Integrated sensing and communication (ISAC) systems enable communication and sensing functions via three protocols, including ($\text{TP}_{1}$) concurrently using the same time-frequency resources, ($\text{TP}_{2}$) scheduling them independently, or ($\text{TP}_{3}$) a hybrid technique that combines both. Nevertheless, all existing studies rely on the first approach, and none provide a comprehensive performance evaluation of all three protocols. Thus, this paper offers an extensive performance evaluation of these protocols, emphasizing their advantages, limitations, and trade-offs. Specifically, we maximize the sum communication and/or sensing rate for all protocols in a full-duplex ISAC system with multiple users and targets. Numerical results reveal that protocols ($\text{TP}_{1}$) and ($\text{TP}_{3}$) necessitate a sensing metric considering both transmit and receiver beams to ensure adequate sensing performance. In contrast, although protocol ($\text{TP}_{2}$) can utilize more straightforward sensing metrics and avoid communication-sensing interference, it may not achieve high communication performance.

Cell-free (CF) integrated sensing and communication (ISAC) combines CF architecture with ISAC. CF employs distributed access points, eliminates cell boundaries, and enhances coverage, spectral efficiency, and reliability. ISAC unifies radar sensing and communication, enabling simultaneous data transmission and environmental sensing within shared spectral and hardware resources. CF-ISAC leverages these strengths to improve spectral and energy efficiency while enhancing sensing in wireless networks. As a promising candidate for next-generation wireless systems, CF-ISAC supports robust multi-user communication, distributed multi-static sensing, and seamless resource optimization. However, a comprehensive survey on CF-ISAC has been lacking. This paper fills that gap by first revisiting CF and ISAC principles, covering cooperative transmission, radar cross-section, target parameter estimation, ISAC integration levels, sensing metrics, and applications. It then explores CF-ISAC systems, emphasizing their unique features and the benefits of multi-static sensing. State-of-the-art developments are categorized into performance analysis, resource allocation, security, and user/target-centric designs, offering a thorough literature review and case studies. Finally, the paper identifies key challenges such as synchronization, multi-target detection, interference management, and fronthaul capacity and latency. Emerging trends, including next-generation antenna technologies, network-assisted systems, near-field CF-ISAC, integration with other technologies, and machine learning approaches, are highlighted to outline the future trajectory of CF-ISAC research.

Cell-free (CF) architectures and full-duplex (FD) communication are leading candidates for next-generation wireless networks. The CF framework removes cell boundaries in traditional cell-based systems, thereby mitigating the inter-cell interference and improving the coverage probability. In contrast, FD communication allows simultaneous transmission and reception on the same frequency-time resources, effectively doubling the spectral efficiency (SE). The integration of these technologies, known as CF FD communication, leverages the advantages of both approaches to enhance the spectral and energy efficiency in wireless networks. CF FD communication is particularly promising due to the low-power and cost-effective FD-enabled access points (APs), which are ideal for short-range transmissions between APs and users. Despite its potential, a comprehensive survey or tutorial on CF FD communication has been notably absent. This paper aims to address this gap in the literature. It begins with an overview of FD communication fundamentals, self-interference cancellation techniques, and CF technology principles, including their implications for current wireless networks. The discussion then moves to the integration and compatibility of CF and FD technologies, focusing on channel estimation, performance analysis, and resource allocation in CF FD massive multiple-input multiple-output (mMIMO) networks, supported by an extensive literature review and case studies. The potential of combining a sub-category of CF architecture—network-assisted CF technology—with FD technology is also explored, including a detailed case study on fundamentals, performance analysis, AP operation, and mode assignments. Finally, emerging CF FD paradigms, like millimeter-wave communications, unmanned aerial vehicles, and reconfigurable intelligent surfaces, are discussed, highlighting existing contributions and unresolved issues.

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.

In a large area such as a warehouse, using a bistatic backscatter network of passive tags for coverage brings up the issue of insufficient energy harvested by the tags, resulting in poor communication performance. To overcome this problem, we propose a solution that involves the use of distributed radio frequency emitters in a cell-free architecture to deliver more power to the tags. Our approach optimizes the emitter power allocation coefficients while ensuring the tags' energy harvesting requirements are met. By doing so, we provide the same rate quality for all tags and mitigate the effect of the tags' spatial distribution. Compared to the equal-power benchmark, our algorithm yields significant improvements. For example, it achieves$\sim 51\%$and$\sim 16\%$gains in harvested power and tag rate, respectively, for 0 dBm and 20 dBm with 100 emitters, respectively.

This study introduces and investigates the integration of a cell-free architecture with bistatic backscatter communication (BiBC), referred to as cell-free BiBC or distributed access point (AP)-assisted BiBC, which can enable potential applications in future (EH)-based Internet-of-Things (IoT) networks. To that purpose, we first present a pilot-based channel estimation scheme for estimating the direct, cascaded, and forward channels. Next, we utilize the channel estimates to design the optimal beamforming weights at the APs, reflection coefficients at the tags, and reception filters at the reader to maximize the tag sum rate while meeting the tags’ minimum energy requirements. Because the proposed maximization problem is non-convex, we propose a solution based on alternative optimization, fractional programming, and Rayleigh quotient techniques. We also quantify the computational complexity of the developed algorithms. Finally, we present extensive numerical results to validate the proposed channel estimation scheme and optimization framework, as well as the performance of the integration of these two technologies. Our algorithm yields impressive gains compared to the random beamforming/combining benchmark. For example, it achieves ~ 64.8% and ~ 253.5% gains in harvested power and tag sum rate, respectively, for 10dBm with 36 APs and 3 tags.

Current backscatter channel estimators employ an inefficient silent pilot transmission protocol, where tags alternate between silent and active states. To enhance performance, we propose a novel approach where tags remain active simultaneously throughout the entire training phase. This enables a one-shot estimation of both the direct and cascaded channels and accommodates various backscatter network configurations. We derive the conditions for optimal pilot sequences and also establish that the minimum variance unbiased (MVU) estimator attains the Cramér-Rao lower bound. Next, we propose new pilot designs to avoid pilot contamination. We then present several linear estimation methods, including least square (LS), scaled LS, and linear minimum mean square error (MMSE), to evaluate the performance of our proposed scheme. We also derive the analytical MMSE estimator using our proposed pilot designs. Furthermore, we adapt our method for cellular-based passive Internet-of-Things (IoT) networks with multiple tags and cellular users. Extensive numerical and simulation results are provided to validate the effectiveness of our approach. Notably, at least 10dBm and 12dBm power savings compared to the prior art are achieved when estimating the direct and cascaded channels. These findings underscore the practical benefits and superiority of our proposed approach.

Ambient internet-of-things networks are just emerging to support sixth-generation wireless goals. We thus investigate a symbiotic radio (SR) system for a single-user primary network and a backscatter communication network that supports non-orthogonal multiple access. The primary base station (BS) concurrently supports the primary user and multiple tags, which modulate and reflect their data using the primary BS signal. The user decodes its data and the tags’ data using the successive interference cancellation technique. We propose a novel optimization framework to accommodate the requirements of both the primary user and the tags while also improving SR network performance. By constructing the beamforming vectors to support both primary and backscatter networks, we develop a BS transmit power minimization problem. The problem formulation ensures the various quality-of-service demands of the user and the tags and the tag energy harvesting requirements. Because of the non-convexity of the problem, we employ semi-definite relaxation techniques to obtain a sub-optimal solution. We evaluate the computational complexity of the proposed algorithm. Finally, we present extensive numerical results and simulations that establish the validity and performance gains of the proposed optimization scheme without modifying the fundamental passive tag architecture.

Optimal beamforming design is developed for a nonorthogonal multiple access (NOMA)-aided symbiotic radio (SR) system where a base station (BS) simultaneously serves multiple NOMA users and a secondary ambient tag. The nearest user of the tag decodes its own data and the tag data using the successive interference cancellation (SIC) technique. We design optimal transmit beamforming and power allocation at the BS to maximize the weighted sum rate of NOMA users and the tag, under the minimum rate requirements while satisfying the tag’s minimum energy requirement. Because the problem is nonconvex, we propose algorithms using alternative optimization and fractional programming techniques. Our results reveal that significant performance gains can be achieved while keeping the tag design intact. For example, the proposed beamforming can increase harvested power and data rate by 2.16×103% and 314.5% compared to random beamforming.

This paper investigates the performance of intelligence reflective surface (IRS)-assisted relay systems. To this end, we quantify the optimal signal-to-noise ratio (SNR) attained by smartly controlling the phase-shifts of impinging electromagnetic waves upon an IRS. Thereby, a tightly approximated cumulative distribution function is derived to probabilistically characterize this optimal SNR. Then, we derive tight approximations/bounds for the achievable rate, outage probability, and average symbol error rate. Monte-Carlo simulations are used to validate our performance analysis. We present numerical results to reveal that the IRS-assisted relay system can boost the performance of end-to-end wireless transmissions.

In this paper, the feasibility of adopting an intelligent reflective surface (IRS) in a cell-free wireless communication system is studied. The received signal-to-noise ratio (SNR) for this IRS-enabled cell-free set-up is optimized by adjusting phase-shifts of the passive reflective elements. Then, tight approximations for the probability density function and the cumulative distribution function for this optimal SNR are derived for Rayleigh fading. To investigate the performance of this system model, tight bounds/approximations for the achievable rate and outage probability are derived in closed form. The impact of discrete phase-shifts is modeled, and the corresponding detrimental effects are investigated by deriving an upper bound for the achievable rate in the presence of phase-shift quantization errors. Monte-Carlo simulations are used to validate our statistical characterization of the optimal SNR, and the corresponding analysis is used to investigate the performance gains of the proposed system model. We reveal that IRS-assisted communications can boost the performance of cell-free wireless architectures.

We investigate the coexistence of underlay spectrum sharing in cell-free massive multiple-input multiple-output (MIMO) systems. A primary system with geographically distributed primary access points (P-APs) serves a multitude of primary users (PUs), while a secondary system serves a large number of secondary users (SUs) in the same primary/licensed spectrum by exploiting the underlay spectrum sharing. To mitigate the secondary co-channel interference inflected at PUs, stringent secondary transmit power constraints are defined for the secondary access points (S-APs). A generalized pilots sharing scheme is used to locally estimate the uplink channels at P-APs/S-APs, and thereby, conjugate precoders are adopted to serve PUs/SUs in the same time-frequency resource element. Moreover, the effect of a user-centric AP clustering scheme is investigated by assigning a suitable set of APs to a particular user. The impact of estimated downlink (DL) channels at PUs/SUs via DL pilots beamformed by P-APs/S-APs is investigated. The achievable primary/secondary rates at PUs/SUs are derived for the statistical DL and estimated DL CSI cases. User-fairness for PUs/SUs is achieved by designing efficient transmit power control policies based on a multi-objective optimization problem formulation of joint underlay spectrum sharing and max-min criteria. The proposed orthogonal multiple-access based analytical framework is also extended to facilitate non-orthogonal multiple-access. Our analysis and numerical results manifest that the primary/secondary performance of underlay spectrum sharing can be boosted by virtue of the average reduction of transmit powers/path-losses, uniform coverage/service, and macro-diversity gains, which are inherent to distributed transmissions/receptions of cell-free massive MIMO.

In this paper, the performance of a distributed intelligent reflective surface (IRS)-aided communication system is investigated. To this end, the optimal signal-to-noise ratio (SNR) achievable through the direct and reflected channels is quantified by controlling the phase-shifts of the distributed IRS. This optimal SNR is statistically characterized by deriving tight approximations to the exact probability density function and cumulative distribution function for Nakagami- m fading. Thereby, the outage probability and achievable rate bounds are derived in closed-form, and they are validated via Monte-Carlo simulations. Our numerical results reveal that the distributed IRS-aided communication set-ups can boost the outage and rate performance of wireless systems.

We investigate the feasibility of employing non-orthogonal multiple-access (NOMA) in cell-free massive multiple-input multiple-output (MIMO) operating with underlay spectrum-sharing. In our proposed system model, multiple clusters of NOMA-enabled secondary users (SUs) are concurrently served by geographically distributed secondary access-points (S-APs) via conjugate beamforming. The uplink channels are estimated locally at each S-AP via pilots sent by SUs. A set of orthogonal pilots is shared among the secondary and primary clusters to strike a balance between the throughput and training overhead, while enabling massive connectivity in primary and secondary systems. We derive the achievable rates of the secondary system by capturing the adverse effects of inter/intra-cluster interference, primary/secondary pilot contamination, imperfect successive interference cancellation (SIC) and partial channel state information (CSI). We propose a transmit power allocation policy for the secondary system to mitigate the detrimental impact of near-far effects by virtue of max-min fairness criterion. Through an achievable rate analysis, we reveal that although the number of concurrently served users can be substantially boosted by employing the proposed system model, the achievable rates are adversely affected due to detection uncertainties with imperfect SIC and statistical CSI at NOMA-enabled SUs.

Active pilot attack detection in time division duplexing based sub-6GHz massive multiple-input multiple-output (MIMO) non-orthogonal multiple-access (NOMA) systems is investigated. A practically realizable generalized likelihood ratio test (GLRT) is formulated when the eavesdropper's signal parameters are unknown to the massive MIMO base-station. The performance of this detector is analyzed by deriving the probability of false alarm, probability of detection and receiver operating characteristics. The underlying performance is compared with respect to an optimal Neyman-Pearson (NP) based Clairvoyant detector, which is designed by assuming the perfect knowledge of eavesdropper's signal parameters. Thereby, we conclude that the limited knowledge of the eavesdropper's signal parameters must be taken into account in designing practically viable active pilot detectors because the Clairvoyant detector overestimates the detection performance. Nevertheless, we show that the proposed GLRT based detector asymptotically becomes optimal in NP sense when the number of antennas at the base-station grows without bound. Moreover, we reveal that there is a fundamental trade-off between the number of NOMA users that can be served simultaneously in the same time-frequency resource element and the detection performance of active pilot attacks.

In this paper, the coexistence of cell-free massive multiple-input multiple-output (MIMO) and underlay spectrum-sharing is investigated. Thereby, the fundamental performance limits are established to characterize the feasibility of this coexistence. A set of spatially-distributed secondary access points (S-APs), which are underlaid in a primary cell-free massive MIMO system, serves many secondary users (SUs) in the same licensed spectrum of the primary access points (P-APs). Stringent secondary transmit power constraints are defined for the S-APs to mitigate undesired secondary cochannel interference (CCI) at the primary users (PUs). The uplink channels are estimated locally at the P-APs and S-APs via a generalized pilot sharing scheme, and thereby, conjugate precoders are used to serve PUs/SUs simultaneously. The achievable rates for both primary and secondary systems are derived for imperfectly estimated channels at the P-APs/S-APs, and the impact of intra-system pilot contamination is investigated. User-fairness for SUs is guaranteed by designing an efficient transmit power control policy based on the max-min criterion and secondary transmit power constraints. Through a rigorous analysis, we reveal that massive distributed primary/secondary transmissions can be exploited to mitigate detrimental impact of secondary CCI on PUs, and thereby, the achievable rates of primary/secondary systems can be boosted.