In this paper, we propose an integrated sensing and backscatter communication system utilizing high scanning-rate slot array antennas (SAAs) in a frequency-diverse configuration, leveraging an inverse scattering approach. This system employs a frequency diversity scheme to facilitate rapid spatial data acquisition, significantly outperforming traditional mechanical and electronic scanning methods in terms of speed and cost-efficiency. The proposed integrated system achieves simultaneous localization, identification, and backscatter communication of tags within cluttered environments. Our approach analyzes backscatter process based on the structural and antenna modes of tags, as well as the presence of clutter scatterers. By modulating the tags between open-circuit and short-circuit states, we effectively extract the structural and antenna mode components. The structural mode component allows us to sense both the tags and the surrounding clutter scatterers, while the antenna mode component is used for precise tag identification by incorporating both inverse scattering and compressive sensing (CS) algorithms. During these processes, channel state information (CSI) is gathered through the antenna mode, enhancing the backscatter communication capability of the system. Additionally, the efficacy of backscatter communication is assessed using the bit error rate (BER) with the maximum ratio combining (MRC) technique. Simulations and experimental results demonstrate that our proposed system can accurately sense and identify tags amidst clutter scatterers while maintaining robust backscatter communication over a 40 MHz bandwidth within the 3.98-4.02 GHz range. These results highlight the significant advantages of employing the proposed frequency-diverse antennas and the inverse scattering approach in integrated backscatter communication and sensing applications.

This paper proposes an innovative framework that integrates electromagnetic inverse scattering with improved quadrature spatial modulation (IQSM) to simultaneously accomplish sensing, identification, and backscatter communication. Specifically tailored for energy- and spectrum-efficient wireless operations in highly cluttered environments, the proposed system employs distinct load impedance modulation at the tags to effectively separate structural and antenna mode signatures. Structural mode signals are processed using inverse scattering combined with compressive sensing techniques, enabling precise localization of both tags and surrounding clutter. Concurrently, antenna mode signals are utilized for accurate tag identification. In addition, the antenna mode enables the acquisition of the reliable channel state information (CSI), facilitating the integration of IQSM schemes into the backscatter communication module. Furthermore, we present a theoretical analysis by deriving the bit error probability (BEP) for the proposed system. The proposed system is validated through a proof-of-concept experimental setup consisting of transmit and receive arrays, each configured as a 3×3 uniform linear antenna array (ULA). Both simulation and experimental results confirm that the inverse scattering-based approach achieves high-precision sensing and accurate identification of tags individually or in combination. Additionally, the integration of IQSM significantly enhances spectral efficiency (SE) and data throughput, compared with the state-of-the-art systems that do not incorporate spatial modulation (SM) techniques. These findings highlight the effectiveness and practical viability of the proposed integrated sensing and communication system for challenging clutter-rich scenarios.

Artificial intelligence is expected to play a central role in enabling future 6 G networks. Developing foundation models that support a wide range of downstream tasks is critical for advancing 6 G standardization. This paper proposes a general framework for compressing wireless channel state information (CSI) using pretrained wireless foundation models. The foundation model is pre-trained using self-supervised learning with a masked reconstruction objective, achieving a normalized mean square error on the order of$10^{-3}$during pretraining. The model is evaluated on a range of wireless communication and sensing tasks, including classification tasks where compressed CSI is directly used for prediction, and regression tasks that require full CSI reconstruction. For regression tasks such as massive MIMO CSI feedback, the pre-compressed output from the foundation model is used as an auxiliary input to the downstream compressor, effectively enhancing the reconstruction quality. Compared to the Type-I codebook with comparable number of feedback bits, our method improves SGCS by 16.22% and reduces NMSE by 93.24%. Additionally, it achieves comparable SGCS performance to the Type-II codebook while using only 29% of the feedback bits. Comparison with existing research further confirms the contribution of the foundation model's compressed output in improving CSI compression performance. For classification tasks such as WiFi-based human activity recognition and human identification, the compressed representations produced by the foundation model can be directly utilized without additional fine-tuning. These representations achieve over 97% accuracy, outperforming conventional AI-based methods even under higher compression ratios. These findings demonstrate that leveraging a pretrained wireless foundation model consistently enhances performance across both classification and regression tasks, underscoring its versatility and potential in wireless CSI processing.

In this work, we propose a compact millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system utilizing electronically steerable parasitic array radiator (ESPAR). We analyze the system and channel models for the compact mmWave massive MIMO system using a beamspace formulation and demonstrate that we can optimize the spectral efficiency of the compact mmWave massive MIMO system by jointly adjusting the variable reactive loads in the ESPAR with a digital beamformer. We formulate the compact mmWave massive MIMO system optimization problem to maximize spectral efficiency and propose an unconstrained optimization based algorithm with an initialization method. The spectral and energy efficiencies of the compact mmWave massive MIMO system are evaluated in comparison to conventional mmWave massive MIMO systems of the same antenna size using full-digital, fully-connected hybrid, and partially-connected hybrid beamforming. The results show that the compact mmWave massive MIMO system provides higher spectral efficiency than partially-connected hybrid beamforming. On top of this, it can provide higher energy efficiencies of around 3.21, 2.88, and 1.22 times compared to full-digital, fully-connected hybrid, and partially-connected hybrid beamforming but with lower hardware complexity and lower cost. Therefore, compact mmWave massive MIMO systems are a promising and effective alternative to conventional massive MIMO systems in millimeter wave (mmWave) communications.

This paper considers cell-free multiple-input multiple-output (MIMO) systems with multi-antenna access points (APs) and multi-antenna users in constrained spaces. Two main effects emerge in such a space-constrained design: spatial correlation and mutual coupling. We analytically study the resulting performance with conjugate beamforming and reducing inter-antenna distance. We derive the closed-form expression for the spectral efficiency (SE) as a function of the spatial correlation and mutual coupling matrices. Additionally, we consider a practical power consumption model to investigate the energy efficiency (EE) with power control coefficients. The obtained ergodic and analytical results will show essential insights into the system performance. The decreasing inter-antenna distance introduces performance loss, while an appropriate number of APs could balance the SE and EE trade-off. The number of users improves the SE performance, while an optimal number of antennas per user can be selected to achieve maximum SE performance. Moreover, more users will introduce a smaller optimal number of antennas per user.

In this paper, we propose a new solution to improve the achievable rate of radio frequency (RF) powered cognitive radio networks (CRNs) with ambient backscatter communication (AmBC). Assisted with AmBC, the secondary transmitter (ST) can harvest energy and backscatter ambient signals when the primary channel is busy, which enhances the achievable rate compared with conventional RF-powered CRNs adopting the harvest-then-transmit (HTT) protocol. Our work proposes an RF-powered CRN that uses a multi-antenna ST since implementing multiple antennas on ST can enhance energy harvesting and increase the data rate. We discuss the corresponding time sharing and antenna selection tradeoffs and propose a low-complexity and time-efficient block coordinate descent (BCD)-assisted exhaustive search algorithm to find the optimal tradeoff that maximizes the data rate of the system. Simulation results show that our proposed scheme outperforms both the HTT mode and the ambient backscatter technique, leading to improved overall system performance.

A novel analog beamforming technique that utilizes single radio frequency (single-RF) electronically steerable parasitic array radiator (ESPAR) antennas is proposed. The ESPAR beamforming techniques are aimed at precoding in multiple antenna wireless communication systems using the single or hybrid RF approach. One advantage of ESPAR analog beamforming compared to conventional analog beamforming using phase shifters is that arbitrary patterns can be straightforwardly formed. Therefore, ESPAR analog beamforming can provide performance almost identical to digital beamforming. More importantly, ESPAR analog beamforming does not require phase shifters and power splitters, overcoming implementation challenges of conventional analog beamforming. It also has a more compact size compared to conventional beamforming using uniform antenna arrays. We analyze and optimize ESPAR analog beamforming and propose a channel approach based on beamspace pilot transmission to acquire channel state information (CSI). In addition, we propose limited feedback ESPAR analog beamforming based on a beamforming selection strategy. Simulation results for average gain show that ESPAR analog beamforming has almost identical performance to digital beamforming and better performance than analog beamforming using phase shifters. Energy efficiency simulations show that the technique has the highest efficiency among all the beamforming techniques. Therefore, ESPAR analog beamforming has near optimal performance, high energy efficiency, low circuit complexity, low cost, compact size and can be an effective alternative to conventional analog beamforming.