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This work analyses the performance of the orthogonal frequency division multiplexing (OFDM) technique, which is widely employed in wireless communication. The modified Jakes model for expeditious fading coefficients generation is adopted aiming to analyse and evaluate realistic communication channel scenarios. Multiple-input multiple-output (MIMO)...
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Citations
... Indeed, because the ML detector solution requires an exhaustive search through all possible symbol combinations [8], while linear closed solutions such as ZF and MMSE result in a poor performance for highly correlated channels [9], evolutionary heuristic algorithms are strong candidates for producing better solutions than linear detectors, and they result in reduced computational complexity compared to ML because heuristic approaches do not evaluate all possibilities. ...
This work analyzes the performance of implementable detectors for the multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system conditions, including antenna correlation and array configuration. A time‐domain channel model was used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order, and antenna array configurations. Several MIMO‐OFDM detectors were analyzed for the purpose of achieving high performance combined with high capacity systems and manageable computational complexity. Numerical Monte Carlo simulations demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic‐based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO‐OFDM context.
... Authors in [9] discuss how the performance of SISO-OFDM systems scale with the number of subcarriers. In the MIMO-OFDM context, the performance of ZF and MMSE linear detectors are analyzed under spatial correlation scenarios. ...
... In the MIMO-OFDM context, the performance of ZF and MMSE linear detectors are analyzed under spatial correlation scenarios. This work extends the results reported in [9]. In particular and differently of [9], herein, we propose a hybrid detection approach, where particle swarm optimization (PSO) and differential evolution (DE) evolutionary heuristics are combined with linear detectors (two detection steps), aiming to improve performance with reduced increment in complexity. ...
... This work extends the results reported in [9]. In particular and differently of [9], herein, we propose a hybrid detection approach, where particle swarm optimization (PSO) and differential evolution (DE) evolutionary heuristics are combined with linear detectors (two detection steps), aiming to improve performance with reduced increment in complexity. ...
In this paper, we analyze the performance of evolutionary heuristic‐aided linear detectors deployed in multiple‐input–multiple‐output (MIMO) orthogonal frequency‐division multiplexing (OFDM) systems, considering realistic operating scenarios. Hybrid linear‐heuristic detectors under different initial solutions provided by linear detectors are considered, namely, differential evolution and particle swarm optimization. Numerical results demonstrated the applicability of hybrid detection approach, which can improve considerably the performance of minimum mean‐square error and matched filter detectors. Furthermore, we discuss how the complexity of the presented algorithms scales with the number of antennas, besides of verifying the spatial correlation effects on MIMO‐OFDM performance assisted by linear, heuristic, and hybrid detection schemes. The influence of the initial point in the performance improvement and complexity reduction is evaluated numerically.
