Question

What kind of interference is reduced by frequency planning in cellular networks and how does it get reduced?

Answer 1

The common types of interference in cellular networks are: self-interference, multiple access interference, co-channel interference (CCI) and adjacent channel interference (ACI). Self-interference is induced by signals that are transmitted on a shared transmitter.

The design process of selecting and allocating channel groups for all cellular base stations within a system is called frequency reuse or frequency planning. It is an important area to increase the efficiency and quality of the service by optimally using the frequency band. The main goal of the frequency-planning task is to increase the efficiency of the spectrum usage, keeping the interference in the network below some predefined level. Therefore it is always related to interference predictions. The frequency allocation is based on cell-to-cell interference probability estimation according to the network topology, field strength distribution and traffic load. This results in customized frequency performance of the selected radio network elements. With the usage of propagation model based on digital maps, we are able to obtain the interference predictions very near to reality.

Self-interference :- Self-interference is due to interference induced among signals that are transmitted from a shared transmitter. The amount of interference induced depends on the modulation type. In OFDM, self interference among sub carriers due to carrier frequency offsets caused by oscillator mismatches, Doppler Effect and fast fading caused by motion of the transceivers. Transceiver non-idealities such as amplifier non-linearity and IQ imbalance may also be source self-interference. Interference between the uplink and downlink transmissions in a FD duplex system may be also classified as self interference, as it occurs among signals send on the same two-way connection. This interference is mitigated by employing duplex filters. The impact of self-interference may be reduced by selecting the physical layer numerology such that the operating conditions and implementation technology are taken into account.

Multiple access interference :- Multiple access interference refers to the interference induced among the transmission from multiple radios using the same frequency resource to a single receiver. In theory, the physical layer will allow orthogonal multiple accesses, however, factors such as synchronization errors, RF circuitry non-idealities, and the effect of wireless propagation channel will not allow orthogonality to be maintained in practice. An essential method of maintaining orthogonality in multiple access scenarios is power control.

Co-channel interference :- CCI between links that reuse the same frequency band (channel). It is also referred to as inter-cell interference in cellular systems. The effect of CCI may be minimized by employing fixed frequency re-use patterns. Common methods for CCI management in cellular networks include: Frequency reuse, MIMO techniques, Interference alignment, and adaptation to interference variation. High frequency re-use factor implies a constant data rate across the service area (reference). This situation leads to similar throughput experience by users at different locations of the cell, and the service rate distribution. Another method of dealing with CCI is by considering co-operation among transmitters. Such techniques have been investigated in the literature under the name of network MIMO (Gesbert et al., 2010; Huang, Lau, & Chen, 2009). In network MIMO, the interference channel is transformed to a broadcast channel by considering the co-operating transmitter as a single transmitter (Janis, 2013). Data traffic may be shared between multiple cooperating transmitters from which it may then be coherently transmitted to the destination receiver.

Adjacent channel interference :- ACI is the interference induced between links that communicate in the same geographical location using neighboring frequency bands. A transmitter occupying a certain frequency band also leaks energy on frequency adjacent to that band. The out-of-band emissions are perceived as interference by other receivers. The effect of the out-of-band emissions may be quantified using the adjacent channel power ratio (ACPR). Signals outside the nominal frequency band generate interference components on the in-band frequencies at the receiver. The adjacent channel sensitivity (ACS) determines the ability of the receiver to cope with an out-of-band interferer. The properties of the RF chain that contribute to ACS characteristics include; the quality of channel selection filters, analogue-to-digital converter bit width, and the linearity of amplifiers and mixers.

Interference in the communication networks comes as undesirable nuisance. In this work, techniques employed in the management of interference encountered in cellular networks are reviewed. Propositions by different Authors include intermodulation solutions, frequency planning methods, genetic algorithms, Simulated annealing models, ordering heuristic, ant colony and multi-agent optimization, artificial neural networks model, evolutionary strategy approach (EAs), and hybrid EAs. While many techniques work more effectively in some areas of operations, others show strong performances in other areas. For instance, the technique of weak cooperation among base station proposed by some authors offer significant network performance when employed as it provides framework for optimal performances of adjacent base stations under some performance objectives. Also evolutionary strategy approach employed by some other authors shows proficiency in the management of channels allocation aimed at minimizing the problems of interference, which can manifest as call blocking or dropping. Furthermore, some authors designed hybrid EAs integrating local search and constraint programming into evolutionary operators, which was shown to speed up performance. Despite all efforts, challenges of interference still abound in communication networks. Among several techniques available to address interference networks, EAs has been given wider applications and the results show good performance of this technique. Meanwhile, genetic algorithm is also efficient where applied, but to reduce interference to the minimum in the current and future mobile communication networks, this review proposes synchronization of reduction techniques, where traffic-driven factors are considered.