Integrated sensing and communications (ISAC) is a disruptive technology enabling future sixth-generation (6G) networks. This paper investigates target detection in a bistatic ISAC system, in which the base station (BS) transmits superimposed ISAC signals comprising both Gaussian information-bearing and deterministic sensing components to simultaneously provide communication and sensing functionalities. First, we develop a Neyman-Pearson (NP)-based detector that effectively utilizes both the deterministic sensing and random communication signals. Closed-form analysis reveals that both signal components contribute to improving the overall detection performance. Subsequently, we optimize the BS transmit beamforming to maximize the detection probability, subject to a minimum signal-to-interference-plus-noise ratio (SINR) constraint for the communication user (CU) and a total transmit power budget at the BS. The resulting non-convex beamforming optimization problem is addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which leverages both types of signals, over benchmark schemes that treat information signals as interference. They also reveal that a higher communication-rate threshold directs more transmit power to Gaussian information-bearing signals, thereby diminishing deterministic-signal power and weakening detection performance.

This paper investigates an unlicensed millimeter-wave (mmWave) coexistence system, where the new radio-based access to unlicensed spectrum (NR-U) network and the incumbent Wireless Gigabit (WiGig) network share the same spectrum resources to transmit data packets. The total data rate of NR-U is maximized by jointly optimizing the user equipment (UE) scheduling and hybrid beamforming, subject to the constraints of the quality-of-service (QoS) requirements of all UEs and the maximum transmit power at the gNB. Besides, NR-U needs to avoid excessive interference to WiGig, without acquiring any prior information about WiGig. To circumvent this problem, we put forth a model-free joint optimization scheme, referred to as Deep Q-Policy Gradient (DQPG), based on the deep reinforcement learning (DRL) technique. Specifically, a hybrid DRL framework is first proposed to support DQPG, where the deep double Q-network (D2QN) and double-critic-based deep deterministic policy gradient (D3PG) algorithms are invoked to optimize UE scheduling in the discrete action domain and hybrid beamforming in the continuous action domain, respectively. Thereafter, a new reward function and a scaled action selection policy are judiciously designed for DQPG to satisfy various constraints. To capture the complex coupling relationship between different optimization variables, we further propose a parallel experience replay mechanism to maintain training synchronization between D2QN and D3PG. In addition, a transfer learning approach is introduce to accelerate the convergence of DQPG. Simulation results demonstrate that while satisfying the QoS requirements of all UEs, compared with other coexistence schemes, DQPG (i) attains a higher total data rate of NR-U, (ii) causes less interference to WiGig, and (iii) converges faster. Furthermore, under different numbers of UEs and QoS requirements of UEs, DQPG is more robust than benchmarks.

This paper studies an intelligent omni-surface (IOS)-aided integrated sensing and communication (ISAC) system, where a base station (BS) provides simultaneous target sensing and communication services with an IOS under outdated and imperfect channel state information (CSI). Both the communication sum-rate and sensing signal-to-noise ratio are maximized through joint optimization of BS beamforming and IOS configuration. To address this problem, we propose an intelligent joint optimization scheme called meta multi-objective hybrid deep reinforcement learning (meta-MHDRL). Specifically, the meta-MHDRL framework first introduces a hybrid deep reinforcement learning (DRL) approach that integrates double-critic-based deep deterministic policy gradient with deep double Q-network algorithms, enabling parallel optimization of both continuous-domain variables (i.e., BS beamforming, IOS reflecting phase shift, and IOS reflecting/refracting amplitudes) and the discrete-domain variable (i.e., IOS refracting phase shift). Thereafter, an objective-preference weight is incorporated into the hybrid DRL framework, such that meta-MHDRL can capture the trade-off between communication and sensing performance. To address the complex coupling relationships among different optimization variables, we further put forth a synchronized experience replay mechanism for meta-MHDRL, which maintains training synchronization among different neural networks. In addition, a meta-learning approach is developed to enhance the generalization ability of meta-MHDRL across different objective-preference weights. Simulation results show that meta-MHDRL attains more Pareto-efficient solutions than other schemes under outdated and imperfect CSI while maintaining stronger robustness across various simulation setups. Besides, we demonstrate the generalization ability of meta-MHDRL for unseen tasks

This paper investigates a new integrated sensing and communication (ISAC) scheme for underwater acoustic (UWA) networks based on deep reinforcement learning, referred to as Deep UWA-ISAC (DeepUSC). Specifically, we consider a UWA-ISAC system, where an autonomous underwater vehicle (AUV) transmits the collected environmental data to the buoy, while sensing the sea area to monitor the unauthorized mobile target. The expected communication rate over a given navigation period is maximized by jointly optimizing the AUV's beamforming and trajectory, subject to the constraints on the average signal-to-noise ratio requirement for target sensing as well as the navigation mission, collision avoidance, and maximum transmit power limit of the AUV. Three key challenges for DeepUSC are: (i) long propagation delays in the UWA-ISAC system may cause interference from the previous echo to the current ISAC signal; (ii) the mobility pattern of the target is unknown in advance; and (iii) the AUV navigation-oriented ISAC problem is a long-term optimization problem as the navigation mission typically lasts for a long period. To circumvent the above challenges, DeepUSC is developed based on a specific partially observable Markov decision process model termed episode task, where each navigation period is considered as an episode and the navigation mission corresponds to the episode task. Through judicious design of a reward function and action selection policy, DeepUSC can satisfy various preset constraints without requiring prior knowledge of the target's mobility. Besides, to enable efficient learning in episode tasks, we propose an episodic experience replay mechanism that dynamically prioritizes high-value recent experiences and utilizes all experiences generated within each episode to jointly train the neural network. Simulation results demonstrate that compared with benchmarks, DeepUSC yields a higher communication rate while satisfying all constraints, converges faster, and is more robust against different simulation setups.

This paper studies a wireless control system at network edge, in which a base station (BS) wirelessly coordinates the closed-loop control of multiple subsystems each consisting of a plant, a sensor, and an actuator. In this system, the BS first collects the state information from the sensors of plants, then processes the information via edge computing, and finally sends the obtained command signals back to the actuators for controlling the plants. In particular, we consider the hyper-reliable and low-latency communications (HRLLC) for the state and command signal transmission, by using the rate formulas based on short-packet communication. Under this setup, we first present a time-division-multiple-access (TDMA) protocol for coordinating the sensing, communication, and computation among the multiple plants. Then, we jointly optimize the task scheduling as well as the communication and computation resource allocations to minimize the closed-loop control latency while ensuring the stability of the multiple control subsystems. The considered problem is a highly non-convex combinatorial optimization problem that is difficult to solve. To resolve this issue, we present efficient algorithms by first optimizing the communication and computation resource allocations under given task scheduling via the techniques of alternating optimization and successive convex approximation, and then designing the task scheduling based on the exhaustive search or the low-complexity flow-shop scheduling. Numerical results show that the proposed joint resource allocation design with exhaustive search based task scheduling significantly outperforms other benchmark schemes without such joint optimization, and the proposed low-complexity task scheduling based on flow-shop scheduling achieves performance close to the upper bound by exhaustive search.

In this paper, we investigate a bistatic integrated sensing and communications (ISAC) system, consisting of a base station (BS) with multiple transmit antennas, a sensing receiver with multiple receive antennas, a single-antenna communication user (CU), and a point target to be sensed. Specifically, the BS transmits a superposition of Gaussian information and deterministic sensing signals to support ISAC. The BS aims to deliver information symbols to the CU, while the sensing receiver aims to estimate the target’s direction-of-arrival (DoA) with respect to the sensing receiver by processing the echo signals reflected by the target. For the sensing receiver, we assume that only the sequences of the deterministic sensing signals and the covariance matrix of the information signals are perfectly known, whereas the specific realizations of the information signals remain unavailable. Under this setup, we first derive the corresponding Cram´er-Rao bounds (CRBs) for DoA estimation and propose practical estimators to accurately estimate the target’s DoA. Subsequently, we formulate the transmit beamforming design as an optimization problem aiming to minimize the CRB, subject to a minimum signal-to-interference-plus-noise ratio (SINR) requirement at the CU and a maximum transmit power constraint at the BS. When the BS employs only Gaussian information signals, the resulting beamforming optimization problem is convex, enabling the derivation of an optimal solution. In contrast, when both Gaussian information and deterministic sensing signals are transmitted, the resulting problem is non-convex and a locally optimal solution is acquired by exploiting successive convex approximation (SCA). Finally, numerical results demonstrate that the utilization of additional deterministic sensing signals is critical for sensing performance enhancement, while solely employing Gaussian information signals leads to a notable performance degradation for target sensing. It is unveiled that the proposed transmit beamforming design achieves a superior ISAC performance boundary compared with various benchmark schemes.

Due to the harsh channel conditions and operational difficulties in battery recharging, energy-efficient transmission is critical in underwater acoustic communications (UACs). This paper investigates a new link adaptation technique for UACs that jointly optimizes transmission frequency, power, and rate to maximize energy efficiency. Conventional optimization-based approaches typically require real-time and perfect channel state information and have high computational complexity, making them difficult to implement in realistic systems. To circumvent this problem, we put forth MetaDT, a model-free link adaptation technique combining deep reinforcement learning with meta-learning. To enable powerful reasoning and fast decision-making, we further propose a dueling echo state network (ESN) with separate output architecture for incorporation into MetaDT. Besides, to enable MetaDT to quickly adapt to diverse new/unseen environments, a low-complexity meta-learning is developed to find the optimal meta-parameters of the dueling ESN architecture. Numerical results show that compared to various benchmarks, MetaDT attains significant energy efficiency gains and is more robust against different transmission distances and numbers of multi-paths. In comparison to conventional neural networks, dueling ESN shortens the run-time of MetaDT by more than 89.58% and is more efficient for temporal inference. In addition, we demonstrate the generalization capability of MetaDT with meta-learning to new/unseen environment configurations.

This paper exploits the networked integrated sensing and communications (ISAC) to support low-altitude economy (LAE), in which a set of networked ground base stations (GBSs) cooperatively transmit joint information and sensing signals to communicate with multiple authorized uncrewed aerial vehicles (UAVs) and concurrently detect unauthorized objects over the interested region in the three-dimensional (3D) space. We assume that each GBS is equipped with uniform linear array (ULA) antennas, which are deployed either horizontally or vertically to the ground. We also consider two types of UAV receivers, which have and do not have the capability of canceling the interference caused by dedicated sensing signals, respectively. Under each setup, we jointly design the coordinated transmit beamforming at multiple GBSs together with the authorized UAVs’ trajectory control and their GBS associations, for enhancing the authorized UAVs’ communication performance while ensuring the sensing requirements. In particular, we aim to maximize the average sum rate of authorized UAVs over a given flight period, subject to the minimum illumination power constraints toward the interested 3D sensing region, the maximum transmit power constraints at individual GBSs, and the flight constraints of UAVs. These problems are highly non-convex and challenging to solve, due to the involvement of binary UAV-GBS association variables as well as the coupling of beamforming and trajectory variables. To solve these non-convex problems, we propose efficient algorithms by using the techniques of alternating optimization, successive convex approximation, and semi-definite relaxation. Numerical results show that the proposed joint coordinated transmit beamforming and UAV trajectory designs efficiently balance the sensing-communication performance tradeoffs and significantly outperform various benchmarks. It is also shown that the horizontally placed antennas lead to enhanced performance compared with their vertical counterparts due to the more flexible multi-beam design, and the sensing interference cancellation ability at UAV receivers is advantageous for further enhancing ISAC performance.

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully- and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze both the sensing signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB) for estimating the target’s direction-of-arrival (DoA) with joint transmit and reflective beamforming optimization. First, we characterize the maximum sensing SNR when the BS-IRS channel follows line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elementsNequipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally toN2andN4for the semi- and fully-passive IRSs, respectively. Then, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. It is shown that whenNgrows, the minimum CRB decreases inversely proportionally toN4andN6for the semi- and fully-passive IRS, respectively. Finally, numerical results are presented to corroborate our analysis across general channel conditions. It is shown that the fully-passive IRS outperforms the semi-passive counterpart whenNexceeds a certain threshold due to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss.

Reconfigurable intelligent surfaces (RIS) play a crucial role in enhancing the security of integrated sensing and communication (ISAC) systems. In this paper, RIS is explored to assist the secure transmission of user data in ISAC system. Through the joint design of the transmit beamforming and RIS discrete phase shifter, we aim to maximize user's secure rates while ensuring target sensing performance. Due to the coupling of optimization variables, conventional optimization methods are hard to address this formulated problem. Therefore, a deep reinforcement learning (DRL) scheme by utilizing the soft actor-critic (SAC) and alternating optimization (AO) algorithms is employed to design the transmit beamforming and the RIS discrete phase shifter, respectively. Simulation results indicate that the problem scheme could obtain a significant improvement in enhancing user secure rates compared to other benching scheme.

The scope of the 6G wireless communication system is envisioned to expand beyond delivering data to humans and towards connecting machines that constantly upload computation-intensive status updates to obtain real-time situational awareness. Under dynamic environments, the amount of useful information contained in status updates degrades over time, which could be measured based on the concept of age of information. In this paper, we develop an analytical framework to investigate the temporal value of status updates, in terms of the peak age of information. Given the temporal dynamics of observed physical process, the procedure of transmission and computing is modeled as tandem queues for both parallel processing and series processing modes at the edge server. The obtained closed-form expressions explicitly characterize the coupling among information generation, transmission, and usage, which can be exploited as performance metrics for task-oriented resource optimization. The accuracy of our analysis is verified with simulation results. Based on the theoretical analysis, we formulate an optimization problem to simultaneously minimize the age of status updates and energy consumption for multiple devices. Numerical results reveal that the computation and transmission time could be traded off to obtain timely status updates at low energy cost.

This paper investigates the performance tradeoff for a multi-antenna integrated sensing and communication (ISAC) system with simultaneous information multicasting and multi-target sensing, in which a multi-antenna base station (BS) sends the common information messages to a set of single-antenna communication users (CUs) and estimates the parameters of multiple sensing targets based on the echo signals concurrently. We consider two target sensing scenarios without and with prior target knowledge at the BS, in which the BS is interested in estimating the complete multi-target response matrix and the target reflection coefficients/angles, respectively. First, we consider the capacity-achieving transmission and characterize the fundamental tradeoff between the achievable rate and the multi-target estimation Cramér-Rao bound (CRB) accordingly. To this end, we design the optimal transmit signal covariance matrix at the BS to minimize the estimation CRB for each of the two scenarios, subject to the minimum multicast rate requirement and the maximum transmit power constraint. It is shown that the optimal covariance matrix consists of two parts for ISAC and dedicated sensing, respectively. Next, we consider the transmit beamforming designs, in which the BS sends one information beam together with multiple a-priori known dedicated sensing beams for effective ISAC and each CU can cancel the interference caused by the sensing signals. By exploiting the successive convex approximation (SCA) technique, we develop efficient algorithms to obtain the joint information and sensing beamforming solutions to the resultant rate-constrained CRB minimization problems. Finally, we provide numerical results to validate the CRB-rate (C-R) tradeoff achieved by our proposed designs, as compared to two benchmark schemes, namely the isotropic transmission and the joint beamforming without sensing interference cancellation. It is shown that the proposed optimal transmit covariance solution achieves much better C-R performance than the benchmark schemes and the proposed joint beamforming with sensing interference cancellation performs close to the optimal transmit covariance solution when the number of CUs is small. We also conduct simulations to show the practical estimation performance achieved by our proposed designs, by considering randomly generated information signals and practical estimators.

This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communications (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one target at the non-line-of-sight (NLoS) region of the BS. The IRS is deployed to not only assist the communication from the BS to the CUs, but also enable the BS’s NLoS target sensing based on the echo signals from the BS-IRS-target-IRS-BS link. We consider two types of targets, namely the extended and point targets, for which the BS aims to estimate the complete target response matrix and the target’s direction-of-arrival (DoA) with respect to the IRS, respectively. To provide full degrees of freedom for sensing, we consider that the BS sends dedicated sensing signals in addition to the communication signals. Accordingly, we model two types of CU receivers, namely Type-I and Type-II CU receivers, which do not have and have the capability of canceling the interference from the sensing signals, respectively. Under each setup, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the IRS to minimize the Cramér-Rao bound (CRB) for target estimation, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs and the maximum transmit power constraint at the BS. We present efficient algorithms to solve the highly non-convex SINR-constrained CRB minimization problems, by using the techniques of alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design achieves lower estimation CRB than other benchmark schemes, and the sensing signal interference cancellation at Type-II CU receivers is beneficial when the number of CUs is greater than one.

This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communication (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one extended target at the non-line-of-sight (NLoS) region of the BS. The IRS is deployed to not only assist the communication from the BS to the CUs, but also enable the BS's NLoS target sensing based on the echo signals from the BS-IRS-target-IRS-BS link. To provide full degrees of freedom for sensing, we suppose that the BS sends additional dedicated sensing signals combined with the information signals. Accordingly, we consider two types of CU receivers, namely Type-I and Type-II receivers, which do not have and have the capability of cancelling the interference from the sensing signals, respectively. Under this setup, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the IRS to minimize the Cramer-Rao bound (CRB) for estimating the target response matrix with respect to the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs and the maximum transmit power constraint at the BS. We present efficient algorithms to solve the highly non-convex SINR-constrained CRB minimization problems, by using the techniques of alternating optimization and semi-definite relaxation. Numerical results show that the proposed design achieves lower estimation CRB than other benchmark schemes, and the sensing signal interference pre-cancellation is beneficial when the number of CUs is greater than one.

This paper studies the wireless control system at network edge, in which one base station (BS) coordinates the closed-loop wireless control of multiple subsystems each consisting of a plant, sensor, and actuator. In this system, the BS first collects the state information from the sensors of plants, then processes the information via edge computing, and finally sends the obtained command signals back to the actuators for controlling the plants. In particular, we consider the ultra-reliable low-latency communication (URLLC) for the state and command signal transmission, by using the rate formulas based on short-packet communication. Under this setup, we first present a time-division-multiple-access (TDMA) protocol for coordinating the sensing, communication, and computation among the multiple plants. Then, we jointly optimize the communication and computation resource allocations to minimize the closed-loop control latency while ensuring the stability of the controlled plants. Though the considered problem is difficult to solve, we transform it into a non-convex problem with semi-definite constraints, and then present an efficient solution via the techniques of alternating optimization and convex approximation. Numerical results show that the proposed solution efficiently reduces the closed-loop control latency as compared to other benchmark schemes with heuristic resource allocations.

Moving beyond just interconnected devices, the increasing interplay between communication and computation has fed the vision of real-time networked control systems. To obtain timely situational awareness, IoT devices continuously sample computation-intensive status updates, generate perception tasks and offload them to edge servers for processing. In this sense, the timeliness of information is considered as one major contextual attribute of status updates. In this paper, we derive the closed-form expressions of timeliness of information for computation offloading at both edge tier and fog tier, where two-stage tandem queues are exploited to abstract the transmission and computation process. Moreover, we exploit the statistical structure of Gauss-Markov process, which is widely adopted to model temporal dynamics of system states, and derive the closed-form expression for process-related timeliness of information. The obtained analytical formulas explicitly characterize the dependency among task generation, transmission and execution, which can serve as objective functions for system optimization. Based on the theoretical results, we formulate a computation offloading optimization problem at edge tier, where the timeliness of status updates is minimized among multiple devices by joint optimization of task generation, bandwidth allocation, and computation resource allocation. An iterative solution procedure is proposed to solve the formulated problem. Numerical results reveal the intertwined relationship among transmission and computation stages, and verify the necessity of factoring in the task generation process for computation offloading strategy design.

This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate an extended target and communicate with a multi-antenna communication user (CU) at the same time. We investigate the fundamental tradeoff between the estimation Cramér-Rao bound (CRB) for sensing and the data rate for communication, by characterizing the Pareto boundary of the achievable CRB-rate (C-R) region. Towards this end, we formulate a new MIMO rate maximization problem by optimizing the transmit covariance matrix at the BS, subject to a new form of maximum CRB constraint together with a maximum transmit power constraint. We derive the optimal transmit covariance solution in a semi-closed form, by first implementing the singular-value decomposition (SVD) to diagonalize the communication channel and then properly allocating the transmit power over these subchannels for communication and other orthogonal subchannels (if any) for dedicated sensing. It is shown that the optimal transmit covariance is of full rank, which unifies the conventional rate maximization design with water-filling power allocation and the CRB minimization design with isotropic transmission. Numerical results are provided to validate the performance achieved by our proposed optimal design, in comparison with other benchmark schemes.

This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS with a uniform linear array (ULA) is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing potential targets at areas with LoS links blocked. We consider that the BS transmits combined information and sensing signals for ISAC. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS and the reflective beamforming at the IRS, to maximize the IRS’s minimum beampattern gain towards the desired sensing angles, subject to the minimum signal-to-noise ratio (SNR) requirement at the CU and the maximum transmit power constraint at the BS. Although the formulated SNR-constrained beampattern gain maximization problem is non-convex and difficult to solve, we present an efficient algorithm to obtain a high-quality solution by using the techniques of alternating optimization and semi-definite relaxation (SDR). Numerical results show that the proposed joint beamforming design achieves improved sensing performance while ensuring the communication requirement as compared to benchmarks without such joint optimization. It is also shown that the use of dedicated sensing beams is beneficial in enhancing the performance for IRS-assisted ISAC.