Next-generation wireless networks are expected to develop a novel paradigm of integrated sensing and communications (ISAC) to enable both the high-accuracy sensing and high-speed communications. However, conventional mono-static ISAC systems, which simultaneously transmit and receive at the same equipment, may suffer from severe self-interference, and thus significantly degrade the system performance. To address this issue, this paper studies a multi-static ISAC system for cooperative target localization and communications, where the transmitter transmits ISAC signal to multiple receivers (REs) deployed at different positions. We derive the closed-form of weighted sum Cramér-Rao bound (CRB) on the joint estimations of both the transmission delay and Doppler shift for cooperative target localization, and the weighted sum CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements for the REs. To solve this problem, we first decouple it into two subproblems for RE selection and transmit beamforming, respectively. Then, a minimax linkage-based method is proposed to solve the RE selection subproblem, and a successive convex approximation algorithm is adopted to deal with the transmit beamforming subproblem with non-convex constraints. Finally, numerical results validate our analysis and reveal that our proposed multi-static ISAC scheme achieves better ISAC performance than the conventional mono-static ones with ideal SI cancellation when the number of cooperative REs is large.

Hybrid reconfigurable intelligent surface (HRIS) as one of the key technologies has the potential to improve the sensing and communication performance in the upcoming sixth-generation systems by creating virtual links among the entities. This paper proposes a HRIS-assisted integrated sensing and communication (ISAC) system to simultaneously perform the sensing and communication by co-designing transmit beamforming at base station and the reflection coefficients at the HRIS. Specifically, we derive the Fisher information matrix on the estimations of transmission delay and angle of departure, and derive the corresponding closedform expression of the Cramér-Rao bound (CRB) to describe the sensing performance. Then, a CRB minimization problem is formulated by taking into account the communication requirements and power constraints, which is non-convex and generally difficult to solve. To address this problem, we propose an algorithm that combines the Schur complement technique with sequential parametric convex approximation to approximate the original problem into a convex version. Finally, numerical results indicate that the proposed method effectively enhances the performance of the ISAC system by appropriately increasing the number of active reflecting elements. It also demonstrates that our proposed HRIS outperforms the conventional passive RIS and active RIS for the ISAC system under limited power budget.

Integrated sensing and communications (ISAC) as one of the key technologies is capable of supporting high-speed communication and high-precision sensing for the upcoming 6G. This paper studies a waveform strategy by designing the orthogonal frequency division multiplexing (OFDM)-based reference signal (RS) for sensing and communication in ISAC system. We derive the closed-form expressions of Cramér-Rao bound (CRB) for the distance and velocity estimations, and obtain the communication rate under the mean square error of channel estimation. Then, a weighted sum CRB minimization problem on the distance and velocity estimations is formulated by considering communication rate requirement and RS intervals constraints, which is a mixed-integer problem due to the discrete RS interval values. To solve this problem, some numerical methods are typically adopted to obtain the optimal solutions, whose computational complexity grow exponentially with the number of symbols and subcarriers of OFDM. Therefore, we propose a relaxation and approximation method to transform the original discrete problem into a continuous convex one and obtain the sub-optimal solutions. Finally, our proposed scheme is compared with the exhaustive search method in numerical simulations, which show slight gap between the obtained sub-optimal and optimal solutions, and this gap further decreases with large weight factor.

This paper studies a multi-static integrated sensing and communications (ISAC) system for multi-user downlink communications and cooperative target sensing, where the base station transmits ISAC signal and the users deployed at different positions receive it. We analyze the joint estimation of transmission delay and Doppler shift of cooperative users for target sensing, and derive the corresponding Cramér-Rao bound (CRB) in closed form. Then, a CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements among these users. To solve this problem, we propose a minimax linkage-based cooperative method for user selection, and then design an approximation non-convex transforming algorithm for transmit beamforming. Finally, simulation results validate our analysis and reveal significant performance improvement of our proposed methods over the state-of-the-art benchmarks.

This paper studies a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the ISAC transmitter sends excitation signals to power multiple passive backscatter devices (BD), and the ISAC receiver performs joint sensing (localization) and communication tasks based on the backscattered signals from all BDs. Specifically, the localization performance is characterized by the Cramér-Rao bound (CRB) on the transmission delay and direction of arrival (DoA) of the backscattered signals, whose closed-form expression is obtained by deriving the corresponding Fisher information matrix (FIM), and the communication performance is characterized by the sum transmission rate of all BDs. Then, a CRB minimization problem is formulated by considering the communication rate constraint, and is shown to be non-convex in general. To solve this problem, we propose an approach that combines fractional programming (FP) and Schur complement techniques to transform the original problem into an equivalent convex form. Finally, numerical results reveal the trade-off between the localization and communication performances.

This paper studies the performance trade-off in a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the multi-antenna ISAC transmitter sends excitation signals to power multiple single-antenna passive backscatter devices (BD), and the multi-antenna ISAC receiver performs joint sensing (localization) and communication tasks based on the backscattered signals from all BDs. Specifically, the localization performance is measured by the Cramér-Rao bound (CRB) on the transmission delay and direction of arrival (DoA) of the backscattered signals, whose closed-form expression is obtained by deriving the corresponding Fisher information matrix (FIM), and the communication performance is characterized by the sum transmission rate of all BDs. Then, to characterize the trade-off between the localization and communication performances, the CRB minimization problem with the communication rate constraint is formulated, and is shown to be non-convex in general. By exploiting the hidden convexity, we propose an approach that combines fractional programming (FP) and Schur complement techniques to transform the original problem into an equivalent convex form. Finally, numerical results reveal the trade-off between the CRB and sum transmission rate achieved by our proposed method.

This paper considers smart-duplex (SD) powered user-centric ultra dense networks (UC-UDN), which shifts the conventional access point-centric paradigm to the user-centric one by de-cellular concept, to provide good quality-of-service for a large number of users via flexibly designing the user association, power allocation, and duplex mode. The maximization average ratio of satisfied users for the considered SD UC-UDN in the long-term time scale is firstly formulated as a Markov decision process (MDP) problem with large discrete action space. To reduce the action space, the user association and power allocation processes are modeled as a two-layer tree structure, and then selecting an action is equivalent to finding the path from root to one of the leaf nodes of the tree. A multi-agent tree-structured policy gradient (MATSPG) based deep reinforcement learning (DRL) algorithm is proposed to solve this problem by directly mapping the action space for user association and power allocation to the two layers of the tree, respectively, whose training is shown to be equivalent to the training of neural networks on two-layer paths. The time and space complexity for searching one action in the proposed MATSPG is also proved to be lower than other conventional DRL algorithms. Simulations show that the proposed MATSPG algorithm significantly improves the average ratio of the satisfied users than the conventional DRL methods in typical scenarios.

Ultra dense networks (UDNs) with smart-duplex (SD), which allows the base stations (BSs) to flexibly switch between the half-duplex (HD) and full-duplex (FD), are expected to support high-density transmissions. However, to centrally handle a large network is costly, while distributed processing may suffer from the severe performance loss due to the complicated intercell interferences in the UDNs. This article aims to balance the system performance and clustering cost of the SD UDNs by dividing all small cells into several clusters. A Markov decision process (MDP) problem is formulated to maximize the average weighted sum of network throughput and clustering cost for all clusters. To approximately solve this problem, we first adopt an affinity propagation method to determine the number of clusters and the center of each cluster. Then, by treating small cells as agents, the original MDP problem is proved to be equivalent to a multiagent MDP to maximize the average reward of all small cells. Next, a multiagent deep reinforcement learning (DRL) is proposed to jointly implement the dynamic clustering for noncenter small cells, resource allocation, and duplex mode selection. Simulation results show that SD has prominent advantages over both the HD and FD in UDNs, and the proposed multiagent DRL outperforms other clustering schemes under the considered scenarios.

This article considers a smart-duplex (SD) powered user-centric ultra dense networks (UC-UDNs), where each user is served cooperatively by multiple access points (APs) adopting the de-cellular concept to achieve desired Quality-of-Service (QoS). The average QoS satisfaction ratio maximization problem for the considered SD UC-UDN is formulated as a Markov decision process (MDP) with large discrete action space by designing the user association and power allocation. To reduce the action space, user association and power allocation are modeled as a two-layer tree, and selecting an action for each user is equivalent to finding the path from the root to one leaf of the constructed tree. Then, a multiagent tree-structured policy gradient (MATSPG)-based deep reinforcement learning (DRL) algorithm is proposed to solve the MDP problem, whose training process is shown to be equivalent to that of the two-layer neural networks. Next, the time and space complexity of searching one action in the proposed MATSPG are also proved to be lower than the conventional DRL algorithms. Finally, simulations show that the proposed MATSPG algorithm significantly improves the average QoS satisfaction ratio than the conventional multiagent deep deterministic policy gradient and multiagent deep Q-network methods in typical scenarios.

This paper considers an ultra dense network (UDN) with smart-duplex (SD), which allows the base stations (BSs) to flexibly switch between half-duplex (HD) and full-duplex (FD) modes over time. All the small cells are divided into several clusters, where the BSs in the same cluster jointly serve their users. A Markov decision process (MDP) problem is formulated to maximize the average weighted sum of network throughput and clustering cost for all clusters. To approximately solve this problem, we first adopt an affinity propagation method to determine the number of clusters and the center of each cluster. Then, by treating small cells as agents, the original MDP problem is proved to be equivalent to a multi-agent MDP to maximize the average reward of all small cells. Next, a multi-agent deep reinforcement learning (DRL) algorithm is proposed to jointly implement the dynamic clustering for the non-center small cells, resource allocation, and duplex mode selection. Simulation results show that SD has prominent advantages over both the HD and FD modes in UDNs, and the proposed algorithm outperforms other clustering schemes under the considered scenarios.

In this paper, we focus on a two-transmitter and two-receiver interference channel (IC), where each transmitter sends a message to the desired receiver. Especially, the full-duplex (FD) amplify-and-forward (AF) protocol is adopted to build up the receiver cooperations. With the considered scheme, the equivalent channel model is analyzed, and the statistics of the accumulated residual interference and noise (ARIN), generated by the imperfect self interference (SI) cancellation and AF scheme, are calculated. Then, the achievable rate regions for both the single-user and joint decoding schemes are characterized by a concave-convex procedure (CCCP). Next, from the achievable rates, one-side cooperation is analyzed to explain its optimality. Simulation results show that the achievable rate regions can be improved by the proposed scheme in certain scenarios.