Multi-user MIMO

About: Multi-user MIMO is a research topic. Over the lifetime, 10265 publications have been published within this topic receiving 227206 citations. The topic is also known as: multi user mimo & MU-MIMO.

LDPC-coded MIMO systems with unknown block fading channels: soft MIMO detector design, channel estimation, and code optimization

Abstract: This paper considers the design of a practical low-density parity check (LDPC)-coded multiple-input multiple-output (MIMO) system composed of M transmit and N receive antennas operating in a flat-fading environment where channel state information (CSI) is assumed to be unavailable both to the transmitter and the receiver. A soft iterative receiver structure is developed, which consists of three main blocks: a soft MIMO detector and two LDPC component soft decoders. We first propose at the component level several soft-input soft-output MIMO detectors whose performances are much better than the conventional minimal mean square error (MMSE)-based detectors. In particular, one optimal soft MIMO detector and two simplified suboptimal detectors are developed that do not require an explicit channel estimate and offer an effective tradeoff between complexity and performance. In addition, a modified expectation maximization (EM)-based MIMO detector is developed that completely removes positive feedback between input and output extrinsic information and provides much better performance compared with the direct EM-based detector that has strong correlations especially in fast-fading channels. At the structural level, the LDPC-coded MIMO receiver is constructed in an unconventional manner where the soft MIMO detector and LDPC variable node decoder form one super soft-decoding unit, and the LDPC check node decoder forms the other component of the iterative decoding scheme. By exploiting the proposed receiver structure, tractable extrinsic information transfer functions of the component soft decoders are obtained, which further lead to a simple and efficient LDPC code degree profile optimization algorithm with proven global optimality and guaranteed convergence from any initialization. Finally, numerical and simulation results are provided to confirm the advantages of the proposed design approach for the coded MIMO system.

System and method for combination multiple input, multiple output (mimo) and beamforming

Abstract: Specifically, a method and system are provided that performs MIMO and beamforming at a base station (102) based on an uplink channel sounding (ULCS) from only one of a mobile station's (130) antennas (132, 134) and closed-loop multiple input, multiple output (MIMO) schemes based on the singular value decomposition (SVD) of the channel matrix. The ULCS is limited to sounding and the channel uses fewer than an optimal number of transmit antennas, for example, one for WiMAX. The base station's array (118, 120, 122, 124) may be configured for a full array transmitting mode (800, 900) or a sub-array transmitting mode (1200, 1300).

Effect of Mutual Coupling on the Channel Capacity of MIMO Systems

Abstract: The reciprocity theorem is applied to derive an exact expression that is used to remove the coupling effects embedded in the receiving voltages of an antenna array. The correlation coefficients of the multiple-input-multiple-output (MIMO) channel established with the antenna array is also modified to incorporate the direction-dependent coupling effects more accurately. The effects of mutual coupling on the channel capacity of MIMO systems in a line-of-sight (LOS), as well as a multipath environments, under different coupling assumptions, are simulated and compared.

Large System Analysis of the Energy Consumption Distribution in Multi-User MIMO Systems With Mobility

Abstract: In this work, we consider the downlink of a single-cell multi-user MIMO system in which the base station (BS) makes use of $N$ antennas to communicate with $K$ single-antenna user equipments (UEs) . The UEs move around in the cell according to a random walk mobility model. We aim at determining the energy consumption distribution when different linear precoding techniques are used at the BS to guarantee target rates within a finite time interval $T$ . The analysis is conducted in the asymptotic regime where $\protect{N}$ and $K$ grow large with fixed ratio under the assumption of perfect channel state information (CSI) . Both recent and standard results from large system analysis are used to provide concise formulae for the asymptotic transmit powers and beamforming vectors for all considered schemes. These results are eventually used to provide a deterministic approximation of the energy consumption and to study its fluctuations around this value in the form of a central limit theorem. Closed-form expressions for the asymptotic means and variances are given. Numerical results are used to validate the accuracy of the theoretical analysis and to make comparisons. We show how the results can be used to approximate the probability that a battery-powered BS runs out of energy and also to design the cell radius for minimizing the energy consumption per unit area. The imperfect CSI case is also briefly considered.

SIEVE: Scalable user grouping for large MU-MIMO systems

Abstract: Multi-user multiple input and multiple output (MU-MIMO) is one predominate approach to improve the wireless capacity. However, since the aggregate capacity of MU-MIMO heavily depends on the channel correlations among the mobile users in a beamforming group, unwisely selecting beamforming groups may result in reduced overall capacity, instead of increasing it. How to select users into a beamforming group becomes the bottleneck of realizing the MU-MIMO gain. The fundamental challenge for user selection is the large searching space, and hence there exists a tradeoff between search complexity and achievable capacity. Previous works have proposed several low complexity heuristic algorithms, but they suffer a significant capacity loss. In this paper, we present a novel MU-MIMO MAC, called SIEVE. The core of SIEVE design is its scalable multi-user selection module that provides a knob to control the aggressiveness in searching the best beamforming group. SIEVE maintains a central database to track the channel and the coherence time for each mobile user, and largely avoids unnecessary computing with a progressive update strategy. Our evaluation, via both small-scale testbed experiments and large-scale trace-driven simulations, shows that SIEVE can achieve around 90% of the capacity compared to exhaustive search.