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Proposed is an adaptive fully transmitter-based block-diagonalisation scheme for multiple input multiple output (MIMO) multiuser downlink systems. A relaxation to the beamforming optimisation constraints is proposed, which introduces interference diversity and attains a more efficient performance optimisation. The trade-off to the performance improvement is an increase in the precoding complexity imposed by the adaptive nature of the proposed beamforming. A sub-optimal adaptive-decomposition beamforming scheme is also proposed with a reduced complexity overhead. Comparative analytical and simulation results to conventional beamforming demonstrate the significant diversity gains offered by the proposed scheme. [PUBLICATION ABSTRACT]

In this paper, a robust minimum variance (MV) beamforming approach is proposed for improving the robustness of multiple-input multiple-output (MIMO) radar against the mismatches of the steering vector and the finite sample effects. In contrast to existing robust MV beamformers (RMVB), the proposed RMVB utilises a specific structured model of virtual steering vector, also named transmit-receive steering vector, of MIMO radar rather than the commonly used unstructured model in phased-array radar. The basic idea of the proposed RMVB is to estimate the desired transmit and receive steering vectors under two quadratic constraints. To solve this problem, an iterative algorithm is developed. Simulations are provided to confirm the effectiveness of the proposed method.

Wireless Sensor Networks (WSNs) are composed of spatially distributed autonomous sensor devices, named motes. These motes have their own power supply, processing unit, sensors and wireless communications However with many constraints, such as limited energy, bandwidth and computational capabilities. In these networks, at least one mote called a sink, acts as a gateway to connect with other networks. These sensor networks run monitoring applications and then the data gathered by these motes needs to be retrieved by the sink. When this sink is located in the far field, there have been many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications to reach the sink. In this paper, we conduct a thorough study of the related work and analyze the requirements to do CB. In order to implement these communications in real scenarios, we will consider if these requirements and the assumptions made are feasible from the point of view of commercial motes and their constraints. In addition, we will go a step further and will consider different alternatives, by relaxing these requirements, trying to find feasible assumptions to carry out these types of communications with commercial motes. This research considers the nonavailability of a central clock that synchronizes all motes in the WSN, and all motes have identical hardware. This is a feasibility study to do CB on WSN, using a simulated scenario with randomized delays obtained from experimental data from commercial motes.

Hybrid beamforming is a viable method for lowering the complexity and expense of massive multiple-input multiple-output systems while achieving high data rates on track with digital beamforming. To this end, the purpose of the research reported in this paper is to assess the effectiveness of the three architectural beamforming techniques (Analog, Digital, and Hybrid beamforming) in massive multiple-input multiple-output systems, especially hybrid beamforming. In hybrid beamforming, the antennas are connected to a single radio frequency chain, unlike digital beamforming, where each antenna has a separate radio frequency chain. The beam formation toward a particular angle depends on the channel state information. Further, massive multiple-input multiple-output is discussed in detail along with the performance parameters like bit error rate, signal-to-noise ratio, achievable sum rate, power consumption in massive multiple-input multiple-output, and energy efficiency. Finally, a comparison has been established between the three beamforming techniques.

In order for the pre-coding technology to suppress the influence of pilot contamination, a hybrid pre-coding scheme is proposed in this paper by combining a full-digital pre-coding technology and an analog beamforming in the context of current massive MIMO. Simulation results show that the proposed pre-coding scheme effectively suppresses the pilot contamination while reducing the system structure complexity and ensuring the system’s basic performance, which is of important significance in practical communication applications.

Intelligent reflecting surfaces (IRS) recently gained prominence due to their ability to adapt and tweak their configuration in real-time to create an intelligent wireless environment. Hence, it can elevate wireless connectivity, signal strength, data rate, coverage, and mitigate signal blockage or interference in future wireless networks. A comprehensive review of IRSs has been conveyed in this paper, emphasizing beamforming optimization strategies in the realm of energy harvesting with IRS assistance. The discussion encompasses an overview of IRS hardware design, practical IRS prototypes for hardware design, a summary of related works, and an equivalent RLC circuit model. Additionally, an extensive comparative analysis of IRS architecture, shape, size, advantages, drawbacks, and applications is presented, considering existing research. Further, the paper examines the most pivotal cost and economic aspects of IRS to optimize energy harvesting and coverage enhancement. The paper explores beamforming techniques and examines various optimization methods aimed at maximizing the potential of IRS for energy harvesting. Furthermore, the paper delves into the wide range of potential applications that IRS-assisted wireless communication networks can offer. Despite the significant promises of IRS technology, it faces substantial research challenges in optimization. This paper addresses and highlights these challenges and limitations associated with the IRS, paving the way for future research directions.

Array probes are commonly used in ultrasound equipment. These are transducers arranged in an array. This system requires multiple transmitter and receiver circuits, making it an expensive system; three-dimensional ultrasound imaging requires a two-dimensional array, which is even more expensive and difficult to popularize. We propose an imaging system with a single transducer and a single transmitter/receiver circuit for low-cost implementation of ultrasound imaging. The system uses a distributed electrode, which consists of multiple electrodes on a single transducer. Each electrode is connected to a single transmit/receive circuit. In this paper, we evaluate the performance of the proposed and existing array probe systems and systems by simulation.

Integrated sensing and communication (ISAC) has been envisioned as a key enabler in the next-generation wireless networks. In this paper, we consider the joint information and sensing beamforming design in a multi-user and multi-target multi-input multi-output ISAC system, where a transmit BS and a sensing BS collaborate to sense targets, and the transmit BS sends information streams to communication users at the same time. To optimize the sensing performance and guarantee the communication throughput, we formulate a joint beamforming design problem to minimize the trace of the weighted Cramer–Rao bound of target parameters subject to the sum-rate constraint. The problem is challenging to solve due to the intricate non-convex objective function and constraints. We firstly exploit the weighted mean square error minimization (WMMSE) and semidefinite relaxation (SDR) techniques to devise a WMMSE–SDR algorithm that can achieve a KKT point of the problem. The SDR can be shown to be tight for a subproblem in the WMMSE–SDR algorithm, which implies zero duality for the subproblem. Based on this property and fractional programming techniques, we further reformulate the beamforming problem as a min–max form with simple constraints which then can be efficiently solved by first-order min–max optimization algorithms. Finally, the proposed algorithms are evaluated extensively in simulations. Numerical results show that both proposed algorithms can achieve promising performance in sensing and communication, and the low-complexity algorithm has a significantly reduced computation time.

In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multiple-output (MIMO) systems is presented. The ResNeSt-based deep learning method is harnessed to simplify and optimize the beamforming process, consequently improving performance and efficiency of 5G and beyond communication networks. A study of beamforming capabilities has revealed potential to maximize channel capacity while minimizing interference, thus eliminating inherent limitations of the traditional methods. The proposed model shows superior adaptability to dynamic channel conditions and outperforms traditional techniques across various interference scenarios.