Affine frequency division multiplexing (AFDM) achieves full diversity in doubly selective channels. By exploiting its clear interference structure, decision feedback equalization (DFE) offers a robust, non-iterative receiver, yet suffers from severe error propagation. A permutation-driven DFE (P-DFE) framework is proposed to optimize both single-trajectory reliability and parallel structural diversity. For the single-branch P-DFE, permutation acts as an interference-shaping mechanism. Guided by derived statistical bounds, a low-complexity energy graph (EG) algorithm is developed to leverage the sparse AFDM channel for approximating optimal reliability sorting, reshaping the signal-to-interference-plus-noise ratio (SINR) profile to suppress initial errors. To decouple error propagation in high order constellations, permutation is elevated to a branch-generation tool for a parallel scheme. Specifically, a combination of cyclic shifts and time reversal is applied to the base order, generating a quality-preserving branch family. An optimal permutation-driven quad-branch DFE (P-QDFE) is configured by maximizing the harmonic mean of Kendall–Tau (KT) distances, minimizing inter-branch correlation. Simulations show P-QDFE effectively suppresses error floors in high-order modulations. At practical pre-decoding thresholds, this non-iterative scheme outperforms highly-iterated orthogonal approximate message passing (OAMP) under low-density parity-check (LDPC) coding, while integrating incomplete Cholesky factorization (ICHOL) ensures linear minimum mean square error (LMMSE)-level complexity.
The advent of sixth-generation (6G) mobile networks forecasts the widespread deployment of edge artificial intelligence (AI), where AI inference tasks are offloaded from resource-constrained edge devices to edge servers. This paradigm promises low latency and high-efficiency processing, yet faces critical challenges in meeting the stringent latency requirements of emerging applications such as autonomous driving and realtime robotics. Traditional ultra-reliable and low-latency communication (URLLC) paradigms fall short in the context of edge inference, where the high-dimensional nature of extracted features introduces a fundamental trade-off between reliability and latency. In this paper, we exploit the inherent robustness of AI models to channel distortions to design an ultra-low-latency edge inference framework that jointly optimizes computation and communication resources. We focus on both multi-snapshot (sequential sensing) and multi-view (distributed sensing) scenarios. For each, we derive upper bounds on inference accuracy as a function of the number of snapshots/views and their average bit error rate (BER). These bounds guide the formulation of optimization problems that minimize total system latency while satisfying accuracy constraints. The joint optimization is decomposed into subproblems involving snapshot/view selection, transmit power control, and allocation of computation and communication time. To solve this efficiently, we develop a low-complexity iterative algorithm. Experimental results on synthetic and real-world datasets validate our approach, demonstrating significant latency reductions while maintaining inference performance. Our findings provide a foundation for robust and efficient edge AI design in future 6G networks.
Optical reconfigurable intelligent reflecting surface (ORIS) has attracted widespread attention since it was proposed. As a new type of programmable communication device, ORIS can intelligently reconfigure the optical wireless communication (OWC) channel, thereby improving system performance and expanding communication scenarios. In OWC networking, multiple ORISs can serve as communication nodes to access users in different areas and support resource allocation. This work studies the deployment optimization of multiple ORISs in OWC networking scenarios with obstacles. When designing the ORIS deployment path, we consider the transceiver position, obstacle position, obstacle shape and size, ORIS output attenuation, ORIS jitter, transmitter jitter, and atmospheric turbulence. A geometry-guided deployment optimization algorithm is proposed to generate feasible ORIS deployment paths and select the best candidate solution according to the outage probability. Under the condition that the transceiver location, obstacle map, and deployable region are known, the algorithm can automatically generate candidate ORIS positions and assist communication-path planning. Simulation results show that the proposed deployment optimization method can effectively improve the performance of the OWC system compared with geometry-only and distance-only baselines.
Integrated sensing and communication (ISAC) in multi-hop wireless networks represents a transformative solution for enabling next-generation Internet-of-things (IoT) applications, addressing critical challenges such as spectrum scarcity and limited coverage. By leveraging shared resources and multi-antenna techniques, this architecture simultaneously supports high-quality end-to-end communication and accurate environmental sensing, thus a methodical exploration of the trade-offs between these two functionalities is required. To achieve this goal, this paper focuses on maximizing the end-to-end communication rate while ensuring the overall sensing performance in multiple-input multiple-output (MIMO) ISAC multi-hop wireless networks. In particular, a joint optimization problem for routing strategy and transmit covariance matrix design is formulated, which is, however, highly non-convex and challenging to solve directly. To tackle this problem with implicit routing variables, we first transform it into an equivalent but more tractable mixed-integer nonlinear programming (MINLP) problem through a series of equivalent transformations. Then, instead of relying on computationally expensive exhaustive search methods, we propose an efficient (centralized) algorithm based on generalized Benders decomposition (GBD), which can obtain the optimal solution of the MINLP problem with much lower complexity as compared to the exhaustive search method. Moreover, to further alleviate the communication overhead and computational complexity of solving the MINLP problem in a centralized manner, we also develop a low-complexity distributed algorithm that integrates the alternating direction method of multipliers (ADMM), the penalty method and the successive convex approximation (SCA) method. Finally, numerical results demonstrate that: 1) the centralized algorithm is able to achieve the optimal performance obtained by the exhaustive search method, while the low-complexity distributed algorithm attains near-optimal performance; 2) as the sum-CRB threshold is relaxed from 0.041 to 0.055, the average end-to-end achievable rate of both proposed algorithms increases from approximately 14 to 24.5 bps/Hz. Moreover, compared with the greedy and random routing baselines, the distributed algorithm achieves rate improvements of about 43%–69% and 32%–40%, respectively.
As a pivotal direction for future 6G systems, low-altitude wireless networks present a promising application ground for integrated sensing and communication (ISAC) technologies. In a typical use case where a base station performs sensing on unmanned aerial vehicles (UAVs), the system encounters new challenges including highly dynamic targets, rapidly time-varying channels, multipath effects, and complex interference. These demand integrated solutions that jointly support high-precision sensing and highly reliable communication. To address these challenges, this paper adopts the Cramér-Rao Bound (CRB) as the estimation performance metric to introduce an estimation-performance-oriented robust beamforming method, formulating an optimization problem that simultaneously guarantees the CRB-based estimation performance and outage-constrained communication reliability. For the single-user scenario, the optimization problem is reformulated into Semi-Definite Programming (SDP). In the multi-user case, probabilistic SINR constraints are relaxed via Bernstein-type inequalities, ensuring computational tractability while guaranteeing outage performance across multiple users. Simulation results show that the proposed method effectively balances the sensing-communication trade-off, showing notable performance gains, particularly in enhancing target estimation accuracy under low SINR conditions.
