Integrated Sensing and Communications Over the Years: An Evolution Perspective

Integrated Sensing and Communications (ISAC) enables efficient spectrum utilization and reduces hardware costs for beyond 5G (B5G) and 6G networks, facilitating intelligent applications that require both high-performance communication and precise sensing capabilities. This survey provides a comprehensive review of the evolution of ISAC over the years. We examine the expansion of the spectrum across RF and optical ISAC, highlighting the role of advanced technologies, along with key challenges and synergies. We further discuss the advancements in network architecture from single-cell to multi-cell systems, emphasizing the integration of collaborative sensing and interference mitigation strategies. Moreover, we analyze the progress from single-modal to multi-modal sensing, with a focus on the integration of edge intelligence to enable real-time data processing, reduce latency, and enhance decision-making. Finally, we extensively review standardization efforts by 3GPP, IEEE, and ITU, examining the transition of ISAC-related technologies and their implications for the deployment of 6G networks.

Over-the-Air Time-Frequency Synchronization in Distributed ISAC Systems

A distributed integrated sensing and communication (D-ISAC) system offers significant cooperative gains for both sensing and communication performance. These gains, however, can only be fully realized when the distributed nodes are perfectly synchronized, which is a challenge that remains largely unaddressed in current ISAC research. In this paper, we propose an over-the-air time-frequency synchronization framework for the D-ISAC system, leveraging the reciprocity of bistatic sensing channels. This approach overcomes the impractical dependency of traditional methods on a direct line-of-sight (LoS) link, enabling the estimation of time offset (TO) and carrier frequency offset (CFO) between two ISAC nodes even in non-LoS (NLOS) scenarios. To achieve this, we introduce a bistatic signal matching (BSM) technique with delay-Doppler decoupling, which exploits offset reciprocity (OR) in bistatic observations. This method compresses multiple sensing links into a single offset for estimation. We further present off-grid super-resolution estimators for TO and CFO, including the maximum likelihood estimator (MLE) and the matrix pencil (MP) method, combined with BSM processing. These estimators provide accurate offset estimation compared to spectral cross-correlation techniques. Also, we extend the pairwise synchronization leveraging OR between two nodes to the synchronization of $N$ multiple distributed nodes, referred to as centralized pairwise synchronization. We analyze the Cramer-Rao bounds (CRBs) for TO and CFO estimates and evaluate the impact of D-ISAC synchronization on the bottom-line target localization performance. Simulation results validate the effectiveness of the proposed algorithm, confirm the theoretical analysis, and demonstrate that the proposed synchronization approach can recover up to 96% of the bottom-line target localization performance of the fully-synchronous D-ISAC.

Latency-Aware Resource Allocation for Integrated Communications, Computation, and Sensing in Cell-Free mMIMO Systems

In this paper, we investigate a cell-free massive multiple-input and multiple-output (MIMO)-enabled integration communication, computation, and sensing (ICCS) system, aiming to minimize the maximum computation latency to guarantee the stringent sensing requirements. We consider a two-tier offloading framework, where each multi-antenna terminal can optionally offload its local tasks to either multiple mobile-edge servers for distributed computation or the cloud server for centralized computation while satisfying the sensing requirements and power constraint. The above offloading problem is formulated as a mixed-integer programming and non-convex problem, which can be decomposed into three sub-problems, namely, distributed offloading decision, beamforming design, and execution scheduling mechanism. First, the continuous relaxation and penalty-based techniques are applied to tackle the distributed offloading strategy. Then, the weighted minimum mean square error (WMMSE) and successive convex approximation (SCA)-based lower bound are utilized to design the integrated communication and sensing (ISAC) beamforming. Finally, the other resources can be judiciously scheduled to minimize the maximum latency. A rigorous convergence analysis and numerical results substantiate the effectiveness of our method. Furthermore, simulation results demonstrate that multi-point cooperation in cell-free massive MIMO-enabled ICCS significantly reduces overall computation latency, in comparison to the benchmark schemes.

Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience

Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development.

Signaling Design for Noncoherent Distributed Integrated Sensing and Communication Systems

The ultimate goal of enabling sensing through the cellular network is to obtain coordinated sensing of an unprecedented scale, through distributed integrated sensing and communication (D-ISAC). This, however, introduces challenges related to synchronization and demands new transmission methodologies. In this paper, we propose a transmit signal design framework for D-ISAC systems, where multiple ISAC nodes cooperatively perform sensing and communication without requiring phase-level synchronization. The proposed framework employing orthogonal frequency division multiplexing (OFDM) jointly designs downlink coordinated multi-point (CoMP) communication signals and multi-input multi-output (MIMO) radar signals, leveraging both collocated and distributed MIMO radars to estimate angle-of-arrival (AOA) and time-of-flight (TOF) from all possible multi-static measurements for target localization. To design the optimal D-ISAC transmit signal, we use the target localization Cram\'er-Rao bound (CRB) as the sensing performance metric and the signal-to-interference-plus-noise ratio (SINR) as the communication performance metric. Then, an optimization problem is formulated to minimize the localization CRB while maintaining a minimum SINR requirement for each communication user. Moreover, we present three distinct transmit signal design approaches, including optimal, orthogonal, and beamforming designs, which reveal trade-offs between ISAC performance and computational complexity. Unlike single-node ISAC systems, the proposed D-ISAC designs involve per-subcarrier sensing signal optimization to enable accurate TOF estimation, which contributes to the target localization performance. Numerical simulations demonstrate the effectiveness of the proposed designs in achieving flexible ISAC trade-offs and efficient D-ISAC signal transmission.

Federated Learning Strategies for Coordinated Beamforming in Multicell ISAC

We propose two cooperative beamforming frameworks based on federated learning (FL) for multi-cell integrated sensing and communications (ISAC) systems. Our objective is to address the following dilemma in multicell ISAC: 1) Beamforming strategies that rely solely on local channel information risk generating significant inter-cell interference (ICI), which degrades network performance for both communication users and sensing receivers in neighboring cells; 2) conversely centralized beamforming strategies can mitigate ICI by leveraging global channel information, but they come with substantial transmission overhead and latency that can be prohibitive for latency-sensitive and source-constrained applications. To tackle these challenges, we first propose a partially decentralized training framework motivated by the vertical federated learning (VFL) paradigm. In this framework, the participating base stations (BSs) collaboratively design beamforming matrices under the guidance of a central server. The central server aggregates local information from the BSs and provides feedback, allowing BSs to implicitly manage ICI without accessing the global channel information. To make the solution scalable for densely deployed wireless networks, we take further steps to reduce communication overhead by presenting a fully decentralized design based on the horizontal federated learning (HFL). Specifically, we develop a novel loss function to control the interference leakage power, enabling a more efficient training process by entirely eliminating local channel information exchange. Numerical results show that the proposed solutions can achieve significant performance improvements comparable to the benchmarks in terms of both communication and radar information rates.

Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance.

Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems II

This study introduces an innovative beamforming design approach that incorporates the reliability of antenna array elements into the optimization process, termed "antenna health-aware selective beamforming". This method strategically focuses transmission power on more reliable antenna elements, thus enhancing system resilience and operational integrity. By integrating antenna health information and individual power constraints, our research leverages advanced optimization techniques such as the Group Proximal-Gradient Dual Ascent (GPGDA) to efficiently address nonconvex challenges in sparse array selection. Applying the proposed technique to a Dual-Functional Radar-Communication (DFRC) system, our findings highlight that increasing the sparsity promotion weight ($\rho_s$) generally boosts spectral efficiency and communication data rate, achieving perfect system reliability at higher $\rho_s$ values but also revealing a performance threshold beyond which further sparsity is detrimental. This underscores the importance of balanced sparsity in beamforming for optimizing performance, particularly in critical communication and defense applications where uninterrupted operation is crucial. Additionally, our analysis of the time complexity and power consumption associated with GPGDA underscores the need for optimizing computational resources in practical implementations.

ISAC Super-Resolution Receivers: The Effect of Different Dictionary Matrices

This paper presents an off-the-grid estimator for ISAC systems using lifted atomic norm minimization (LANM). The main challenge in the ISAC systems is the unknown nature of both transmitted signals and radar-communication channels. We use a known dictionary to encode transmit signals and show that LANM can localize radar targets and decode communication symbols when the number of observations is proportional to the system's degrees of freedom and the coherence of the dictionary matrix. We reformulate LANM using a dual method and solve it with semidefinite relaxation (SDR) for different dictionary matrices to reduce the number of observations required at the receiver. Simulations demonstrate that the proposed LANM accurately estimates communication data and target parameters under varying complexity by selecting different dictionary matrices.

ISAC Super-Resolution Receiver via Lifted Atomic Norm Minimization

This paper introduces an off-the-grid estimator for integrated sensing and communication (ISAC) systems, utilizing lifted atomic norm minimization (LANM). The key challenge in this scenario is that neither the transmit signals nor the radar-and-communication channels are known. We prove that LANM can simultaneously achieve localization of radar targets and decoding of communication symbols, when the number of observations is proportional to the degrees of freedom in the ISAC systems. Despite the inherent ill-posed nature of the problem, we employ the lifting technique to initially encode the transmit signals. Then, we leverage the atomic norm to promote the structured low-rankness for the ISAC channel. We utilize a dual technique to transform the LANM into an infinite-dimensional search over the signal domain. Subsequently, we use semidefinite relaxation (SDR) to implement the dual problem. We extend our approach to practical scenarios where received signals are contaminated by additive white Gaussian noise (AWGN) and jamming signals. Furthermore, we derive the computational complexity of the proposed estimator and demonstrate that it is equivalent to the conventional pilot-aided ANM for estimating the channel parameters. Our simulation experiments demonstrate the ability of the proposed LANM approach to estimate both communication data and target parameters with a performance comparable to traditional radar-only super-resolution techniques.