Copy of report - 123seminarsonly.com · Transmitter detection ... Aero Radar, Military: 1300-1400 MHz ... For, example for a delay sensitive transmission the modulation scheme

DOCTORAL QUALIFICATION EXAM ‐ II

Cognitive Radios Spectrum Sensing and Allocation Techniques

Farrukh Javed

7/28/2008

F‐05‐020/07‐UET_PHD‐CASE‐CP‐40, Electrical and Computer Engineering Department

Centre for Advanced Studies in Engineering, Islamabad, Pakistan

Advisor: Dr Riaz Inayat

Abstract

The un‐precedented and exponential expansion in field of telecommunications is yet to face a challenge that cannot be amicably resolved. But the lightning pace of development itself poses a question which if not effectively addressed will bring this development to a dead halt. The ultimate human limitation in the field of telecommunication: “The available spectrum is but finite”. Latest advancements in research have offered various solutions but definitely none as viable as “Cognitive Radios”. The term next generation Radios Networks or dynamic spectrum access is also used for the same paradigm. This paper is formulated to fulfill the requirements of doctoral qualification exam and encompasses, “the introduction to the spectrum sensing and allocation techniques used in cognitive radios”.

2 Farrukh Javed F‐05‐020. Spectrum Sensing and Allocation in Cognitive Radios (DQE ‐ II)

Contents Section I – Cognitive Radios

1. Introduction .......................................................................................................................................... 4

2. Next Generation (xG) networks ............................................................................................................ 5

3. Cognitive Radio ..................................................................................................................................... 6

3.1 Cognitive Capability ...................................................................................................................... 7

3.1.1 Spectrum Sensing .................................................................................................................. 7

3.1.2 Spectrum allocation .............................................................................................................. 7

3.2 Re‐Configurability ......................................................................................................................... 8

Section II ‐ Spectrum Sensing

4. Transmitter detection ......................................................................................................................... 10

4.1 Matched filler detection ............................................................................................................. 11

4.1.1 Opportunities ...................................................................................................................... 11

4.1.2 Challenges ........................................................................................................................... 11

4.2 Energy detection ......................................................................................................................... 11

4.2.1 Opportunities ...................................................................................................................... 11

4.2.2 Challenges ........................................................................................................................... 12

4.3 Cyclo‐Stationary feature detection ............................................................................................. 12

4.3.1 Opportunities ...................................................................................................................... 13

4.3.2 Challenges ........................................................................................................................... 13

5. Cooperative detection ........................................................................................................................ 13

5.1 Opportunities .............................................................................................................................. 14

5.2 Challenges ................................................................................................................................... 14

6. Interference based detection ............................................................................................................. 14

6.1 Opportunities .............................................................................................................................. 15

6.2 Challenges ................................................................................................................................... 15

7. Spectrum Sensing Challenges ............................................................................................................. 15

7.1 Interference Temperature .......................................................................................................... 15

7.2 Spectrum Sensing In Multi‐User Networks ................................................................................. 16

7.3 Speed of detection ...................................................................................................................... 16

Farrukh Javed F‐05‐020. Spectrum Sensing and Allocation in Cognitive Radios (DQE ‐ II) 3

Section III ‐ Spectrum Allocation

8. Spectrum Analysis ............................................................................................................................... 17

8.1 Channel capacity ......................................................................................................................... 17

8.2 Primary and xG network user info .............................................................................................. 17

8.3 Channel Capacity ......................................................................................................................... 19

8.4 Spectrum analysis Challenges ..................................................................................................... 19

8.4.1 Opportunities ...................................................................................................................... 20

9. Spectrum Decision .............................................................................................................................. 20

9.1 Spectrum Mgmt: ......................................................................................................................... 21

9.2 Spectrum Mobility....................................................................................................................... 21

9.2.1 Spectrum mobility challenges ............................................................................................. 22

9.2.2 Opportunities for spectrum mobility .................................................................................. 23

9.3 Spectrum Sharing ........................................................................................................................ 24

9.3.1 Architecture based classification ........................................................................................ 24

9.3.2 Challenges and Opportunities ............................................................................................. 24

9.3.3 Spectrum Sharing based on the access behaviour ............................................................. 24


9.3.5 Spectrum sharing based on access technology .................................................................. 25


9.4 Spectrum Sharing Challenges ...................................................................................................... 26

10. Conclusion ....................................................................................................................................... 27

Section I – Cognitive Radios

1. Introduction

The finite nature of available spectrum is undoubtedly the biggest question mark on the phenomenal expansion in the field of telecommunication. The question that “Are we headed for a dead stop?” has been answered in negative in very mean ways but none is apparently more suitable than the idea of cognitive radios. The human limitation of finites is answered by human nature to cognitivity. The term “Cognitive” as elaborated in encyclopaedia Encarta as the ability of acquiring knowledge by the use of reasoning, intuition or perception. The concept of cognitive radios is based on humanising the communication networks by giving them ability to sense, analyse, decide and adjust. Though the barrier of finite nature of spectrum cannot be crossed but it sure can be bypassed by the most optimum utilization of available spectrum.

Presently the spectrum is accessed using fixed spectrum access techniques FSA. Government agencies allocate spectrum bands to different users and vendors based on policies and monetary agreements. The fixed allocation, due to it’s convenience of use has survived for so many years and is sure to last for many more. But the biggest drawback of the technique is its non‐flexibility. The mass variations in spectrum concentration at diff time, space and freq bands results in wastage of major

portion of spectrum at most of the time instances. Federal communication commission (FCC) [1] places the spectrum usage between the ranges 15% ‐ 85% at all times. This implies that if some means can be adopted to effectively utilize, unused spectrum slots in different freq bands the spectrum can be used much more efficiently and economically.

Fig. 1. Spectrum Utilisation [1]


Another estimation of spectrum concentration is given by Jean‐Pierre Hubaux in [2]. He has observed spectrum allocation and usage at six locations(Locations: New York city; Riverbend Park, Great Falls, VA; Tysons Corner, VANSF Roof, Arlington, VA; NRAO, Greenbank, WV; SSC Roof, Vienna, VA). He has come up with the results shown in fig. 2. He goes on to comment that in Europe, cellular operators have spent nearly 100 billion Euros to buy spectrum for the 3rd generation.

2. Next Generation (xG) networks

The xG Networks are heterogeneous networks which provide dynamic spectrum access by using cognitive radios as nodal points in the network. The xG Networks can be used in parallel to licensed user networks and can opportunistically utilize the under‐utilised spectrum without disturbing the licensed user network.

The inherent flexibility and adaptability in the xG networks necessitates elaborate protocols that can address a multitude of situation that are un‐encountered in fixed spectrum access scenarios. The situation is further complicated by the fact that the xG network will almost work not only in parallel to the licensed user network but also subservient in the regards that it will always be the xG network that has to adept to the changing needs of the licensed user band. There might also be situations in which various xG networks are to work in conjunction with each other.

Though the protocols defining xG networks are still a popular area for study and have not evolved into a very refined form however the generally suggested architecture are of multiple cross

Measured Spectrum Occupancy Averaged over Six Locations

0.0% 25.0% 50.0% 75.0% 100.0%

PLM, Amateur, others: 30-54 MHzTV 2-6, RC: 54-88 MHz

Air traffic Control, Aero Nav: 108-138 MHzFixed Mobile, Amateur, others:138-174 MHz

TV 7-13: 174-216 MHzMaritime Mobile, Amateur, others: 216-225 MHz

Fixed Mobile, Aero, others: 225-406 MHzAmateur, Fixed, Mobile, Radiolocation, 406-470 MHz

TV 14-20: 470-512 MHzTV 21-36: 512-608 MHzTV 37-51: 608-698 MHzTV 52-69: 698-806 MHz

Cell phone and SMR: 806-902 MHzUnlicensed: 902-928 MHz

Paging, SMS, Fixed, BX Aux, and FMS: 928-906 MHzIFF, TACAN, GPS, others: 960-1240 MHz

Amateur: 1240-1300 MHzAero Radar, Military: 1300-1400 MHz

Space/Satellite, Fixed Mobile, Telemetry: 1400-1525 MHzMobile Satellite, GPS, Meteorologicial: 1525-1710 MHz

Fixed, Fixed Mobile: 1710-1850 MHzPCS, Asyn, Iso: 1850-1990 MHz

TV Aux: 1990-2110 MHzCommon Carriers, Private, MDS: 2110-2200 MHz

Space Operation, Fixed: 2200-2300 MHzAmateur, WCS, DARS: 2300-2360 MHz

Telemetry: 2360-2390 MHzU-PCS, ISM (Unlicensed): 2390-2500 MHz

ITFS, MMDS: 2500-2686 MHzSurveillance Radar: 2686-2900 MHz

Spectrum Occupancy

Fig. 2. Spectrum Concentration [2]


layers. Akyildiz in [7] suggested a cross‐layer architecture which is shown in fig. 3. The architecture exhibits the cooperative and complex functionalities of xG Network.

3. Cognitive Radio

Joseph Mitolla and Gerald Maguire [4] in their ground breaking work described the novel idea as the situation in which wireless nodes and the related networks are sufficiently computationally intelligent about radio resources and related computer to computer communication to detect the user communication needs as a function of use context and to provide resources and wireless resources most required. In simpler words, a cognitive radio is a radio that can interact with its radio environments by sensing its parameters and adapting to them. The definition indicates two basis characteristics of cognitive radios.

• Cognitive capability: Interaction with environment in order to detect the spectrum parameters. The spectrum needs to be analysed for spectrum concentration, power level, extent and nature of temporal and spatial variations, modulation scheme and existence of any other xG network operating in the neighbourhood.

• Reconfigurability: The beauty of cognitive radio is its flexibility. The radio is capable to adopt itself so as to meet the spectrum needs in the most optional method. This flexibility in design has been rendered only recently due to the advancements in concept of software radios DSP techniques, antenna technology etc.

The paper is restricted to the spectrum sensing and allocation of cognitive radios which constitute the cognitive capability of a radio. Hence the re‐configurability is only briefly discussed and then the spectrum sensing and allocation techniques and concepts have been discussed in detail.

Fig. 3. xG Network Functionalities [3]


3.1 Cognitive Capability

Cognitive capability of a radio enables it to interact with its radio environment. The interaction is a closed loop function called as cognitive cycle. The spectrum is analysed for its various parameters and decisions made on the basis of this analysis are used for reconfiguration of cognitive radio. This is a continual process and the spectrum sensing and analysis is not ceased at any instant. The cycle is depicted as in fig. 4 in [3]

The implementation of this cycle is still an open area for research and multiple options have been suggested. However, a broad categorization can be made as under:

• Spectrum Sensing • Spectrum allocation

o Spectrum analysis o Spectrum decision

3.1.1 Spectrum Sensing

Spectrum sensing implies the data collection from the radio neighbourhood not only for the identification of temporally unused slots in the spectrum but also for all other relevant details. These unused slots are called as spectrum holes or white space [5]. The monitoring is continuous and over the complete spectrum.

3.1.2 Spectrum allocation

Spectrum allocation is made based not only on the sensed spectrum parameters but various other functions. The spectrum allocation follows two steps.

3.1.2.1 Spectrum analysis

The analysis of spectrum parameters and identifying the spectrum holes is carried out in order to determine various parameters that are required to choose a suitable band for transmission.

Fig. 4. Cognitive cycle [3]


3.1.2.2 Spectrum decision

The decision about the selection of a suitable band for transmission is not only depending on the spectrum information acquired by spectrum analysis but also by various other consideration equally important such as:‐

• Transmission characteristics

• Spectrum management

• Spectrum mobility

• Spectrum sharing

3.2 Re‐Configurability

After selection of a suitable band and transmission parameters for transmission the next biggest design challenge is to reconfigure or adapt the radio to suit these parameters. The recent technological breakthroughs in software radios can be best utilised for giving this flexibility. There are various parameters that can be considered for reconfiguration. Incorporation of each factor adds to the design flexibility.

• Operating frequency: This is the ability to transmit at different operating frequencies in order to use a spectrum hole. This is the primal requirement for a dynamic spectrum access network.

• Modulation Scheme: A cognitive radio should reconfigure for adaptation to the channel and user requirements. For, example for a delay sensitive transmission the modulation scheme should be chosen whose delay characteristic are better than the error rate. Conversely, a low sensitive transmission would be more suited to a low error rate channel. Another example can be the use of CDMA for security sensitive transmissions.

• Transmission Power: The cognitive nature of a cognitive radio enables it to use a transmission most suited to the requirement. The transmission power level can be adjusted based on the information of the intended receiver. The adjustment of transmission power to minimum gives two big advantages. Firstly, it enables reduced power consumption and secondly it enhances the numbers of users linked to the network by decreasing interference.

• Communication Technology: The inherent design flexibility enables a cognitive radio to be interoperable between different communication systems.

• Directivity of transmission: The directivity of transmission gives another dimension of expansion to cognitive radios. The spectrum holes cannot only be in temporal sense but also spatial distribution of holes can be considered and put to optimum utilisation.

Fi 5 S H l C

Thprevailingcognitive

Farrukh Ja

he reconfigug spectrum coradio conside

aved F‐05‐020.

rable parameonditions. Theering the topi

Power

Spectrum Sen

eters of the ce discussion fic under discu

Fig. 5. S

sing and Alloca

cognitive radfrom here on ussion.

Time

Spectrum Hole C

ation in Cognit

io are changis focused on

Fr

Concept

tive Radios (DQ

ging continuon the cognitiv

equency

QE ‐ II)

usly based ove capability o

9

on the of the


Section II – Spectrum Sensing

As already introduced, the cognitive nature of a cognitive radio is manifested by its interaction with the spectrum and the very first step in this regard is the identification of spectrum holes. Actual detection of a channel between the transmitter and receiver is very difficult. However, the recent work utilises the interactive nature of a cognitive radio by detecting the transmitter based observation of spectrum users. The broad classification of cognitive radio sensing is as follows

4. Transmitter detection

Spectrum concentration in a spatial domain can be determined by receiving transmission from all the perceived transmitters in that domain at any time instant. The approach depends on observation of signals received at various cognitive radios in an xG network and analysing the context for the transmission by the primary transmission in that vicinity the hypothesis mode would be

, ,

Or

, ,

Where x(t)� is the signal received at the cognitive radio, n(t)is the AWGN noise present in the channel and h(t)is the impulse response of the channel. The second model is the frequency domain translation of the same hypothetical model. Both hypotheses are checked for correctness based on probabilistic models and if hypothesis H1 is found true then the presence of transmitted signal is concluded and further analyzed.

Spectrum Sensing

Transmitter Detection

Matched Filter Detection Energy Detection Cyclostationary

Feature Detection

Cooperative Detection

Interference Bssed Detection


As indicated already, the techniques used for detection can be broadly categorized as matched filter detection, energy detection and cyclo‐stationary feature detection.

4.1 Matched filler detection

The common sense means to detect a transmission is to design an inverse filler to the impulse response of the primary transmitter. The additional requirement would be to make a precise noise model of the channel which can be used to decode the signal optimally.

4.1.1 Opportunities

• Matched filter detection is a widely used technique in communication and not much effort is needed to adapt it to the requirements of a cognitive radio.

• The filter takes very little time to achieve high processing gains.

4.1.2 Challenges

• Even If inter symbol interference is completely eliminated from a transmission even than the best performance of a matched filler is bounded by a theoretical bound known as matched filler bound.

• The biggest challenge for a matched filter is that it requires apriory knowledge of the transmission in order to decode a signal. In case of an xG network this information can only be obtained if the licensed spectrum user can extend the leverage. If the licensed user intends to transmit to a matched filter detector than some information in the form of pilots, preambles, synchronization work or spreading code will be transmitted by the transmitter which might be used by the cognitive radio. Otherwise, the technique can only be used if licensed user intends cooperating.

4.2 Energy detection

If the transmission information is not available with the cognitive radio then the optimum receiver is the energy detector. The detector is simply the integrated output Y of a band pass filter with bandwidth W over a time period T. The output is compared with a threshold λ to decide whether some signal is present in the band or not. The threshold selection can be made as a fixed value or a flexible choice, that is though complicated but more suitable for a cognitive radio.

In [6] it is deduced that the probability of detections Pd and probability of false alarm Pf is

Pd = P [Y > λ / H1] = Qm (√2γ, √λ),

Pf = P [Y > λ / H0] = Г (m, λ/2) / Г (m)

Where λ is SNR, n = TW (time bandwidth product) and Г ( . ) , Г (. , .) are complete and incomplete Г (gamma) functions. Qm is generalized Marcum Q function.

4.2.1 Opportunities

• As it is the most easily implement‐able detector hence it is the most widely considered detector in research.


• The detector has been sufficiently studied for multi‐path and fading channels and satisfactory results have been obtained. In [7] it is given that Pf in this case is independent of λ. When the amplitude gain of channel h, varies due to the shadowing and fading, Pd gives the probability of detection conditioned on instantaneous SNR as follows

Pd = Qm (√2γ, √λ) fr(x) dx

Where fr(x) is the probability distribution function of SNR under fading conditions.

4.2.2 Challenges

• If the Pd is very low then the failure to detect a licensed primary user will erroneously create a false hole. Consequently the interference for primary user will increase. Conversely, if the Pf is very high, it will result in under‐utilized spectrum. This necessitates a very careful selection of comparison threshold λ.

• Pilot detector is susceptible to noise power variations. A pilot tone from transmitter can be considered to address this problem but again, the detectors major benefit that no transmitter information is required at the receiver is compromised.

• An energy detector cannot differentiate communication types, hence is prone to false indication if some un‐desirable signal is present in the considered band.

• The lack of information about the type of transmission, reduces the re‐configurable parameters, hence results in reduced flexibility of the cognitive radio.

4.3 Cyclo‐Stationary feature detection

Cyclo‐stationary detector is based on the inherent redundancy in the transmission signals. Modulated signals in general are associated with sine wave carriers, digital sequences in the farm of pulse trains, repeating spreading or having cyclic prefixes. These all periodicities in these communication signals results in an inherent autocorrelation. A cyclo‐stationary detector detects the presence of a

Fig. 6. (a) Receiver Uncertainty (b) Shadowing Uncertainty [3]


signal on basis of presence of periodicity in the transmission by using a spectral correlation function. The beauty of this detector is that it can differentiate the signal easily from the noise because the noise pattern in general, is wide‐sense stationary and indicates no periodicity.

4.3.1 Opportunities

• A cyclo‐stationary feature detector can perform better than the energy detector because of its robustness in presence of uncertain noise powers.

• No transmitter information is required at the cognitive radio to obtain the communication information.

• Neural network based cyclo‐stationary detectors are studied in [8] and are found extremely useful.

4.3.2 Challenges

• Cyclo‐stationary detector is computationally very complex to implement.

• Cyclo‐stationary detectors cannot detect type of communication and hence is prone to erroneous detection from an un‐intended transmission source.

• The lack of information about communication types renders reduced flexibility to the cognitive radio.

Though the challenges of each transmission detector technique are discussed separately but one common problem that transmitter detection has to face is its frequent isolation from primary network. This isolation results in what are called as “receiver uncertainty” and “shadowing uncertainty” [3]. Receiver uncertainty exists when the receiver is unable to detect a primary transmitter due to weakness of its signal but is adversely affecting the reception of primary receiver. Shadowing un‐certainty is similar to receiver uncertainty except that the cause of weak signal is some obstruction in the transmission and not the distance involved.

5. Cooperative detection

The cooperative detection is based on the cooperation between various xG users for contention of spectrum by sharing information about their radio environment. This not only enhances the optimality of spectrum utilization but also reduces the chances of interference to primary uses. The two approaches to implement the cooperative detection are in a centralised or distributed manner. In centralised approach a xG base station or hub is responsible for acquiring information from all xG users, makes a broad picture of primary users and xG users in the complete radio environment and disseminate the information to xG users on as required basis. In distributed approach each xG users acts as a node and shares its neighbourhood information with all others users of xG network.


5.1 Opportunities

• Biggest advantage of cooperative detection is the manifold reduction in the uncertainties of transmitter detection, due to sharing of information at various xG users.

• Effects of degrading factors such as multiple paths fading and shadowing are mitigated.

• Primary users’ interference is appreciably reduced.

5.2 Challenges

• In [9] the problems created due to co‐location of spectrum sensing (cooperative method) and transmission functions are discussed and it is suggested that two separate networks for sensing and transmission be adopted. Though problem might be resolved but at the cost of added complexity.

• In case of resource constrained network, cooperative detection might be a difficult option considering the additional operations and overhead traffic.

• Primary receiver un‐certainty due to the passive nature of primary receiver is still un‐resolved.

6. Interference based detection

All the detection techniques discussed thus far have focused on reducing the interference to the primary transmitter. This is because of the difficulty to detect primary receivers due to their passive nature. However the aim remains to reduce the interference to the primary receiver irrespective of the transmitter.

Interference based detection model has been recently proposed by FCC [10] which introduces the idea of interference temperature. The fig 7 [10] shows an interference temp model. The power received at primary receiver reduces exponentially with distance, until it falls to the level of noise floor. At this point the receiver treats this communication as simply noise and not transmission. The noise

Fig. 7. Interference Temperature Model [10]


floor, though a theoretical threshold has spikes in it which are to be treated as noise by the receiver. If an interference cap is introduced above the maximum noise level, all noise will be completely removed. This increase in threshold results in slightly reduced range for transmitter but makes a very useful corridor for xG networks to operate. A cognitive radio aware of the existing noise level can use the spectrum of its choice unless the transmission power does not exceed the interference cap. Below this level the primary receiver will treat this transmission as simply noise and no interference will occur.

The interference base detection is a new concept and is being widely researched. Novel approaches for its implementation are still being suggested. In [11] a direct receiver detection method is presented which uses the leakage power of LO (local oscillator) by the RF front‐end of a primary receiver. The idea proposed is to have low cast sensor nodes which detect the leakages from primary receiver and feed to the cognitive radios in the xG networks. This information is used by un‐licensed users to deduce the spectrum band of choice.

6.1 Opportunities

• The biggest advantage of interference temperature based detector is the shift of focus from primary transmitter to primary receiver. It implies that no undue effort will be made to reduce interference e.g. If no primary receiver is around, a cognitive radio may use any frequency band of its choice irrespective of primary transmitters in the vicinity.

• If the transmission power of a cognitive radio remains below the interference cap, it may utilise any frequency parameters of its choice.

6.2 Challenges

The proposal is still in embryonic stage of research but is sure to break ground for a vast improvement. The biggest challenge is the receiver interference temperature detection. The complexity of the problem is compounded considering multiple primary and xG users.

7. Spectrum Sensing Challenges

The challenges and opportunities offered by various spectrum sensing techniques have been discussed above. But there are few spectrum sensing challenges that are not specific to any specific technique but are an open area for research.

7.1 Interference Temperature

As already discussed the major research done so far is to reduce interference to the primary transmitter rather than the primary receiver. This is due to the problems faced in finding interference temperature at the receiver due to its passive nature. A cognitive radio is generally aware of its own transmission power levels, its location and surrounding noise level. But in order to cause minimum disturbance to the primary users, it is mandatory to gather information about their transmission characteristics and parameters. The main focus in this regard should be the primary receiver but currently there exists no feasible method of detecting the interference temp at the primary receivers.


This is solely due to the passive nature of these receivers. Hence, major work till to date is focused on reducing interference to primary transmitters. Furthermore, even if temp interference at a precise loc is deduced, it’ll remain useless for a cognitive radio unless it can conclude the effect of its transmission on all primary receivers.

7.2 Spectrum Sensing In Multi‐User Networks

Most of the cognitive radios will have to operate in multi‐user environments, requiring not only minimal interference to primary users but also competing with other xG users for the same spectrum. Most of the research done does not cater for multi‐user scenarios. However, a cooperative scheme which is subsequently discussed can be considered. This will greatly minimize the problems arising due to multi‐user interactions.

7.3 Speed of detection

The complete cycle of sensing, analyzing and adapting is happening in real time. Hence the speed of acquiring spectrum information is extremely essential to avoid interference and / or missed opportunities for spectrum utilization. This is greatly dependent on the modulation scheme of the transmission. In [12, 13, and 14] it has been deduced that OFDM is the most suited transmission scheme for cognitive radios. Once a primary user is detected by a single carrier, detecting all other carriers is not necessary. This at present is also another area open for reach.


Section III – Spectrum Allocation

Spectrum sensing gives a broad‐based information to proceed on to the more important functionality of spectrum allocation. Spectrum allocation not only comprises of the process of spectrum analysis and spectrum decision but encompasses a variety of issues such as spectrum mobility, spectrum sharing, spectrum access etc. Whereas the spectrum sensing primarily deals with physical layer, spectrum allocation deals generally with the higher layers. The spectrum allocation is a widely researched topic and major issues need to be compromised as yet. A broad of spectrum allocation functionality is as follows:

8. Spectrum Analysis

The physical layer of spectrum sensing provides all the raw information to the spectrum analysis functionality. The analysis not only examines the time varying radio environment but also measures various parameters of primary and co‐existing xG users of the spectrum. The information is collected on basis of various matrices. Generally the spectrum is analysed for three things:

• Channel capacity

• Primary user related information

• xG user information

8.1 Channel capacity

The following factors might be considered for channel capacity analysis.

• Path Loss: Path loss is directly proportional to the operating frequency. Hence it is a major consideration for transmission power. In order to avoid interference to other users the transmission power should be kept to the minimum but it should remain high enough to be of use. Hence the transmission power is to be adjusted as per operating freq.

• Wireless Link Error: The error rate of the channel changes with the transmission scheme and interference level of the spectrum band. Hence related information is of importance.

• Link Layer Delay: Due to the inherent flexibility in the design all the proposed networks are multi‐layer concepts. This brings into consideration the delay involved in various protocol layers.

• Noise Info: The noise existent in the neighbouring radio environment must be considered in all analyses. Especially the interference temperature model which appears to be the most feasible spectrum sensing option utilises the noise floor as the basis of all calculations.

8.2 Primary and xG network user info

Similar information is required about xG and primary users. The following information will be feasible in this regard.

18

• Interfanalysto det

• Holdincognitbecauinformthis in

• User user aerror

Farrukh Ja

ference: sed for informtect the spect

ng Time: tive radio. Thuse it will redmation can benformation fo

transmission are useful to rate, entropy

aved F‐05‐020.

Some specmation abouttrum holes.

Holding timhe greater thduce the hane acquired baor all the user

Parameters:decide the sy etc are usef

Spectrum Sen

ctrum bandst the xG and p

me is the ese holding timndoffs a cogased on statisrs.

: The transmuitable spectful for the cog

Fig . 9. Spectru

sing and Alloca

s are more primary users

stimated timme, the bettenitive radio hstical models

ission charactrum for trangnitive radio t

um Sensing and

ation in Cognit

congested ts utilising the

me the vacaner is the perfohas to underor a coopera

cteristics of tsmission. Datto predict the

Allocation

tive Radios (DQ

han others. spectrum. Th

nt slot can bormance of trgo due to pative network

the primary ata such as moe behaviour o

QE ‐ II)

The spectruhis info is ana

be occupied the cognitive primary usersk can also gen

and co‐existinodulation schof these users

um is alysed

by a radio s. The nerate

ng xG heme, s.


8.3 Channel Capacity

Channel capacity though theoretically defined as mean average entropy but here it is considered as a term encompassing all channel characteristics.

The spectrum characterised by channel capacity have generally made the SNR parameters as the basis. Various models have been considered to utilise the SNR as a measure of channel capacity but channel characteristics can never be fully encompassed utilising SNR only. All the above mentioned factors form matrices that must be considered for channel analysis. Various models have been suggested in this regard which take additional factors into consideration e.g. in [3] the interference temperature model discussed in previous section is suggested for measures of channel capacity. In [14] bandwidth and permissible transmission power are suggested as the basis of system capacity measurement C.

C = B log ( 1 + S/ (N+I) )

Where B is the bandwidth, S is the received signal power from cognitive radio, N is the cognitive radio noise power and I is the interference power received at the cognitive radio due to primary users. In [15] OFDM based cognitive radio channel capacity is defined as

12log 1

Where Ω is the collection of unused spectrum segments, G (f) is the channel power gain at freq f. So & No are the signal and noise power per unit frequency respectively.

8.4 Spectrum analysis Challenges

• Heterogeneous Spectrum Sensing: All un‐conventional and unconventional analysis methods are designed to operate on a limited frequency band. Even in software radios the general approach is to follow a frequency filtration mechanism immediately after the RF front end and then the analogue to digital conversion stage. However, in case of a cognitive radio the complete spectrum is to be analysed for various parameters. The more elaborate is the analysis the more flexible and interference free performance will be acquired.

• Non Cooperative Primary and xG users: The biggest analytical challenge is the analysis of primary and co‐existing xG users. The data is extremely important for deciding a suitable band for transmission. All decisions for switching from one spectrum hole to another are also based on the same data. In a non‐cooperative xG network this poses to be a big design challenge.

• Varying Transmission Parameters: The spectrum holds multiple users at all time utilising different modulation schemes, data, error rate etc. In order to accommodate the requisite flexibility to the channel, cognitive radio should be able to adapt to all the varying conditions.


• Real Time Analysis: The Cognitive nature of a cognitive radio demands continuous real time analysis. The broad spectrum time varying parameter over which the cognitive radio is operating make the real time analysis very difficult to accomplish.

• Delays in Processing: In order to cause minimum disturbance to the primary network (licensed users) and have better spectrum sharing with co‐existing cognitive radios it is primal to undergo a handoff. Another situation for handoff may arise, if the conditions in the occupied slot deteriorate below an acceptable level. Or a situation in which a more feasible slot is offered for occupation for which the handoff losses to performance are a worthy bargain. The handoffs cause processing delays which might be very taxing for the cognitive radio performance.

Special considerations are to be made to minimise the no of handoffs and the performance degradation so that the cognitive radio must be able to adapt to its surroundings in minimum possible time. This necessitates the minimisation of processing delays in the analysis stage. Though, the advanced DSP techniques and processors have evolved to a level that makes this task possible but still highly cumbersome.

8.4.1 Opportunities

As discussed spectrum sensing proves to be a design challenge at physical layer but the spectrum analysis is a software design problem. The evolution of software radios has for the time made it possible to visualise the existence a level of flexibility primal for a cognitive radio. This has rendered many discussions to describe cognitive radio as an extension of software radios. In any case, as the technology for a cognitive radio matures, the software radio concepts have already evolved to a level where they can be effectively utilised for spectrum analysis and decision.

9. Spectrum Decision

The spectrum once analysed for all relevant parameters is chosen for transmission at a particular slot. The decision also includes the transmission characteristics. This is the most computationally extensive and complex block of cognitive radio. The decision not only caters for the spectrum characteristics but also the protocols involved for spectrum sharing and spectrum mobility. Based on this information few holes are identified for transmission. These are than compared with transmission requirements to decide which slot will most suitably fulfil the user’s requirements of data rate, modulation scheme, bandwidth etc. A suitable slot is finally selected which can provide the required QoS for the transmission. The decision is made in real time and has to be changed with the changing parameters. The complexity of decision block is evident from multifarious challengers it poses. The research issues that need to be investigated to implementation of spectrum decision functionality are:

• Spectrum management

• Spectrum mobility

• Spectrum sharing


9.1 Spectrum Mgmt:

The term is being utilised to indicate those functions of the spectrum decision block directly related with spectrum characteristics. These include all the elements discussed in section 8.1. Based on the discussed factors, conclusions about the spectrum utilisation are to be made. The challengers faced in spectrum management functionality include.

• Decision Model: As mentioned in the previous section, the criterion for spectrum selection is not restricted to SNR parameters, various models are discussed e.g. [14] and [15] included additional spectrum characteristics for choosing the optimum spectrum characteristics. However, no proposed criterion as yet includes all relevant requirements for measuring true channel capacity.

• Multiple Spectrum decision: A very interesting opportunistic idea discussed in [3] and [16] is to use multiple non‐contiguous spectrum bands for transmission. This has various advantages. First, the limitation on bandwidth to transmission is lifted, as multiple slots are simultaneously being utilized for transmission. This will enable to use wide band transmission techniques that greatly enhance the performance of the transmission. It is because of the fact that even if some of the slots are compromised due to interference, still complete data will not be lost. The temperature interference model previously discussed is also suited for wide band transmission.

• Reduced Transmission Power: Another major advantage is that transmission power required for each slot will be much lesser. How to determine number of transmission slots and the set of appropriate bands are still open research issues in xG networks.

• Cooperation with reconfiguration: The spectrum allocation stage is succeeded by the re‐configurability functionality of a cognitive radio. The decisions made must be able to consider the re‐configurability limitations in order to make as correct decision about the transmission slot and parameters as possible.

• Heterogeneous Spectrum: The heterogeneity of spectrum creates un‐conventional and varying problems. The spectrum contains primary users with licensed bands with greater priority use and xG networks co‐existing and competing for spectrum resources. Spectrum sharing is accomplishing by protocols which are at times well decided or in some cases evolving or amorphous. These all considerations make the spectrum management problems a complex one. So spectrum slots avail as holes, still need to be prioritized for occupation.

9.2 Spectrum Mobility

A cognitive radio is design to switch its operating frequency on the run and switch from are white space to another, all the time. The aim is to always occupy the most suitable transmission slot. This “Get the best channel” strategy results in spectrum mobility. The switching at a channel from one hole to another is called “Spectrum Handoff” [3].


A spectrum handoff may be initiated in various situations. If a primary user appears, the cognitive radio has to undergo a handoff. Another situation may be, if the conditions in the occupied slot deteriorate below an acceptable level or a situation in which a more feasible slot is available for occupation for which the handoff losses to performance are a worthy bargain.

Special considerations are to be made to minimise the no of handoffs and the performance degradation that is involved in each hand off. Well defined network protocols are required for regulating the process of handoffs. These handoffs should be smooth and arrangements should be made in the protocols to cater for the latency involved in each had off.

[7] Suggests a multi‐layered mobility management protocol required to accomplish the mobility functionalities. The examples suggested include use of “Wait Status” in a TCP network during the process of handoff. The switching of transmission parameters has to be accomplished during the same period smoothly. For a data transmission e.g. in FTP the protocol is suggested to store some packets to transmit in the handoff period. For a real time application this packets storage would not be practical.

9.2.1 Spectrum mobility challenges

There are many open research issues in spectrum mobility. A few may be:

• Latency: The delay is one major problem. Reduction of this delay is a design challenge and refining the process of handoff is a standardized protocol requirement. The process should be smooth and arrangements for minimised performance degradation are to be made.

• Suitable Algorithms for mobility: Sophisticate algorithms are required to be devised to decide suitability of channels for mobility. Thong the spectrum management functionality decides the suitability of a slot but spectrum mobility block should indicate if the switch off is suitable in terms of hand off losses.

• Appearance of a primary user: Appearance of a primary user creates a situation in which handoff is to be made immediately with minimum losses. Special algorithms are to be defined that minimise the performance loss in this situation. An example is to keep a “Most suitable next slot(s)” always pre‐decided and in case a handoff is to be made, it may be accomplished without delay.

• Vertical and inter‐cell handoff schemes: Inter‐cell handoff is simply a switch from one slot to another due to performance considerations while vertical handoffs are the switching at frequencies between two different networks. In such a diverse environment it is highly necessary to devise special procedures and mechanisms to implement these handoffs.

• Suited threshold for inter‐cell handoffs: As a handoff may be carried out if conditions at a certain freq slot have deteriorated below an acceptable level or if another slot promises must enhanced performance. Deciding an optimum threshold or level is highly important for both

situatrequir

• Spectflexib

• Spectmove

9.2.2 O

Thbeen prop

• Priorican boccupspectoptimmay bFIFO qthe qflexibproce

• Soft aadaptsoft h

Farrukh Ja

tions. The threments. Firs

rum mobilityility renders a

rum mobility ement of the u

Opportunities

he spectrum posed on the

tised white spbe consideredpation. Similarum sharing a

mum slot readbe immediatequeue of suitqueues. The sle such that tessing delay.

and hard spetation to cognhandoff woul

aved F‐05‐020.

hresholds shtly the optim

y in time doma complexity

in space: Tusers. This ad

for spectrum

mobility is ansubject. Som

pace: Insd to allow thar queues shand user requdy for occupaely occupied wtable slots easequence in the block wit

ectrum handonitive radio isd be the one

Spectrum Sen

hould be seum performa

main: The mthat needs ve

The availabledds another d

m mobility

n open area fme areas open

tead of waitihe spectrum hould be mauirements blotion. Whenevwithout delayach having a which the dh maximum

off: The ters a useful onee in which th

Fig. 10. Spectru

sing and Alloca

lected to mance and seco

mobility requiery elaborate

band and thdimension the

or research an for research

ng for a situamobility modade by the socks. The firstver a situatioy. The spectr“duration of decision blockrejections is a

rm handoff ise because it be next slot is

um Decision Fun

ation in Cognit

make a comond, minimisi

irements are e arrangemen

he user reque spectrum m

and not manyh are indicate

ation where adule to makespectrum shat intersectionon arises that rum decision suitability” tk consults itsalways first c

s more suitebrings some res occupied be

nctionality

tive Radios (DQ

promise beting number o

continuouslynts to sustain

irements alsomobility challe

y algorithms ad below.

a handoff bece a queue ofaring, spectrn of these quenecessitate ablock shouldtag with it afts predecessoconsulted. Th

d to cellular eadily made sefore leaving

QE ‐ II)

tween two pof hand offs.

y varying andit.

o change witenge.

and protocols

comes necessf suitable slorum managemeues should ba handoff, th also be carryter which it lors should alsis will reduce

networks busolutions withthe previous

23

prime

d this

th the

s have

sary it ts for ment, be the e slot ying a eaves so be ed the

ut the h it. A s slot.


This results in almost no loss in performance. There might be a standby arrangement which is ready to take over at any time having the next most feasible slot as its operating frequency. Whenever the situation arises the handoff is made with little or no delay. This might results in enhanced complication and increase cell occupancy but major issues related to spectrum handoff performance degradation might be resolved.

9.3 Spectrum Sharing

The ultimate goal of a cognitive radio is to share the spectrum with the primary and co‐existing xG users. The term “spectrum sharing” has been variedly used in different works. In [7] the spectrum sharing is described as the complete process including spectrum sensing, spectrum allocation, spectrum access, transmitter receiver hand‐shake and transmitter mobility. In this paper the terminology is used to indicate the functionality of a cognitive radio that enables it contest with other xG users and interact with primary users for sharing the spectrum.

The spectrum sharing techniques can be classified on the basis of there different aspects i.e. According to their architectural assumption, spectrums allocation behaviour and/or spectrum access techniques. Each approach must also elaborate the inter‐network or intra‐network sharing.

9.3.1 Architecture based classification

• Centralised spectrum sharing: As indicated by the name, there is a central entity that controls the spectrum sharing process. In order to aid the process a network of sensor nodes is suggested which feed the controlling central entity. Based on the information of theses sensors, a spectrum allocation map is formulated.

• Distributed spectrum sharing: In distributed spectrum sharing each node is self sufficient with its own sensing mechanism. The sharing is done on basis of local (or if possible global) policies.

9.3.2 Challenges and Opportunities

Centralised spectrum sharing is by far the better option as it accommodates not only the spectrum sharing but also as a hub for control / Coordination of various cognitive radios in the xG network. Sharing of interference information and spectrum concentration can also be communicated to each user through the central hub. The implementation of protocols will be much easier and less delay will be involved.

Distributed spectrum sharing is considered in situations where development of an infrastructure is not possible due to limited resources. This results in reduced spectrum utilisation but economic use of resources.

9.3.3 Spectrum Sharing based on the access behaviour

The spectrum sharing technique may also be classified on basis of access behaviour. The spectrum access can be co‐operative or non‐cooperative.


• Cooperative Spectrum Sharing: Cooperative or collaborative spectrum sharing is a mutual co‐existing scheme in which each node shares its transmission information with the other. The information regarding the interference measured at each node is also shared among all users. This results in a much more elaborate and accurate spectrum concentration map. All centralised spectrum sharing networks are collaborative while distributive networks may or may not be collaborative.

• Non‐cooperative Spectrum sharing: The non‐cooperation solutions more appropriately called as selfish solutions, only consider the node at hand. This results in reduced spectrum utilization but offers a trade off for practical solutions due to the minimal communication requirements.


The compromise like previous case is again between the simplicity and performance. A co‐operative scheme is of course more optimal than the non‐cooperative scheme. In [20] it is studied that the spectrum utilization in case of a cooperative spectrum sharing is much higher than a non‐cooperative spectrum sharing scenario even approaching the global optimum. The study is carried at on basis of spectrum utilization, fairness, throughput, channel allocation and potential neighbours.

Similar results are obtained in [21]. However, limitation on these works is the assumption that location and transmission power at primary users is known which may not generally be the case. The non‐cooperative sharing can be considered in restrained resources situation where they are beneficial due to the minimal communication requirements.

9.3.5 Spectrum sharing based on access technology

• Overlay spectrum sharing: This is the situation where a cognitive radio occupies only the vacant slots (holes) in the spectrum and vacates them on appearance of primary users. As a result interference to primary system is minimised.

• Underlay Spectrum Sharing: The underlay spectrum access techniques utilises the spread spectrum techniques developed for cellular networks. The interference temperature model previously discussed can be utilised for this purpose. The transmission is made on a broad band with much reduced transmission power. This renders the primary user to treat it as noise. The transmission can be made until the noise plus the cognitive radio transmission power does not exceed the noise threshold of the receiver. Though this technique requires sophisticated spread spectrum techniques but can utilise increased bandwidth compared to overlay techniques.


Spectrum sharing requirement based on access technology are a direct dictate of the spectrum access technique being utilized. Only from a spectrum sharing point of view, the underlay technique is more feasible as there is no requirement of information about the co‐existing primary and xG users. The only consideration is the noise level. However, performance improvement can be acquired by having a


centralised or cooperative network which renders each node capable of differentiating between noise and another xG user. Another advantage is the availability of much wider bandwidth which makes the completion for bandwidth less fierce. An overlay spectrum sharing techniques has the primal advantage of creating less interference for the primary network.

9.4 Spectrum Sharing Challenges

Spectrum sensing challenges because of the type of technique being utilised have been discussed but some well recognized common spectrum sharing challengers are discussed below:

• Common control Channel: Many spectrum sharing solutions, whether centralised or distributed consider a common control channel for spectrum sharing. A common control channel may be used for various spectrum sharing functionalities, such as sharing interference information, transmitter receiver handshake or communication with a central entity. The challenge is that like all other cognitive channel the common control channel also cannot be a fixed channel. As soon as a primary user appears, the control channel will have to switch to another hole. Moreover, a common channel for various xG users is highly topology dependent and may need to change itself overtime. [27] Suggested that for protocols requiring common control channel, either a mitigation technique needs to be devised or local common control channel, for clusters of nodes can be considered. If a common control channel is not used transmitter receiver handshakes become a challenge. Receiver driven techniques can be considered in this case.

• Dynamic radio range: As interference varies with the operating frequency due to attenuation variation hence radio range is a function of the operating frequency. Another consequence of varying operating frequency is the change in neighbouring spectrum users. This results in varying interference profile as well as routing decisions. Another consideration is the location of control channels. The control channels are placed in lower portion of the spectrum to increase range and data channels are placed in higher channel where a localized operation can be utilized with minimum interference. There is much room for research in operating frequency aware spectrum sharing techniques, which can cater for the direct inter‐dependence between interference and radio range.

Fig. 11. Spectrum Unit [3]


• Spectrum Unit: The complete operation of xG networks focuses on switching of channel. Hence the definition of channel or spectrum unit is highly critical. Different modulation schemes use different spectrum units. This aspect has not been discussed in detail in most work done so far. In [28] a spectrum space is introduced for xG networks with power, freq, time, space and signal as its possible dimensions. Although not orthogonal, but these dimensions can be used to distinguish signals. In [3] a virtual cube model is discussed. The resource is modelled in a 3‐dimensional resource space with time, rate and power/code as dimensions. The rate dimension models the data rate of the network the time dimension models the time required to transfer information.

10. Conclusion

It is not an over‐statement to declare the cognitive radio as the future of telecommunication rather than the part of the future. An environment aware, intelligent network with cognitive radios as its nodes is the only available option for sustaining the enhanced and varying needs that continue to appear with every passing day. In this paper cognitive radios are discussed with special emphasis on the spectrum sensing and allocation functionalities. Various techniques used are discussed along with the challenges and opportunities offered by them. The sub‐functionalities of each major block are also explained in detail. The discussion on spectrum allocation also includes the discussion on spectrum sharing, spectrum mobility and spectrum management as part of spectrum allocation. The paper is a survey of latest trends in spectrum sensing and allocation functionalities of cognitive radios and also highlights open areas for research.


References

[1] FCC, ET Docket No 03‐222 Notice of proposed rule making and order, December 2003.

[2] Jean‐Pierre Hubaux, M. H. Manshaei, M. Felegyhazi, J. Freudiger, and P. Marbach: Spectrum Sharing Games of Network Operators and Cognitive Radios. EPFL, August 2005.

[3] Ian F. Akyildiz, Won‐Yeol Lee, Mehmet C. Vuran, Shantidev Mohanty: NeXt generation/ dynamic spectrum access / cognitive radio wireless networks; A survey. School of Electrical and Computer Engineering, Georgia Institute of Technology, United States. January 2006.

[4] J. Mitola III, Cognitive radio: an integrated agent architecture for software defined radio, Ph.D Thesis, KTH Royal Institute of Technology, 2000.

[5] S. Haykin, Cognitive radio: brain‐empowered wireless communications, IEEE Journal on Selected Areas in Communications 23 (2) (2005) 201–220.

[6] F. Digham, M. Alouini, M. Simon, On the energy detection of unknown signals over fading channels, in: Proc. IEEE ICC 2005, vol. 5, May 2003, pp. 3575–3579.

[7] A. Ghasemi, E.S. Sousa, Collaborative spectrum sensing for opportunistic access in fading environment, in: Proc. IEEE DySPAN 2005, November 2005, pp. 131–136.

[8] A. Fehske, J.D. Gaeddert, J.H. Reed, A new approach to signal classification using spectral correlation and neural networks, in: Proc. IEEE DySPAN 2005, November 2005, pp. 144–150.

[9] S. Shankar, Spectrum agile radios: utilization and sensing architecture, in: Proc. IEEE DySPAN 2005, November 2005, pp. 160–169.

[10] FCC, ET Docket No 03‐237 Notice of inquiry and notice of proposed Rulemaking, November 2003. ET Docket No. 03‐ 237.

[11] B. Wild, K. Ramchandran, Detecting primary receivers for cognitive radio applications, in: Proc. IEEE DySPAN 2005, November 2005, pp. 124–130.

[12] I.F. Akyildiz, Y. Li, OCRA: OFDM‐based cognitive radio networks, Broadband and Wireless Networking Laboratory Technical Report, March 2006.

[13] H. Tang, Some physical layer issues of wide‐band cognitive radio system, in: Proc. IEEE DySPAN 2005, November 2005, pp. 151–159.

[14] T.A. Weiss, F.K. Jondral, Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency, IEEE Radio Communication Magazine 42 (March) (2004) 8–14.

[15] H. Tang, Some physical layer issues of wide‐band cognitive radio system, in: Proc. IEEE DySPAN 2005, November 2005, pp. 151–159.

[16] I.F. Akyildiz, Y. Li, OCRA: OFDM‐based cognitive radio networks, Broadband and Wireless Networking Laboratory Technical Report, March 2006.


[17] V. Brik, E. Rozner, S. Banarjee, P. Bahl, DSAP: a protocol for coordinated spectrum access, in: Proc. IEEE DySPAN 2005, November 2005, pp. 611–614.

[18] C. Raman, R.D. Yates, N.B. Mandayam, Scheduling variable rate links via a spectrum server, in: Proc. IEEE DySPAN 2005, November 2005, pp. 110–118.

[19] S.A. Zekavat, X. Li, User‐central wireless system: ultimate dynamic channel allocation, in: Proc. IEEE DySPAN 2005, November 2005, pp. 82–87.

[20] C. Peng, H. Zheng, B.Y. Zhao, Utilization and fairness in spectrum assignment for opportunistic spectrum access, in: ACM Mobile Networks and Applications (MONET), 2006.

[21] H. Zheng, C. Peng, Collaboration and fairness in opportunistic spectrum access, in: Proc. IEEE ICC 2005, vol. 5, May 2005, pp. 3132–3136.

[22] V. Brik, E. Rozner, S. Banarjee, P. Bahl, DSAP: a protocol for coordinated spectrum access, in: Proc. IEEE DySPAN 2005, November 2005, pp. 611–614.

[23] L. Cao, H. Zheng, Distributed spectrum allocation via local bargaining, in: Proc. IEEE Sensor and Ad Hoc Communications and Networks (SECON) 2005, September 2005, pp. 475–486.

[24] L. Ma, X. Han, C.‐C. Shen, Dynamic open spectrum sharing MAC protocol for wireless ad hoc network, in: Proc. IEEE DySPAN 2005, November 2005, pp. 203–213.

[25] S. Sankaranarayanan, P. Papadimitratos, A. Mishra, S. ershey, A bandwidth sharing approach to improve licensed spectrum utilization, in: Proc. IEEE DySPAN 2005, November 2005, pp. 279–288.

[26] J. Zhao, H. Zheng, G.‐H. Yang, Distributed coordination in dynamic spectrum allocation networks, in: Proc. IEEE DySPAN 2005, November 2005, pp. 259–268.

[27] H. Zheng, L. Cao, Device‐centric spectrum management, in: Proc. IEEE DySPAN 2005, November 2005, pp. 56–65.

Documents

Copy of report - 123seminarsonly.com · Transmitter detection ... Aero Radar, Military: 1300-1400 MHz ... For, example for a delay sensitive transmission the modulation scheme