In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

As a key technology for the evolution to the sixth generation (6G) systems, cell-free massive multiple-input multiple-output (CF-mMIMO) can effectively improve the spectrum efficiency, peak rate, and reliability of wireless communication systems. Starting from the scalable implementation of CF-mMIMO, we study a cell-free RAN (CF-RAN) with distributed cooperative transceivers under the open RAN (ORAN) architecture. Through theoretical analysis and numerical simulation, we investigate the uplink and downlink spectral efficiencies of CF-mMIMO with the distributed transceivers. We then discuss the implementation issues of CF-RAN under ORAN architecture, including time-frequency synchronization and over-the-air reciprocity calibration, low layer splitting, deployment of ORAN radio units (O-RU), and artificial intelligent-based user associations. Finally, we present some representative experimental results for the uplink distributed reception and downlink coherent joint transmission of CF-RAN with commercial off-the-shelf O-RUs.

Abstract A novel multistatic integrated sensing and communication (ISAC) system based on cellular network is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved upon the mobile communication network at low complexity and cost without modifying the architecture of BSs for full duplexing. In this work, the topology of sensing cell is first provided, which can be duplicated to seamlessly cover the cellular network. Each sensing cell consists of a single central BS transmitting signals and multiple neighboring BSs receiving reflected signals from sensing objects. Then an estimating approach is described for obtaining position and velocity of sensing objects that locate in the sensing cell. Joint data processing with an efficient optimization method is also provided. In addition, key issues in the cellular network based multistatic ISAC system are analyzed. Simulation results show that the multistatic ISAC system can reduce interference power by over 10 dBm and significantly improve position and velocity estimation accuracy of objects when compared with the monostatic ISAC system, demonstrating the effectiveness and promise of implementing the proposed system in the mobile network.

The 6th generation mobile communication system (6G) will provide everything as a service (XaaS), where X includes communication, sensing, computing, artificial intelligence (AI), big data and security and more. Novel features such as sensing as a Service (SaaS) will contribute to further realising Internet of Everything (IoE). Integrated Sensing and Communication (ISAC) is identified as one of the six usage scenarios for 6G by the International Telecommunication Union Radiocommunication Sector (ITU-R), and the corresponding studies on the detailed technical performance requirements and evaluation methodologies have begun in 2024. Although ISAC has become a popular topic, there are no systematic performance requirements metrics and corresponding evaluation methodologies defined for SaaS in a mobile communication system, while conventional key performance indicators (KPI) for radar systems have been borrowed currently. Therefore, to fill this gap, this paper proposes SensCAP, a systematic CAPability performance metric composed of Sensing Capacity, Accuracy and Probability. The sensing capacity reflects the comprehensive sensing performance, which can be expressed as the number of targets that can be detected per unit area within unit time, given Sensing Quality of Service (QoS) requirements consisting of sensing accuracy and probability. Furthermore, the performance evaluation of the SensCAP is conducted through system simulation using proposed evaluation methodologies, and the KPI values are suggested as the guidelines for further study in ITU-R.

The combination of communication and sensing is envisioned as a novel feature in the forthcoming sixth-generation (6G) wireless communication. The conventional approach to the joint sensing and communication (JSAC) system is utilizing one base station (BS) as both a sensing transmitter and a sensing receiver, which is known as monostatic sensing. However, the resulting self-interference issue requires additional hardware promotion to achieve full-duplexing. To overcome this issue, in this paper, we focus on cooperative sensing where the transmitter and receivers are non-co-located, which includes the bistatic and multistatic sensing. Specifically, the system model of cooperative sensing based on mobile networks is established. To demonstrate the feasibility of cooperative sensing, the bistatic radar cross section (RCS) is provided. As for the sensing method, a refined orthogonal matching pursuit (R-OMP) method is proposed to estimate the channel parameters and data fusion is also provided to derive the objects’ positions and velocities. Considering the non-negligible interference in the cooperative JSAC networks, we also discuss interference management in this paper. Simulation results show that the proposed cooperative sensing system improves the position and velocity estimation accuracy by over 20% when compared with monostatic sensing. The preliminary experiment results also verify the feasibility of the proposed system.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) based integrated localization and communication (ILAC) signal transmission scheme, where communication and localization signals of different user equipments (UEs) are superimposed respectively. We analyze the performance of localization and communication in terms of position error bound (PEB) and effective data rate (EDR) theoretically. We further compare the proposed NOMA-ILAC method with current NOMA-orthogonal multiple access (OMA) method where different UEs’ communication signals are superposed while their localization signals are transmitted orthogonally, both from theoretical analysis and simulations. Performance trade-off is then carried out w.r.t. the time-domain resource allocation. Numerical results demonstrate that by adapting the time allocation ratio, the proposed method is able to improve the communication performance by up to 33%, when the PEB of the two methods are equal.

In this article, we consider a cloud-radio access network (C-RAN) with multiple enhanced remote radio heads (RRHs), which support for wireless charging, computation offloading and information transmission. The employment of enhanced RRHs requires a redesign for efficient resource allocation. Our goal is to minimize the weighted sum of energy transfer time and computation time by jointly optimizing energy transmission time, energy beamforming vectors, offloading factors, information transmission time, and combining vectors, while satisfying the communication performance requirements of terminal nodes. The problem is formulated as a mixed-integer nonlinear programming, which can be solved by alternating optimization (AO) utilizing semidefinite relaxation (SDR) and nonlinear transformation. To reduce the online computation delay of AO algorithm, we propose an optimization method based on the deep learning framework, which can determine an optimal solution with a trained neural network in real-time application. To improve the performance of the learning algorithm, we use two methods to set labels for training deep neural network to obtain the optimal offloading decision: 1) preoptimization and 2) self-evolution. Simulation results show that with the trained neural network, the algorithms based on deep learning can achieve a similar performance to the AO algorithm with less computation time.

Downlink coherent multiuser transmission is an essential technique for cell-free massive multiple-input multiple-output (MIMO) systems, and the availability of channel state information (CSI) at the transmitter is a basic requirement. To avoid CSI feedback in a time-division duplex system, the uplink channel parameters should be calibrated to obtain the downlink CSI due to the radio frequency circuit mismatch of the transceiver. In this paper, a design of a reference signal for over-the-air reciprocity calibration is proposed. The frequency domain generated reference signals can make full use of the flexible frame structure of the fifth-generation (5G) new radio, which can be completely transparent to commercial off-the-shelf (COTS) remote radio units (RRU) and commercial user equipments. To further obtain the calibration of multiple RRUs, an interleaved RRU grouping with a genetic algorithm is proposed, and an averaged Argos calibration algorithm is also presented. We develop a cell-free massive MIMO prototype system with COTS RRUs, demonstrate the statistical characteristics of the calibration error and the effectiveness of the calibration algorithm, and evaluate the impact of the calibration delay on the different cooperative transmission schemes.

Integrated Sensing and Communication (ISAC) is an emerging technology that realizes both communication and sensing functionalities on the same sets of hardware as well as same frequency bands. Such technique will bring tremendous benefits such as spectrum efficiency improvement and cost reduction. In this paper, we firstly define three different classes of ISAC with the integration of both resources and functionalities for communication and sensing as the ultimate form. Furthermore, target at the full-integration level, we propose a downlink-pilot-based OFDM ISAC signal processing algorithm for base-station-orientated sensing. The OFDM architecture guarantees forward compatibility, and downlink reference signals including DMRS and CSI-RS ensure the communication throughput and address privacy concerns as well. Simulation results show that the proposed method is able to achieve outstanding sensing performance with range estimation error of 0.15m and velocity estimation error of 0.2m/s in the low SNR region.