This paper introduces a universal optimization framework for integrated sensing and communication (ISAC) systems, which are expected to be fundamental aspects of sixth-generation networks. In particular, we develop an iterative augmented Lagrangian manifold optimization (IALMO) framework designed to maximize communication sum rate while satisfying sensing beampattern gain targets, users’ minimum rate requirements, and base station (BS) transmit power limits. IALMO applies the principles of Riemannian manifold optimization to navigate the complex, non-convex landscape of the resource allocation problem. It efficiently leverages the augmented Lagrangian method to ensure adherence to constraints. Comprehensive numerical results are presented to validate our framework, which illustrates the IALMO method’s superior capability to enhance the dual functionalities of communication and sensing in ISAC systems. For instance, with 12 antennas and 30 dBm BS transmit power, our proposed IALMO algorithm delivers a 4.2% sum rate gain over a benchmark optimization-based algorithm. Remarkably, the suggested method performs better in complexity and execution time. For instance, the proposed IALMO algorithm reduces average algorithm execution time by 89.5% with 20 BS transmit antennas compared to the standard optimization-based benchmark. This work demonstrates significant improvements in system performance and contributes a new algorithmic perspective to ISAC resource management.

Cell-free integrated sensing and communication (CF-ISAC) systems are just emerging as an interesting technique for future communications. Such a system comprises several multiple-antenna access points (APs), serving multiple single-antenna communication users and sensing targets. However, efficient beamforming designs that achieve high precision and robust performance in densely populated networks are lacking. This paper proposes a new beamforming algorithm by exploiting the inherent Riemannian manifold structure. The aim is to maximize the communication sum rate while satisfying sensing beampattern gains and per AP transmit power constraints. To address this constrained optimization problem, a highly efficient augmented Lagrangian model-based iterative manifold optimization for the CF-ISAC (ALMCI) algorithm is developed. This algorithm exploits the geometry of the proposed problem and uses a complex oblique manifold. Conventional convex-concave procedure (CCPA) and multidimensional complex quadratic transform (MCQT)-SCA algorithms are also developed as comparative benchmarks. The ALMCI algorithm significantly outperforms both of these. For example, with 16 APs having 12 antennas and 30 dBm transmit power each, our proposed ALMCI algorithm yields 22.7 % and 6.7 % sum rate gains over the CCPA and MCQT-SCA algorithms, respectively. In addition to improvement in communication capacity, the ALMCI algorithm achieves superior beamforming gains and reduced complexity.

The era of ubiquitous, affordable wireless connectivity has opened doors to countless practical applications. In this context, ambient backscatter communication (AmBC) stands out, utilizing passive tags to establish connections with readers by harnessing reflected ambient radio frequency (RF) signals. However, conventional data detectors face limitations due to their inadequate knowledge of channel and RF-source parameters. To address this challenge, we propose an innovative approach using a deep neural network (DNN) for channel state information (CSI) estimation and signal detection within AmBC systems. Unlike traditional methods that separate CSI estimation and data detection, our approach leverages a DNN to implicitly estimate CSI and simultaneously detect data. The DNN model, trained offline using simulated data derived from channel statistics, excels in online data recovery, ensuring robust performance in practical scenarios. Comprehensive evaluations validate the superiority of our proposed DNN method over traditional detectors, particularly in terms of bit error rate (BER). In high signal-to-noise ratio (SNR) conditions, our method exhibits an impressive approximately 20% improvement in BER performance compared to the maximum likelihood (ML) approach. These results underscore the effectiveness of our developed approach for AmBC channel estimation and signal detection. In summary, our method outperforms traditional detectors, bolstering the reliability and efficiency of AmBC systems, even in challenging channel conditions.

Monostatic backscatter (MBS) networks provide connectivity for ultra-low power and low-cost tags for numerous applications. However, the reader operates in the full-duplex (FD) mode and experiences self-interference (SI) levels much higher (e.g., 160 dB) than the desired signal. However, hardware-based SI cancellation (SIC) techniques can remove SI partially only. Therefore, residual SI (RSI) dramatically degrades system performance. To remedy this problem, we develop a sum-rate maximization algorithm that suppresses the RSI and ensures that the tags harvest sufficient energy. It jointly optimizes the reader precoder and combiners and the tags reflection coefficients. Because of the non-convexity of this problem, we utilize alternating optimization (AO) to split it into three parts. They are then solved using successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to yield the precoder, a generalized Rayleigh quotient-based closed-form solution for the combiners, and geometric programming (GP) to get the reflection coefficients. Simulation results validate the fast convergence of the algorithm and show significant sum rate improvements (more than 21%) over the baselines.

e investigate a wireless power transfer (WPT)-based backscatter-mobile edge computing (MEC) network with a reconfigurable intelligent surface (RIS).In this network, wireless devices (WDs) offload task bits and harvest energy, and they can switch between backscatter communication (BC) and active transmission (AT) modes. We exploit the RIS to maximize energy efficiency (EE). To this end, we optimize the time/power allocations, local computing frequencies, execution times, backscattering coefficients, and RIS phase shifts.e investigate a wireless power transfer (WPT)-based backscatter-mobile edge computing (MEC) network with a reconfigurable intelligent surface (RIS).In this network, wireless devices (WDs) offload task bits and harvest energy, and they can switch between backscatter communication (BC) and active transmission (AT) modes. We exploit the RIS to maximize energy efficiency (EE). To this end, we optimize the time/power allocations, local computing frequencies, execution times, backscattering coefficients, and RIS phase shifts.WThis goal results in a multi-objective optimization problem (MOOP) with conflicting objectives. Thus, we simultaneously maximize system throughput and minimize energy consumption via the Tchebycheff method, transforming into two single-objective optimization problems (SOOPs). For throughput maximization, we exploit alternating optimization (AO) to yield two sub-problems. For the first one, we derive closed-form resource allocations. For the second one, we design the RIS phase shifts via semi-definite relaxation, a difference of convex functions programming, majorization minimization techniques, and a penalty function for enforcing a rank-one solution. For energy minimization, we derive closed-form resource allocations. We demonstrate the gains over several benchmarks. For instance, with a 20-element RIS, EE can be as high as 3 (Mbits/Joule), a 150% improvement over the no-RIS case (achieving only 2 (Mbits/Joule)).

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave ($\mu \text{W}$) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the$\mu \text{W}$channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs’ phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs’ trajectories/velocities, RISs’ phase shifts, and subcarrier allocations for$\mu \text{W}$; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS.