The Future of Radio Network Load Balancing White Paper

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Text of The Future of Radio Network Load Balancing White Paper

  • November 2011

    White Paper

    The Future of Cellular Radio Network Load Balancing

    A special report for mobile operators and optimization engineers

  • White paper: The Future of Cellular Radio Network Load Balancing

    2

    Executive summary The RF conditions in the cellular radio network were never static. For load balancing

    functions in the predictable world of voice and low volume data, however, this

    approximation was close enough. The introduction of smartphones and mobile data

    devices brought an exponential increase in mobile data usage, in turn increasing the

    unpredictability of subscribers demand which causes loading on network resources.

    A handful of subscribers, using bandwidth-hungry services, can

    push cells to congestion anywhere, anytime.

    Traditional optimization methods are slow, based on long term network statistics, and

    have a long turnaround time basically changing from one static network

    configuration working point to another. Iterations must be followed by a labor-

    intensive verification stage. Operators who chose to maintain these methods might

    find themselves facing underutilized resources, premature expansion costs to support

    peak loads, and customer dissatisfaction resulting from overcrowded networks.

    In order to support the changing network needs, operators require a fully integrated

    automated load balancing application with a built in feedback mechanism. The radio

    engineers can leave the tedious roles of manual optimization to a machine, and focus

    on defining network policies, performance goals and performing radio planning

    activities.

    A configuration

    that was optimal

    early this morning

    could fall short

    before lunchtime

  • White paper: The Future of Cellular Radio Network Load Balancing

    3

    Dealing with dynamic networks Live networks have dynamic RF traffic patterns that change throughout the week, and

    over the course of a day. Changes in behavior of voice verses data usage, roads and

    business areas compared to adjacent residential areas.

    Unexpected load imbalances due to massive gatherings, cell malfunction or

    introduction of new cells in an area, all effect the load distribution, and are rarely dealt

    with as soon as they occur.

    Since dealing with such dynamics is impossible from practical engineering practice

    perspective, a preventive RNP (Radio Network Planning) approach is normally taken.

    By this approach, cells are dimensioned to handle the busy hour traffic, regardless of

    what is occurring in others cells in the area.

    The high cost of imbalance

    The sunk cost of underutilized resources In Static networks with no time-sharing of geographically distributed resources, costly

    resources supporting peak traffic are very often unused.

    In some cases, there might be underutilized cells nearby resources that the operator

    already paid for, i.e. sunk costs.

    This situation is further emphasized in cases of bursty traffic demand and high

    variability of the active users locations. Furthermore, exponential increase in demand

    for mobile broadband increases this gap greatly.

    Denied admissions pattern in two ajacent cells. Note that the peak load does not follow the same time pattern.

    At certain times,

    underutilized

    resources already

    paid for might be

    available next to

    loaded cells.

  • White paper: The Future of Cellular Radio Network Load Balancing

    4

    The mobile data crunch Mobile operators today are facing an avalanche of demand, driven by the mobile data

    crunch - fast penetration of smartphones and mobile broadband. The impact is

    colossal. According to a mobile data usage study conducted by Cisco, an iPhone

    generates as much traffic as 96 non-smart phones; a tablet generates as much as 122

    non-smart phones and a laptop with a data card consumes as much traffic as 515 non-

    smart phones (Cisco, 2011).

    Mobile data traffic prediction by Cisco

    The operational cost of networks increases to meet the increasing demands. According

    to a network cost analysis conducted by Informa telecoms & media, network costs are

    expected to increase by 30% in the next 2 years, and continue in this pace (Informa

    Telecoms & Media, 2011b).

    To support this increase in traffic would require a similar increase in resources, causing

    more increase the underutilized resource gap.

    Increased uneven distribution of bandwidth demands

    increases this gap greatly The top 1 percent of mobile data subscribers generate over 20 percent of mobile data

    traffic, and the top 10 percent of mobile data subscribers now generate approximately

    60 percent of mobile data (Cisco, 2011). The impact of the concentration of usage on

    network planners is that it is becoming increasingly difficult to predict load patterns in

    both time and place.

    This, in turn, magnifies the resource gap in unpredictable amounts.

    A handful of

    subscribers, using

    bandwidth-hungry

    services, can push

    cells to congestion

    anywhere, anytime.

  • White paper: The Future of Cellular Radio Network Load Balancing

    5

    The Empty bus syndrome:

    Expansions performed to maintain high level QoS at peak

    usage times In order to support the peak traffic, radio planners dimensioning rules dictate adding

    new cells or resources according to the peak traffic in the busy hours measured per

    cell. In unbalanced networks, the load is uneven, and the busy hours are not the same

    in all their cells, and there might be underutilized available resources already paid for

    in the vicinity of a certain loaded cell.

    Overloaded cells cause customer dissatisfaction, increased

    churn, and lost revenue The option of leaving the network unbalanced, without expansion will risk

    congestions, call setup failures and reduced data QoE at peak traffic conditions.

    Leaving the network unbalanced without expansion will limit the data throughput

    available to subscribers at peak time, lowering subscriber satisfaction, and possible

    loss of revenue.

    Existing solutions to handle load balancing Current network optimization processes are handled manually by radio engineers.

    The granularity of existing optimization cycles is quite large, and can take days or even

    weeks thus making long term adjustments which are normally based on large scale

    time averaging of traffic loading. By their very nature, such solutions can fit long term

    or predicted load issues, and will, at best, provide a passible compromise between the

    needs of different areas.

    What types of solutions are currently available?

    Decision supporting tools Using decision-supporting tool to perform the optimization calculations such as

    required expansions, RF parameter changes, and the predicted impact on

    performance, improves the capability of taking more inputs into consideration.

    However, these types of tools provide reports not actions, and are prone to error

    due to the high degree of sensitivity to initial conditions. The radio engineers are still

    left with the tasks of verifying the resulting recommendations, updating the OSS /

    NMS, and checking the results. This is an open-loop solution, where the entire end-to-

    end process includes manual stages to complete.

    Local load balancing between carriers Some equipment vendors offer solutions of inter-frequency load balancing. These

    solutions can balance loads between carriers - generally co-sectors in the same base

    station. Traditional

    optimization works

    through big

    periodical jumps

    from one static

    working point to

    another

  • White paper: The Future of Cellular Radio Network Load Balancing

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    These solutions, while efficient in resolving localized load imbalance cases, do not

    provide a solution for a balancing the load in a cluster of cells, and require an

    infrastructure of multiple carriers in each sector.

    Wi-Fi or Femto offloading For local areas with consistent capacity problems, operators can elect to offload the

    data portion to a local Wi-Fi or femtocell. However, according to a recent report by

    Informa, in order to extract value from Wi-Fi offload mobile operators will require

    carrier-grade Wi-Fi networks that are more tightly integrated into the operators

    network and back office environments than at present, and deployment of which will

    incur significant costs (Informa Telecoms & Media, 2011a).

    Additionally, both Femto and Wi-Fi offloading are complex techniques which require

    high level of backoffice configuration management, installation and supporting

    equipment, on the network side (such as Femto Gateways) or in the UE side (the client

    has to support controlled Wi-Fi offloading) etc. These reasons make those techniques

    non trivial and not suitable for every operator.

    In any case, this solution can only add capacity to a fixed location, in the vicinity of the

    Wi-Fi AP of the Femto itself, and does not provide a solution to congestion situations

    that change over time and place.

    Furthermore, by passing the data to a separate network, the operator faces a potential

    loss of revenue, and has less control over the QoS and SLAs toward the customers.

    With F