Employing temporal channel correlation in user selection for MIMO broadcast systems

Taking the time varying nature of wireless channels into account, two user selection schemes with lower complexity are developed for multiple-input multiple-output broadcast （MIMO BC）systems. According to the relationship between coherence time and Doppler frequency, an information frame is divided into several segments. At the beginning of each segment, the user selection is carded out with the greedy selection algorithm. In the simplified user selection algorithms employing the temporal correlation （SUSTC）, the selection results are applied for all the remaining slots in each segment. But in the improved simplified user selection algorithms employing the temporal correlation（ISUSTC）, at the remaining slots, users are kept with favorable channel conditions selected at the previous slot, and other users are updated from the candidate pool to communicate simultaneously. Simulations show that compared with the greedy user selection method, the proposed algorithms can reduce the selection complexity with a little sum capacity loss.

TRANSMIT ANTENNA SELECTION IN V-BLAST SYSTEM

In order to reduce the cost of Radio Frequency (RF) chains in the spatial multiplexing systems with Vertical-Bell Labs Layered Space-Time (V-BLAST) nonlinear receiver, a novel transmit antenna selection cri-terion is proposed with the motivation of minimizing the Vector Symbol Error Rate (VSER). In the proposed scheme, both the number of substreams and the mapping of substreams to antennas are dynamically adjusted based on the knowledge of channel. Simulation results illustrate that the proposed two-step selection criterion outperforms the existing eigenmode based selection criterion by 0.3dB at a VSER of 10?3.

Antenna selection for V-BLAST systems in correlated channel

Spatial multiplexing systems can provide significant capacity improvement but are sensitive to channel correlation. Antenna selection is a low-cost low-complexity ahemative to resolve these problems. This paper proposes a new transmit antenna selection algorithm for the spatial multiplexing systems with the Vertical-Bell Labs Layered Space-Time （V-BLAST） nonlinear receiver in correlated channel. The proposed scheme separates the optimization into two parts： it first chooses the optimal number of substreams in terms of the singular values of the channel matrix, then adapts the mapping of substreams to antennas according to the post-detection signal-to-noise ratio （SNR）. Simulation results illustrate that the proposed two-step selection criterion can provide greater selection gain than the existing singnlar value based selection criterion as SNR and scattering angle increases. The proposed criterion outperforms the existing one by 0.3 dB at a vector symbol error rate of 10^- 3 and scattering angle of 20 degree.

Joint User Scheduling and Antenna Selection in Distributed Massive MIMO Systems with Limited Backhaul Capacity

Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station(BS).In a distributed massive MIMO system,the capacity of fiber backhaul that links base station and remote radio heads is usually limited,which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink.To solve this problem,we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity.Three sub-optimal iterative algorithms with the objective of sumrate maximization are proposed for the joint optimization of antenna selection and user scheduling,either based on greedy fashion or Frobenius-norm criteria.Convergence and complexity analysis are presented for the algorithms.The provided Monte Carlo simulations show that,one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.