Recent breakthroughs in reconfigurable intelligent surface (RIS) technology are reshaping next-generation wireless networks. In multi-RIS deployments, however, the received signal is a superposition of numerous single- and double-reflection paths, some arrive from different angles while others overlap at the same angle, which makes channel estimation challenging. Conventional approaches attempt to separate these paths by sequentially turning each RIS on and off, but the required switching overhead grows rapidly with the number of channels and becomes impractical. To overcome this limitation, we propose a two-stage channel estimation method for multi-RIS systems. In the first stage, the received signal is projected onto angle subspaces to separate components from different directions. In the second stage, the remaining components that share the same angle are resolved by exploiting the reflection coefficient design of the RISs. Both stages operate on the same superposed received signal, enabling path separation without exhaustive switching. The method further extends beyond the two-RIS case to an arbitrary number of RISs. Simulation results demonstrate that the proposed scheme maintains estimation accuracy while reducing switching overhead, thereby providing a practical solution for large-scale multi-RIS deployments in future wireless networks.

From the perspective of joint source-channel coding (JSCC), there has been significant research on utilizing semantic communication, which inherently possesses analog characteristics, within digital device environments. However, a single-model approach that operates modulation-agnostically across various digital modulation orders has not yet been established. This article presents the first attempt at such an approach by proposing a universal joint source-channel coding (uJSCC) system that utilizes a single-model encoder-decoder pair and trained vector quantization (VQ) codebooks. To support various modulation orders within a single model, the operation of every neural network (NN)-based module in the uJSCC system requires the selection of modulation orders according to signal-to-noise ratio (SNR) boundaries. To address the challenge of unequal output statistics from shared parameters across NN layers, we integrate multiple batch normalization (BN) layers, selected based on modulation order, after each NN layer. This integration occurs with minimal impact on the overall model size. Through a comprehensive series of experiments, we validate that the modulation-agnostic semantic communication framework demonstrates superiority over existing digital semantic communication approaches in terms of model complexity, communication efficiency, and task effectiveness.

This paper addresses the intricate task of hybrid-field channel estimation in extremely large-scale MIMO (XL-MIMO) systems, critical for the progression of 6G communications. Within these systems, comprising a line-of-sight (LoS) channel component alongside far-field and near-field scattering channel components, our objective is to tackle the channel estimation challenge. We encounter two central hurdles for ensuring dependable sparse channel recovery: the design of pilot signals and channel estimators tailored for hybrid-field communications. To overcome the first challenge, we propose a method to derive optimal pilot signals, aimed at minimizing the mutual coherence of the sensing matrix within the context of compressive sensing (CS) problems. These optimal signals are derived using the alternating direction method of multipliers (ADMM), ensuring robust performance in sparse channel recovery. Additionally, leveraging the acquired optimal pilot signal, we introduce a two-stage channel estimation approach that sequentially estimates the LoS channel component and the hybrid-field scattering channel components. Simulation results attest to the superiority of our co-designed approach for pilot signal and channel estimation over conventional CS-based methods, providing more reliable sparse channel recovery in practical scenarios.

This paper introduces a novel privacy-enhanced over-the-air Federated Learning (OTA-FL) framework using client-driven power balancing (CDPB) to address privacy concerns in OTA-FL systems. In recent studies, a server determines the power balancing based on the continuous transmission of channel state information (CSI) from each client. Furthermore, they concentrate on fulfilling privacy requirements in every global iteration, which can heighten the risk of privacy exposure as the learning process extends. To mitigate these risks, we propose two CDPB strategies—CDPB-n (noisy) and CDPB-i (idle)—allowing clients to adjust transmission power independently, without sharing CSI. CDPB-n transmits noise during poor conditions, while CDPB-i pauses transmission until conditions improve. To further enhance privacy and learning efficiency, we show a mixed strategy, CDPB-mixed, which combines CDPB-n and CDPB-i. Our experimental results show that CDPB outperforms traditional approaches in terms of model accuracy and privacy guarantees providing a practical solution for enhancing OTA-FL in resource-constrained environments.

In this study, we delve into the intricacies of semantic communication-control codesign (CoCoCo) for wireless mixed logical dynamical (MLD) systems operating under signal temporal logic (STL) specifications. Our novel contribution, the MLD-Koopman autoencoder (AE), emerges as a method to linearize the progression of system states within a feature space. This linearization effectively mitigates the communication and computation costs associated with MLD system control. To surmount the challenges posed by multiple correlated MLD systems that possess distinct logical control rules while sharing baseline dynamics, we present the compositional logical dynamical (CLD)-Koopman AE as a remedy to the scalability limitations of the MLD-Koopman AE. This innovative approach incorporates two pivotal models—the dynamics semantic Koopman (DSK) model, capturing semantic correlations among MLD systems, and the logical semantic Koopman (LSK) model, encoding logical control rules. These models portray the linear evolution of baseline dynamics and control rules within a feature space, facilitating predictions of future states for multiple MLD systems with constrained communication. Validation comes from simulations on large-scale inverted cart-pole systems, demonstrating the prowess of the CLD-Koopman AE in achieving an average state prediction performance 82.77% higher than other predictive benchmarks, particularly evident at a signal-to-noise ratio (SNR) of 10 dB.

This work deals with a heterogeneous semantics-native communication (SNC) problem. When agents do not share the same communication context, the effectiveness of contextual reasoning (CR) is compromised calling for agents to infer other agents’ context before communication. This article proposes a novel framework for solving the inverse problem of CR in SNC using two Bayesian inference methods, namely: Bayesian inverse CR (iCR) and Bayesian inverse linearized CR (iLCR). The first proposed Bayesian iCR method utilizes Markov Chain Monte Carlo (MCMC) sampling to infer the agent’s context while being computationally expensive. To address this issue, a Bayesian iLCR method is leveraged which obtains a linearized CR (LCR) model by training a linear neural network. Experimental results show that the Bayesian iLCR method requires less computation and achieves higher inference accuracy compared to Bayesian iCR. Additionally, heterogeneous SNC based on the context obtained through the Bayesian iLCR method shows better communication effectiveness than that of Bayesian iCR. Overall, this work provides valuable insights and methods to improve the effectiveness of SNC in situations where agents have different contexts.

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LEO SAT networks exhibit extremely long link distances of many users under time-varying SAT network topology. This makes existing multiple access protocols, such as random access channel (RACH) based cellular protocol designed for fixed terrestrial network topology, ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH). In stark contrast to existing model-based and standardized protocols, eRACH is a model-free approach that emerges through interaction with the non-stationary network environment, using multi-agent deep reinforcement learning (MADRL). Furthermore, by exploiting known SAT orbiting patterns, eRACH does not require central coordination or additional communication across users, while training convergence is stabilized through the regular orbiting patterns. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput with around two times lower average access delay while achieving 0.989 Jain’s fairness index.

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. However, such wide coverage rather makes it difficult to apply existing multiple access protocols, such as random access channel (RACH). To overcome this issue, in this paper, we propose a novel random access solution for LEO SAT networks, called as S-RACH. In contrast to existing standardized protocols, S-RACH is a model-free approach using deep reinforcement learning (DRL). Compared to RACH, we show from various simulations that our proposed S-RACH yields around 2x lower average access delay.

Real-time remote control over wireless is an important yet challenging application in fifth-generation and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultrareliable and low-latency communication (URLLC) links but also predicting future states, which may consume enormous communication resources and struggle with a short prediction time horizon. To fill this void, in this article we propose a novel two-way Koopman autoencoder (AE) approach wherein: 1) a sensing Koopman AE learns to understand the temporal state dynamics and predicts missing packets from a sensor to its remote controller and 2) a controlling Koopman AE learns to understand the temporal action dynamics and predicts missing packets from the controller to an actuator co-located with the sensor. Specifically, each Koopman AE aims to learn the Koopman operator in the hidden layers while the encoder of the AE aims to project the nonlinear dynamics onto a lifted subspace, which is reverted into the original nonlinear dynamics by the decoder of the AE. The Koopman operator describes the linearized temporal dynamics, enabling long-term future prediction and coping with missing packets and closed-form optimal control in the lifted subspace. Simulation results corroborate that the proposed approach achieves a$38\times $lower mean squared control error at 0-dBm signal-to-noise ratio (SNR) than the nonpredictive baseline.

Due to the rapid paradigm shift in Internet of Things networks from wired and centralized to flexible wireless and decentralized networks, building effective and reliable distributed consensus mechanisms over wireless is becoming essential. Especially, since the performance of consensus over communication endpoints in a large-scale wireless network is limited by their communication capability, it requires a careful co-design of communication and consensus to attain a fast and scalable distributed wireless consensus mechanism with high resiliency against faulty nodes. Within this context, this article addresses such problem by designing two wireless consensus mechanisms that well-suit in large-scale wireless networks. On the one hand, as a reinterpretation of the conventional referendum consensus (RC) in a large-scale wireless network, gossip-broadcasting-based RC (GB-RC) is proposed. On the other hand, to overcome the scalability issue of the GB-RC, cooperative-broadcast-based electoral-college consensus (CB-EC) is proposed. By mathematically analyzing the performance of both of the consensus mechanisms, in terms of consensus latency and resiliency against the faulty nodes, we show that the GB-RC outperforms the conventional RC, while the CB-EC significantly reduces the consensus latency compromising the stochastic resiliency. We further evaluate their performance numerically to show their effectiveness and feasibility under realistic large-scale wireless environments.

Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension but also learns the system dynamics by lifting it via a Koopman operator, thereby allowing the observer to locally predict future states after training convergence. Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.

Designing distributed, fast, and reliable wireless consensus protocols is instrumental in enabling mission-critical decentralized systems, such as robotic networks in the Industrial Internet of Things (IIoT), drone swarms in rescue missions, and so forth. However, chasing both low-latency and reliability of consensus protocols is a challenging task. The problem is aggravated under wireless connectivity that may be slower and less reliable, compared to wired connections. To tackle this issue, we investigate fundamental relationships between consensus latency and reliability through the lens of wireless connectivity, and co-design communication and consensus protocols for low-latency and reliable decentralized systems. Specifically, we propose a novel communication-efficient distributed consensus protocol, termed random representative consensus (R2C), and show its effectiveness under gossip and broadcast communication protocols. To this end, we derive a closed-form end-to-end (E2E) latency expression of the R2C that guarantees target reliability, and compare it with a baseline consensus protocol, referred to as referendum consensus (RC). The result shows that the R2C is faster compared to the RC and more reliable compared when co-designed with the broadcast protocol compared to that with the gossip protocol.

The fundamental limits of private information retrieval (PIR) with unknown cache prefetching at the user are investigated in this paper. To this end, a novel random linear combination (RLC)-based PIR scheme that can solve the basic PIR problem and its variation is proposed. The proposed scheme is based on random coding approach and achieves the capacity of the basic PIR asymptotically. Then, we investigate PIR with unknown cache prefetching (PIRC) problem at different cache-to-file size ratio. Specifically, we propose RLC-based PIRC method, which prefetches RLC-based side information and leverages them to retrieve desired information at small download cost. Furthermore, by applying time and memory sharing on the proposed RLC-based PIRC, RLC-based basic PIR and some other known approach in literature, we derive the achievable normalized download cost bound of PIRC. The derived achievable bound outperforms the existing bound in literature and the case study provides numerical results that verifies it.

This paper studies a receive beamwidth controlling method in vehicle-to-infrastructure (V2I) wireless communication system using millimeter wave (mm-wave) band. We use a triangular beam pattern to model and characterize a mm-wave receive beam pattern. First of all, channel coherence time for line-of-sight (LoS) downlink transmission is derived under the given vehicular scenario. Then, we derive an attainable data rate for the time varying vehicular channel, by supposing that the beam is realigned whenever the channel coherence time is elapsed. In addition, the optimal receive beamwidth, which achieves the maximum point of the derived attainable data rate, is obtained. The effectiveness and feasibility of the proposed receive beamwidth controlling method is underpinned by both analytic and numerical simulation results. The results are also compared with a uniform linear array (ULA) beam pattern model and show that the triangular beam pattern model can well characterize the practical antenna model.

In this paper, we propose a resource management method based on deep learning, which controls both the transmit power and the power splitting ratio to maximize the sum rate with low computational complexity in D2D networks with energy harvesting requirements. The introduction of the energy harvesting requirements to D2D networks makes it hard to design an effective resource management solution since the treatment of interference signals should be completely different from the conventional resource management focusing only on the rate maximization. To deal with drawbacks of the conventional deep learning-based approach, we propose a new training algorithm suitable for our resource management problem. Numerical simulations show that the proposed learning-based method outperforms the benchmark methods, which are derived from some relevant works, in most situations and achieves performances comparable to an exhaustive search in terms of the sum rate and energy outage probability. Although the conventional optimization-based method is derived to achieve the asymptotic optimal performance for a large network, the proposed deep learning method is shown to achieve almost the same performance with much lower computational complexity. Furthermore, simulation results offer new insights to the impact of the energy harvesting requirements on the behaviour of the optimal resource management.

In Compressed Sensing based Random Access CHannel (CS-RACH) protocol, a base station leverages compressed sensing technique to detect the active users in the cell coverage and estimate the channel gain between the users and the base station. In a real communication scenario, activity of a specific user usually varies time to time and thus can be seen as a random variable following ON/OFF distribution. Meanwhile, the performance of compressed sensing technique is dependent on the sparsity of the estimating vector, which is closely related to the user activity in CS-RACH scenario. In this perspective, we analyze the condition of the user activity for the stable operation of the protocol. Particularly, we use the least absolute shrinkage and selection operator (LASSO) approach, which gives a closed form expressions of the sparsity condition for the successful active user detection in an asymptotic manner. As a result, we obtain the condition of the user activity for stable operation of CS-RACH and verify the result with numerical simulations.

This paper proposes a compressed sensing-based random access protocol (CS-RACH), which is suitable for servicing a large number of machine-type communication devices in Internet of Things (IoT) network. In CS-RACH, we utilize a larger number of unique preambles compared to conventional LTE-RACH, however, the compressed sensing technique makes it possible to simultaneously detect the users with high accuracy. Compared to the user detection in conventional LTE-RACH, the proposed user detection can get rid of preamble collisions and decrease the collision probability, thereby the overall access latency is significantly reduced. To prove the benefits of the proposed CS-RACH, we mathematically analyze and compare access latency performance of LTE-RACH and CS-RACH. In particular, based on the least absolute shrinkage and selection operator approach, we derive a normalized throughput, access success probability, and average access latency. Our simulation results also exhibit that the proposed CS-RACH considerably reduces the access latency under reasonable conditions in IoT environments.

We propose a novel scheme that achieves the capacity of the PIR, which is the maximum number of bits of the desired messages that can be privately retrieved by a single bit information download from the databases. The proposed scheme is based on the stochastic approach, where the queries are generated randomly and the downloading information is the random linear combinations of the message bits. Consequently, we claim that one-shot query generation, based on the stochastic approach, can guarantee the privacy of the user and the correctness of the retrieved information while achieving the capacity, when the number of answering bits are sufficiently large.