Distributed Multi-input Multi-output Research Articles

Topological Pilot Assignment in Large-Scale Distributed MIMO Networks

We consider the pilot assignment problem in large-scale distributed multi-input multi-output (MIMO) networks, where a large number of remote radio head (RRH) antennas are randomly distributed in a wide area, and jointly serve a relatively smaller number of users (UE) coherently. By artificially imposing structures on the UE-RRH connectivity, we model the network by a partially-connected interference network, so that the pilot assignment problem can be cast as a topological interference management problem with multiple groupcast messages. Building upon such connection, we formulate the topological pilot assignment (TPA) problem in two different ways with respect to whether or not the to-be-estimated channel connectivity pattern is known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> . When it is known, we formulate the TPA problem as a low-rank matrix completion problem that can be solved by a simple alternating projection algorithm. Otherwise, we formulate it as a sequential maximum weight induced matching problem that can be solved by either a mixed integer linear program or a simple yet efficient greedy algorithm. With respect to two different formulations of the TPA problem, we evaluate the efficiency of the proposed algorithms under the cell-free massive MIMO setting.

A Novel Receiver Design and Maximum-Likelihood Detection for Distributed MIMO Systems in Presence of Distributed Frequency Offsets and Timing Offsets

In distributed multi-input multi-output (MIMO) systems, antennas may not be able to utilize common oscillators at the transmitter side or the receiver side. Consequently, there are multiple carrier frequency offsets (CFOs) and multiple timing offsets (TOs) due to distributed antennas that may significantly degrade the system performance if not properly addressed. In this paper, we analyze the effect of multiple CFOs and multiple TOs on receiver signal energy at the front end of each receive antenna pulse matched filter output in a distributed MIMO system. We then propose a novel receiver design for distributed MIMO systems that can accommodate multiple CFOs and multiple TOs. The proposed receiver structure utilizes a bank of pulse matched filters at each receive antenna where each filter accommodates one CFO specifically. Each filter output is then sampled at the symbol rate, with sampling timing selected according to the corresponding TO, followed by an information symbol detector. For the proposed receiver configuration, we derive the maximum-likelihood (ML) symbol detector for both space-time block coded and uncoded distributed MIMO systems. In addition, we design sub-optimal detectors and show that under certain conditions, the sub-optimal detectors perform close to the ML-optimal detector with significantly lower complexities. Extensive simulation studies illustrate our theoretical development for distributed MIMO systems with various transceiver configurations and channel conditions. The simulation results indicate that the proposed receiver structure together with the ML detection offers significant performance gains compared to the current state of the art.

On the Performance of Reconfigurable Distributed MIMO in Mobile Networks

We propose a new distributed Multi-Input Multi-Output (MIMO) architecture for mobile networks, which we refer to as reconfigurable distributed MIMO (RD-MIMO), where the communicating mobile nodes temporarily recruit adjacent nodes to operate as distributed antenna arrays. To best serve the communicating nodes, the node clusters are continuously reconfigured due to the node mobility and varying channel conditions. The frequency of reconfiguration depends on the required system performance, exhibiting a tradeoff between performance and complexity. We propose a practical node selection scheme, which activates only a small subset of transmitters. We evaluate the asymptotic performance of the scheme as a function of the number of recruited nodes, demonstrating that there is an optimal number of such nodes. Compared with the system that blindly activates all available transmitting nodes, our results show that the proposed RD-MIMO architecture with node selection achieves superior performance, especially as evident at low SNR. Furthermore, assuming the Brownian motion model, an analytical expression for the reconfiguration time to select a new cluster of transmitting nodes is obtained. Numerical results show that the obtained expression serves as a good estimation for the order of magnitude of the cluster reconfiguration time for other mobility patterns, such as the random walk model.

Joint Frequency Offset and Channel Estimation in Distributed MIMO-OFDM Systems

In recent times, cooperative communications or distributed multi-input multi-output (DMIMO) systems are being considered as key enabler for increasing spectral efficiency and coverage area in 4G and beyond wireless communication system. DMIMO system employs multiple relays each with single or multiple antennas in between the source and destination to form a virtual antenna array. However, a few of the fundamental difficulties in deployment of DMIMO networks with coherent communications are synchronization, tracking of independent oscillators, and channel estimation. The signal received at the destination is characterized by multiple carrier frequency offsets (MCFOs) and MIMO channel effects due to sparsely distributed relay nodes. In this paper, we address the issue of joint estimation of frequency offset and channel gains in a DMIMO orthogonal frequency division multiplexing (OFDM) systems deploying multiple antennas at the relays. We consider estimate-and-forward (EF) protocol for relaying where the coarse estimation process at the low cost relays generates inter carrier interference (ICI) in a multi-carrier system resulting in severe performance degradation and decrease in reliability of the network. The two iterative estimators, expectation conditional maximization (ECM) and space-alternating generalized expectation maximization (SAGE), are proposed to jointly estimate MCFOs and channel in the presence of ICI. The robustness of the estimators are evaluated through mathematical analysis and validated using simulations. The performance of the estimators is measured in terms of mean square error (MSE) and bit error rate (BER). Simulation results show that the proposed estimators provide a significant performance gain in DMIMO-OFDM systems with MIMO configuration at the relays compared to single-input single-output (SISO) system.

Near-Capacity Wireless System Design Principles

In this tutorial, the design procedure of near-capacity channel code design invoking non-binary EXtrinsic Information Transfer (EXIT) charts is illustrated by using design examples of Irregular Concatenated Coding Arrangements (ICCAs) relying on Irregular Convolutional Codes (IrCCs). More specifically, in order to benchmark the near-capacity design examples, both the capacity and the Outage Probability (OP) of the Continuous-input Continuous-output Memoryless Channel (CCMC), of the Discrete-input Continuous-output Memoryless Channel (DCMC) as well as of the Differential Discrete-input Continuous-output Memoryless Channel (D-DCMC) are characterised. Furthermore, the EXIT-chart aided near-capacity design principles are illustrated by detailing the design process of three characteristic prototype schemes, which represent the family of coherent and non-coherent detection based systems as well as Multi-Input Multi-Output (MIMO) systems, respectively. Moreover, the beneficial application of the EXIT-chart based design principle is also demonstrated in the context of cooperative systems using a specific distributed MIMO prototype scheme.

Spectral Efficiency of Distributed MIMO Systems

Distributed multi-input multi-output (D-MIMO) system is a promising system to greatly improve the spectral efficiency and power efficiency of the cellular system. The performance analysis of the spectral efficiency of D-MIMO system is a fundamental problem for both theoretical study and technique evaluation. In this paper, the theoretical performances of spectral efficiency for D-MIMO system and traditional collocated MIMO (C-MIMO) system are studied and compared. First, a composite D-MIMO channel including path loss, shadow fading and multipath fading is given. Conditioned on the desired user position, by using the tight bounds and central limit theory, the analytical approximations of the mean and the cumulative distribution function (CDF) of the mutual information (MI) are derived for C-MIMO and D-MIMO channels at both high signal to noise ratio (SNR) and low SNR. Assuming that the users are randomly distributed in the cell, the CDFs of the spectral efficiency are also given for the C-MIMO and D-MIMO cellular system, and the closed-form expressions for the mean spectral efficiency, mean outage spectral efficiency are derived. Finally, the theoretical comparisons between C-MIMO and D-MIMO for large number of antennas are given, and simulation results are presented which validate the analytical results.

Outage Capacity Study of the Distributed MIMO System with Antenna Cooperation

This letter analyzes the outage capacity of the distributed multi-input multi-output (D-MIMO) systm with antenna cooperation over the Rayleigh-Log-normal fading channel. Firstly, an analytical expression of outage capacity is derived under the cooperative transmission scheme (CTS). Moreover, to optimize the system performance, an adaptive CTS (ACTS) is proposed, and a theoretical expression is derived to perform the ACTS. Simulation results are presented to verify the theoretical outcome.

Optimum Resource Allocation in Distributed MIMO Systems

Distributed multi-input multi-output (MIMO) system is a promising architecture to provide reliable communications over spatially separated relaying nodes. In this paper, we will investigate the optimum resource allocation techniques in distributed MIMO systems, employing differential (de)modulation and various relaying protocols. Instead of limiting to energy optimization, we solve this problem via a two-dimensional energy and location optimization. The benefits of our resource optimization approaches are illustrated through extensive analysis and simulations. Both the comparisons between different optimization techniques and systems with different protocols are included. Based on our previous work, we provide an upper bound of the symbol error rate (SER) for decode-and-forward (DF) systems and an approximated SER for amplify-and-forward (AF) systems. We proved that the system SER is a convex function in both the transmit energy and the relay location, and then carried out the two-dimensional energy and location optimization through numerical search. As we know, system energy is a limited resource, especially for mobile terminals, and the coverage area is a critical factor to judge the system performance. Therefore, we evaluate the benefits of our optimization techniques in terms of the system energy saving and the coverage distance extension. The simulations reveal several interesting results. For both the DF and the AF protocols, the optimized systems always outperform the unoptimized systems with either less energy consumption or longer transmission range. It is also noticed that the benefits of both energy and location optimizations vary a lot for different protocols, and with different system configurations. Uniform energy allocation and midpoint relay location are normally chosen as an initial system setup. For such a configuration with the DF protocol, location optimization is more critical than energy optimization, and the unoptimized system receives prominent benefits from both optimizations, and tremendous system resources savings. For the AF protocol, however, the location and energy optimizations are equally important for the unoptimized system. It turns out that the uniform energy allocation and the midpoint relay location result in fairly good system performance, since it is reasonably close to the global optimum. For other initial system setups, the optimization benefits are also distinct in the AF and the DF systems. In DF systems, more optimization benefits can be achieved when the relays are either close to the destination or have more transmit energy allocated to the relay(s). On the contrary, in the AF systems, remarkable optimization benefits will be achieved when the relays are far from the midpoint, or when the relays are only able to transmit at low energy levels.