In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input multiple-output (MIMO) antenna channels are proposed. We consider a large-scale MIMO system consisting of multiple users and one base station with several distributed antenna sets. Each link between a user and an antenna set forms a two-sided spatially correlated MIMO channel with line-of-sight (LOS) components. Our derivations are based on novel techniques from large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the terminals approach to infinity with a fixed ratio. The deterministic equivalent results (the deterministic equivalent of ergodic sum rate and the capacity-achieving input covariance matrices) are easy to compute and shown to be accurate for realistic system dimensions. In addition, they are shown to be invariant to several types of fading distribution.

On Capacity of Large-Scale MIMO Multiple Access Channels with Distributed Sets of Correlated Antennas

This article focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal matching pursuit algorithm is utilized for the extraction of the UL channel parameters, including channel gains, directions of arrival, delays, and Doppler frequencies, over the antenna-time-frequency domain. Thirdly, we carefully analyze energy dispersion and power leakage of the 3D angle-delay-Doppler channels. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides and to assure that the observation regions for different users do not overlap over the 3D cubic area, i.e., angle-delay-Doppler domain. After scheduling, different users can map their respective data to the scheduled delay-Doppler domain grids, and simultaneously send the data to base station (BS) without inter-user interference in the same OTFS block. Correspondingly, the signals at desired grids within the 3D resource space of BS are separately collected to implement the 3D channel estimation and maximal ratio combining-based data recovery over the angle-delay-Doppler domain. Then, we construct a low complexity beamforming scheme over the angle-delay-Doppler domain to achieve inter-user interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

A New Path Division Multiple Access for the Massive MIMO-OTFS Networks

Many real-world optimization problems involve multiple conflicting objectives. Such problems are called multiobjective optimization problems (MOPs). Typically, MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution. To assist the decision maker (DM) in finding his/her most preferred solution, we propose an interactive multiobjective evolutionary algorithm (MOEA) called iDMOEA-eC, which utilizes the DM’s preferences to compress the objective space directly and progressively for identifying the DM’s preferred region. The proposed algorithm employs a state-of-the-art decomposition-based MOEA called DMOEA-eC as the search engine to search for solutions. DMOEA-eC decomposes an MOP into a series of scalar constrained subproblems using a set of evenly distributed upper bound vectors to approximate the entire Pareto front. To guide the population toward only the DM’s preferred part on the Pareto front, an adaptive adjustment mechanism of the upper bound vectors and two-level feasibility rules are proposed and integrated into DMOEA-eC to control the spread of the population. To ease the DM’s burden, only a small set of representative solutions is presented in each interaction to the DM, who is expected to specify a preferred one from the set. Furthermore, the proposed algorithm includes a two-stage selection procedure, allowing to elicit the DM’s preferences as accurately as possible. To evaluate the performance of the proposed algorithm, it was compared with other interactive MOEAs in a series of experiments. The experimental results demonstrated the superiority of iDMOEA-eC over its competitors.

Interactive multiobjective evolutionary algorithm based on decomposition and compression

This paper investigates the optimal training duration and the optimal power allocation for the training and the data transmission that maximize the ergodic sum rate in single-cell uplink massive MIMO with MMSE receivers. Our channel model assumes that each user experiences the same spatial channel correlation. The expression for the ergodic sum rate is obtained in the large system regime where the number of antennas ( $N$ ) at the base station and the number of users ( $K$ ) tend to infinity with a fixed ratio. Interestingly, we show that the optimal training duration is equal to $K$ and independent of the spatial correlation. We also derive the optimal power allocation that in facts depends on the spatial correlation, the channel coherence interval, and the uplink SNR. We show that more energy should be allocated for the training if the data transmission duration ( $t_{d}$ ) is less than $K$ , and vice versa. Moreover, equal power allocation is optimal when $t_{d}=K$ . We also obtain an approximation for the optimal power allocation that depends on the mean of the correlation matrix eigenvalues. Numerical simulations show that our results based on the large system approximation are accurate and applicable for finite-size systems. The simulations also show that (1) the resulting ergodic sum rates obtained by employing the optimal power allocation and its approximation are indistinguishable, and (2) the optimal power allocation obtained from the uncorrelated channel model can be applied to the cases involving the correlated channels with indiscernible penalties on the ergodic sum rates.

/pdf/optimal-power-allocation-and-training-duration-for-uplink-tsju9k6yjs.pdf

Optimal Power Allocation and Training Duration for Uplink Multiuser Massive MIMO Systems With MMSE Receivers

Performance analysis of distributed MIMO with ZF receivers over gamma shadowed correlated Rician fading channels

This paper is on the resource allocation problem for pilot-assisted multi-user massive multiple-input-multiple-output (MIMO) uplink with linear minimum mean-squared error (MMSE) channel estimation and detection. We utilize the angular domain channel representation for uniform linear antenna arrays, and adopt its equivalent independent and nonidentical distributed channel model. For a given coherence interval and total energy budget, we study the joint optimization of the training length and the training power to maximize the achievable sum-rate. For tractable analysis and low-complexity solution, a tight approximation on the achievable sum-rate is derived first. Then the training length optimization for fixed training power and the training power optimization for fixed training length with respect to the approximate sum-rate maximization are both shown to be concave. An alternative optimization that solves the training length and power iteratively is proposed for the joint resource allocation. In addition, for the special case that the training and data transmission powers are equal, we derive the optimal training lengths for both high and low signal-to-noise- ratio (SNR) regions. Numerical results show the tightness of the derived sum-rate approximation and also the significant performance advantage of the proposed resource allocation.

Resource allocation for pilot-assisted massive MIMO transmission

On achievable rate of massive MIMO multiple access channels via virtual representation

Intelligent reflecting surface-assisted secrecy wireless communication with imperfect CSI

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Resource allocation for pilot-assisted massive MIMO transmission

On achievable rate of massive MIMO multiple access channels via virtual representation

Intelligent reflecting surface-assisted secrecy wireless communication with imperfect CSI