Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models

Generative foundation AI models have recently shown great success in synthesizing natural signals with high perceptual quality using only textual prompts and conditioning signals to guide the generation process. This enables semantic communications at extremely low data rates in future wireless networks. In this paper, we develop a latency-aware semantic communications framework with pre-trained generative models. The transmitter performs multi-modal semantic decomposition on the input signal and transmits each semantic stream with the appropriate coding and communication schemes based on the intent. For the prompt, we adopt a re-transmission-based scheme to ensure reliable transmission, and for the other semantic modalities we use an adaptive modulation/coding scheme to achieve robustness to the changing wireless channel. Furthermore, we design a semantic and latency-aware scheme to allocate transmission power to different semantic modalities based on their importance subjected to semantic quality constraints. At the receiver, a pre-trained generative model synthesizes a high fidelity signal using the received multi-stream semantics. Simulation results demonstrate ultra-low-rate, low-latency, and channel-adaptive semantic communications.

AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication Networks

Motivated by the need for increased spectral efficiency and the proliferation of intelligent applications, the sixth-generation (6G) mobile network is anticipated to integrate the dual-functions of communication and sensing (C&S). Although the millimeter wave (mmWave) communication and mmWave radar share similar multiple-input multiple-output (MIMO) architecture for integration, the full potential of dual-function synergy remains to be exploited. In this paper, we commence by overviewing state-of-the-art schemes from the aspects of waveform design and signal processing. Nevertheless, these approaches face the dilemma of mutual compromise between C&S performance. To this end, we reveal and exploit the synergy between C&S. In the proposed framework, we introduce a two-stage frame structure and resort artificial intelligence (AI) to achieve the synergistic gain by designing a joint C&S channel semantic extraction and reconstruction network (JCASCasterNet). With just a cost-effective and energy-efficient single sensing antenna, the proposed scheme achieves enhanced overall performance while requiring only limited pilot and feedback signaling overhead. In the end, we outline the challenges that lie ahead in the future development of integrated sensing and communication networks, along with promising directions for further research.

Index Modulation for Fluid Antenna-Assisted MIMO Communications: System Design and Performance Analysis

In this paper, we propose a transmission mechanism for fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) communication systems based on index modulation (IM), named FA-IM, which incorporates the principle of IM into FAs-assisted MIMO system to improve the spectral efficiency (SE) without increasing the hardware complexity. In FA-IM, the information bits are mapped not only to the modulation symbols, but also the index of FA position patterns. Additionally, the FA position pattern codebook is carefully designed to further enhance the system performance by maximizing the effective channel gains. Then, a low-complexity detector, referred to efficient sparse Bayesian detector, is proposed by exploiting the inherent sparsity of the transmitted FA-IM signal vectors. Finally, a closed-form expression for the upper bound on the average bit error probability (ABEP) is derived under the finite-path and infinite-path channel condition. Simulation results show that the proposed scheme is capable of improving the SE performance compared to the existing FAs-assisted MIMO and the fixed position antennas (FPAs)-assisted MIMO systems while obviating any additional hardware costs. It has also been shown that the proposed scheme outperforms the conventional FA-assisted MIMO scheme in terms of error performance under the same transmission rate.

AFDM-SCMA: A Promising Waveform for Massive Connectivity over High Mobility Channels

This paper studies the affine frequency division multiplexing (AFDM)-empowered sparse code multiple access (SCMA) system, referred to as AFDM-SCMA, for supporting massive connectivity in high-mobility environments. First, by placing the sparse codewords on the AFDM chirp subcarriers, the input-output (I/O) relation of AFDM-SCMA systems is presented. Next, we delve into the generalized receiver design, chirp rate selection, and error rate performance of the proposed AFDM-SCMA. The proposed AFDM-SCMA is shown to provide a general framework and subsume the existing OFDM-SCMA as a special case. Third, for efficient transceiver design, we further propose a class of sparse codebooks for simplifying the I/O relation, referred to as I/O relation-inspired codebook design in this paper. Building upon these codebooks, we propose a novel iterative detection and decoding scheme with linear minimum mean square error (LMMSE) estimator for both downlink and uplink channels based on orthogonal approximate message passing principles. Our numerical results demonstrate the superiority of the proposed AFDM-SCMA systems over OFDM-SCMA systems in terms of the error rate performance. We show that the proposed receiver can significantly enhance the error rate performance while reducing the detection complexity.

BER Analysis of SCMA-OFDM Systems in the Presence of Carrier Frequency Offset

Sparse code multiple access (SCMA) building upon orthogonal frequency division multiplexing (OFDM) is a promising wireless technology for supporting massive connectivity in future machine-type communication networks. However, the sensitivity of OFDM to carrier frequency offset (CFO) poses a major challenge because it leads to orthogonality loss and incurs intercarrier interference (ICI). In this paper, we investigate the bit error rate (BER) performance of SCMA-OFDM systems in the presence of CFO over both Gaussian and multipath Rayleigh fading channels. We first model the ICI in SCMA-OFDM as Gaussian variables conditioned on a single channel realization for fading channels. The BER is then evaluated by averaging over all codeword pairs considering the fading statistics. Through simulations, we validate the accuracy of our BER analysis and reveal that there is a significant BER degradation for SCMA-OFDM systems when the normalized CFO exceeds 0.02.

Design and Performance Analysis of Index Modulation Empowered AFDM System

In this letter, we incorporate index modulation (IM) into affine frequency division multiplexing (AFDM), called AFDM-IM, to enhance the bit error rate (BER) and energy efficiency (EE) performance. In this scheme, the information bits are conveyed not only by $M$-ary constellation symbols, but also by the activation of the chirp subcarriers (SCs) indices, which are determined based on the incoming bit streams. Then, two power allocation strategies, namely power reallocation (PR) strategy and power saving (PS) strategy, are proposed to enhance BER and EE performance, respectively. Furthermore, the average bit error probability (ABEP) is theoretically analyzed. Simulation results demonstrate that the proposed AFDM-IM scheme achieves better BER performance than the conventional AFDM scheme.

Resource Management for IRS-assisted WP-MEC Networks with Practical Phase Shift Model

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for low-power wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming and uplink multi-user detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penalty-based optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

Enhancing Signal Space Diversity for SCMA Over Rayleigh Fading Channels

Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems.

Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

Sum Secrecy Rate Maximization for IRS-aided Multi-Cluster MIMO-NOMA Terahertz Systems

Intelligent reflecting surface (IRS) is a promising and disruptive technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted Terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding in the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. Cluster heads are firstly selected for transmissions, and discrete phase-based analog precoding is designed for the transmit beamforming. Subsequently, based on the channel conditions, the users are grouped into multiple clusters, and each cluster is transmitted by using the NOMA technique. In addition, a low complexity zero-forcing method is employed to design digital precoding so as to eliminate interference between clusters. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS under the constraints of system transmission power, achievable rate requirement of each user, and IRS phase shifts. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results are demonstrated to validate the convergence of the proposed algorithm, and confirm that the deployment of IRS can significantly improve the secrecy performance.