RFF Based Parallel Detection for Massive MIMO

Multi user massive multiple input multiple output (MU-m-MIMO) has emerged as a viable technology for scaling up existing communication systems, and in serving increasing number of users for the next-generation communication systems. Several signal processing algorithms exist for mitigating the performance-limiting artefacts encountered in MU-m-MIMO systems (like inter-symbol interference, inter-channel interference, and device nonlinearities), among which, reproducing kernel Hilbert space (RKHS) based approaches have emerged to provide effective solutions. However, most of the existing RKHS based detectors for MU-m-MIMO are dictionary-based, which makes it difficult to gauge the memory requirements beforehand, and are prone to error in the presence of noisy observations. Hence, to reduce the computational complexity, a Random Fourier Features (RFF) based parallel detection algorithm is proposed for MU-m-MIMO, that uses decomposed blocks of high dimensional observations, and makes the proposed detector scalable for parallel computation using modern multicore compute-units at the receivers (which is possible today due to advances in computing). Further, the RFF based explicit feature map to RKHS alleviates the requirement of a dictionary, and facilitates ease of practical implementation. Simulations are performed over realistic MU-m-MIMO systems, which indicates that the proposed approach delivers an acceptable uncoded BER performance, whilst maintaining a finite implementation budget, which makes the proposed approach attractive for implementation. Lastly, the error-rate analysis of the proposed detector is performed, and validated through simulations.