Statistical Eigenmode Transmission for the MU-MIMO Downlink in Rician Fading

Abstract

Abstract-In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen's inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SE-SDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician K-factor on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician K-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zeroforcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician K-factor regime. Finally, numerical results are presented to validate our analysis.

MU‐MIMO Downlink Capacity Analysis and Optimum Code Weight Vector Design for 5G Big Data Massive Antenna Millimeter Wave Communication

“…Assume that each user is equipped with a single antenna element [3]. Additionally, the number of the BS antenna elements is assumed to be larger than that of users ( ≥ ); and the power is equally allocated among all users [11,12].…”

“…It was found that when the users are homogeneously distributed and the number of antenna elements is large enough ( → ∞), the SLNR is asymptotically equal to SINR. In [11], an efficient statistical Eigen-mode space division multiple access (SE-SDMA) scheme for downlink ergodic sum-rate analysis based on SLNR was proposed. To maximise the approximate ergodic sum-rate capacity, authors in [12] designed an optimal beamforming vector for each user based on a three-dimensional beamforming algorithm.…”

FDM-Structured Preamble Optimization for Channel Estimation in MIMO-OQAM/FBMC Systems

“…Due to the above superiorities over OFDM, OQAM/FBMC is being considered as a promising technique for cognitive radio [8], professional mobile radio (PMR) evolution [9], and 5G cellular networks [10], [11]. As another promising technique in future communication systems, multiple-input multipleoutput (MIMO) is able to increase the system throughput and the link reliability [12], [13]. Therefore, it is believed that the successful combination of MIMO and OQAM/FBMC is important and is envisioned to achieve higher spectral efficiency [14]- [16].…”

Revisiting MMSE Combining for Massive MIMO over Heterogeneous Propagation Channels

“…Due to its extremely short wavelengths, mmWaves are unable to propagate over large distances, and are primarily envisaged to operate in small-cellular scenarios, where there is a considerably higher probability of a terminal experiencing LoS propagation. To this end, several investigations have examined the performance of massive MIMO with Ricean fading [10][11][12][13], including studies which consider variability in the Ricean K-factors across multiple terminals [9,14].…”