Group Movable Antenna With Flexible Sparsity: Joint Array Position and Sparsity Optimization

Movable antenna (MA) is a promising technology to exploit the spatial variation of wireless channel for performance enhancement, by dynamically varying the antenna position within a certain region. However, for multi-antenna communication systems, moving each antenna independently not only requires prohibitive complexity to find the optimal antenna positions, but also incurs sophisticated movement control in practice. To address this issue, this letter proposes a new MA architecture termed group MA (GMA), enabling the group movement of all elements collectively in a continuous manner, and simultaneously achieving flexible array architecture by antenna selection (AS). In this letter, we focus on the uniform sparse array based GMA, where equally spaced antenna elements are selected to achieve desired array sparsity. The array position and sparsity level are jointly optimized to maximize the sum rate of the multi-user communication system. Numerical results verify the necessity to optimize the position and sparsity of GMA, and considerable performance gain is achieved as compared to the conventional fixed-position antenna (FPA).

6D Motion Parameters Estimation in Monostatic Integrated Sensing and Communications System

In this paper, we propose a novel scheme to estimate the six dimensional (6D) motion parameters of dynamic target for monostatic integrated sensing and communications (ISAC) system. We first provide a generic ISAC framework for dynamic target sensing based on massive multiple input and multiple output (MIMO) array. Next, we derive the relationship between the sensing channel of ISAC base station (BS) and the 6D motion parameters of dynamic target. Then, we employ the array signal processing methods to estimate the horizontal angle, pitch angle, distance, and virtual velocity of dynamic target. Since the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the dynamic target's radial velocity, horizontal angular velocity, and pitch angular velocity from these virtual velocities. Simulation results demonstrate the effectiveness of the proposed 6D motion parameters estimation scheme, which also confirms a new finding that one single BS with massive MIMO array is capable of estimating the horizontal angular velocity and pitch angular velocity of dynamic target.

OFDM Achieves the Lowest Ranging Sidelobe Under Random ISAC Signaling

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC): What is the optimal communication-centric ISAC waveform for ranging? Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

Near-Optimal MIMO Detection Using Gradient-Based MCMC in Discrete Spaces

The discrete nature of transmitted symbols poses challenges for achieving optimal detection in multiple-input multiple-output (MIMO) systems associated with a large number of antennas. Recently, the combination of two powerful machine learning methods, Markov chain Monte Carlo (MCMC) sampling and gradient descent, has emerged as a highly efficient solution to address this issue. However, existing gradient-based MCMC detectors are heuristically designed and thus are theoretically untenable. To bridge this gap, we introduce a novel sampling algorithm tailored for discrete spaces. This algorithm leverages gradients from the underlying continuous spaces for acceleration while maintaining the validity of probabilistic sampling. We prove the convergence of this method and also analyze its convergence rate using both MCMC theory and empirical diagnostics. On this basis, we develop a MIMO detector that precisely samples from the target discrete distribution and generates posterior Bayesian estimates using these samples, whose performance is thereby theoretically guaranteed. Furthermore, our proposed detector is highly parallelizable and scalable to large MIMO dimensions, positioning it as a compelling candidate for next-generation wireless networks. Simulation results show that our detector achieves near-optimal performance, significantly outperforms state-of-the-art baselines, and showcases resilience to various system setups.

Deep Learning-based CSI Feedback in Wi-Fi Systems

In Wi-Fi systems, channel state information (CSI) plays a crucial role in enabling access points to execute beamforming operations. However, the feedback overhead associated with CSI significantly hampers the throughput improvements. Recent advancements in deep learning (DL) have transformed the approach to CSI feedback in cellular systems. Drawing inspiration from the successes witnessed in the realm of mobile communications, this paper introduces a DL-based CSI feedback framework, named EFNet, tailored for Wi-Fi systems. The proposed framework leverages an autoencoder to achieve precise feedback with minimal overhead. The process involves the station utilizing the encoder to compress and quantize a series of matrices into codeword bit streams, which are then fed back to the access point. Subsequently, the decoder installed at the AP reconstructs beamforming matrices from these bit streams. We implement the EFNet system using standard Wi-Fi equipment operating in the 2.4 GHz band. Experimental findings in an office environment reveal a remarkable 80.77% reduction in feedback overhead compared to the 802.11ac standard, alongside a significant boost in net throughput of up to 30.72%.

Electromagnetic Property Sensing Based on Diffusion Model in ISAC System

Integrated sensing and communications (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area. Specifically, we first estimate the sensing channel by using both the communications and the sensing signals echoed back from the target. Then we employ the diffusion model to generate the point cloud that represents the target and thus enables 3D visualization of the target's EM property distribution. In order to minimize the mean Chamfer distance (MCD) between the ground truth and the estimated point clouds, we further design the communications and sensing beamforming matrices under the constraint of a maximum transmit power and a minimum communications achievable rate for each user equipment (UE). Simulation results demonstrate the efficacy of the proposed method in achieving high-quality reconstruction of the target's shape, relative permittivity, and conductivity. Besides, the proposed method can sense the EM property of the target effectively in any position of the sensing area.

Coding-Enhanced Cooperative Jamming for Secret Communication in Fluid Antenna Systems

This letter investigates the secret communication problem for a fluid antenna system (FAS)-assisted wiretap channel, where the legitimate transmitter transmits an information-bearing signal to the legitimate receiver, and at the same time, transmits a jamming signal to interfere with the eavesdropper (Eve). Unlike the conventional jamming scheme, which usually transmits Gaussian noise that interferes not only with Eve but also with the legitimate receiver, in this letter, we consider that encoded codewords are transmitted to jam Eve. Then, by employing appropriate coding schemes, the legitimate receiver can successfully decode the jamming signal and then cancel the interference, while Eve cannot, even if it knows the codebooks. We aim to maximize the secrecy rate through port selection and power control. Although the problem is non-convex, we show that the optimal solution can be found. Simulation results show that by using the FAS technique and the proposed jamming scheme, the secrecy rate of the system can be significantly increased.

Interference Cancellation Based Neural Receiver for Superimposed Pilot in Multi-Layer Transmission

In this paper, an interference cancellation based neural receiver for superimposed pilot (SIP) in multi-layer transmission is proposed, where the data and pilot are non-orthogonally superimposed in the same time-frequency resource. Specifically, to deal with the intra-layer and inter-layer interference of SIP under multi-layer transmission, the interference cancellation with superimposed symbol aided channel estimation is leveraged in the neural receiver, accompanied by the pre-design of pilot code-division orthogonal mechanism at transmitter. In addition, to address the complexity issue for inter-vendor collaboration and the generalization problem in practical deployments, respectively, this paper also provides a fixed SIP (F-SIP) design based on constant pilot power ratio and scalable mechanisms for different modulation and coding schemes (MCSs) and transmission layers. Simulation results demonstrate the superiority of the proposed schemes on the performance of block error rate and throughput compared with existing counterparts.

Sparse MIMO for ISAC: New Opportunities and Challenges

Multiple-input multiple-output (MIMO) has been a key technology of wireless communications for decades. A typical MIMO system employs antenna arrays with the inter-antenna spacing being half of the signal wavelength, which we term as compact MIMO. Looking forward towards the future sixth-generation (6G) mobile communication networks, MIMO system will achieve even finer spatial resolution to not only enhance the spectral efficiency of wireless communications, but also enable more accurate wireless sensing. To this end, by removing the restriction of half-wavelength antenna spacing, sparse MIMO has been proposed as a new architecture that is able to significantly enlarge the array aperture as compared to conventional compact MIMO with the same number of array elements. In addition, sparse MIMO leads to a new form of virtual MIMO systems for sensing with their virtual apertures considerably larger than physical apertures. As sparse MIMO is expected to be a viable technology for 6G, we provide in this article a comprehensive overview of it, especially focusing on its appealing advantages for integrated sensing and communication (ISAC) towards 6G. Specifically, assorted sparse MIMO architectures are first introduced, followed by their new benefits as well as challenges. We then discuss the main design issues of sparse MIMO, including beam pattern synthesis, signal processing, grating lobe suppression, beam codebook design, and array geometry optimization. Last, we provide numerical results to evaluate the performance of sparse MIMO for ISAC and point out promising directions for future research.

Rethinking Waveform for 6G: Harnessing Delay-Doppler Alignment Modulation

Waveform design has served as a cornerstone for each generation of mobile communication systems. The future sixth-generation (6G) mobile communication networks are expected to employ larger-scale antenna arrays and exploit higher-frequency bands for further boosting data transmission rate and providing ubiquitous wireless sensing. This brings new opportunities and challenges for 6G waveform design. In this article, by leveraging the super spatial resolution of large antenna arrays and the multi-path spatial sparsity of highfrequency wireless channels, we introduce a new approach for waveform design based on the recently proposed delay-Doppler alignment modulation (DDAM). In particular, DDAM makes a paradigm shift of waveform design from the conventional manner of tolerating channel delay and Doppler spreads to actively manipulating them. First, we review the fundamental constraints and performance limitations of orthogonal frequency division multiplexing (OFDM) and introduce new opportunities for 6G waveform design. Next, the motivations and basic principles of DDAM are presented, followed by its various extensions to different wireless system setups. Finally, the main design considerations for DDAM are discussed and the new opportunities for future research are highlighted.