Network Association in Machine-Learning Aided Cognitive Radar and Communication Co-Design

Wang, Jingjing; Guan, Sanghai; Jiang, Chunxiao; Alanis, Dimitrios; Ren, Yong; Hanzo, Lajos

doi:10.1109/jsac.2019.2933778

Cited by 25 publications

(11 citation statements)

References 28 publications

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“…D'Andrea et al [14] studied the effect of a wide-beam search based radar on the uplink performance of a massive MIMO communication system. As a further advance, Wang et al [15] invoked machine learning tools for beneficially configuring the network association scheme for the communication user in RCC, where the communication throughput was maximized, while simultaneously coordinating the radar's received interference.…”

Section: A State-of-the-artmentioning

confidence: 99%

NOMA-Aided Joint Radar and Multicast-Unicast Communication Systems

Liu

Guo

et al. 2022

IEEE J. Select. Areas Commun.

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The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radarcentric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R-and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, a beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality nearoptimal solution. Next, the system is extended to the scenario of multiple pairs of R-and C-users, where a cluster-based NOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed schemes over the benchmark schemes employing conventional transmission strategies.

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Section: A State-of-the-artmentioning

confidence: 99%

NOMA-Aided Joint Radar and Multicast-Unicast Communication Systems

Liu

Guo

et al. 2022

IEEE J. Select. Areas Commun.

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“…where J q P T ti;q;t k À � is the Fisher information matrix of prior information in (11), and problem P2 is solved by the heuristic search method proposed in Algorithm 1.…”

Section: Two-stage Solution Algorithm Based On Kld-hpsomentioning

confidence: 99%

“…Furthermore, based on the reinforcement learning (RL) algorithms, a novel partially observable Markov decision process-based network association scheme and a non-linear value function approximation-based deep RL approach are proposed in Ref. [11,12], respectively. The former is able to maximise the network throughput while minimising the interference imposed on the radar user, and the latter can mitigate interference between radar and CSs while achieving improved radar detection performance.…”

Section: Introduction 1| Background and Motivationmentioning

confidence: 99%

Communication‐awareness adaptive resource scheduling strategy for multiple target tracking in a multiple radar system

Cheng

et al. 2022

IET Signal Processing

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In this study, a communication‐awareness adaptive resource scheduling (CARS) strategy for multiple target tracking in a multiple radar system (MRS) is proposed. The CARS strategy aims to maximise the tracking performance of MRS, whilst minimising the interference from MRS to communication systems (CSs), whose mechanism is to simultaneously control the revisit frame interval of each target and determine the activation of radar nodes, the radar‐target assignment and the allocation of transmitted power. Mathematically, the CARS strategy is formulated as an optimization problem, which contains both the continuous variable and the discrete (integer) variable. To tackle the resultant mixed‐integer, non‐convex, and non‐linear problem efficiently, incorporating with the proposed hybrid particle swarm optimization algorithm based on the Kullback–Leibler divergence, a two‐stage solution technique is developed to obtain the near‐optimal solution. Numerical simulation results are provided to validate the proposed CARS strategy and demonstrate its superiority over the traditional scheduling strategies.

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“…In addition to robots, RL algorithms have been used to design the trajectory for other kinds of craft, such as vehicles [26,27] and UAVs [28][29][30][31]. From a perspective of the communication, since RL algorithms have a prominent ability to solve non-convex problems [32], it manifests extraordinary potential in wireless network optimization [33,34]. The author of [35] optimized the reflecting beamforming of an IRS by an RL approach and their results suggest that the participation of the RL significantly improves the secrecy and quality of service (QoS) satisfaction probability of the system.…”

Section: ) Reinforcement Learning (Rl) In Robotic Control and Communi...mentioning

confidence: 99%

Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots

Zhong¹,

Liu²,

Liu³

et al. 2020

Preprint

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A communication enabled indoor intelligent robots (IRs) service framework is proposed, where nonorthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications.In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state in order to construct the radio map. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent, which can save training time and hardware costs. Build on the proposed communication model, motions of IRs who need to reach designated mission destinations and their corresponding down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to solve this optimization problem, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation results demonstrate that 1) With the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.

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Network Association in Machine-Learning Aided Cognitive Radar and Communication Co-Design

Cited by 25 publications

References 28 publications

NOMA-Aided Joint Radar and Multicast-Unicast Communication Systems

NOMA-Aided Joint Radar and Multicast-Unicast Communication Systems

Communication‐awareness adaptive resource scheduling strategy for multiple target tracking in a multiple radar system

Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots

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