Search

Minimum mean square error (MMSE) detection algorithm can achieve nearly optimal performance when the number of antennas at the base station (BS) is large enough compared to the number of users. But the traditional MMSE involves complicated matrix inversion. In this paper, we propose a modified MMSE algorithm which exploits the channel characteristics occurring in massive multiple-input multiple-output (MIMO) channels and the relaxation iteration (RI) method to avoid the matrix inversion. A proper initial solution is given to accelerate the convergence speed. In addition, we point out that the channel estimation scheme used in channel hardening-exploiting message passing (CHEMP) receiver is very appropriate for our proposed detection algorithm. Simulation results verify that the proposed algorithm can achieve very close performance of the traditional MMSE algorithm with a small number of iterations.

As an important part of the channel fading, large scale fading should be considered in downlink massive multiple-input multiple-output (MIMO) systems. This paper investigates the downlink massive MIMO system over a large scale fading channel, when the base station takes advantage of maximum-radio transmission (MRT) precoding. The cases when the base station has perfect and imperfect channel state information (CSI) are considered respectively. Specially, through the analysis of single user's ergodic achievable rate, some novel and approximate expressions for the spectral efficiency (SE) are derived. Based on the approximate SE, the effect of the channel estimation error is analyzed intuitively. In addition, the average SE of all the users with different large-scale fading parameters is carefully investigated. Simulations validate that all the theoretical results coincide with numerical results and the large scale fading factors have little influence on SE reduction resulted from channel estimation.

We consider the problem of nearly optimal energy efficiency in massive (Multiinput Multi-output) MIMO systems. Considering the correlated channel in practice, we derive the ergodic expression with zero-forcing precoding and analyze the simplified antennas selection method. Aiming at optimizing the energy efficiency, the closed form expressions of the nearly optimal number of transmit antennas and transmit power are given under the circuit consumption model. The joint solution of the number of transmit antennas and transmit power was replaced to only solve transmit power. Based on the expression only related with transmit power, we give an energy efficiency optimization algorithm. The simulation results show that the proposed algorithm can achieve nearly optimal energy efficiency with fast convergence speed.

This paper investigates the multiuser multiple-input–multiple-output (MIMO) linear cooperative spectrum sensing optimization problem, in which the primary user (PU) and the cognitive radio (CR) are equipped with multiple antennas. By optimizing the different weights assigned on the received signals of CRs, the cooperative spectrum sensing optimization aims at maximizing the probability of detection given a targeted probability of false alarm. Statistical characteristics of parameters in MIMO cooperative spectrum sensing systems have been determined for the PU with a single antenna and the CR with multiple antennas, the PU with multiple antennas and the CR with a single antenna, and both the PU and the CR with multiple antennas. Because of the non-convex characteristic of the optimization problem, an alternative approach based on a genetic algorithm (GA) instead of convex approaches is proposed to find the optimal weight vectors without solution domain restrictions and convexity constraints. Furthermore, several classical GA crossover operators have been provided to investigate their effect on sensing performance. The simulation results show that, the reliability of spectrum sensing in cooperative spectrum sensing system can be significantly improved with multiple antennas. Furthermore, the GA method is a promising approach in addressing the cooperative spectrum sensing problem.

This paper investigates the detecting algorithm for MIMO system, in which the Multi-layered orthogonal space-time block code (OSTBC) is employed to transmit the data symbols. First, the matched filter design problem based on maximizing the SINR of each layer is investigated, which is characterized as a generalized eigen-value problem that can be efficiently solved by the Generalized Rayleigh-Quotient (R-Q) Theorem. Then a new efficient space time detecting algorithm is proposed for Multi-layered OSTBC with this R-Q matched filter, in which first a linear recombination strategy is applied on the received signal to construct a high-dimensional equivalent channel derived from the OSTBC encoding pattern, as a joint characterization of channel fading and OSTBC encoding. Then using the high-dimensional equivalent channel matrix, a group of R-Q matched filters are designed to effectively extract the receive signal of the desired layer and suppress the interference from other layers. Finally, linear decoding is used to detect the transmitted symbols by exploiting the OSTBC-induced structure in the equivalent channel matrix of each layer after interference cancellation. Furthermore, the proposed algorithm in presence of the channel estimation error is also analyzed. Theory analysis and simulation results reveal that the proposed algorithm has the benefits of good robustness, better BER and capacity characteristics.