Large-scale Multiuser MIMO Systems Research Articles

Performance of ZF and CB Precoding Techniques for Large-Scale MU-MIMO System

Large Scale Multi User-MIMO (MU-MIMO) is a key technology with reference to 5G to achieve higher spectrum as well as energy efficiency. The new technology refers to the use of a large number of antennas at the base station serving many user terminals in the same time and frequency resource allowing the channel vectors nearly orthogonal as a result, there is a reduction in inter-user interference and users may be served with the significant data rate. The linear precoding techniques play a vital role in the reduction of interference among users and cells. In this paper, we have derived, analyzed and compared two important precoding techniques i.e. Zero-forcing (ZF) and Conjugate beamforming (CB) for large-scale multiuser-MIMO. We analyze these precoding techniques with respect to spectral efficiency and downlink power with imperfect channel state information (CSI) as well as with perfect CSI. It is shown that ZF performs better as compared to CB precoding for achieving higher spectral efficiency and requires lower downlink power. CB outperforms the ZF in terms of downlink transmit power when there is a requirement to achieve low spectral efficiency and also for cell- edge users, hence energy efficient in these cases. It is shown from simulation results that ZF precoding is the better choice for attaining higher spectral and energy efficiency for a large scale multiuser-MIMO communication system.

Asymptotic Analysis of RZF in Large-Scale MU-MIMO Systems Over Rician Channels

In this paper, we focus on the downlink ergodic sum rate of a single-cell large-scale multiuser MIMO system in which the base station employs N antennas to communicate with K single-antenna user equipments (TIEs). A regularized zero-forcing (RZF) scheme is used for precoding under the assumption that each TIE uses a specific power and each link forms a spatially correlated MIMO Rician fading channel. The analysis is conducted assuming that N and K grow large with a given ratio and perfect channel state information is available at the base station. New results from random matrix theory and large system analysis are used to compute an asymptotic expression of the signal-to-interference-plus-noise ratio as a function of system parameters, spatial correlation matrix, and Rician factor. Numerical results are used to validate the accuracy of asymptotic approximations in the finite system regime and to evaluate the performance under different operating conditions. It turns out that the asymptotic expressions provide accurate approximations even for relatively small values of N and K.

Upper Bound on Uplink Sum Rate for Large-Scale Multiuser MIMO Systems With MRC Receivers

In this letter, uplink sum rate of large-scale multiuser MIMO systems with maximum ratio combining receivers is analyzed. To be practical, no perfect channel state information is assumed, and both large scale path loss and small scale Rayleigh fading are considered. The large scale path losses experienced by multiple users are also assumed to be non-identical due to different locations of the users. A tight upper bound is derived in closed-form. Its tightness is verified by simulations. It is shown that the proposed upper bound is tighter than other bounds and the tightness is further improved when the system becomes larger, i.e., more antennas are deployed at the receiver. It thus provides a solid foundation for system design and optimization.

Linear Precoding Based on Polynomial Expansion: Large-Scale Multi-Cell MIMO Systems

Large-scale MIMO systems can yield a substantial improvement in spectral efficiency for future communication systems. Due to the finer spatial resolution achieved by a huge number of antennas at the base stations, these systems have shown to be robust to inter-user interference and the use of linear precoding is asymptotically optimal. However, most precoding schemes exhibit high computational complexity as the system dimensions increase. For example, the near-optimal RZF requires the inversion of a large matrix. This motivated our companion paper, where we proposed to solve the issue in single-cell multi-user systems by approximating the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are optimized to maximize the system performance. We have shown that the proposed TPE precoding with a small number of coefficients reaches almost the performance of RZF but never exceeds it. In a realistic multi-cell scenario involving large-scale multi-user MIMO systems, the optimization of RZF precoding has thus far not been feasible. This is mainly attributed to the high complexity of the scenario and the non-linear impact of the necessary regularizing parameters. On the other hand, the scalar weights in TPE precoding give hope for possible throughput optimization. Following the same methodology as in the companion paper, we exploit random matrix theory to derive a deterministic expression for the asymptotic SINR for each user. We also provide an optimization algorithm to approximate the weights that maximize the network-wide weighted max-min fairness. The optimization weights can be used to mimic the user throughput distribution of RZF precoding. Using simulations, we compare the network throughput of the TPE precoding with that of the suboptimal RZF scheme and show that our scheme can achieve higher throughput using a TPE order of only 3.