In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-driven cooperative integrated sensing, computing, and communication (ISCC) network to meet the requirement for a diverse network with low energy consumption. The cooperative base stations (BSs) are equipped with TRIS transceivers to accomplish sensing data acquisition, communication offloading, and computation in a time slot. In order to obtain higher cooperation gain, we utilize a signal-level spatial registration algorithm, which is realized by adjusting the beamwidth. Meanwhile, for more efficient offloading of the computational task, multistream communication is considered, and rank-N constraints are introduced, which are handled using an iterative rank minimization (IRM) scheme. We construct an optimization problem with the objective function of minimizing the total energy consumption of the network to jointly optimize the beamforming matrix, time slot allocation, sensing data allocation and sensing beam scheduling variables. Due to the coupling of the variables, the proposed problem is a non-convex optimization problem, which we decouple and solve using a block coordinate descent (BCD) scheme. Finally, numerical simulation results confirm the superiority of the proposed scheme in improving the overall network performance and reducing the total energy consumption of the network.

In this paper, we propose a transmissive reconfigurable intelligent surface (TRIS)-empowered distributed cooperative integrated sensing and communication (ISAC) network, which enhances the coverage and wireless environment understanding through the joint design of cooperative users (CUEs) and destination users (DUEs). Rate-splitting multiple access (RSMA) is implemented at the base station (BS), where the common stream is decoded and recoded by the CUEs and forwarded to the DUEs, while the private stream meets the CUEs’ own communication requirements. We construct an optimization problem with the objective of maximizing the minimum Radar mutual information (RMI), and jointly optimize the BS beamforming matrix, the CUE beamforming matrixs, common stream rate, and user scheduling vectors. To address the challenges of the nonconvex optimization problem, the consensus alternating direction multiplier framework (ADMM) is utilized to decouple the variables, and the subproblems are solved independently through iterative optimization until overall convergence is achieved. Numerical results validate the superiority of the proposed scheme in terms of improving communication sum-rate and RMI, and greatly reduce the algorithm complexity.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-enhanced robust and secure integrated sensing and communication (ISAC) network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise (AN), while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent (BCD)-based second-order cone programming (SOCP) algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein’s inequality and successive convex approximation (SCA) are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency (SEE) and the Cramér-Rao boundary (CRB).

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service. We introduce the sensing quality of service (QoS) criteria based on this structure and construct an optimization problem with the sensing QoS criteria as the objective function to optimize the sensing stream precoding matrix and the communication stream precoding matrix. Due to the coupling of optimization variables, the formulated problem is a non-convex optimization problem that cannot be solved directly. To tackle the above-mentioned challenging problem, alternating optimization (AO) is utilized to decouple the optimization variables. Specifically, the problem is decoupled into three subproblems about the sensing stream precoding matrix, the communication stream precoding matrix, and the auxiliary variables, which is solved alternatively through AO until the convergence is reached. For solving the problem, successive convex approximation (SCA) is applied to deal with the sum-rate threshold constraints on communications, and difference-of-convex (DC) programming is utilized to solve rank-one non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in terms of improving the communication and sensing QoS.

This paper studies the fair transmission design for an intelligent reflecting surface (IRS) aided rate-splitting multiple access (RSMA). IRS is used to establish a good signal propagation environment and enhance the RSMA transmission performance. The fair rate adaption problem is constructed as a max-min optimization problem. To solve the optimization problem, we adopt an alternative optimization (AO) algorithm to optimize the power allocation, beamforming, and decoding order, respectively. A generalized power iteration (GPI) method is proposed to optimize the receive beamforming, which can improve the minimum rate of devices and reduce the optimization complexity. At the base station (BS), a successive group decoding (SGD) algorithm is proposed to tackle the uplink signal estimation, which trades off the fairness and complexity of decoding. At the same time, we also consider robust communication with imperfect channel state information at the transmitter (CSIT), which studies robust optimization by using lower bound expressions on the expected data rates. Extensive numerical results show that the proposed optimization algorithm can significantly improve the performance of fairness. It also provides reliable results for uplink communication with imperfect CSIT.

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.