Mixed-Precision Quantization: Make the Best Use of Bits Where They Matter Most

Mixed-precision quantization offers superior performance to fixed-precision quantization. It has been widely used in signal processing, communication systems, and machine learning. In mixed-precision quantization, bit allocation is essential. Hence, in this paper, we propose a new bit allocation framework for mixed-precision quantization from a search perspective. First, we formulate a general bit allocation problem for mixed-precision quantization. Then we introduce the penalized particle swarm optimization (PPSO) algorithm to address the integer consumption constraint. To improve efficiency and avoid iterations on infeasible solutions within the PPSO algorithm, a greedy criterion particle swarm optimization (GC-PSO) algorithm is proposed. The corresponding convergence analysis is derived based on dynamical system theory. Furthermore, we apply the above framework to some specific classic fields, i.e., finite impulse response (FIR) filters, receivers, and gradient descent. Numerical examples in each application underscore the superiority of the proposed framework to the existing algorithms.

Optimizing Air-borne Network-in-a-box Deployment for Efficient Remote Coverage

Among many envisaged drivers for the sixth generation, one is from the United Nations Sustainability Development Goals 2030 to eliminate digital inequality. Remote coverage in sparsely populated areas, difficult terrains, or emergency scenarios requires on-demand access and flexible deployment with minimal capex and opex. In this context, network-in-a-box (NIB) is an exciting solution that packs the whole wireless network into a single portable and re-configurable box to support multiple access technologies such as WiFi/2G/3G/4G/5G etc. In this paper, we propose low-altitude platform stations (LAPS) based NIBs with stratospheric high-altitude platform station (HAPS) as backhaul. Specifically, backhaul employs non-orthogonal multiple access (NOMA) with superposition coding at the transmitting HAPS and successive interference cancellation (SIC) at the receiving NIBs, whereas the access link (AL) employs superposition coding along with the regularized zero-forcing (RZF) precoding at the NIB in order to elevate the computational overhead from the ground users. The required number of airborne NIBs to serve a desired coverage area, their optimal placement, user association, beam optimization, and resource allocation are optimized by maximizing the sum rate of the AL while maintaining the quality of service. Our findings reveal the significance of thorough system planning and communication parameters optimization for enhanced system performance and best coverage under limited resources.

Edge Information Hub-Empowered 6G NTN: Latency-Oriented Resource Orchestration and Configuration

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

Finite-Precision Arithmetic Transceiver for Massive MIMO Systems

Efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we conduct the rounding error analysis and derive the lower bound of the achievable rate for single-input-multiple-output (SIMO) using maximal ratio combining (MRC) and multiple-input-single-output (MISO) systems using maximal ratio transmission (MRT) with finite-precision arithmetic. Then, considering the multi-user scenario, the rounding error analysis of zero-forcing (ZF) detection and precoding is derived by using the normal equations (NE) method. The corresponding lower bounds of the achievable sum rate are also derived and asymptotic analyses are presented. Built upon insights from these analyses and lower bounds, we propose a mixed-precision architecture for massive MIMO systems to offset performance gaps due to finite-precision arithmetic. The corresponding analysis of rounding errors and computational costs is obtained. Simulation results validate the derived bounds and underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

Radio Map-Based Spectrum Sharing for Joint Communication and Sensing

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fraction programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results collaborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.

Joint Channel Estimation and Data Recovery for Millimeter Massive MIMO: Using Pilot to Capture Principal Components

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods, the proposed PF-assisted method is utilized to capture the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The theoretical analysis demonstrates that the PF-assisted JCD method can achieve equivalent performance to the Bayesian-optimal DF-based method, while greatly reducing the computational complexity. Simulation results are also presented to validate the analytical results.

Self-supervised Adaptive Weighting for Cooperative Perception in V2V Communications

Perception of the driving environment is critical for collision avoidance and route planning to ensure driving safety. Cooperative perception has been widely studied as an effective approach to addressing the shortcomings of single-vehicle perception. However, the practical limitations of vehicle-to-vehicle (V2V) communications have not been adequately investigated. In particular, current cooperative fusion models rely on supervised models and do not address dynamic performance degradation caused by arbitrary channel impairments. In this paper, a self-supervised adaptive weighting model is proposed for intermediate fusion to mitigate the adverse effects of channel distortion. The performance of cooperative perception is investigated in different system settings. Rician fading and imperfect channel state information (CSI) are also considered. Numerical results demonstrate that the proposed adaptive weighting algorithm significantly outperforms the benchmarks without weighting. Visualization examples validate that the proposed weighting algorithm can flexibly adapt to various channel conditions. Moreover, the adaptive weighting algorithm demonstrates good generalization to untrained channels and test datasets from different domains.

Knowledge Distillation Based Semantic Communications For Multiple Users

Deep learning (DL) has shown great potential in revolutionizing the traditional communications system. Many applications in communications have adopted DL techniques due to their powerful representation ability. However, the learning-based methods can be dependent on the training dataset and perform worse on unseen interference due to limited model generalizability and complexity. In this paper, we consider the semantic communication (SemCom) system with multiple users, where there is a limited number of training samples and unexpected interference. To improve the model generalization ability and reduce the model size, we propose a knowledge distillation (KD) based system where Transformer based encoder-decoder is implemented as the semantic encoder-decoder and fully connected neural networks are implemented as the channel encoder-decoder. Specifically, four types of knowledge transfer and model compression are analyzed. Important system and model parameters are considered, including the level of noise and interference, the number of interfering users and the size of the encoder and decoder. Numerical results demonstrate that KD significantly improves the robustness and the generalization ability when applied to unexpected interference, and it reduces the performance loss when compressing the model size.

Cooperative Perception with Learning-Based V2V communications

Cooperative perception has been widely used in autonomous driving to alleviate the inherent limitation of single automated vehicle perception. To enable cooperation, vehicle-to-vehicle (V2V) communication plays an indispensable role. This work analyzes the performance of cooperative perception accounting for communications channel impairments. Different fusion methods and channel impairments are evaluated. A new late fusion scheme is proposed to leverage the robustness of intermediate features. In order to compress the data size incurred by cooperation, a convolution neural network-based autoencoder is adopted. Numerical results demonstrate that intermediate fusion is more robust to channel impairments than early fusion and late fusion, when the SNR is greater than 0 dB. Also, the proposed fusion scheme outperforms the conventional late fusion using detection outputs, and autoencoder provides a good compromise between detection accuracy and bandwidth usage.

Near-Field Positioning and Attitude Sensing Based on Electromagnetic Propagation Modeling

Positioning and sensing over wireless networks are imperative for many emerging applications. However, traditional wireless channel models cannot be used for sensing the attitude of the user equipment (UE), since they over-simplify the UE as a point target. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cram\'er-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.