Distributed Beamforming Technique Research Articles

C-Cube

Recent innovations significantly advance the communication capabilities of backscatter networks, while energy harvesting remains the bottleneck of these backscatters. To tackle this problem, existing studies explore distributed beamforming techniques to beam enhanced energy for a battery-free tag. However, they cannot concurrently transfer high-density power to multiple tags since the system is unable to estimate accurate channel state information (CSI). In this paper, we argue that by deploying a lightweight beamforming helper, we can push the limit of CSI estimation with negligible overhead on the tag. The big gap between the wireless charging threshold and the CSI estimation sensitivity can provide stable estimation result of CSI for the charging system even the signal experiences double fading. On this basis, we propose C-Cube, a concurrent charging system for backscatter networks. C-Cube employs distributed beamforming with the help of CSI to indicate the phase alignment process. To bring it into practice, we design a cold start scheme to activate tags by transmitting a distributed orthogonal frequency (DOF) signal. Moreover, to ensure all tags' CSIs have been successfully received, a spatial-based CSI classifier is explored in C-Cube. We further align the beamforming phase to optimize a novel metric called relative power, instead of the received power metric. The new metric can eliminate the impact of the unknown tag-to-helper channel. We implement our system with USRP/GNURadio platform and customized tags in a real environment. C-Cube can achieve the same coverage as the state-of-the-art and reduce the charge time by 44.1% and 30.2%.

Distributed Beamforming Techniques for Cell-Free Wireless Networks Using Deep Reinforcement Learning

In a cell-free network, a large number of mobile devices are served simultaneously by several base stations (BSs)/access points(APs) using the same time/frequency resources. However, this creates high signal processing demands (e.g., for beamforming) at the transmitters and receivers. In this work, we develop centralized and distributed deep reinforcement learning (DRL)-based methods to optimize beamforming at the uplink of a cell-free network. First, we propose a fully centralized uplink beamforming method (i.e., centralized learning) that uses the Deep Deterministic Policy Gradient algorithm (DDPG) for an offline-trained DRL model. We then enhance this method, in terms of convergence and performance, by using distributed experiences collected from different APs based on the Distributed Distributional Deterministic Policy Gradients algorithm (D4PG) in which the APs represent the distributed agents of the DRL model. To reduce the complexity of signal processing at the central processing unit (CPU), we propose a fully distributed DRL-based uplink beamforming scheme. This scheme divides the beamforming computations among distributed APs. The proposed schemes are then benchmarked against two common linear beamforming schemes, namely, minimum mean square estimation (MMSE) and the simplified conjugate symmetric schemes. The results show that the D4PG scheme with distributed experience achieves the best performance irrespective of the network size. Furthermore, although the proposed distributed beamforming technique reduces the complexity of centralized learning in the DDPG algorithm, it performs better than the DDPG algorithm only for small-scale networks. The performance superiority of the fully centralized DDPG model becomes more evident as the number of APs and/or UEs increases. The codes for all of our DRL implementations are available at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/RayRedd/Distributed_beamforming_rl</uri></monospace> .

New distributed beamforming techniques based on optimized elliptical arc geometry for back lobe cancellation of linear antenna arrays

The presence of array back lobe is a major source of interference, power loss, and degrades the signal-to-interference ratio (SIR) in wireless communication systems. In this paper, new distributed beamforming techniques based on an optimized elliptical arc geometry (EAG) are proposed for back lobe cancellation of linear antenna arrays (LAA). The optimized EAG allows for flexible control of the elements' locations and initial transmission phases of their radiations, allowing them to add constructively towards the main beam direction and add destructively towards the back lobe direction. Firstly, two EAG based beamforming techniques are introduced using either a single ellipse-EAG (SE-EAG) or double ellipses-EAGs (DE-EAG). In these techniques, the back lobes are minimized by optimizing both the minor axes of the elliptical arcs and the elements' angular distributions using the particle swarm optimization (PSO) technique. While the elements' transmission energies are kept the same as that of the original LAA. For further back lobe minimization, the double ellipses-EAG with optimized energies (DE-EAG-WOE) beamforming technique is introduced, where both elements' transmission energies and positions are optimized. To verify the superiority and the possibility of practical validation of the proposed techniques, the proposed distributed array configurations based on the DE-EAG-WOE are realized using the CST Microwave Studio software package using dipole elements.

Real‐time implementation of distributed beamforming for simultaneous wireless information and power transfer in interference channels

In this paper, we propose one-bit feedback-based distributed beamforming (DBF) techniques for simultaneous wireless information and power transfer in interference channels where the information transfer and power transfer networks coexist in the same frequency spectrum band. In a power transfer network, multiple distributed energy transmission nodes transmit their energy signals to a single energy receiving node capable of harvesting wireless radio frequency energy. Here, by considering the Internet-of-Things sensor network, the energy harvesting/information decoding receivers (ERx/IRx) can report their status (which may include the received signal strength, interference, and channel state information) through one-bit feedback channels. To maximize the amount of energy transferred to the ERx and simultaneously minimize the interference to the IRx, we developed a DBF technique based on one-bit feedback from the ERx/IRx without sharing the information among distributed transmit nodes. Finally, the proposed DBF algorithm in the interference channel is verified through the simulations and also implemented in real time by using GNU radio and universal software radio peripheral.

Design and Performance Analysis of Secure Multicasting Cooperative Protocol for Wireless Sensor Network Applications

This letter proposes a new security cooperative protocol, for dual phase amplify-and-forward large wireless sensor networks. In such a network, a portion of the ${K}$ relays can be potential eavesdroppers. The source agrees to share with the destination a given channel state information (CSI) of a source-trusted relay-destination link to encode the message. Then, in the first hop, the source will use this CSI to map the right message to a certain sector while transmitting fake messages to the other sectors using sectoral transmission. In the second hop, the relays retransmit their received signals to the destination, using the distributed beamforming technique. We derived the secrecy outage probability and demonstrated that the probability of receiving the right encoded information by an untrustworthy relay is inversely proportional to the number of sectors. We also showed that the aggressive behavior of the cooperating untrusted relays is not effective compared to the case where each untrusted relay is trying to intercept the transmitted message individually.

Secrecy Analysis in Wireless Network With Passive Eavesdroppers by Using Partial Cooperation

This paper proposes a new location-based multicasting technique, for dual phase amplify-and-forward (AF) large networks, aiming to improve the security in the presence of non-colluding passive eavesdroppers. These eavesdroppers could also be part of this cooperative network as relays. In order to reduce the impact of these eavesdroppers on the network security, we propose a new transmission strategy where, for the first hop of each transmission time, while the destination is jamming, the source randomly chooses a different subset $K$ of the total $T$ relays, to transmit its message toward the destination. For practical implementation, sectoral transmission can be achieved with analog beamforming at the source's side. In the second hop, using the distributed beamforming technique, the $K$ AF relays retransmit the received signal to the destination. We analytically demonstrated that the proposed technique decreases the probability of choosing the same sector that has certain eavesdroppers again, for each transmission time, to $K/T$ . Moreover, we also show that the secrecy capacity scaling of our technique is still the same as for broadcasting. Hereafter, the lower and upper bounds of the secrecy outage probability are calculated, and it is shown that the security performance is remarkably enhanced, compared to conventional multicasting technique.

Worst-case-based robust beamforming for wireless cooperative networks

Abstract It is known that distributed beamforming techniques can improve the performance of relay networks by using channel state information (CSI). In practical applications, there exist unavoidably estimation errors of the CSI, which results in outage of quality of service (QoS) or overconsumption of transmit power. In this paper, we propose two worst-case-based distributed beamforming techniques that are robust to the channel estimation errors. In the worst-case-based approaches, the worst case in a set that includes the actual case is optimized. Therefore, the performance of the actual case can be guaranteed. In our first approach, the maximal total relay transmit power in the set is minimized subject to the QoS constraint. This distributed beamforming problem can be approximately solved using second-order cone programming (SOCP). In our second method, the worst QoS in the set is maximized subject to the constraints of individual relay transmit powers. It is shown that the resultant problem can be approximately formulated as a quasi-convex problem and can be solved by using a bisection search method. Simulation results show that the proposed beamforming techniques are robust to the CSI errors and there is no outage of QoS or power in the proposed methods.

Optimal spectrum sensing in cooperative cognitive two-way relay networks

This paper investigates a network consisting of some CR terminals distributed between two primary transceivers. In the proposed network model, CR nodes assist the primary transmission as a two-way amplify-and-forward relaying scheme when the primary transceivers are in operation. By using distributed beamforming techniques among CR nodes, we propose an optimal solution to maximize the performance of spectrum sensing in terms of signal-to-noise ratio (SNR) at the CBS while satisfying the quality of service requirements of primary receivers. We demonstrate the superiority of sensing performance in the proposed method by relaxing the problem into the semidefinite form and we achieve the maximum value of SNR in the CBS by using an iterative algorithm.