Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis

Abstract

In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and crosscorrelation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-tointerference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.

Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

“…The IRWLS procedure presented in this section is performed to obtain the optimal solution by solving the primal problem, whereas the conventional QP optimization problem mentioned in Section 3 obtains the solution by solving the dual problem. The IRWLS procedure for the dual problem can be constructed by forcing the solution to be expanded into a linear combination of whitened vectors, such as (17). The number of unknowns for dual problems is 2N , where N is the number of inequality constraints established by sampling the beampattern, whereas the number of unknowns for the primal problem is 2M , where M is the length of the weighted vector.…”

“…However, such methods fail when the SNR is low or the dimension of signal plus interference subspace is high [11]. In addition to the above two kinds of approaches, other robust beamforming methods include the Bayesian beamformer [15] [16], the lowrank cross-correlation-based technique [17], the convex optimization-based method [18] [19], and the semidefinite programming-based approach [20].…”

“…Then, the weighted vectors w can be obtained in accordance with(17). Each complex weighted coefficient can be formed as ( ) iM = .…”

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

“…For the wireless sensors, it is envisioned that a new design paradigm is needed to support large numbers of heterogeneous sensing devices with diverse requirements and unique traffic characteristics. Comparing to the sensors in traditional IoT networks, those deployed in extreme environments need to operate in harsh (sometimes hazardous) conditions and are prone to wear and tear, and cannot be easily replaced, posing major challenges in designing resilient networks for robust communications [40]. Our work assumes a centralised control mechanism where sensors are connected to a fusion node via wireless links.…”

Precoding and Resource Allocation for Cell-Free Massive Mimo Systems

“…In addition, algorithms based on different gradients have been derived to update the beamforming weights, reducing computational cost. In the same way, cross-correlation methods have been applied to relay systems, where the correlation is explored between the system's output and received data from the relays at the destination [50]. The proposed distributed beamforming with low-rank and cross-correlation has the objective to maximize the output SINR under total relay power constraint.…”