Multi-stage Antenna Selection for Adaptive Beamforming in MIMO Arrays

Increasing the number of transmit and receive elements in multiple-input-multiple-output (MIMO) antenna arrays imposes a substantial increase in hardware and computational costs. We mitigate this problem by employing a reconfigurable MIMO array where large transmit and receive arrays are multiplexed in a smaller set of k baseband signals. We consider four stages for the MIMO array configuration and propose four different selection strategies to offer dimensionality reduction in post-processing and achieve hardware cost reduction in digital signal processing (DSP) and radio-frequency (RF) stages. We define the problem as a determinant maximization and develop a unified formulation to decouple the joint problem and select antennas/elements in various stages in one integrated problem. We then analyze the performance of the proposed selection approaches and prove that, in terms of the output SINR, a joint transmit-receive selection method performs best followed by matched-filter, hybrid and factored selection methods. The theoretical results are validated numerically, demonstrating that all methods allow an excellent trade-off between performance and cost.Comment: Submitted for publicatio

Integrated Sensing and Communication for Network-Assisted Full-Duplex Cell-Free Distributed Massive MIMO Systems

In this paper, we combine the network-assisted full-duplex (NAFD) technology and distributed radar sensing to implement integrated sensing and communication (ISAC). The ISAC system features both uplink and downlink remote radio units (RRUs) equipped with communication and sensing capabilities. We evaluate the communication and sensing performance of the system using the sum communication rates and the Cramer-Rao lower bound (CRLB), respectively. We compare the performance of the proposed scheme with other ISAC schemes, the result shows that the proposed scheme can provide more stable sensing and better communication performance. Furthermore, we propose two power allocation algorithms to optimize the communication and sensing performance jointly. One algorithm is based on the deep Q-network (DQN) and the other one is based on the non-dominated sorting genetic algorithm II (NSGA-II). The proposed algorithms provide more feasible solutions and achieve better system performance than the equal power allocation algorithm.Comment: 14 pages, 7 figures,submit to China Communication February 28, 2023, date of major revision July 09, 202

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks

Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Commmunications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks

Joint transmitter selection and resource management strategy based on low probability of intercept optimization for distributed radar networks

In this paper, a joint transmitter selection and resource management (JTSRM) strategy based on low probability of intercept (LPI) is proposed for target tracking in distributed radar network system. The basis of the JTSRM strategy is to utilize the optimization technique to control transmitting resources of radar networks in order to improve the LPI performance, while guaranteeing a specified target tracking accuracy. The weighted intercept probability and transmit power of radar networks is defined and subsequently employed as the optimization criterion for the JTSRM strategy. The resulting optimization problem is to minimize the LPI performance criterion of radar networks by optimizing the revisit interval, dwell time, transmitter selection, and transmit power subject to a desired target tracking performance and some resource constraints. An efficient and fast three‐step solution technique is also developed to solve this problem. The presented mechanism implements the optimal working parameters based on the feedback information in the tracking recursion cycle in order to improve the LPI performance for radar networks. Numerical simulations are provided to verify the superior performance of the proposed JTSRM strategy

Cognitive radar network design and applications

PhD ThesisIn recent years, several emerging technologies in modern radar system design are attracting the attention of radar researchers and practitioners alike, noteworthy among which are multiple-input multiple-output (MIMO), ultra wideband (UWB) and joint communication-radar technologies. This thesis, in particular focuses upon a cognitive approach to design these modern radars. In the existing literature, these technologies have been implemented on a traditional platform in which the transmitter and receiver subsystems are discrete and do not exchange vital radar scene information. Although such radar architectures benefit from these mentioned technological advances, their performance remains sub-optimal due to the lack of exchange of dynamic radar scene information between the subsystems. Consequently, such systems are not capable to adapt their operational parameters “on the fly”, which is in accordance with the dynamic radar environment. This thesis explores the research gap of evaluating cognitive mechanisms, which could enable modern radars to adapt their operational parameters like waveform, power and spectrum by continually learning about the radar scene through constant interactions with the environment and exchanging this information between the radar transmitter and receiver. The cognitive feedback between the receiver and transmitter subsystems is the facilitator of intelligence for this type of architecture. In this thesis, the cognitive architecture is fused together with modern radar systems like MIMO, UWB and joint communication-radar designs to achieve significant performance improvement in terms of target parameter extraction. Specifically, in the context of MIMO radar, a novel cognitive waveform optimization approach has been developed which facilitates enhanced target signature extraction. In terms of UWB radar system design, a novel cognitive illumination and target tracking algorithm for target parameter extraction in indoor scenarios has been developed. A cognitive system architecture and waveform design algorithm has been proposed for joint communication-radar systems. This thesis also explores the development of cognitive dynamic systems that allows the fusion of cognitive radar and cognitive radio paradigms for optimal resources allocation in wireless networks. In summary, the thesis provides a theoretical framework for implementing cognitive mechanisms in modern radar system design. Through such a novel approach, intelligent illumination strategies could be devised, which enable the adaptation of radar operational modes in accordance with the target scene variations in real time. This leads to the development of radar systems which are better aware of their surroundings and are able to quickly adapt to the target scene variations in real time.Newcastle University, Newcastle upon Tyne: University of Greenwich