Integrated Sensing and Communication (ISAC) is becoming increasingly important in next-generation wireless networks. This paper focuses on an ISAC system supported by a reconfigurable intelligent surface (RIS), where a full-duplex base station (BS) simultaneously performs uplink multi-user communication, downlink multi-user communication, and radar sensing tasks with the assistance of the RIS. To maximize the sum rate of all downlink and uplink users, an optimization problem is formulated, subject to multiple constraints, including target detection signal-to-interference-plus-noise ratio, self-interference, BS transmission power, user transmission power, and unit-modulus constraints of RIS reflection coefficients. To address the complex non-convex optimization problems, efficient solving algorithms are proposed, and their performance is validated through simulations. The results demonstrate that the RIS-assisted full-duplex ISAC (RAFD-ISAC) system significantly enhances both communication and sensing performance. The proposed joint beamforming and reflection design offers a novel solution for the deep integration of sensing and communication in next-generation networks.

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task; ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder.

Integrated sensing and communications (ISAC) is emerging as one of the six application scenarios for future wireless networks. Characterizing the Pareto boundary is an urgent issue in multiple-input multiple-output (MIMO) ISAC systems. The lack of unified sensing metrics and the neglect of the instantaneous worst-case sensing requirement in the existing works present challenges to this issue. In this paper, we propose a more universal and operable theoretical limit analysis framework where the high-signal-to-noise ratio (SNR) channel capacity is characterized under instantaneous covariance mismatch constraint. We use the covariance mismatch that implies the distance to optimal covariance as the sensing metric. The optimal covariance can be computed by optimizing any key sensing metric. An MIMO ISAC Pareto boundary can be obtained by computing channel capacity under fine-grained sensing thresholds, below which the mismatch must be constrained. In the experiments, three radar modes are considered, and the results show that different radar modes affect capacity performance and a trade-off exists between communication and sensing. In addition, pure communication capacity is the upper bound of the communication capacity in ISAC. Moreover, capacity under instantaneous constraint approaches that under average one in pure MIMO communications when signal length approaches infinity.

We consider the problem of estimating the directions of multiple target sources by exploiting auxiliary sources, focusing on a single snapshot obtained by the distributed array with position errors and angular offsets of subarrays. Former calibration methods generally assume the directions of auxiliary sources are unknown, while prior knowledge of the auxiliary sources is usually available in practice. In order to quantify the effects of auxiliary sources and their prior information, we model the directions of auxiliary sources as Gaussian random variables and use their standard deviations to quantify the prior information. We derive the prior Cramér-Rao lower bound (CRB) of the direction estimations in the new model. Simulation results show that calibration with auxiliary sources performs better than self-calibration and the prior CRB is much lower than the existing counterparts assuming unknown auxiliary sources, implying much potential to improve the estimation performance by employing the prior information.

It is a fundamental problem to analyze the performance bound of multiple‐input multiple‐output dual‐functional radar‐communication systems. To this end, we derive a performance bound on the communication function under a constraint on radar performance. To facilitate the analysis, in this paper, we consider a simplified situation where there is only one downlink user and one radar target. We analyze the properties of the performance bound and the corresponding waveform design strategy to achieve the bound. When the downlink user and the radar target meet certain conditions, we obtain analytical expressions for the bound and the corresponding waveform design strategy. The results reveal a tradeoff between communication and radar performance, which is essentially caused by the energy sharing and allocation between radar and communication functions of the system.

Integrated sensing and communications (ISAC) has received considerable attention from both industry and academia. By sharing the spectrum and hardware platform, ISAC significantly reduces costs and improves spectral, energy, and hardware efficiencies. To support the large number of communication users (CUs) and sensing targets (STs), the design of multiple access (MA) is a fundamental issue in ISAC. MA techniques in ISAC are expected to avoid mutual interference between sensing and communicating functions under the critical constraints of both functions. In this article, we present an overview on approaches of MA for ISAC, from orthogonal transmission strategies to nonorthogonal ones, realized in time, frequency, code, spatial, delay-Doppler, power, and/or multiple domains. We discuss their individual implementation schemes and corresponding resource allocation strategies, as well as highlight future research opportunities.

In this paper, we consider the design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In contrast to the previous designs which guarantee communication performance, we require the covariance of the transmit waveform to be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. By numerical simulations, we first demonstrate the radar performance loss caused by previous designs, and then show the communication performance for our proposed design in terms of balanced SINR versus transmit signal-to-noise ratio.

Abstract Radar and communication are two key applications of radio technology, and they occupy a large portion of the frequency spectrum. Traditionally, radar and communication systems are operated at different frequencies, owing to their different functions and application areas. For instance, radar was mainly employed for sensing (target detection, localization, recognition, imaging, etc.) in the military field, while wireless communication was mainly for information delivery. However, along with the fast development of radio technologies and huge demand for information, the radio frequency (RF) spectrum is becoming increasingly congested, and the spectra of the radar system will be overlaid with those of wireless communication devices. Moreover, radar and communication are becoming increasingly merged in both technologies and applications. Besides the military field, radar has been widely employed in daily life including weather service, air traffic control, autonomous driving and security monitoring. Meanwhile, these applications rely Largely on information transmission through wireless communications. In this regard, integration of radar and communication (IRC) has proved to be a very promising development to address the spectrum congestion issue between radar and communications devices. This also brings us a number of key challenges in signal processing for both implementation of IRC and joint optimization between the two systems.

In this paper, we consider the waveform design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In addition to a power constraint, we require the covariance of the transmit waveform be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. Considering that the interference cannot be completely eliminated with this constraint, we introduce dirty paper coding (DPC) to further cancel the interference, and formulate the SINR balancing and sum rate maximization problem in the DPC regime. Although both of the two problems are non-convex, we show that they can be reformulated to convex optimizations via the Lagrange and downlink-uplink duality. In addition, we propose gradient projection based algorithms to solve the equivalent dual problem of SINR balancing, in both transmit beamforming and DPC regimes. The simulation results demonstrate significant performance improvement of DPC over transmit beamforming, and also indicate that the degrees of freedom for the communication transmitter is restricted by the rank of the covariance.

Block-sparse signal recovery has drawn increasing attention in many areas of signal processing, where the goal is to recover a high-dimensional signal whose non-zero coefficients only arise in a few blocks from compressive measurements. However, most off-the-shelf data-driven reconstruction networks do not exploit the block-sparse structure. Thus, they suffer from deteriorating performance in block-sparse signal recovery. In this paper, we put forward a block-sparse reconstruction network named Ada-BlockLISTA based on the concept of deep unfolding. Our proposed network consists of a gradient descent step on every single block followed by a block-wise shrinkage step. We evaluate the performance of the proposed Ada-BlockLISTA network through simulations based on the signal model of two-dimensional (2D) harmonic retrieval problems.

Dual function radar and communications (DFRC) systems are the focus of growing research attention. The common DFRC setup considers simultaneous probing and information transmission to a remote receiver, typically involving complex radar-oriented waveforms, whose detection can induce a notable burden on the receiver. In many DFRC applications, the communication receivers are devices which are limited in terms of hardware, power, and memory resources. These receivers are required to extract the desired information from the received dual-function waveform, while operating with a given bit budget. In this paper, we design bit constrained communication receivers in dual-function systems, by considering hybrid analog/digital architectures and treating their operation as task-based quantization. We study two forms of analog processing in these hybrid receivers, allowing to combine inputs in different time instances and antennas or only in different antennas at the same time instance. Simulation results demonstrate that the proposed task-based quantization strategy outperforms receivers operating only in the digital domain with the same total number of quantization bits.

Multistatic radar system (MSRS) is considered an effective scheme to suppress mainlobe jamming, since it has higher spatial resolution enabling jamming cancellation from spatial domain. To develop electronic countermeasures against MSRS, a random array subset selection (RASS)jamming method is proposed in this paper. In the RASS jammer, elements of the array antenna are activated randomly, leading to stable mainlobe and random sidelobes, different from the traditional jammer that applies the complete antenna array enjoying constant mainlobe and sidelobes. We study the covariance matrix of jamming signals received by radars, and derive its rank, revealing that the covariance matrix is of full rank. We also calculate the output jamming to signal and noise ratio (JSNR) after the subspace-based jamming suppression methods used in MSRS under the proposed jamming method, which demonstrates that the full rank property invalidates such suppression methods. Numerical results verify our analytical deduction and exhibit the improved countermeasure performance of our proposed RASS jamming method compared to the traditional one.

Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware. An emerging DFRC strategy embeds transmission of digital messages into agility-based radar schemes in the form of index modulation (IM). This approach provides the ability to communicate without entailing degradation in radar performance, at the cost of increased decoding complexity at the receiver side. In this work we propose schemes for reducing the decoding complexity associated with IM-based DFRC systems. We first focus on the receiver side, developing a sub-optimal low complexity scheme for recovering IM symbols embedded in radar waveforms. Then, we propose a method to modify the radar waveform to facilitate the recovery of the communicated bits with minimal effect on the radar performance. Our numerical results demonstrate that the proposed techniques allow the receiver to reliably recover the transmitted symbols with an affordable computational burden.

Modern radar systems are expected to operate reliably in congested environments under cost and power constraints. A recent technology for realizing such systems is frequency agile radar (FAR), which transmits narrowband pulses in a frequency hopping manner. To enhance the target recovery performance of FAR in complex electromagnetic environments, and particularly, its range-Doppler recovery performance, multi-Carrier AgilE phaSed Array Radar (CAESAR) was proposed. CAESAR extends FAR to multi-carrier waveforms while introducing the notion of spatial agility. In this paper, we theoretically analyze the range-Doppler recovery capabilities of CAESAR. Particularly, we derive conditions which guarantee accurate reconstruction of these range-Doppler parameters. These conditions indicate that by increasing the number of frequencies transmitted in each pulse, CAESAR improves performance over conventional FAR, especially in complex environments where some radar measurements are severely corrupted by interference.

Sub-Nyquist radars require fewer measurements, facilitating low-cost design, flexible resource allocation, etc. By applying compressed sensing (CS) method, such radars achieve close performance to traditional Nyquist radars. However in low signal-to-noise ratio (SNR) scenarios, detecting weak targets is challenging: low probability of detection and many spurious targets could occur in the recovery results of traditional CS method. To overcome this issue, we propose a weighted sparse recovery based track-before-detect (TBD) method for weak targets detection by accumulating multi-frame information. Particularly, tracking results of targets are utilized as prior knowledge to enhance the recovery accuracy, thus improving the detection performance. Numerical results show that our method improves the detection performance particularly and reduces the occurrence of spurious targets in low SNR situations compared with traditional CS method.

Partitioning the antenna array into different subarrays is a flexible scheme in the joint radar and communication system. However, the traditional fixed partition of the antenna array cannot make full use of the complete aperture. In this paper, we propose a novel antenna partition scheme. In this scheme, the antenna is randomly and dynamically chosen as radar or communication unit. The dynamic randomness introduces extra channel capacity of the communication system, and enables the radar system approximately obtain the resolution and sidelobe level of a full antenna array simultaneously. The channel capacity, Cramér Rao Bound and the ambiguity function are theoretically analyzed. Pareto Front is used to demonstrate the performance improvement of the proposed system over the traditional fixed partition system.

This letter considers waveform design of orthogonal frequency division multiplexing (OFDM) signal for radar applications, and aims at mitigating the envelope fluctuation in OFDM. A novel method is proposed to reduce the peak-to-mean envelope power ratio (PMEPR), which is commonly used to evaluate the fluctuation. The proposed method is based on the tone reservation approach, in which some bits or subcarriers of OFDM are allocated for decreasing PMEPR. We introduce the coefficient of variation of envelopes (CVE) as the cost function for waveform optimization, and develop an iterative least squares algorithm. Minimizing CVE leads to distinct PMEPR reduction, and it is guaranteed that the cost function monotonically decreases by applying the iterative algorithm. Simulations demonstrate that the envelope is significantly smoothed by the proposed method.

This letter considers the problem of observation strategy design for compressed sensing. An adaptive method, based on Cramer-Rao bound minimization, is proposed to design the sensing matrix. Simulation results demonstrate that the adaptively constructed sensing matrix can lead to much lower recovery errors than those of traditional Gaussian matrices and some existing adaptive approaches.