Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery

This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse arrays and subarrays: i) the subarrays are generated from a main sparse array geometry (Type-I sparse array), and ii) the sparse subarrays that are directly designed and grouped together to generate the whole sparse array (Type-II sparse array). Numerical results demonstrate that the proposed MMV model can benefit from multiple data records and that Type-II sparse noncoherent arrays are superior in performance for DOA estimation

Study of Adaptive LLR-based AP selection for Grant-Free Random Access in Cell-Free Networks

This paper presents an iterative detection and decoding scheme along with an adaptive strategy to improve the selection of access points (APs) in a grant-free uplink cell-free scenario. With the requirement for the APs to have low-computational power in mind, we introduce a low-complexity scheme for local activity and data detection. At the central processing unit (CPU) level, we propose an adaptive technique based on local log-likelihood ratios (LLRs) to select the list of APs that should be considered for each device. Simulation results show that the proposed LLRs-based APs selection scheme outperforms the existing techniques in the literature in terms of bit error rate (BER) while requiring comparable fronthaul load.

Tomlinson-Harashima Cluster-Based Precoders for Cell-Free MU-MIMO Networks

Cell-free (CF) multiple-input multiple-output (MIMO) systems generally employ linear precoding techniques to mitigate the effects of multiuser interference. However, the power loss, efficiency, and precoding accuracy of linear precoders are usually improved by replacing them with nonlinear precoders that employ perturbation and modulo operation. In this work, we propose nonlinear user-centric precoders for CF MIMO, wherein different clusters of access points (APs) serve different users in CF multiple-antenna networks. Each cluster of APs is selected based on large-scale fading coefficients. The clustering procedure results in a sparse nonlinear precoder. We further devise a reduced-dimension nonlinear precoder, where clusters of users are created to reduce the complexity of the nonlinear precoder, the amount of required signaling, and the number of users. Numerical experiments show that the proposed nonlinear techniques for CF systems lead to an enhanced performance when compared to their linear counterparts.

Study of Iterative Detection and Decoding with Log-Likelihood Ratio Based Access Point Selection for Cell-Free Networks

This paper proposes an iterative detection and decoding (IDD) scheme and an approach to improve the selection of access points (APs) in uplink cell-free massive multiple-antenna systems. A cost-effective scheme for selection of APs based on local log-likelihood ratios (LLRs) is developed that provides sufficient statistics to the central processing unit and selects which APs should be considered for each user. {Numerical results show that the proposed IDD scheme works very well and the proposed LLRs-based approach to select APs outperforms the existing techniques in terms of bit error rate and spectral efficiency while requiring a comparable fronthaul load.

Sequential Multiuser Scheduling and Power Allocation for Cell-Free Multiple-Antenna Networks

Resource allocation is a fundamental task in cell-free (CF) massive multi-input multi-output (MIMO) systems, which can effectively improve the network performance. In this paper, we study the downlink of CF MIMO networks with network clustering and linear precoding, and develop a sequential multiuser scheduling and power allocation scheme. In particular, we present a multiuser scheduling algorithm based on greedy techniques and a gradient ascent {(GA)} power allocation algorithm for sum-rate maximization when imperfect channel state information (CSI) is considered. Numerical results show the superiority of the proposed sequential scheduling and power allocation scheme and algorithms to existing approaches while reducing the computational complexity and the signaling load.

Study of Multiuser Multiple-Antenna Wireless Communications Systems Based on Super-Resolution Arrays

This work studies multiple-antenna wireless communication systems based on super-resolution arrays (SRAs). We consider the uplink of a multiple-antenna system in which users communicate with a multiple-antenna base station equipped with SRAs. In particular, we develop linear minimum mean-square error (MMSE) receive filters along with linear and successive interference cancellation receivers for processing signals with the difference co-array originating from the SRAs. We then derive analytical expressions to assess the achievable sum-rates associated with the proposed multiple-antenna systems with SRAs. Simulations show that the proposed multiple-antenna systems with SRAs outperform existing systems with standard arrays that have a larger number of antenna elements.

Study of MMSE-Based Resource Allocation for Clustered Cell-Free Massive MIMO Networks

In this paper, a downlink cell-free massive multiple-input multiple-output (CF massive MIMO) system and a network clustering is considered. Closed form sum-rate expressions are derived for CF and the clustered CF (CLCF) networks where linear precoders included zero forcing (ZF) and minimum mean square error (MMSE) are implemented. An MMSE-based resource allocation technique with multiuser scheduling based on an enhanced greedy technique and power allocation based on the gradient descent (GD) method is proposed in the CLCF network to improve the system performance. Numerical results show that the proposed technique is superior to the existing approaches and the computational cost and the signaling load are essentially reduced in the CLCF network.

Study of Robust Adaptive Beamforming Algorithms Based on Power Method Processing and Spatial Spectrum Matching

Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for dealing with covariance matrix reconstruction issues. The proposed method involves INC matrix reconstruction using an idea in which the power and the steering vector of the interferences are estimated based on the power method. Furthermore, spatial match processing is computed to reconstruct the desired signal-plus-noise covariance matrix. Then, the noise components are excluded to retain the desired signal (DS) covariance matrix. A key feature of the proposed technique is to avoid eigenvalue decomposition of the INC matrix to obtain the dominant power of the interference-plus-noise region. Moreover, the INC reconstruction is carried out according to the definition of the theoretical INC matrix. Simulation results are shown and discussed to verify the effectiveness of the proposed method against existing approaches.

Study of Enhanced MISC-Based Sparse Arrays with High uDOFs and Low Mutual Coupling

In this letter, inspired by the maximum inter-element spacing (IES) constraint (MISC) criterion, an enhanced MISC-based (EMISC) sparse array (SA) with high uniform degrees-of-freedom (uDOFs) and low mutual-coupling (MC) is proposed, analyzed and discussed in detail. For the EMISC SA, an IES set is first determined by the maximum IES and number of elements. Then, the EMISC SA is composed of seven uniform linear sub-arrays (ULSAs) derived from an IES set. An analysis of the uDOFs and weight function shows that, the proposed EMISC SA outperforms the IMISC SA in terms of uDOF and MC. Simulation results show a significant advantage of the EMISC SA over other existing SAs.

Efficient Covariance Matrix Reconstruction with Iterative Spatial Spectrum Sampling

This work presents a cost-effective technique for designing robust adaptive beamforming algorithms based on efficient covariance matrix reconstruction with iterative spatial power spectrum (CMR-ISPS). The proposed CMR-ISPS approach reconstructs the interference-plus-noise covariance (INC) matrix based on a simplified maximum entropy power spectral density function that can be used to shape the directional response of the beamformer. Firstly, we estimate the directions of arrival (DoAs) of the interfering sources with the available snapshots. We then develop an algorithm to reconstruct the INC matrix using a weighted sum of outer products of steering vectors whose coefficients can be estimated in the vicinity of the DoAs of the interferences which lie in a small angular sector. We also devise a cost-effective adaptive algorithm based on conjugate gradient techniques to update the beamforming weights and a method to obtain estimates of the signal of interest (SOI) steering vector from the spatial power spectrum. The proposed CMR-ISPS beamformer can suppress interferers close to the direction of the SOI by producing notches in the directional response of the array with sufficient depths. Simulation results are provided to confirm the validity of the proposed method and make a comparison to existing approaches