Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection a...
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Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.
The communication industry is rapidly advancing towards 5G and beyond 5G (B5G) wireless technologies in order to fulfill the ever-growing needs for higher data rates and improved quality-of-service (QoS). Emerging app...
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The communication industry is rapidly advancing towards 5G and beyond 5G (B5G) wireless technologies in order to fulfill the ever-growing needs for higher data rates and improved quality-of-service (QoS). Emerging applications require wireless connectivity with tremendously increased data rates, substantially reduced latency, and growing support for a large number of devices. These requirements pose new challenges that can no longer be efficiently addressed by conventional approaches. Artificial intelligence (AI) is considered as one of the most promising solutions to improve the performance and robustness of 5G and B5G systems, fueled by the massive amount of data generated in 5G and B5G networks and the availability of powerful data processing fabrics. As a consequence, a plethora of research on AI-based communication technologies has emerged recently, promising higher data rates and improved QoS with affordable implementation overhead. In this overview paper, we summarize the state-of-the-art of AI-based 5G and B5G techniques on the algorithm, implementation, and optimization levels. We shed light on the advantages and limitations of AI-based solutions, and we provide a summary of emerging techniques and open research problems.
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