Low-Complexity Signal Detection for Large-Scale MIMO in Optical Wireless Communications

Abstract

Optical wireless communication (OWC) has been a rapidly growing research area in recent years. Applying multiple-input multiple-output (MIMO), particularly large-scale MIMO, into OWC is very promising to substantially increase spectrum efficiency. However, one challenging problem to realize such an attractive goal is the practical signal detection algorithm for optical MIMO systems, whereby the linear signal detection algorithm like minimum mean square error (MMSE) can achieve satisfying performance but involves complicated matrix inversion of large size. In this paper, we first prove a special property that the filtering matrix of the linear MMSE algorithm is symmetric positive definite for indoor optical MIMO systems. Based on this property, a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method is proposed to reduce the overall complexity by one order of magnitude with a negligible performance loss. The performance guarantee of the proposed SOR-based algorithm is analyzed from the following three aspects. First, we prove that the SOR-based algorithm is convergent for indoor large-scale optical MIMO systems. Second, we prove that the SOR-based algorithm with the optimal relaxation parameter can achieve a faster convergence rate than the recently proposed Neumann-based algorithm. Finally, a simple quantified relaxation parameter, which is independent of the receiver location and signal-to-noise ratio, is proposed to guarantee the performance of the SOR-based algorithm in practice. Simulation results verify that the proposed SOR-based algorithm can achieve the exact performance of the classical MMSE algorithm with a small number of iterations.Index Terms-Optical wireless communications, large-scale MIMO, signal detection, minimum mean square error (MMSE), low complexity.

Beam Domain Massive MIMO for Optical Wireless Communications With Transmit Lens

“…Remark 3: Since LED chips are small, a common LED array could contain a large number of LEDs, such as 30 × 30 LEDs [17] and 60 × 60 LEDs [8]. Therefore, the asymptotic property of massive LED arrays is meaningful.…”

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

“…where r t denotes the sequence of decreasing step sizes [27]. In addition, we can simplify formulas (30) and (31), because (1 − p u, t − 1 )p u, t − 1 and (1 − g m, t − 1 )g m, t − 1 has no significant impact on the convergence of (30) and (31). Hence, Eqs.…”

“…The asymptotic notations, which reflect the computational complexity, was used to evaluation how the scheme responds to changes of parameters which are M, N, U, and K. From Table 2, we can easily observe the computational complexity gap between the four methods. Note that C N M denotes the binomial coefficient, and the matrix inverse operation [29][30][31][32] makes the computational complexity of per sample up to O(N 3 ). Thus, the overall complexity of our proposed Algorithm 1 for the problem of (11) is O(N 3 tL MCMC ), where t × L MCMC is the total number of target function evaluations.…”

Structured Compressive Sensing-Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO

“…For Step 2.1, the correlation operation has the complexity of O(RLM G N p ). For Step 2.2, both the support merger and Π s (·) have the complexity of O(L) [38], [39], while the norm operation has the complexity of O(RLM G ). To quantitatively compare the computational complexity of different operations, we consider the parameters used in Fig.…”