Collaborative Beamforming Research Articles

Collaborative Beamforming for UAV Networks Exploiting Swarm Intelligence

Unmanned aerial vehicle (UAV)-enabled communication is an essential component of the promising next generation networks owning to its advantages of high maneuverability and low cost. However, the limited onboard energy and weak communication ability of the UAV, as well as the open nature of the wireless networks, may result in the insufficient service time, low coverage and worrying security. Collaborative beam-forming is an effective technique for boosting the communication performance of the multi-antenna systems, which is able to achieve an energy-efficient, long-range and secure transmission. In this work, we focus on the UAV-enabled collaborative beamforming via swarm intelligence approaches. First, we present some preliminaries such as collaborative beamforming, swarm intelligence and multi-objective optimization, and artificial intelligence (AI)-enabled UAV swarm networks. Then, several typical applications of UAV-enabled collaborative beamforming are briefly presented. Subsequently, we propose two UAV communication schemes via collaborative beamforming to enhance communication performance. Specifically, we jointly optimize the schedules of UAVs and the communication order of multiple base stations (BSs) to achieve a secure and energy-efficient transmission. Moreover, we also deploy the UAVs and configure the communication order of the BSs for a time and energy minimization communication via collaborative beamforming. Visualization and numerical results are provided to verify the performance of the proposed schemes. Finally, we also point out some future directions worthy of further investigation.

Security Energy Efficiency Analysis of Analog Collaborative Beamforming With Stochastic Virtual Antenna Array of UAV Swarm

To extend the transmission range governed by limited transmit power, collaborative beamforming (CB) can be employed with a virtual antenna array (VAA) constructed by an unmanned aerial vehicle (UAV) swarm. CB techniques combined with physical layer security (PLS) algorithms are known to better secure transmissions of sensitive information in the presence of eavesdropping attacks, compared to co-located antenna-based schemes. In particular, analog collaborative beamforming (ACB) with random VAA topologies, which is suitable for UAV networks due to its lower signaling/channel estimation overhead compared to the digital CB, can provide a significantly higher secrecy rate by creating noise-like signals at eavesdroppers. However, the increase in energy consumption to improve secrecy rate had been ignored in the existing ACB-based PLS schemes, which is of paramount importance considering the limited onboard energy of UAVs. Thus, motivated by such limitation, in this paper, we examine the security energy efficiency of the ACB-based PLS technique, where a group of UAVs (or VAA elements) randomly change their locations in a distributed manner to randomize the array factor of the VAA, i.e., a stochastic VAA. The proposed stochastic VAA outperforms the existing static VAA in terms of secrecy energy efficiency as verified by analytic and numerical results. Further, the impact of phase errors of the proposed scheme is also presented, which indicates the significance of the accurate phase estimation.

Secure and Energy-Efficient UAV Relay Communications Exploiting Collaborative Beamforming

Unmanned aerial vehicle (UAV) is a promising communication platform to assist terrestrial networks. In this work, we aim to provide relay communication to the blocked or low-quality terrestrial networks via an aerial relay. Nevertheless, major issues of the considered system are the worrying security and limited service time. Thus, we study a novel aerial relay system via collaborative beamforming (CB) by exploiting a UAV-enabled virtual antenna array (UVAA) to achieve a secure and energy-efficient communication for remote ground users (GUs). Specifically, we formulate a secure and energy-efficient communication multi-objective optimization problem (SECMOP) to circumvent the effects of the known and unknown eavesdroppers and minimize the propulsion energy consumption of UAVs, by optimizing the hovering positions and excitation current weights of UAVs and the scheduling for communicating with the remote GUs. The formulated SECMOP is challenging and proven to be NP-hard. Thus, we propose an improved evolutionary computation method with several enhanced designs to solve this problem. Simulation results demonstrate the benefits of the proposed IMODAOM against various benchmark algorithms. Moreover, we find that the UVAA-based relay can achieve substantial energy consumption reduction as compared to the multi-hop relay scheme.

Beam Coordination via Diffusion Reduced-Rank Adaptation Over Array Networks With Parametric Matrix Combination

Distributed and collaborative beamforming (DCBF) has been attracting increased interest due to its desirable scalability and enhanced robustness, whereas slow convergence would hinder practical applications of the conventional DCBF algorithms for massive arrays. In this work, we consider the distributed reduced-rank beam coordination over an array network, equipped with a collection of potentially massive arrays interconnected via a certain topology. We develop a diffusion reduced-rank beam coordination algorithm along with an inherently adaptive combination scheme based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">combination matrix</i> for beam coordination, leveraging the joint iterative alternating optimization methodology. We propose two efficient adaptive implementations with parametric matrix combination schemes, which could further enhance the robustness of the distributed reduced-rank beam coordination algorithm against the spatial variations of the signal and noise powers. The proposed distributed reduced-rank beam coordination algorithm could not only remarkably speed the initial convergence in comparison with its full-rank counterparts under the small-sample conditions, but also achieve comparable steady-state performance with sufficiently large sample size. The tracking capability could also be enhanced with the proposed algorithm as a by-product. Illustrative simulations validate the efficiency of the proposed reduced-rank beam coordination algorithm.

Collaborative Beamforming Aided Fog Radio Access Networks

The success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs’ caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms.

Collaborative one-shot beamforming under localization errors: A discrete optimization approach

We consider a mobile multi-agent network in which the agents locate themselves in an environment through imperfect measurements and aim to transmit a message signal to a far-field base station via collaborative beamforming. The agents imperfect measurements yield localization errors that degrade the quality of service at the base station due to unknown phase offsets in the channels. Assuming that the localization errors follow Gaussian distributions, we study the design of a one-shot (non-iterative) beamforming strategy that ensures reliable communication between the agents and the base station despite the localization errors. We formulate a risk-sensitive discrete optimization problem to choose an agent subset for transmission so that the desired signal-to-interference-plus-noise ratio (SINR) at the base station is attained with minimum variance. We show that, when the localization errors have small variances characterized in terms of the carrier frequency, greedy algorithms globally minimize the variance of the received SINR. Moreover, when the localization errors have large variances, we show that the variance of the received SINR can be locally minimized by exploiting the supermodularity of the mean and variance of the received SINR. Simulations demonstrate that the proposed algorithms synthesize beamformers orders of magnitude faster than convex optimization-based approaches while achieving comparable performance with fewer agents.

Design and analysis of a multiple collaborative beamforming scheme in the realm of Wireless Sensor Networks featuring 3-dimension node configuration

Collaborative Beamforming (CB) is an essential tool towards achieving long-range transmission in Wireless Sensor Networks (WSNs). In some instances, there may be multiple intended data destinations (sinks) in a WSN. This calls for multi-CB. In comparison to sink-by-sink CB, multi-CB implies improved data rates and decreased co-channel interference; and consequently increased network capacity. In current literature, there is no research in multi-CB particularly in 3-dimension WSNs. In this paper, a novel multi-CB mechanism is brought to the fore. This is from the point of view of a random arrangement of sensor nodes in a 3-dimension manner. It is assumed that all sinks' directions are known at the CB cluster head. Node transmit amplitude and phase are optimized using a Particle Swarm Optimization (PSO) algorithm variant to concurrently achieve balanced multiple narrow beams and minimal radiation in undesired directions. The performance of the proposed scheme is checked against that of a pure multiple beam steering approach (without beam power balancing and minimization of radiation in undesired directions). Moreover, an analysis of beam power, width and steering accuracy is done upon varying the number of collaborating nodes and the collaborating cluster radius. Increasing the count of collaborating nodes yields improved beam precision/accuracy, lower radiation in undesired directions and appreciable stability in beam power performance Increasing the collaborating nodes' cluster radius yields narrow beams, improved beam precision and appreciably lower radiation in undesired directions. The contributions of this work to current literature include: (i) formulation and analysis of a multiple beamforming scheme in the realm of 3-dimension WSNs; (ii) design of a multi-CB scheme taking into account minimization of radiation in undesired directions; (iii) a statistical multi-CB performance analysis upon varying collaborating nodes' cluster radius and collaborating node count.

Joint optimization of SNR and motion energy consumption for UAV-enabled collaborative beamforming

Joint optimization of SNR and motion energy consumption for UAV-enabled collaborative beamforming

Analysis of Planar Random Arrays With Stochastic Geometry Tools

The performance of 2-D random arrays is analyzed using the distance distributions of an isotropic homogeneous binomial point process. The array factor is expressed as the superposition of the patterns of equal-amplitude isotropic elements uniformly randomly distributed in a disk area. The distribution of the distance to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> th nearest element from the center of the disk is utilized in the summation, providing a novel representation of the array factor in terms of ordered distances. Furthermore, an alternative distribution is examined by setting a constraint to the distance of the farthest element from the origin. The mean range of the elements in the disk and a numerical calculation of the mean interelement distance is provided. The statistical average and the variance of the array factor as well as the average power pattern are derived and compared to previous results. The 3 dB beamwidth, directivity, nulls, and sidelobe peaks are discussed in terms of the number of elements and the array density. Simulation results for various random arrays and comparison of ensemble-averaged parameters with those of deterministic and sparse arrays from the literature are provided. The results are applicable to collaborative beamforming for distributed wireless ad hoc networks.

Time synchronization enhancements in wireless networks with ultra wide band communications

The emergence of low cost Ultra Wide Band (UWB) transceivers has enabled the implementation of Wireless Sensor Networks (WSN) based on this communication technology. These networks are composed of distributed autonomous low cost nodes (also known as motes) with their own processing unit, memory and communications. Usually these nodes are power-limited and due to the poor performance and quality of their clocks, time synchronization is in the order of milliseconds and in some specific scenarios till microseconds. The integration of commercial UWB transceivers in these nodes can improve the synchronization accuracy. In particular, we focus on WSN nodes based on off-the-shelf commercial products and commodity hardware. In this paper we analyze step by step, from a practical and experimental point of view, the different elements involved in the time synchronization using UWB technology on WSN with static nodes. From our experimental results, with timestamps captured during the packet exchanges, we analyze and discuss the application of different communication schemes and simple statistical methods (in order to be run in WSN nodes). The results obtained with timestamps captured at the UWB transceivers and by using linear regression show that the lowest time synchronization error achieved between two nodes is 0.14 ns. Employing the same setup and performing the synchronization with the timestamps captured internally at the microcontrollers of the nodes, the error rises to 31 ns, due to the higher time period of the microcontrollers’ timers and the inaccuracies that affect the acquisition of the timestamps. Nevertheless, the synchronization of the microcontrollers’ clocks allows the setting up of a common time reference at the network nodes, enabling the implementation of applications with tight synchronization requirements, such as collaborative beamforming and ranging.

Time and Energy Minimization Communications Based on Collaborative Beamforming for UAV Networks: A Multi-Objective Optimization Method

Unmanned aerial vehicle (UAV) communications and networks are of utmost concern. However, they have challenges such as the limited on-board energy and restricted transmit power. In this paper, we study a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). To achieve a better transmission performance, the UAV elements can fly to optimal positions by using optimal speeds and adjust to optimal excitation current weights for performing CB transmissions. However, there are some trade-offs between energy consumption and transmission performance. Thus, we formulate a time and energy minimization communication multi-objective optimization problem (TEMCMOP) of CB in UAV networks to simultaneously minimize the total transmission time, total performing time of VAAs and total motion and hovering energy consumptions of UAVs by jointly optimizing the positions, flight speeds and excitation current weights of UAVs, as well as the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated TEMCMOP, we propose an improved multi-objective ant lion optimization (IMOALO) algorithm with chaos-opposition based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed IMOALO can effectively solve the formulated TEMCMOP and it has better performance than some other benchmark approaches.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

Collaborative Beamforming (CBF) is an essential tool towards increasing transmission range in Wireless Sensor Networks (WSNs). Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. Particle Swarm Optimization (PSO) algorithm is a good candidate for use in CBF owing to its simplicity and low computation complexity. However, the basic PSO algorithm suffers from premature convergence particularly in highly multimodal functions (typical of CBF). This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF. A new fuzzy-logic based confidence and inertia weight parameters adaptation scheme has been developed with an aim of enhancing exploration and exploitation capabilities of the PSO algorithm. Normalized particle quality and iteration count have been used as the inputs to the designed fuzzy-logic inference system. The fuzzy logic based parameters adaptation scheme has been implemented in the form of a lookup table to minimize “on-line” computation complexity. Furthermore, a particle culling/ re-initialization procedure is utilized at half the number of maximum iterations to enhance overall swarm diversity. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. The developed CFAPSO algorithm is noted to outperform other metaheuristic algorithms in a statistical performance analysis procedure (on the basis of a set of standard unimodal and multimodal functions). Upon application to CBF, the CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering.

Decentralized Frequency Alignment for Collaborative Beamforming in Distributed Phased Arrays

A new approach to distributed synchronization (frequency alignment) for the coordination of nodes in open loop coherent distributed antenna arrays to enable distributed beamforming is presented. This approach makes use of the concept of consensus optimization among nodes without requiring centralized control. Decentralized frequency consensus can be achieved through iterative frequency exchange among nodes. We derive a model of the signal received from a coherent distributed array and analyze the effects on beamforming of phase errors induced by oscillator frequency drift. We introduce and discuss the average consensus protocol for frequency transfer in undirected networks where each node transmits and receives frequency information from other nodes. We analyze the following cases: 1) undirected networks with a static topology; 2) undirected networks with a dynamic topology, where connections between nodes are made and lost dynamically; and 3) undirected networks with oscillator frequency drift. We show that all the nodes in a given network achieve average consensus and the number of iterations needed to achieve consensus can be minimized for a given cluster of nodes. We derive the theoretical phase error resulting from both processing noise and measurement noise, and derive an optimal update interval that minimizes the error. Numerical simulations demonstrate that the consensus algorithm enables tolerable errors to obtain high coherent gain of greater than 90% of the ideal gain in an error-free distributed phased array.

Joint power allocation and collaborative beamforming for physical layer security in underlay CR NOMA relay systems

In this paper, we investigate the problem of joint power allocation and collaborative beamforming for physical layer security in underlay cognitive radio non-orthogonal multiple access relay systems. We consider two relaying schemes, i.e., amplify-and-forward and decode-and-forward, with the goal of maximizing the secrecy rate for the central user subjective to the transmit power constraint at the source node, individual transmit power constraints at relays, rate requirements at the cognitive users and the interference power constraint at the primary receiver. The optimization problems are nonconvex and hard to solve. We propose an efficient iterative algorithm by the constrained concave–convex procedure to solve the nonconvex optimization problems. For a comparison, we provide another method, namely alternating optimization (AO) algorithm. In addition, simulations are provided to show the superiority of our proposed methods with comparison to the AO algorithm and traditional orthogonal multiple access schemes.

Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices

Collaborative beamforming (CB) enables uplink transmission in a wireless sensor network (WSN) composed of sensors (nodes) and far-away access points (APs). It can also be applied to the case where the sensors are equipped with beam-switching structures (BSSs). However, as the antenna arrays of the BSSs are randomly headed due to the irregular mounting surface, some sensors form beams that do not illuminate a desired AP and waste their limited energy. Therefore, to resolve this problem, it is required to switch the beams toward the desired AP. While an exhaustive search can provide the globally optimal combination, a greedy search (GS) is utilized to solve this optimization problem efficiently. Simulation and experimental results verify that under certain conditions the proposed algorithm can drive the sensors to switch their beams properly and increase the received signal-to-noise ratio (SNR) significantly with low computational complexity and energy consumption.

Collaborative Beamforming in Wireless Sensor Networks Using a Novel Particle Swarm Optimization Algorithm Variant

Wireless Sensor Network (WSN) nodes inherently feature limited transmission range. A recent solution to the long range transmission problem is Collaborative Beamforming (CBF). The outcome of an ideal CBF process ought to be a radiation pattern whose main beam is oriented towards the intended transmission destination (WSN sink), inherently increasing the transmission range. Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF from the perspective of a 3 dimension WSN configuration (wherein the sink is elevated from the nodes’ plane). The modifications done on the PSO algorithm entail use of fuzzy-adaptive confidence and inertia weight parameters alongside a particle culling procedure. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. Comparisons against use of a linearly-adaptive PSO algorithm variant, basic PSO algorithm and the Genetic Algorithm (GA) in CBF have established the superiority of the proposed CFAPSO algorithm. The CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering. It is noteworthy that conventional beamsteering is inapplicable to beam-pattern optimization with respect to processes such as sidelobe minimization, nulling among others. The developed CFAPSO algorithm befits utilization in a plethora of CBF schemes.

Mobility Speed Effect and Neural Network Optimization for Deep MIMO Beamforming in mmWave Networks

Beamforming for millimetre-wave (mmWave) frequencies has been studied for many years. It is considered as an important enabling technology for communications in these high-frequency ranges and it received a lot of attention in the research community. The special characteristics of the mmWave band made the beamforming problem a challenging one because it depends on many environmental and operational factors. These challenges made any model-based architecture fit only special applications, working scenarios, and specific environment geometry. All these reasons increased the need for more general machine learning based beamforming systems that can work in different environments and conditions. This increased the need for an extended adjustable dataset that can serve as a tool for any machine learning technique to build an efficient beamforming architecture. Deep MIMO dataset has been used in many architectures and designs and has proved its benefits and flexibility to fit in many cases. In this paper, we study the extension of collaborative beamforming that includes many cooperating base stations by studying the impact of User Equipment (UE) speed ranges on the beamforming performance, optimizing the parameters of the neural network architecture of the beamforming design, and suggesting the optimal design that gives the best performance for as a small dataset as possible. Suggested architecture can achieve the same performance achieved before with up to 33% reduction in the dataset size used to train the system which provides a huge reduction in the data collection and processing time.

Collaborative beamforming via diffusion adaptation based on tensor over array networks

Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting the inherent algebraic structure present in the problem, we develop a fully distributed collaborative beamforming algorithm incorporating the diffusion scheme for arrays endowed with the property of multi-linear translation invariance (MLTI). We also derive the convergence constraint of the proposed algorithm. Illustrative simulations validate the superior performance of the proposed algorithm, with notably accelerated convergence rate, reduced computational complexity and enhanced communication efficiency.

A joint optimization approach for distributed collaborative beamforming in mobile wireless sensor networks

The nodes in mobile wireless sensor networks (MWSNs) are usually with limited hardware resources, which leads to the limitation of transmission range and energy. To extend the communication distance of a single sensor node, distributed collaborative beamforming (DCB) based on a virtual node antenna array (VNAA) can be used in MWSNs. The locations and excitation current weights are the key factors that affect the performance of DCB, thus the nodes of a MWSN can move to better locations to achieve a lower maximum sidelobe level (SLL) of the beam pattern, thereby reducing the communication interferences and enhancing the directivity. However, the moving energy consumption will be increased. In this paper, a joint optimization problem for optimizing the maximum SLL of beam pattern, transmission power and moving energy consumption of DCB nodes in MWSNs is proposed, and the NP-hardness of the formulated problem is proven. Then, we propose a distributed parallel cuckoo search algorithm (DPCSA), which is a nature-inspired approach, to solve the formulated joint optimization problem. Simulation results verify that the maximum SLL, transmission power and moving energy consumption of DCB nodes can be optimized effectively. Moreover, the performance and stability of the proposed DPCSA are evaluated.

Capitalizing Backscatter-Aided Hybrid Relay Communications With Wireless Energy Harvesting

In this work, we employ multiple energy harvesting relays to assist information transmission from a multi-antenna hybrid access point (HAP) to a receiver. All the relays are wirelessly powered by the HAP in the power-splitting (PS) protocol. We introduce the novel concept of hybrid relay communications, which allows each relay to switch between two radio modes, i.e., the active RF communications and the passive backscatter communications, according to its channel and energy conditions. We envision that the complement transmissions in two radio modes can be exploited to improve the overall relay performance. As such, we aim to jointly optimize the HAP's beamforming, individual relays' radio mode, the PS ratio, and the relays' collaborative beamforming to enhance the throughput performance at the receiver. The resulting formulation becomes a combinatorial and non-convex problem. Thus, we firstly propose a convex approximation to the original problem, which serves as a lower bound of the relay performance. Then, we design an iterative algorithm that decomposes the binary relay mode optimization from the other operating parameters. In the inner loop of the algorithm, we exploit the structural properties to optimize the relay performance with the fixed relay mode in the alternating optimization framework. In the outer loop, different performance metrics are derived to guide the search for a set of passive relays to further improve the relay performance. Simulation results verify that the hybrid relaying communications can achieve 20% performance improvement compared to the conventional relay communications with all active relays.