Ryo Hayakawa

Massive overloaded MIMO signal detection via convex optimization with proximal splitting

In this paper, we propose signal detection schemes for massive overloaded multiple-input multiple-output (MIMO) systems, where the number of receive antennas is less than that of transmitted streams. Using the idea of the sum-of-absolute-value (SOAV) optimization, we formulate the signal detection as a convex optimization problem, which can be solved via a fast algorithm based on Douglas-Rachford splitting. To improve the performance, we also propose an iterative approach to solve the optimization problem with weighting parameters update in a cost function. Simulation results show that the proposed scheme can achieve much better bit error rate (BER) performance than conventional schemes, especially in large-scale overloaded MIMO systems.

An overloaded MIMO signal detection scheme with slab decoding and lattice reduction

This paper proposes a reduced complexity signal detection scheme for overloaded MIMO (Multiple-Input Multiple-Output) systems. The proposed scheme firstly divides the transmitted signals into two parts, the post-voting vector containing the same number of signal elements as of receive antennas, and the pre-voting vector containing the remaining elements. Secondly, it uses slab decoding to reduce the solution candidates of the pre-voting vector and determines the post-voting vectors for each pre-voting vector candidate by lattice reduction aided MMSE (Minimum Mean Square Error)-SIC (Successive Interference Cancellation) detection. Simulation results show that the proposed scheme can achieve almost the same performance as the optimal ML (Maximum Likelihood) detection while drastically reducing the required computational complexity.

Convex Optimization-Based Signal Detection for Massive Overloaded MIMO Systems

This paper proposes signal detection schemes for massive multiple-input multiple-output (MIMO) systems, where the number of receive antennas is less than that of transmitted streams. Assuming practical baseband digital modulation and taking advantage of the discreteness of transmitted symbols, we formulate the signal detection problem as a convex optimization problem, called sum-of-absolute-value (SOAV) optimization. Moreover, we extend the SOAV optimization into the weighted-SOAV (W-SOAV) optimization and propose an iterative approach to solve the W-SOAV optimization with updating the weights in the objective function. Furthermore, for coded MIMO systems, we also propose a joint detection and decoding scheme, where log likelihood ratios of transmitted symbols are iteratively updated between the MIMO detector and the channel decoder. In addition, a theoretical performance analysis is provided in terms of the upper bound of the size of the estimation error obtained with the W-SOAV optimization. Simulation results show that the bit error rate performance of the proposed scheme is better than that of conventional schemes, especially in large-scale overloaded MIMO systems.