**3. Related work**

This section presents a brief literature review of applications of evolutionary algorithms and their variants to wireless communications problems.

Genetic algorithms (GAs) are among the widely used optimization techniques for address‐ ing design problems in wireless communications. In [26] a Smith prediction filter is proposed for power control design of direct-sequence code-division multiple-access cellular mobile radio systems. A fixed-order robust H∞ loop filter is developed using a genetic algorithm to minimize the worst-case variance of the received SINR from the minimax perspective. The authors in [27] present an antenna selection method for multiple-input multiple-output wireless systems based on a GA that seeks the best subset of antenna elements. The problem of receive antenna selection and symbol detection for multipleinput, multiple-output (MIMO) systems is solved in [28] by applying a genetic algorithm (GA) variant. The paper in [29] addresses the problem of joint transmit/receive antenna selection for MIMO systems using a real-valued genetic algorithm (RVGA). The optimiza‐ tion objective is to improve the channel capacity of multiple-input/multiple-output (MIMO) systems. The study in [30] presents a pattern discovery algorithm for multi-streams mining in wireless sensor networks. This algorithm adapts genetic operators with Elitism Strat‐ egy.The paper in [31] studies joint multiuser linear precoding design in the forward link of fixed multibeam satellite systems. The authors use a generic optimization framework for linear precoding design to handle any objective functions of data rate with general linear and nonlinear power constraints. In [32] an energy-efficient genetic algorithm mechanism is presented to resolve quality of service (QoS) multicast routing problem: The proposed genetic algorithm depends on bounded end-to-end delay and minimum energy cost of the multicast tree.

SI algorithms are among the most commonly used algorithms for solving problems in wireless communications. PSO and several PSO variants have been used in the literature to solve different problems. In [33] optimal power scheduling for distributed detection in a Gaussian sensor network is addressed for both independent and correlated observations. A PSO based technique is developed to find the optimal power allocation for arbitrary correlations. The authors in [34] apply PSO to solve the constrained nonlinear optimisation problem for the minimum bit error rate (MBER) multiuser transmitter (MUT). The proposed PSO aided symbol-specific MBER-MUT and average MBER-MUT schemes provide improved perform‐ ance in comparison to the conventional minimum mean-square error MUT scheme. Several issues in Wireless Sensor Networks (WSNs) can be formulated as multidimensional optimi‐ zation problems, and addressed using bio-inspired algorithms. In [35] the authors present a brief survey of how PSO is used to address these issues. The authors in [36] propose a new approach to estimate the location of a sensor in a wireless sensor network based on a new PSO algorithm with a log-barrier approach. The paper in [37] presents a predistorter based on a cluster-based implementation particle swarm optimization technique with embedded modelsize estimation capability and validates the proposed technique on a Doherty power amplifier prototype.

The ABC algorithm and its variants have been successfully applied to several optimization problems in wireless communications. Among others these include issues in WSN [38-41], WiMax network planning [42] and channel assignment [43].

The ACO algorithm has also been applied to several combinatorial problems in wireless systems which include problems in mobile ad hoc networks [44-46], problems in WSN [47-51], cognitive radio [52], resource allocation in multiuser OFDM systems [53] and MIMO problems [54, 55].

DE variants have also been applied to variety of optimization problems like multi-user detection in multi-carrier CDMA [56], WSNs issues [57, 58], urban area path loss prediction [59], spectrum sharing [60], optimization of interleave-division multiple-access communica‐ tion systems [61]. Other papers use a number of different optimization algorithms and compare results. For example in [62] spectrum allocation methods for cognitive radio based on GA, quantum genetic algorithm (QGA), and PSO, are proposed.
