Integrated Sensing and Communication Meets Smart Propagation Engineering: Opportunities and Challenges

Both smart propagation engineering as well as integrated sensing and communication (ISAC) constitute promising candidates for next-generation (NG) mobile networks. We provide a synergistic view of these technologies, and explore their mutual benefits. First, moving beyond just intelligent surfaces, we provide a holistic view of the engineering aspects of smart propagation environments. By delving into the fundamental characteristics of intelligent surfaces, fluid antennas, and unmanned aerial vehicles, we reveal that more efficient control of the pathloss and fading can be achieved, thus facilitating intrinsic integration and mutual assistance between sensing and communication functionalities. In turn, with the exploitation of the sensing capabilities of ISAC to orchestrate the efficient configuration of radio environments, both the computational effort and signaling overheads can be reduced. We present indicative simulation results, which verify that cooperative smart propagation environment design significantly enhances the ISAC performance. Finally, some promising directions are outlined for combining ISAC with smart propagation engineering.

Widely Separated MIMO Radar Using Matrix Completion

We present a low-complexity widely separated multiple-input-multiple-output (WS-MIMO) radar that samples the signals at each of its multiple receivers at reduced rates. We process the low-rate samples of all transmit-receive chains at each receiver as data matrices. We demonstrate that each of these matrices is low rank as long as the target moves slowly within a coherent processing interval. We leverage matrix completion (MC) to recover the missing samples of each receiver signal matrix at the common fusion center. Subsequently, we estimate the targets' positions and Doppler velocities via the maximum likelihood method. Our MC-WS-MIMO approach recovers missing samples and thereafter target parameters at reduced rates without discretization. Our analysis using ambiguity functions shows that antenna geometry affects the performance of MC-WS-MIMO. Numerical experiments demonstrate reasonably accurate target localization at SNR of 20 dB and sampling rate reduction to 20%.

Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments

The paper studies the problem of designing the Intelligent Reflecting Surface (IRS) phase shifters for Multiple Input Single Output (MISO) communication systems in spatiotemporally correlated channel environments, where the destination can move within a confined area. The objective is to maximize the expected sum of SNRs at the receiver over infinite time horizons. The problem formulation gives rise to a Markov Decision Process (MDP). We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion by constructing the state representation to include the current position of the receiver and the phase shift values and receiver positions that correspond to a window of previous time steps. The channel variability induces high frequency components on the spectrum of the underlying value function. We propose the preprocessing of the critic's input with a Fourier kernel which enables stable value learning. Finally, we investigate the use of the destination SNR as a component of the designed MDP state, which is common practice in previous work. We provide empirical evidence that, when the channels are spatiotemporally correlated, the inclusion of the SNR in the state representation interacts with function approximation in ways that inhibit convergence.

Seventy Years of Radar and Communications: The Road from Separation to Integration

Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age. Although R&C have been historically progressing separately, in recent decades they have been moving from separation to integration, forming integrated sensing and communication (ISAC) systems, which find extensive applications in next-generation wireless networks and future radar systems. To better understand the essence of ISAC systems, this paper provides a systematic overview on the historical development of R&C from a signal processing (SP) perspective. We first interpret the duality between R&C as signals and systems, followed by an introduction of their fundamental principles. We then elaborate on the two main trends in their technological evolution, namely, the increase of frequencies and bandwidths, and the expansion of antenna arrays. Moreover, we show how the intertwined narratives of R\&C evolved into ISAC, and discuss the resultant SP framework. Finally, we overview future research directions in this field.

DFRC with Improved Communication-Sensing Trade-off via Private Subcarrier Permutations and Pairing with Antennas

Dual function radar communication (DFRC) systems can achieve significant improvements in spectrum efficiency, system complexity and energy efficiency, and are attracting a lot of attention for next generation wireless system design. This paper considers DFRC systems using MIMO radar with a sparse transmit array, transmitting OFDM waveforms, and assigning shared and private subcarriers to active transmit antennas. Subcarrier sharing allows antennas to modulate data symbols onto the same subcarriers and enables high communication rate, while the use of private subcarriers trades-off communication rate for sensing performance by enabling the formulation of a virtual array with larger aperture than the physical receive array. We propose to exploit the permutation of private subcarriers among the available subcarriers and the pairing between active antennas and private subcarriers to recover some of the communication rate loss. Exploiting the $1$-sparse property of private subcarriers, we also propose a low complexity algorithm to identify private subcarriers and detect the antenna-subcarrier pairing.

Phased Array With Improved Beamforming Capability via Use of Double Phase Shifters

The passive electronically scanned array (PESA) is widely used due to its simple structure and low cost. {Its antenna weights have unit modulus and thus, only the weights phases can be controlled. PESA has limited degrees of freedom for beampattern design, where only the direction of the main beam can be controlled.} In this paper we propose a novel way to improve the beamforming capability of PESA by endowing it with more degrees of freedom via the use of double phase shifters (DPS). By doing so, both the magnitude and the phase of the antenna weights can be controlled, allowing for more flexibility in beampattern design. We also take into account the physical resolution limitation of phase shifters, and propose a method to approximate a given complex beamformer using DPS. Simulation results indicate significant beamforming improvement even at low phase resolution.

OptM3Sec: Optimizing Multicast IRS-Aided Multiantenna DFRC Secrecy Channel with Multiple Eavesdroppers

With the use of common signaling methods for dual-function radar-communications (DFRC) systems, the susceptibility of eavesdropping on messages aimed at legitimate users has worsened. For DFRC systems, the radar target may act as an eavesdropper (ED) that receives a high-energy signal thereby leading to additional challenges. Unlike prior works, we consider a multicast multi-antenna DFRC system with multiple EDs. We then propose a physical layer design approach to maximize the secrecy rate by installing intelligent reflecting surfaces in the radar channels. Our optimization of multiple ED multicast multi-antenna DFRC secrecy rate (OptM3Sec) approach solves this highly nonconvex problem with respect to the precoding matrices. Our numerical experiments demonstrate the feasibility of our algorithm in maximizing the secrecy rate in this DFRC setup.

Physical Layer Anonymous Precoding: The Path to Privacy-Preserving Communications

Next-generation systems aim to increase both the speed and responsiveness of wireless communications, while supporting compelling applications such as edge and cloud computing, remote-Health, vehicle-to-infrastructure communications, etc. As these applications are expected to carry confidential personal data, ensuring user privacy becomes a critical issue. In contrast to traditional security and privacy designs that aim to prevent confidential information from being eavesdropped upon by adversaries, or learned by unauthorized parties, in this paper we consider designs that mask the users' identities during communication, hence resulting in anonymous communications. In particular, we examine the recent interest in physical layer (PHY) anonymous solutions. This line of research departs from conventional higher layer anonymous authentication, encryption and routing protocols, and judiciously manipulates the signaling pattern of transmitted signals in order to mask the senders' PHY characteristics. We first discuss the concept of anonymity at the PHY, and illustrate a strategy that is able to unmask the sender's identity by analyzing his or her PHY information only, i.e., signalling patterns and the inherent fading characteristics. Subsequently, we overview the emerging area of anonymous precoding to preserve the sender's anonymity, while ensuring high receiver-side signal-to-interference-plus-noise ratio (SINR) for communication. This family of anonymous precoding designs represents a new approach to providing anonymity at the PHY, introducing a new dimension for privacy-preserving techniques.

Towards Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication and Security

Integrated sensing and communication (ISAC) has recently emerged as a candidate 6G technology, aiming to unify the two key operations of the future network in spectrum/energy/cost efficient way. ISAC involves communicating information to receivers and simultaneously sensing targets, while both operations use the same waveforms, the same transmitter and ultimately the same network infrastructure. Nevertheless, the inclusion of information signalling into the probing waveform for target sensing raises unique and difficult challenges from the perspective of information security. At the same time, the sensing capability incorporated in the ISAC transmission offers unique opportunities to design secure ISAC techniques. This overview paper discusses these unique challenges and opportunities for the next generation of ISAC networks. We first briefly discuss the fundamentals of waveform design for sensing and communication. Then, we detail the challenges and contradictory objectives involved in securing ISAC transmission, along with state-of-the-art approaches to address them. We then identify the new opportunity of using the sensing capability to obtain knowledge of the targets, as an enabling approach against known weaknesses of PHY security. Finally, we illustrate a low-cost secure ISAC architecture, followed by a series of open research topics. This family of sensing-aided secure ISAC techniques brings a new insight on providing information security, with an eye on robust and hardware-constrained designs tailored for low-cost ISAC devices.

Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications

While millimeter wave (mmWave) communications promise high data rates, their sensitivity to blockage and severe signal attenuation presents challenges in their deployment in urban settings. To overcome these effects, we consider a distributed cooperative beamforming system, which relies on static relays deployed in clusters with similar channel characteristics, and where, at every time instance, only one relay from each cluster is selected to participate in beamforming to the destination. To meet the quality-of-service guarantees of the network, a key prerequisite for beamforming is relay selection. However, as the channels change with time, relay selection becomes a resource demanding task. Indeed, estimation of channel state information for all candidate relays, essential for relay selection, is a process that takes up bandwidth, wastes power and introduces latency and interference in the network. We instead propose a unique, predictive scheme for resource efficient relay selection, which exploits the special propagation patterns of the mmWave medium, and can be executed distributively across clusters, and in parallel to optimal beamforming-based communication. The proposed predictive scheme efficiently exploits spatiotemporal channel correlations with current and past networkwide Received Signal Strength (RSS), the latter being invariant to relay cluster size, measured sequentially during the operation of the system. Our numerical results confirm that our proposed relay selection strategy outperforms any randomized selection policy that does not exploit channel correlations, whereas, at the same time, it performs very close to an ideal scheme that uses complete, cluster size dependent RSS, and offers significant savings in terms of channel estimation overhead, providing substantially better network utilization, especially in dense topologies, typical in mmWave networks.