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A cooperative inter-cell interference (ICI) mitigation scheme with transmit beamforming for dense wireless LAN systems is proposed. The proposed scheme applies transmit beamforming used for downlink multiuser MIMO in order to mitigate the effect of ICI and selectively determines whether an access point (AP) performs null beamforming for each station (STA) in overlapping basic service sets (OBSSs) according to the ICI power. Null beamforming is used to suppress ICI if its power exceeds a threshold, otherwise, it is not carried out and the transmit antenna is used to obtain a diversity gain for STAs associated with the AP. Computer simulations confirm that the achievable rate obtained with the proposed scheme is superior to that obtained with either time resource sharing or conventional ICI mitigation in an OBSS environment.

Proposed is an adaptive fully transmitter-based block-diagonalisation scheme for multiple input multiple output (MIMO) multiuser downlink systems. A relaxation to the beamforming optimisation constraints is proposed, which introduces interference diversity and attains a more efficient performance optimisation. The trade-off to the performance improvement is an increase in the precoding complexity imposed by the adaptive nature of the proposed beamforming. A sub-optimal adaptive-decomposition beamforming scheme is also proposed with a reduced complexity overhead. Comparative analytical and simulation results to conventional beamforming demonstrate the significant diversity gains offered by the proposed scheme.

One of the challenges to implementing downlink multiuser multiple-input multiple-output in practical systems is to overcome the detrimental impact of the imperfect channel state information at transmitter (CSIT). Researchers have derived a low bound to describe the imperfect CSIT's impact on the achievable rate. However, this low bound is not tight, especially when the quality of CSIT is poor. A tighter low bound using the exponential integral is proposed. A strict proof is given and the simulation results verify the tightness.

Multiuser multiple‐input–multiple‐output (MIMO) has great potential to substantially improve throughput of wireless networks. Unfortunately, it requires a significant amount of feed back for user selection, which prevents practical implementation. The authors propose a feed back reduced downlink multiuser MIMO system, where a signal‐to‐interference‐plus‐noise ratio (SINR) threshold is carefully designed. Only the users whose SINRs are above the threshold feed back their SINRs and channel direction information (CDI) to the base station. They establish a monotonic relation between the threshold and the average feed back overhead. Based on the established relation, they design the threshold that balances the tradeoff between the feed back load and the sum rate. Simulation results show that the proposed approach can achieve the same performance as the conventional full‐feed back scheme (where every user feeds back its SINR and CDI), while the proposed approach is able to reduce the system overhead by more than 40%.

Energy efficiency （EE） is becoming increasingly important for wireless cellular networks. This paper addresses EE optimization problems in downlink multiuser MIMO systems with linear precoding. Referring to different active transmit/receive antenna sets and transmission schemes as different modes, we propose a joint bandwidth/power optimization and mode switching scheme to maximize EE. With a specific mode, we prove that the optimal bandwidth and transmit power is either full transmit power or full bandwidth. After deriving the optimal bandwidth and transmit power, we further propose mode switching to select the mode with optimal EE. Since the optimal mode switching, i.e. exhaustive search, is too complex to implement, an alternative heuristic method is developed to decrease the complexity through reducing the search size and avoiding the EE calculation during each search. Through simulations, we demonstrate that the proposed methods can significantly improve EE and the performance is similar to the optimal exhaustive search.

In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, block diagonalisation (BD) is a well-known precoding technique that eliminates inter-user interference. The number of simultaneously supportable users with BD is limited by the number of base station transmit antennas and the number of user receive antennas. Traditional MU-MIMO scheduling algorithms focus on sum capacity. However, these user scheduling algorithms might not be optimal with respect to energy efficiency. Here, the authors consider the energy-efficient MU-MIMO scheduling that maximises the energy efficiency. The brute-force search for the optimal user set, however, is computationally prohibitive. Therefore they propose a low-complexity user scheduling algorithm for energy-efficient MU-MIMO systems. They first obtain an approximation expression for the energy efficiency by utilising the upper bound of the MU-MIMO system capacity. Then they show that the maximum energy efficiency can be achieved if the scheduling user set is selected to obtain the largest matrix volume. The numerical results show that the proposed algorithm achieves a good tradeoff between energy efficiency and computational complexity. They also consider the problem of maintaining fairness among users, and propose a simplified proportional fair (PF) scheduling algorithm.

This paper proposes a novel receiving antenna allocation scheme for downlink multiuser multiple input multiple output orthogonal frequency division multiplexing (MU-MIMO-OFDM) transmission; an access point (AP) simultaneously transmits data frames to a combination of allocated receiving antennas on a subcarrier basis at each station (STA). The proposed scheme combines a limited channel state information (CSI) feedback sequence with a receiving antenna decision method. In the proposed scheme, each STA estimates the channel responses of all receiving antennas by using the training preamble transmitted from an AP, and then feeds the channel response of the antenna with maximum norm back to the AP when the spatial correlation value between receiving antennas is higher than a threshold. Otherwise, each STA feeds full channel responses back to the AP. This scheme decreases the amount of CSI fed back while exploiting the spatial diversity gain, and the AP’s computational complexity is also decreased regarding the antenna allocation. Moreover, the receiving antenna decision method eliminates the overhead to notify the allocated antenna information from the AP to each STA by simply comparing its own receiving antenna powers. We clarify the effectiveness of the proposed scheme in our computer simulations using channel responses measured in an indoor environment. The results show that the proposed scheme maintains the channel capacity of the downlink MU-MIMO-OFDM transmission while greatly reducing the overhead and computational complexity.

In this study, the authors present a novel low backhaul load cooperative transmission framework for the two-cell multiple-input–single-output systems. In this framework, the neighbouring two base stations (BSs) take turns to transmit data in two consecutive slots. In each slot, only one BS is active, transmitting the preprocessed data symbols of both its own serving user and the cooperative user in neighbouring cell, and sharing its preprocessed data symbols to the other cooperative BS for next transmission. Linear constellation spreading is utilised for preprocessing which helps the system to exploit the macro-diversity without reducing the multiplexing gain. Besides, zero-forcing beamforming is applied in each transmission slot so as to cancel the multiuser interference. In this way, the inter-cell links become beneficial rather than detrimental. Pairwise error probability analysis demonstrates that the multi-cell spatial diversity gain can be achieved for each data stream. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing relevant strategies with less channel estimation overhead. It is shown that because of the higher diversity order it achieved, the proposed scheme can significantly improve the error performance in a distributed manner while maintaining the same multiplexing gain.

Multiuser multiple-input multiple-output (MU-MIMO) has been proposed as a means to improve spectrum efficiency for various future wireless communication systems. This paper reports indoor experimental results obtained for a newly developed and implemented downlink (DL) MU-MIMO orthogonal frequency division multiplexing (OFDM) transceiver for gigabit wireless local area network systems in the microwave band. In the transceiver, the channel state information (CSI) is estimated at each user and fed back to an access point (AP) on a real-time basis. At the AP, the estimated CSI is used to calculate the transmit beamforming weight for DL MU-MIMO transmission. This paper also proposes a recursive inverse matrix computation scheme for computing the transmit weight in real time. Experiments with the developed transceiver demonstrate its feasibility in a number of indoor scenarios. The experimental results clarify that DL MU-MIMO-OFDM transmission can achieve a 972-Mbit/s transmission data rate with simple digital signal processing of single-antenna users in an indoor environment.

In this paper the scheduling problem in downlink multiuser MIMO system is described as an optimization problem and particle swarm optimization (PSO) algorithm is introduced to address such problem. Two PSO scheduling methods with different objective functions applicable to different requirements on capacity and complexity are investigated. One is the capacity based PSO(C-PSO) scheduling method aiming at achieving the near optimal capacity; and the other is the lower bound of eigenvalue based PSO (LBE-PSO) scheduling method with the purpose of reducing computational complexity and at the same time achieving as large as possible capacity gain. Furthermore, convergence analysis of PSO from both the particle and the velocity aspects is also presented to derive the convergent condition, which is validated by several examples of different parameter values. Simulation results reveal that the C-PSO can obtain nearly the same capacity as the exhaustive search method with lower complexity, while the LBE-PSO provides a viable approach by striking a better tradeoff between capacity and computational complexity.