A cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection is developed and a fundamental implementation trade-off is revealed: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Abstract
In this paper, we develop a cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics, which are aggregated at the cloud using weights based on sensing interference and channel quality. We derive the local maximum a posteriori ratio test (MAPRT) detectors under fully informed (FIS) and partially informed (PIS) operation, representing different levels of transmit-signal information at the RX-APs. We further characterize their processing and fronthaul requirements and derive a network power model incorporating radio transmission, AP and cloud processing, and fronthaul infrastructure. We formulate a mixed-integer non-convex problem that jointly optimizes transmit powers, AP modes, UE and sensing-area associations, RX-AP assignments, and active fronthaul and cloud resources subject to communication, sensing, power, processing-capacity, and fronthaul constraints. A two-stage iterative algorithm based on Big-M reformulation, convex--concave programming, penalty-based relaxation, and structured discrete recovery is developed. Numerical results show that the proposed E2E framework reduces total power by up to 50% compared with transmit-power-only optimization and by approximately 11-17% compared with joint radio optimization under full coordination, while maintaining detection probabilities above 0.95. The results also reveal a fundamental implementation trade-off: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Cell-free massive MIMO with wireless fronthaul is a promising architecture for energy-efficient 6G networks, but the access and fronthaul links must then share the same scarce spectrum, and, under the fully centralized (option-8) functional split, the fronthaul rate is dictated by the finite quantization resolution used at the access points (APs). This paper develops a network energy-efficiency (EE) maximization framework for the uplink of such a system, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division (TD) and frequency-division (FD) operating modes in a unified manner. Each AP may be switched off (put to sleep) when it is not worth activating, so the resolution allocation is inherently coupled with AP selection. The resulting mixed-integer, nonconvex fractional program is solved by an alternating-optimization algorithm with per-block optimality guarantees---a closed-form optimal time split, bandwidth bisection, and optimal per-AP bit selection---that applies verbatim to both modes. While the design relies on the tractable additive quantization noise model, the reported performance is obtained end-to-end with the actual Lloyd--Max quantizers and a Bussgang decomposition-based achievable-rate bound.
Integrated sensing and communication (ISAC) under a cell-free (CF) architecture enables seamless connectivity and sensing coverage by allowing multiple distributed access points (APs) to jointly serve users and detect targets, thereby mitigating cell-edge effects and enhancing spatial diversity. However, wideband CF-ISAC also suffers from frequency-selective fading and strong inter-AP interference. To address these challenges, we investigate a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted ISAC framework, which extends full-space coverage and mitigates multiplicative fading and blockage effects. A joint optimization strategy is developed to maximize the weighted ISAC joint rate by jointly optimizing bandwidth and power allocation, receive beamforming, and active STAR-RIS beamforming. To tackle the non-convexity caused by variable coupling and intricate constraints, an efficient alternating optimization algorithm is developed. The original problem is decomposed into several subproblems: first, a closed-form solution for receive beamforming is derived; next, the resource allocation semi-analytical solutions are obtained via Karush-Kuhn-Tucker (KKT) conditions. Subsequently, the active STAR-RIS coefficients are optimized by capitalizing on fractional programming and majorization-minimization (MM) techniques. Finally, simulation results reveal that the proposed scheme achieves a 20.34% weighted ISAC joint-rate gain over the passive scheme, validating its effectiveness in wideband CF-ISAC systems.
Xintong Zhou, Feng Ke, X. Zhang et al.· IEEE Transactions on Communi...· 0 citations
Wireless fronthaul is a key enabler of flexible and scalable cell-free massive MIMO systems, but its limited capacity poses significant challenges for maintaining high and uniform user performance. In this work, we analyze the performance of a cell-free massive MIMO network with wireless fronthaul under realistic low physical layer functional splits. We propose a joint access and fronthaul resource allocation algorithm that maximizes the minimum user equipment (UE) spectral efficiency while satisfying fronthaul load constraints. Our analysis reveals that power allocation over the wireless fronthaul follows a modified water-filling structure, where the water level is jointly determined by the access and fronthaul channel gains. Furthermore, we show that severe fronthaul limitations not only reduce UE rates but also introduce spatial performance disparities depending on the cloud location. Finally, we demonstrate that split option 8 is impractical under wireless fronthaul constraints, underscoring the importance of dynamic fronthaul bit allocation to reduce fronthaul load and enable efficient system operation.
Ozan Alp Topal, Özlem Tuğfe Demir, Emil Björnson et al.· 0 citations
This paper studies integrated sensing and communications (ISAC) over a hybrid system that seamlessly combines legacy cellular base stations with distributed cell-free (CF) access points (APs). We propose a hierarchical ISAC architecture where a central base station (CBS) serves its near users and simultaneously operates as a monostatic radar for aerial target detection, while distributed APs---many idle under user-centric clustering---act as cost-free bistatic receivers. The CBS jointly handles communication processing and multi-static sensing fusion, reducing fronthaul overhead compared to conventional cell-free ISAC. To achieve this, a five-phase time-division duplexing workflow with precise ISAC role assignment is specified. Closed-form expressions for spectral efficiency and multi-static sensing signal-to-noise ratio analytically characterize the communications--sensing Pareto frontier. Numerical results confirm that the proposed hierarchical design simultaneously achieves higher sum throughput and superior sensing accuracy than conventional cell-free ISAC.
Integrated sensing and communication (ISAC) in cloud radio access networks (C-RANs) provides a promising architecture for cooperative sensing and secure transmission in future sixth-generation (6G) networks. However, achieving covert communication in such systems is challenging due to stringent covertness requirements, severe multi-user interference, multi-static sensing constraints, and finite fronthaul capacity. In this paper, we propose a rate-splitting multiple access (RSMA)-enabled integrated direct localization and covert communication framework for fronthaul-constrained C-RAN. In the proposed design, the RSMA common stream acts as an overt public signal, while the private streams convey covert information to legitimate users. A dedicated sensing/artificial-noise beamformer is jointly designed to support multi-static direct localization and shape the received energy profile at the warden. We formulate a sum covert rate maximization problem by jointly optimizing common-rate allocation, BS-stream scheduling, communication precoding, and sensing beamforming under covertness, sensing accuracy, transmit power, and fronthaul constraints. To solve the resulting mixed-integer non-convex problem, we develop an iterative algorithm based on continuous relaxation, semidefinite relaxation, linear matrix inequalities, and successive convex approximation. We further establish the convergence behavior of the proposed algorithm and characterize its computational burden. Numerical results demonstrate that the proposed scheme outperforms SDMA and fixed-scheduling baselines, especially under stringent fronthaul and covertness constraints.
We consider the downlink of a Rician-faded massive multiple-input-multiple-output multi-cell system, where each base station (BS) serves its user equipments (UEs) with assistance from cell-specific intelligent-reflecting surface (IRS). The BSs employ rate splitting multiple access (RSMA) protocol, and maximal-ratio transmission. We derive a closed-form spectral efficiency (SE) expression for this system, and use it to optimize its global energy efficiency (GEE) metric by jointly optimizing the BS transmit power and IRS phases. We develop novel low-complexity closed-form solution to optimize power by using Lagrangian dual, Quadratic and Dinkelbach transforms. We then optimize IRS phases by developing a novel generative diffusion model (GDM)-based deep reinforcement learning (DRL) framework. We numerically characterize, for the first time, the RSMA SE gains over spatial division multiple access in a multi-cell network. We also compare the impact of inter-cell and residual successive interference cancellation interference on the SE of a multi-cell system. We also show that our GDM-DRL framework provides much higher GEE than multiple state-of-the-art solutions.
Sourasis Chatterjee, C. R., Rohit Budhiraja· IEEE Transactions on Communi...· 0 citations