Abstract
Enhanced genetic algorithms (GA) applied in space-time block coded (STBC) multiuser detection (MUD) systems in Rayleigh flat-fading channels are reported in this paper. Firstly, an improved objective function, which is designed to help speed up the search for the optimal solution, is introduced. Secondly, a decorrelating detector (DD) and a minimum mean square error (MMSE) detector have been added to the GA STBC MUD receiver to create the seed chromosome in the initial population. This operation has improved the receiver performance further because some signal information has been intentionally embedded in the initial population. Simulation results show that the receiver employing the improved objective function and the DD or MMSE detector can converge faster with the same bit error rate (BER) performance than the receiver with the initial population chosen randomly. The total signal-to-noise ratio (SNR) improvement contributed by these two modifications can reach 4 dB. Hence the proposed GA receiver is a promising solution of the STBC MUD problem.
Highlights
In wireless communications, space-time block coding (STBC) with diversity gains has been widely studied in multiuser detections (MUDs) [1, 2, 3] because STBC can utilize the information in the spatial and time domains simultaneously [4]
The decorrelating detector (DD) and minimum mean square error (MMSE) detectors have been embedded into the genetic algorithms (GA) STBC MUD system to generate the seed chromosome and provide some signal information to the first generation
The resulting simulations confirm that the receivers designed can converge faster than that with the initial population randomly chosen
Summary
Space-time block coding (STBC) with diversity gains has been widely studied in multiuser detections (MUDs) [1, 2, 3] because STBC can utilize the information in the spatial and time domains simultaneously [4]. The MUD technique that employs a combinatorial optimization process to exploit the information of all the users in order to detect a target user signal has been proposed to mitigate the near-far effect [7]. Among all the MUD techniques, the maximum likelihood (ML) optimal detector can achieve satisfactory bit error rate (BER) performance but the computational complexity varies exponentially with the number of users. The MUD technique is a nondeterministic polynomial (NP)-hard problem [9], which requires unforeseeable huge computing power in order to find a global optimum solution For such NP-hard problems, it is necessary to search for good approximation algorithms that yield solutions close to the optimum, they do not guarantee that a global optimum can be obtained for every instance. Notations Superscripts (·)∗, (·)T , and (·)H denote the complex conjugate, transpose, and Hermit operation respectively; (·)−1 refers to the matrix inverse operation; · is for the matrix/vector Frobenius norm; and · × · represents the dimension of a complex matrix
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