Decongesting telecom networks

By: |
March 2, 2016 12:24 AM

Trai should carry out spectral efficiency studies to determine if operators are using the spectrum optimally

The recent secretary-level change in the DoT was reported in a national daily under the heading ‘Secretary dropped over call drops’. Reportedly, communications and IT minister Ravi Shankar Prasad was not happy with the secretary, because of his inability to solve the problem of network congestion causing call drops.

The problem has become acute in the last six months and has come to the notice of the Prime Minister. Operators are blaming the government because, according to them, they do not have adequate spectrum to give satisfactory services to subscribers. However, the minister does not agree with the stand taken by operators and has pulled them up for not optimising their network. Trai has decided to impose fine on operators and they have been asked to pay R1 for each call dropped. According to operators, Trai is not justified in imposing the fine because, apart from shortage of spectrum, they are also unable to re-engineer their network by splitting the cells, which requires additional towers.

Municipal corporations and RWAs have imposed restrictions on installing of towers. Operators have gone to TDSAT against Trai’s decision to impose fines.

DoT should have referred the matter of optimal utilisation of spectrum bands to Trai. According to the Trai Act, the regulator is responsible for spectrum management as well as QoS of the network. Trai measures Grade of Service (GoS) at periodic intervals—it is the probability of a call not getting adequate network resource to be completed. It is typically 2% for a mobile network, particularly for the air interface, where traffic channels provided by the spectrum are a critical resource. Against the 2% norm, some networks have recorded over 40% call failure rates, according to the latest QoS tests conducted by Trai.

Spectrum is a scarce resource and policy-makers fear this resource will be completely exhausted in the near future.

In this context, it may be relevant to recall the dire predictions about food supply made by Malthus—the 18th century political economist. His prediction of food crises was proved to be wrong because technology and innovation drastically increased productivity beyond what was thought possible at the end of the 18th century. In India, we faced a food crises in early 1960s; however, the Green Revolution brought by innovative farming methods and better seeds made us surplus in food. We escaped Malthus’s dire predictions because technology enabled a far more efficient use of a scarce resource—land. While Malthus’s concerns were based on the ever-increasing population, similar scarcity arguments are being made about the radio spectrum. While the number of spectrum users continues to increase, the amount of spectrum is still considered a limited resource. But the efficiency of utilisation of this scarce resource can be increased dramatically by employing the latest technologies available.

The contention between the government and operators mainly concerns optimal utilisation of spectrum already allocated to the latter, and Trai should develop a methodology to measure spectral efficiency, which quantifies the amount of traffic the network can carry for a given spectrum and for a specified grade of service. Higher spectral efficiency provides higher quality of service to the end-users for specified traffic. It is measured in Erlang (named after a Danish engineer who did pioneering work in traffic engineering in early 20th century). The International Telecommunication Union (ITU) has defined spectral utilisation efficiency in its recommendation SM.1046-1. It states that efficient use of spectrum is achieved by, among other things, the isolation obtained from antenna directivity, geographical spacing, frequency sharing, orthogonal frequency use and time-sharing, i.e. time division.

These factors are reflected in definition of spectrum utilisation factor U, which is the product of frequency bandwidth, geographical space and the time denied to other potential users, i.e. BxSxT, where B is frequency bandwidth, S is geometric space (usually area) and T is time. SM.1046-1 also defines another term spectrum efficiency (SUE) of a radio communication system as SUE = M/U = M/(BxSxT) mobile systems. SUE for mobile systems is equal to traffic in Erlang divided by the amount of spectrum in MHz area in sq km.
A computational model can be employed for measuring the spectral efficiency of various networks. The model is based on the spectrum bandwidth and the technology employed (2G, 3G, 4G), and capacity enhancement techniques employed such as cell splitting, multi-layered techniques, enhanced power control, synthesised frequency hopping, capacity boosted by antenna arrays, network synchronisation, single antenna interference cancellation, dynamic frequency channel allocation, adaptive multi-rate codecs, etc.

We have to carry out a technical audit of networks to check whether capacity enhancement techniques have been employed to optimise the network and to get the maximum throughput from the allotted frequency resource. The Federal Communications Commission’s Technological Advisory Council has developed simulation models to measure the spectral efficiency of all the systems operating in the US, including 4G mobile systems. It should be possible to develop such models to measure the average spectral efficiency of 2G, 3G and 4G systems in the country.

The author is former member, Trai and Telecom Commission

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