Multi-access point coordination (MAPC) is a key technology for enhancing throughput in next-generation Wi-Fi within dense overlapping basic service sets. However, existing MAPC protocols rely on static, protocol-defined rules, which limits their ability to adapt to dynamic network conditions such as varying interference levels and topologies. To address this limitation, we propose a novel Agentic AI Wi-Fi framework where each access point, modeled as an autonomous large language model agent, collaboratively reasons about the network state and negotiates adaptive coordination strategies in real time. This dynamic collaboration is achieved through a cognitive workflow that enables the agents to engage in natural language dialogue, leveraging integrated memory, reflection, and tool use to ground their decisions in past experience and environmental feedback. Comprehensive simulation results demonstrate that our agentic framework successfully learns to adapt to diverse and dynamic network environments, significantly outperforming the state-of-the-art spatial reuse baseline and validating its potential as a robust and intelligent solution for future wireless networks.

Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission in next-generation networks, where only information relevant to a specific task is communicated. This is particularly important in 6G-enabled Internet of Things (6G-IoT) scenarios, where bandwidth constraints, latency requirements, and data privacy are critical. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant information, leading to privacy concerns and suboptimal performance. To address this, we propose an information-bottleneck inspired method, named CLAD (contrastive learning and adversarial disentanglement). CLAD utilizes contrastive learning to effectively capture task-relevant features while employing adversarial disentanglement to discard task-irrelevant information. Additionally, due to the absence of reliable and reproducible methods to quantify the minimality of encoded feature vectors, we introduce the Information Retention Index (IRI), a comparative metric used as a proxy for the mutual information between the encoded features and the input. The IRI reflects how minimal and informative the representation is, making it highly relevant for privacy-preserving and bandwidth-efficient 6G-IoT systems. Extensive experiments demonstrate that CLAD outperforms state-of-the-art baselines in terms of semantic extraction, task performance, privacy preservation, and IRI, making it a promising building block for responsible, efficient and trustworthy 6G-IoT services.

We study a unified information-theoretic framework for integrated sensing and communications (ISAC), applicable to both monostatic and bistatic sensing scenarios. Special attention is given to the case where the sensing receiver (Rx) is required to produce a “soft" estimate of the state sequence, with logarithmic loss serving as the performance metric. We derive lower and upper bounds on the capacity-distortion function, which delineates the fundamental tradeoff between communication rate and sensing distortion. These bounds coincide when the channel between the ISAC transmitter (Tx) and the communication Rx is degraded with respect to the channel between the ISAC Tx and the sensing Rx, or vice versa. Furthermore, we provide a complete characterization of the capacity-distortion function for an ISAC system that simultaneously transmits information over a binary-symmetric channel and senses additive Bernoulli states through another binary-symmetric channel. The Gaussian counterpart of this problem is also explored, which, together with a state-splitting trick, fully determines the capacity-distortion-power function under the squared error distortion measure.

In this paper, we study the lossless analog compression fori.i.d.discrete-continuous mixed signals via the polarization-based framework. We prove that for discrete-continuous mixed source, the error probability of maximum a posteriori (MAP) estimation polarizes under the Hadamard transform, which extends the polarization phenomenon to analog domain. Building on this insight, we propose the partial Hadamard compression and develop the corresponding analog successive cancellation (SC) decoder. The proposed scheme consists of deterministic measurement matrices and non-iterative reconstruction algorithm, providing benefits in both space and computational complexity. Using the polarization of error probability, we prove that our approach achieves the information-theoretical limit for lossless analog compression developed by Wu and Verdú.

Artificial intelligence (AI) and machine learning (ML) have shown tremendous potential in reshaping the landscape of wireless communications and are, therefore, widely expected to be an indispensable part of the next-generation wireless network. This article presents an overview of how AI/ML and wireless communications interact synergistically to improve system performance and provides useful tips and tricks on realizing such performance gains when training AI/ML models. In particular, we discuss in detail the use of AI/ML to revolutionize key physical layer and lower medium access control (MAC) layer functionalities in traditional wireless communication systems. In addition, we provide a comprehensive overview of the AI/ML-enabled semantic communication systems, including key techniques from data generation to transmission. We also investigate the role of AI/ML as an optimization tool to facilitate the design of efficient resource allocation algorithms in wireless communication networks at both bit and semantic levels. Finally, we analyze major challenges and roadblocks in applying AI/ML in practical wireless system design and share our thoughts and insights on potential solutions.

In this paper, we study the lossless analog compression for i.i.d. nonsingular signals. Through analyzing analog polarization under Hadamard transform, we propose efficient successive cancellation (SC) decoding algorithm over analog domain. Thanks to the polarization of Rényi information dimension (RID) and the absorption of discrete entropy, we prove that the proposed scheme achieves the information-theoretical limit for lossless analog compression developed by Wu and Verdú.

Targeting high-throughput and low-power communications, we implement two successive cancellation (SC) decoders for polar codes. Converted to 16nm ASIC technology, the area efficiency and energy efficiency are 4Tbps/mm2and 0.63pJ/bit, respectively, for the unrolled decoder, and 561Gbps/mm2and 1.21pJ/bit, respectively, for the recursive decoder. To achieve such a high throughput, a novel code construction, coined as fast polar codes, is proposed and jointly optimized with a highly-parallel SC decoding architecture. First, we reuse existing modules to fast decode more outer code blocks, and then modify code construction to facilitate faster decoding for all outer code blocks up to a degree of parallelism of 16. Furthermore, parallel comparison circuits and bit quantization schemes are customized for hardware implementation. Collectively, they contribute to an 2.66× area efficiency improvement and 33% energy saving over the state of the art.

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.

In this paper, we design a polar decoding platform for diverse application scenarios that require low-cost and low-power communications. Specifically, prevalent polar decoders such as successive cancellation (SC), SC-list (SCL) and Fano decoders are all supported under the same architecture. Unlike high-throughput or low-latency decoders that promote parallelism, this architecture promotes serialization by repeatedly calling a “sub-process” that is executed by a core module. Surprisingly, the resulting serial SCL-8 decoder is only 3 times the size of an SC decoder, much smaller than the predicted 8 times. Cost and power are minimized through resource sharing and adaptive decoding techniques, etc. We carried out performance simulation and hardware implementation to evaluate the actual chip area and energy consumption.

Identification capacities of randomized and deterministic identification were proved to exceed channel capacity for Gaussian channels with channel side information (CSI). In this work, we extend deterministic identification to the block fading channels without CSI by applying identification codes for both channel estimation and user identification. We prove that identification capacity is asymptotically higher than transmission capacity even in the absence of CSI. And we also analyze the finite-length performance theoretically and numerically. The simulation results verify the feasibility of the proposed blind deterministic identification in finite blocklength regime.

With the rapid development of electronic and information technologies, the Internet of Everything (IoE) has become one of the trendiest topics in both academia and industry. Therein, many types of space/air/ground platforms need to be connected to networks for breaking down the isolation of information islands and providing various services. Space/air/ground platforms, such as satellites, unmanned aerial vehicles (UAVs), airships, balloons, terrestrial vehicles, and high-speed trains (HSTs) have emerged for accomplishing various complex tasks. Wireless communication is one of the most important technologies to support the real-time delivery of control commands and mission-related data. On the other hand, the space-air-ground integrated network has become a promising paradigm for the six-generation (6G) mobile communication network, where the aerospace and terrestrial vehicles may need to connect to existing mobile cellular networks or act as base stations (BSs) or relays to assist terrestrial wireless communications. To meet the ever-increasing demands of high capacity, wide coverage, low latency, and strong robustness for communications, it is promising to adopt large-scale antenna arrays at the transceivers to obtain considerable array gains and improve the channel quality. Antenna array-enabled beamforming technologies can facilitate spectrum reuse, interference mitigation, coverage enhancement, and physical-layer security. Antenna arrays can also be used to promote the sensing capability of space/air/ground networks, where the sensing information may be carefully processed to assist communications. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive, and tricky challenges in antenna array design, physical layer, multiple access control layer, and network layer. As a result, numerous new research issues require to be addressed, which cover a wide range of disciplines including communication theory, network theory, antenna theory, signal processing, protocol design, resource allocation, optimization, hardware implementation, and experimentation.

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.

A distributed lossy compression network with$L$encoders and a decoder is considered. Each encoder observes a source and sends a compressed version to the decoder. The decoder produces a joint reconstruction of target signals with the mean squared error distortion below a given threshold. It is assumed that the observed sources can be expressed as the sum of target signals and corruptive noises which are independently generated from two symmetric multivariate Gaussian distributions. The minimum compression rate of this network versus the distortion threshold is referred to as the rate-distortion function, for which an explicit lower bound is established by solving a minimization problem. Our lower bound matches the well-known Berger-Tung upper bound for some values of the distortion threshold. The asymptotic gap between the upper and lower bounds is characterized in the large$L$limit.

Recently, a permutation-based successive cancellation (PSC) decoding framework for polar codes attracts much attention. It decodes several permuted codewords with indepen-dent successive cancellation (SC) decoders. Its latency thus can be reduced to that of SC decoding. However, the PSC framework is ineffective for permutations falling into the lower-triangular affine (LTA) automorphism group, as they are invariant under SC decoding. As such, a larger block lower-triangular affine (BLTA) group that contains SC-variant permutations was discovered for decreasing polar codes. But it was unknown whether BLTA equals the complete automorphism group. In this paper, we prove that BLTA equals the complete automorphisms of decreasing polar codes that can be formulated as affine transformations.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

During the first phase of the deployment of new radio (NR) networks, base station (BS) and user equipment (UE) are expected to operate alongside the pre-existing long-term evolution (LTE) networks. This paper proposes a solution to be adopted by NR UE to perform an uplink transmission towards NR BS over the same time and frequency resources as the legacy LTE system, which would extend the uplink coverage when operating at the supplementary uplink band. In order to guarantee the absence of interference towards LTE BS, we design a new waveform termed as inverse discrete Fourier transform-based Vandermonde-subspace frequency division multiplexing (IDFT-VFDM) with the following important features. On one hand, an NR UE communicating with an NR BS through IDFT-VFDM would not generate any interference at the legacy LTE BS, regardless of the number of antennas present at both ends of the interference channel. On the other hand, an IDFT-VFDM signal can always be designed to occupy the same signal bandwidth as a legacy single-carrier frequency division multiple access signal for LTE uplink transmission. Numerical results demonstrate the merit of IDFT-VFDM and confirm its potential as a candidate solution to achieve LTE-NR co-existence in multi-antenna multi-user scenarios.

Recently convolutional polar (cpolar) codes have been proposed. A tensor-network-based successive cancellation (SC) decoding was proposed for them under which cpolar codes were shown to outperform polar codes. In this paper we present the notion of m-bit-channels for cpolar codes and give the recursive construction of m-bit-channels for m=3. Then a log likelihood ratio(LLR)-based SC decoding of complexity order O(Nlog(N)) for cpolar codes is presented. We also present the numerical results for performance evaluation of cpolar codes under SC list (SCL) decoding. Our simulation results show that cpolar codes can achieve the performance of polar codes with a list size reduced by a factor of 4.

In this work, we introduce β-expansion, a notion borrowed from number theory, as a theoretical framework to study fast construction of polar codes based on a recursive structure of universal partial order (UPO) and polarization weight (PW) algorithm. We show that polar codes can be recursively constructed from UPO by continuously solving several polynomial equations at each recursive step. From these polynomial equations, we can extract an interval for β, such that ranking the synthetic channels through a closed- form β-expansion preserves the property of nested frozen sets, which is a desired feature for low- complex construction. In an example of AWGN channels, we show that this interval for β converges to a constant close to 1.1892 when the code block-length trends to infinity. Both asymptotic analysis and simulation results validate our theoretical claims.

The indoor hotspot propagation loss has been investigated for several frequency bands from 3.5 GHz to 73 GHz. The measurement campaigns were conducted in multiple different indoor offices world-wide. Analysis of these measurements reveals a number of underlying physical phenomena affecting the propagation behavior. A "Multi-zone propagation" concept is introduced to aid classification and analysis of the millimeter wave specific propagation characteristics. The propagation zones are indicative of differing interaction of the mm-Wave signals with the details of the environment. Many very close range measurements follow a free space propagation phenomenon. For longer LOS propagation, a waveguide propagation zone can be observed. For NLOS propagation, at least two scattering propagation zones can be observed. Together, understanding the environment and the multiple modes of propagation aids the coverage analysis for system deployments.

An important issue for millimeter wave (mmWave) channel model is to introduce variant angles in order to evaluate beam-forming and beam-tracking techniques for mmWave communications. In this paper, we further study the spatial channel model with variant angles (VA-SCM) that has been proposed in [1]. Firstly, VA-SCM model is extended by introducing variant path loss (VA-VP-SCM) in order to describe the power fluctuations in a drop. Secondly, we investigate the variant angles by channel measurements in an indoor NLOS scenario which is done in a dining room in Chengdu. Thirdly, channel simulations verify that the channel characteristics of VA-SCM and VA-VP-SCM are close to SCM in the aspects of power spectrum density and probability distribution of channel coefficients. Thus, VA-SCM and VA-VP-SCM are good candidate models for mmWave channels.

The spectral efficiency enhancement for 5G air interface represents a technical challenge. In addition to the higher capacity requirement, 5G system will also address massive connectivity demand for 5G applications, such as IoT. A straightforward re-design of OFDM based LTE by using different radio parameter set cannot meet the challenges from the diverse 5G usage scenarios. In this paper, we present 5G radio access technologies by exploiting inter-block non-orthogonality and Intra-block non-orthogonality. The interblock non-orthogonality is achieved by block filtering of OFDM sub-block, and the Intra-block non-orthogonality is achieved by Sparse Code Multiple Access based on optimized sequence design. Such a framework can unify the large numbers of radio links for very diverse services and also provides increased spectral efficiency. In particular, when combined with emerging polar codes, we can further increase the spectral efficiency for smaller packets and support excessive number of connection links. Several design details for this novel air-interface will be presented.

A technique is presented to reduce the multiple-access interference in the forward link of a direct-sequence code-division multiple-access system. For each symbol period, an energy-constrained nonlinear transformation is applied at the transmitter output to minimize the mean-squared error at the receiver, subject to a constraint on the peak transmitted energy. The proposed algorithm can be implemented with existing optimization techniques that solve the quadratic trust-region problem. It is shown that in the presence of forward error-correction codes, the proposed method results in a significant gain over earlier known techniques at the cost of a modest increase in computational complexity at the base station. Another advantage of the proposed precoder is that it caps the peak energy (while earlier methods cap the average energy), requiring less sophisticated power amplifiers.

This article presents a technique to reduce multiple access interference in the forward-link of a conventional DS-CDMA system by applying an energy constrained transformation to the transmitter output. For each symbol period, a new transformation is selected which minimizes the mean-squared error at the output of a bank of matched-filter detectors. This selection is subject to a constraint on total transmitted energy. It is shown that the proposed method results in substantial advantages over earlier similar techniques. The proposed algorithm can be implemented efficiently at the transmitter with existing optimization techniques that solve the quadratic trust-region subproblem.

This paper examines the potential benefits of employing downlink feedforward transmit diversity (FF-TxD) in wireless systems which utilise adaptive modulation and coding. Instead of employing FF-TxD in such a system, the results presented suggest that multiple node B transmit antennas could better be utilised to implement a spatial processing technique which exploits parallelism in the MIMO channel, such as one of the space-time coding schemes.

A new block detection receiver is presented for noncoherent orthogonal CDMA (such as the CDMA IS-95A reverse link). This new receiver uses multiple Hadamard (or Walsh) codes for joint detection instead of using a single Walsh code as conventional receivers do. The simulation results show that the new block detection receiver is significantly better than the conventional non-block detection receivers in both additive white Gaussian noise and flat Rayleigh fading channels. A reduced-complexity algorithm for the block detection is also presented with significant complexity reduction and negligible performance degradation.

A novel signal controlled constrained notch filter (SC-CNF) structure with global feedback is presented. The SC-CNF is combined with an adaptive linear enhancer to retrieve a multitone signal corrupted by a strong nonstationary FM interference. Using a time-warping technique, the SC-CNF, by its nature time-varying, is transformed into a CNF which is time variant. Such transformation is implemented by nonequally spaced sampling (NESS) of the input mixture. A closed-loop system is proposed to improve the strategy of NESS. By exploiting the self-synchronization process, nearly complete rejection of FM interference is achieved. The condition for the occurrence of self-synchronization is obtained. The self-synchronized SC-CNF provides significant improvement (up to 30 dB) of the signal-to-interference ratio of the entire system output.>