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In next-generation networks, computing and related applications are expected to be enabled by communications. To accommodate a large number of devices and achieve better resource utilization, over-the-air (OTA) computing has been proposed as an attractive scheme to enable efficient computing. As such, OTA computing could be a candidate for computing applications that are susceptible to frequency-selective fading channels. In current communication systems, orthogonal frequency division multiplexing (OFDM) is the most common approach for dealing with such fading conditions, thus it is important to investigate whether a similar approach can be used for OTA computing. With this in mind, we model an orthogonal frequency domain (OFD) multi-user system where the users utilize OTA computing in each subcarrier. To account for practical scenarios, we study the presence of inter-carrier interference (ICI) among the subcarriers, and an optimization problem is formulated and solved to extract the optimal power allocation policy based on the statistics of the frequency error for the proposed OFD OTA computing system.

Recently, there has been a growing interest in learning-based semantic communication because it can prioritize the preservation of meaningful semantic information over the accuracy of the transmitted symbols, resulting in improved communication efficiency. However, existing learning-based approaches still face limitations in defining semantic level loss and often struggle to find a good trade-off between preserving semantic information and preserving intricate details. In addition, the existing semantic communication approaches cannot effectively train semantic encoders and decoders without the support of downstream models. To address these limitations, this paper proposes a contrastive learning (CL)-based semantic communication system. First, inspired by practical observations, we introduce the concept of semantic contrastive loss and propose a semantic contrastive coding (SemCC) approach that treats data corruption during transmission as a form of data augmentation within the CL framework. Moreover, we propose a semantic re-encoding (SemRE) operation, which uses a duplicate of the semantic encoder deployed at the receiver to guide the entire training process when the downstream model is inaccessible. Further, we design the training procedure for SemCC and SemRE approaches, respectively, to balance the semantic information and intricate details. Finally, simulations are performed to demonstrate the superiority of the proposed approaches over competing approaches. In particular, our approaches achieve a significant accuracy improvement of up to 53% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/24, and also obtain comparable image reconstruction quality as the bandwidth compression ratio is improved.

This letter investigates the coexistence between near-field (NF) and far-field (FF) communications, where multiple FF users are clustered to be served on the beams of legacy NF users, via non-orthogonal multiple access (NOMA). Three different successive interference cancellation (SIC) decoding strategies are proposed and a sum rate maximization problem is formulated to optimize the beam assignment and decoding order. The beam assignment problem is further reformulated as an overlapping coalitional game, which facilitates the design of the proposed clustering algorithm. The optimal decoding order in each cluster is also derived, which can be integrated into the proposed clustering. Simulation results demonstrate that the proposed clustering algorithm is able to significantly improve the sum rate of the considered system, and the developed strategies achieve different trade-offs between sum rate and fairness.

Semantic communications are considered a promising beyond-Shannon paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable “similarity” between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and Shannon communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrier Hybrid Semantic-Shannon communication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-Shannon transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-Shannon communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.

This paper investigates an unconventional superposition scheme, i.e., rate-splitting multiple access (RSMA) to maximize the overall user fairness and high system performance gain for ultra-reliable low-latency communication (URLLC), enhanced mobile-broadband (eMBB) traffic coexistence in uplink scenarios. In particular, we focus on maximizing the worst-case performance of uplink eMBB and URLLC users when multiplexed in a given resource block using an effective rate-splitting approach among multiple sub-messages. Subsequently, a multi-objective optimization problem (MOOP) is formulated to jointly maximize the worst-case rate and minimize the worst-case packet-error probability (PEP) for eMBB and URLLC users, respectively, using effective power splitting and successive interference cancellation (SIC) decoding of the sub-messages. To solve the non-convexity of the formulated MOOP, we adopt a priori articulation scheme combined with the weighted product approach to transforming the MOOP into a single objective optimization problem (SOOP) and later, solve it using a low complex differential evolution (DE)-based meta-heuristic algorithm. We derive an optimal decoding strategy for sub-messages to ensure better user fairness among eMBB-URLLC traffic. Numerical simulations demonstrate the superiority of the considered RSMA-based superposition for hybrid eMBB-URLLC traffic over conventional slicing and superposition techniques. Moreover, the adopted weighted product method-based DE algorithm outperforms the state-of-art solutions.

Ensuring reliable communication in satellite-terrestrial (S-T) networks, especially under composite fading conditions, is a critical challenge in advancing global connectivity. However, existing literature lacks comprehensive models for such networks, particularly under the Generalized-K ( $K_{G}$ ) fading distribution with random ground user deployment. To this end, this work introduces novel closed-form expressions for the outage performance in S-T networks using the Hybrid Automatic Repeat Request with Code Combining (HARQ-CC) protocol. Additionally, the study delves into the network’s delay-limited throughput in scenarios of both perfect and imperfect channel feedback. Finally, simulations validate the theoretical findings, demonstrating the HARQ effectiveness in varied fading conditions and feedback reliability.

In this letter, we develop a novel resource allocation scheme for energy-efficient semantic-aware wireless networks. The wireless users (WUs) extract semantic information from their raw source data during the common feature extraction (FE) phase, and then transmit their semantic features to their paired semantic decoders at the BS over the uplink channels. In random fading channels, the semantic similarity between reconstructed and source data may occasionally fall below some desired threshold resulting in a communications outage, which leads to the notion of average semantic rate. The proposed schemes minimize total energy consumption of WUs by deriving closed-form global optimal solutions for the transmit powers, processor frequencies and transmission duration of each WU, and duration of common FE phase. Compared to conventional bit-based communications, energy gain of proposed schemes increases rapidly with increase of semantic rate and average SNR.

Automatic Modulation Classification (AMC) has become an important component in communication systems for both civil and defense applications. The shortcomings of traditional approaches to AMC have led researchers to develop complex machine learning (ML)-based approaches. In this work, inspired by multi-modal approaches for general Computer Vision tasks like Semantic Segmentation, we propose MM-Net, a multimodal approach to AMC that uses domain-specific features in the form of Higher Order Cumulants (HOCs) to improve classification performance. Furthermore, we explore the usage of HOCs in existing Deep Learning (DL)-based applications for AMC. Simulation results show that for eight modulation classification, MM-Net achieves high classification accuracy even at low SNRs, demonstrating the robustness of the multimodal approach even under challenging channel conditions, while existing methods are improved by utilizing HOCs, especially at low SNR values.

Reconfigurable intelligent surfaces (RIS) can actively manipulate the wave propagation within a space, even in unconventional ways, enabling software-defined wireless propagation. Enabling real-time RIS operation requires a codebook, that is, a set of pre-calculated states of the RIS constituent elements that yield any of the supported macroscopic RIS functionalities, such as anomalous steering, splitting, and wave absorption. The codebook compilation process occurs offline, and requires the time consuming optimization of either an EM simulator, or an automated RIS measurement system. The process is especially resource-demanding in the case of RIS designs with no known analytical performance model, such as metasurfaces in the general sense. This article studies the synergy between metaheuristic optimizers and the RIS codebook compilation process. Specifically, well-known and widely efficient metaheuristic optimizers are imbued with attributes of the RIS physics, yielding considerable gains in the codebook compilation time. This process leverages correlations between microscopic configurations and macroscopic RIS responses, geometric optics, and EM simulators. The evaluation outcomes indicate a significant potential in the design of high-performing and resource-effective metaheuristics for RIS.

In this letter, the case of an aggregated VLC/RF network is presented, where the users can be served simultaneously by both a visible light communication (VLC) carrier and a radio-frequency (RF) carrier to increase their achievable data rate. The optimal resource allocation in the network is dependent on always having accurate channel state information (CSI), otherwise, in the case of outdated CSI, the system is prone to sub-optimally allocating resources and, due to the limited coverage of VLC, users may also experience outage. A closed-form expression for the coverage probability is presented for a user moving randomly in the cell. Using this, a proactive resource allocation algorithm is proposed that takes into account outdated CSI which can occur due to user movement, and ensures the outage probability is low in the network without diminishing the average achievable data rate of users. Finally, simulation results validate the proposed analysis and showcase the effectiveness of this approach.

Over-the-air computing (AirComp) has recently attracted considerable attention as an efficient method of data fusion by integrating uncoded communication transmissions with computation thanks to the signal superposition offered by the multiple access channels. However, appropriate processing is required to neutralize the wireless channel effect. As, internet-of-things (IoT) applications through low-cost devices is the main target of AirComp, perfect availability of channel state information (CSI) is not always practical, there is the need to investigate the effect of imperfect CSI on AirComp. Specifically, we present novel closed-form expressions for tight approximations that can be used to design and evaluate AirComp systems. Furthermore, we design a general optimization framework that takes into account both magnitude and phase errors in the CSI. Finally, a pilot retransmission policy is designed, that offers trade-off between resources cost and the gain in the accuracy of the computations. In order to validate its application, a utility function of the cost of retransmission is introduced, namely, Retransmission Policy Cost (RPC), which can incorporate the power or throughput cost opposing to the expected gain of the selected policy. Simulations show the deterioration caused by the imperfect CSI and highlight the added value of the proposed policy under various system conditions.

Digital twin (DT) has become a game-changing tech- nology in many smart applications, including smart cities, manufacturing, automotive, gaming, entertainment and climate resilience. DTs help push the boundaries of system reliability and are used to support a wide range of func- tions such as diagnostics and fault prediction. Keeping DT up-to-date requires communication means with low latency, high reliability, and high data security protection. The digital virtual twins of physical systems are then used to optimize performance of the system in real time, and one example for such systems is the sixth-generation (6G) wireless networks. There are many challenges in representing a physical system virtually, such as true reflection of attributes, entanglement and composability. Entanglement refers to the truly complete exchange of information between physical objects and their logical twins, while composability deals with using the ex- isting twins of different entities to enable a complete twin- based service. A typical 6G service can be deployed using either a single or multiple twin objects. Multi-tier computing enables the distributed smart devices using the signal pro- cessing and wireless communication techniques to share their idle computing and storage resources, realising the efficient utilisation of multi-tier resources. The sharing of computing, communication and caching resources in multi-tier computing systems is maturing with the continuous development of signal processing and wireless communication technology to create an intelligent interconnected world for the metaverse.

We study and analyze the performance of a wireless communication system that is based on the use of airborne Reconfigurable Intelligent Surfaces, i.e., surfaces mounted on an Unmanned Aerial Vehicle (UAV). To this end, we perform a stochastic analysis that allows us to study a very wide variety of realistic channel conditions, including environments characterized by combined small-scale and large-scale fading and potential spatial correlation in shadowing. For this generic channel model, we derive closed-form expressions for the outage probability and the ergodic capacity, and also investigate the analytical calculation of the average energy efficiency. The presented results allow us, for the first time, to study the impact of different shadowing conditions, including correlation effects, in aerial communications systems that are also supported by reconfigurable intelligent surfaces. Finally, the performance of the considered system is also compared with that of decode-and-forward relaying, highlighting the advantages of combining reconfigurable intelligent surfaces with UAV-assisted communication technologies in composite fading environments.

A primary objective of the forthcoming sixth generation (6G) of wireless networking is to support demanding applications, while ensuring energy efficiency. Programmable wireless environments (PWEs) have emerged as a promising solution, leveraging reconfigurable intelligent surfaces (RISs), to control wireless propagation and deliver exceptional quality-of-service. In this paper, we analyze the performance of a network supported by zero-energy RISs (zeRISs), which harvest energy for their operation and contribute to the realization of PWEs. Specifically, we investigate joint energy-data rate outage probability and the energy efficiency of a zeRIS-assisted communication system by employing three harvest-and-reflect (HaR) methods, i) power splitting, ii) time switching, and iii) element splitting. Furthermore, we consider two zeRIS deployment strategies, namely BS-side zeRIS and UE-side zeRIS. Simulation results validate the provided analysis and examine which HaR method performs better depending on the zeRIS placement. Finally, valuable insights and conclusions for the performance of zeRIS-assisted wireless networks are drawn from the presented results.

Attributing to the rapid growth of AI, the integration of sensing and communication (ISAC) networks has embraced AI in the upcoming new-style mobile communication networks. A FedFog network architecture for ISAC networks is proposed in this article, which consists of the terminal perception layer, the edge base station processing layer, and the cloud data layer. In the context of multiple base stations (BSs), the handover between BSs and user equipment is worthy to be studied. Referring to the concept of coordinated multiples BSs, we design a handover procedures in the ISAC networks. Meanwhile, a federated reinforcement learning scheme of user control is designed. However, due to new unlicensed spectrum bands such as millimeter wave band and Terahertz band, the hybrid beamforming can reduce the expenses of hardware. A learning-based interference management utilizing the hybrid beamforming is designed. Meanwhile, we consider self-interference and mutual interference cancellation with deep neural networks. Simulation results show the performance of AI-driven ISAC networks in terms of mobility and interference management, and further prove that services are boosted for 6G networks.

In this work, we address a novel framework for next-generation distributed radio access. In contrast with existing studies, where all remote radio heads (RRHs) in a distributed network are directly connected to a central unit (CU), an alternative architecture for advanced flexibility is proposed. In our setup, only one of the RRHs, namely the $primary$primary RRH, communicates directly with the CU, while the connectivity between the rest RRHs, namely $secondary$secondary RRHs, and the CU is achieved through the primary RRH via free-space optical links. Assuming that users exploit non-orthogonal multiple access (NOMA) for their transmissions, we introduce two successive interference cancellation (SIC) cooperation schemes, depending on the one-directional or bidirectional communication between the RRHs, as well as, a four-step centralized algorithm for efficient user-RRH association and decoding order operations is proposed. The feasibility of the suggested schemes is adequately demonstrated by deriving analytical expressions for users’ outage probabilities and providing valuable insights into the high signal-to-noise ratio regime. Furthermore, the performance of the proposed system under various weather conditions is investigated via simulation and analytical results. The comparison with a benchmark scheme, where all RRHs are directly connected to the CU and cooperate with each other via ideal links, is provided and it is revealed that although the performance of the proposed system model is weather dependent, in most of the practical cases it achieves similar performance with the ideal benchmark.

Since the unmanned aerial vehicle (UAV) network and intelligent reflecting surface (IRS) technology can flexibly change wireless network links signal, the UAV-IRS network system is a potential solution to increase the communication performance gain. Motivated by the practicality of UAV-IRS networks, a non-orthogonal multiple access (NOMA) heterogeneous UAV communication system with simultaneous wireless information and power transfer (SWIPT) is considered, which consists of multiple UAV base stations (UBSs), a macro base station (MBS), and multiple IRSs for auxiliary communications. This paper pursues a goal to receive the system energy efficiency (EE) maximization by resource allocation and reflecting design of IRSs. Due to the strong coupling among multiple parameters in the original problem, this complex non-convex problem is decomposed into three stages. In the first stage, this paper decouples the problem into two subproblems of NOMA subchannel assignment and SIC decoding order to find the optimal solution separately. For the second stage, under the constraints of UAV’s maximum transmit power, users’ quality of service (QoS) requirements, user energy harvesting threshold and cross-layer interference constraints, a beamforming design based on Lagrangian duality is exploited. For the third stage, the power splitting (PS) factors and the reflecting phases of the IRS are jointly optimized using the penalty-SDR algorithm to approximate the suboptimal solution. Finally, the simulation curves exhibit the validity and excellent performance of the co-design scheme in improving the system EE.

Network slicing via next-generation multiple access techniques and multi-access edge computing (MEC) are considered key enablers for meeting the heterogeneous quality of service requirements of the sixth-generation (6G) networks. Thus, in this work, we investigate the coexistence of further enhanced mobile broadband (feMBB) and ultra-massive machine-type communications (umMTC) devices in a quasi-isolated (QI) heterogeneous uplink MEC network, where users of both services share the same resources, interfering with each other. The feMBB users can partially offload their data to the MEC server utilizing the rate-splitting multiple access (RSMA) protocol, while the umMTC users perform only full offloading. We formulate and optimally solve the problem of maximizing the number of umMTC devices subject to data processing time and data rate constraints by adjusting both the decoding order of the users and the power splitting factor of the RSMA, while a closed-form expression for the optimal partial offloading factor of the feMBBs devices is derived. Simulation results verify that utilizing the RSMA protocol, the QI MEC network has the potential to support more umMTC devices compared to the isolated one.

The evolution of wireless networks toward the sixth generation (6G), and the inclusion of novel application-aware features characterized by sustainability requirements, call for the use of energy-efficient communication technologies, where optical wireless communications can play an important role. In the quest for sustainability, battery recharging through energy harvesting (EH) has also attracted considerable attention. A promising approach consists in combining data transmission and EH into a single scheme, namely, simultaneous lightwave information and power transfer (SLIPT), which capitalizes on the technological maturity and efficiency of optical transceivers to ensure both functionalities. In this article, we investigate SLIPT as a potential technology to help achieve the sustainability goals of 6G by discussing SLIPT transceiver architectures, optical beam propagation, and fundamental design trade-offs. We also categorize the applications of SLIPT with respect to range-from very short to long range -considering indoor, underwater, and aerial scenarios, and present its synergy with other popular technologies that are envisioned for use in 6G networks, for example, artificial intelligence, reconfigurable intelligent surfaces, and quantum communications.

Dynamic spectrum allocation for diverse future applications is anticipated to be supported by sixth-generation (6G) wireless networks. Specifically, automatic modulation classification (AMC) has been highlighted as a technique to enhance spectral utilization. However, its accuracy is influenced not only by additive white Gaussian noise and channel fading but also by phase imperfections (PI) coming from unsynchronized local oscillators and imperfect channel state information (CSI), leading to degraded classification performance. To solve this problem, we propose a convolutional neural network (CNN)-based scheme that transforms the received data to improve the classification accuracy under generalized PI conditions. Moreover, we also modify the kernel dimensions of the CNN layers to further improve the performance based on the geometry of the modulated schemes after the proposed transformation is applied to the received data. Finally, through simulations, we verified the effectiveness of the method in elevating AMC accuracy, even in intense PI conditions.

We investigate the joint optimization problem of stochastic computation offloading, content caching strategy, and dynamic resource allocation to maximize the energy efficiency of mobile edge computing in Internet-of-Things. Specifically, we propose a quantum deep reinforcement learning algorithm to exponentially increase the caching learning speed and content caching delivery efficiency in multi-dimensional continuous and large action spaces. Furthermore, we utilize the modified Grover’s algorithm with faster computation time to improve the processing efficiency and data-content retrieval for transition quantum state probabilities. The numerical results show that our proposed quantum machine learning scheme significantly outperforms other benchmarks in terms of energy-efficiency maximization subject to transmission power, energy consumption, and transmission latency.

In this letter, we propose a novel peak-to-average power ratio (PAPR)-aware adaptive modulation scheme for Orthogonal Frequency Division Multiplexing (OFDM). The aim of the proposed PAPR-aware protocol is to maximize the energy efficiency of the OFDM transmission, where the energy efficiency is defined as the ratio of the OFDM throughput to the PA consumption, and is thus strongly affected by PAPR. For this purpose, we formulate the energy efficiency maximization problem with respect to power and adaptive modulation constraints per subcarrier. Due to the joint optimization of power allocation, adaptive modulation, and the non-convex terms introduced by PAPR, the problem cannot be solved by using convex optimization tools. Therefore, a solution based on online deep learning is proposed. Numerical results showcase that the energy efficiency of the proposed PAPR-aware protocol is greater up to 3-dB compared to the conventional PAPR-unaware protocol, while the PAPR is also reduced by 3-dB.

Grant-free non-orthogonal multiple access (GF-NOMA) and multiple-input multiple-output (MIMO) techniques are key enablers for massive machine-type communications (mMTC) in 5G cellular Internet of Things. On the other hand, compressed sensing (CS) is widely accepted for multiuser detection, due to the sporadic traffic of mMTC. In this letter, we propose two Bayesian-based CS algorithms for uplink grant-free MIMO-NOMA. Exploiting the iterative expectation maximization (EM) and maximum ratio combining techniques, the spatially enhanced sparse Bayesian learning (SE-SBL) algorithm is developed to alternately update hyperparameter values and combined observation signals. Furthermore, we embed the generalized approximate message passing technique into the SE-SBL and propose a low-complexity computational approach. In particular, the aforementioned Bayesian algorithms do not require any prior knowledge of user activity level and noise power. Simulation results show that the proposed Bayesian methods exhibit a superior performance gain over the state-of-the-art.

In Type 1 diabetes, the pancreatic beta cells responsible for producing insulin are destroyed by the immune system. Insulin is needed to activate an insulin-dependent glucose transporter, which is responsible for taking glucose into the muscle cell for metabolism. Recent advances in nanotechnology, bioengineering and synthetic biology are bringing the artificial beta cell (ABC) closer to reality. In this letter, we model glucose regulation by ABCs as a cooperative molecular communication system, in which the glucose source is seen as the transmitter and the muscle as the receiver. the last absorbs the glucose in the presence of insulin, and the ABC is modeled as a a decode-and-forward relay that detects glucose molecules and releases insulin in response. Using this model, we analyze the end-to-end system performance for ABC-assisted glucose regulation by providing closed-form expressions for the probabilities of hyperglycemia and hypoglycemia and the error probability of the system. In addition, we present simulation results for quantifying performance and validation of the analysis.

This letter investigates the multiplexing of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) services in a wireless powered communication network, where a hybrid access point coordinates the wireless energy transfer (WET) to users and receives information from them. The preemptive puncturing is adopted to multiplex URLLC traffic onto eMBB transmission. Apart from the energy used for wireless information transmission (WIT), the rest energy in user’s battery is reserved to avoid insufficient energy for future WIT. The problem is formulated to jointly allocate subcarriers, time, and energy to maximize the uplink eMBB sum rate under the constraints of URLLC latency, radio frequency to direct current (RF/DC) sensitivity, user’s battery capacity, and subcarriers availability. We propose a deep reinforcement learning-based approach named mixed deep deterministic policy gradient (Mixed-DDPG), which decomposes the optimization problem into a discrete subproblem for subcarriers allocation and a continuous subproblem for time and energy allocation, and solves them alternately. Numerical results show that the proposed algorithm achieves a higher eMBB sum rate than the existing schemes.

For future networks, it is highly demanding to satisfy a wide range of time-sensitive and computation-intensive services. This is a very challenging task, since it requires a combination of aspects from information, communication and computation in order to establish a digital representation of the real network environment. This paper introduces a fairness-aware latency minimisation (FALM) framework in the digital twin (DT) aided edge computing with ultra-reliable and low latency communications (URLLC), which jointly optimises various communication and computation parameters, namely, bandwidth allocation, transmission power, task offloading portions, and processing rate of user equipments (UEs) and edge servers (ESs). The formulated problem is highly complicated, due to non-convex constraints and strong coupling among optimisation variables. To deal with this problem, we develop both centralised and distributed optimisation approaches. In particular, we first resort to successive convex approximation (SCA) method to develop a low-complexity iterative algorithm and solve the problem in a centralised manner. Combining tools from SCA and alternating direction method of multipliers (ADMM), we develop an efficient distributed solution with parallel computation processing at ESs under global consensus in each iteration and strong theoretical performance guaranteed. Numerical results are provided to validate the proposed solutions in terms of convergence speed and overall latency as well as improving fairness among all UEs.

In this paper, we investigate a multiuser cache-enabled vehicular mobile edge computing (MEC) network, where one edge server (ES) has some caching and computing capabilities to assist the task computing from the vehicular users. The introduce of caching into the MEC network significantly affects the system performance such as the latency, energy consumption and profit at the ES, which imposes a critical challenge on the system design and optimization. To solve this challenge, we firstly design the vehicular MEC network in a non-competitive environment by maximizing the profit of the ES with a predetermined threshold of user QoE, and jointly exploit the caching and computing resources in the network. We then model the optimization problem into a binary integer programming problem, and adopt the cross entropy (CE) method to obtain the effective offloading and caching decision with a low complexity. Simulation results are finally presented to verify that the proposed scheme can achieve the near optimal performance of the conventional branch and bound (BnB) scheme, while sharply reduce the computational complexity compared to the BnB.

In order to realize massive connectivity, high spectral, and energy efficiency in future internet of things (IoT) networks, reconfigurable intelligent surfaces (RISs) are considered as a promising solution with their low cost and channel-adjusting ability. However, the ever-growing massive number of devices in RIS-empowered IoT networks requires achieving energy self-sustainability for the entire network. Motivated by this, a simultaneous wireless information and power transfer system empowered by a wireless-powered RIS has been investigated under the condition of imperfect channel state information, in which both the RIS and zero-energy devices (ZEDs) harvest energy with the power-splitting (PS) scheme for their operation. Specifically, a non-convex transmit power minimizing problem is formulated to handle the power-limited issue of practical IoT networks, where the PS ratios of the ZEDs, the active beamforming of the access point and RIS reflective coefficients are jointly optimized and solved by an efficient alternating optimization framework. The improved performance of the proposed algorithm against the benchmark schemes and its robustness are verified by simulation results, which highlight the effectiveness of the proposed algorithm in practical IoT networks.

Distributed optimization is ubiquitous in emerging applications, such as robust sensor network control, smart grid management, machine learning, resource slicing, and localization. However, the extensive data exchange among local and central nodes may cause a severe communication bottleneck. To overcome this challenge, over-the-air computing (AirComp) is a promising medium access technology, which exploits the superposition property of the wireless multiple access channel (MAC) and offers significant bandwidth savings. In this work, we propose an AirComp framework for general distributed convex optimization problems. Specifically, a distributed primal-dual (DPD) subgradient method is utilized for the optimization procedure. Under general assumptions, we prove that DPD-AirComp can asymptotically achieve zero expected constraint violation. Therefore, DPD-AirComp ensures the feasibility of the original problem, despite the presence of channel fading and additive noise. Moreover, with proper power control of the users’ signals, the expected non-zero optimality gap can also be mitigated. Two practical applications of the proposed framework are presented, namely, smart grid management and wireless resource allocation. Finally, numerical results confirm DPD-AirComp’s excellent performance, while it is also shown that DPD-AirComp converges an order of magnitude faster compared to two digital orthogonal multiple access schemes, specifically, time-division multiple access (TDMA), and orthogonal frequency-division multiple access (OFDMA).

Optimizing the energy efficiency (EE) of wireless networks is one of the key priorities in the design of beyond 5G mobile technologies. In this pursuit, the use of new frequency bands, in combination with advanced multiple access protocols and cooperative communications strategies, has recently shown promising results. To this end, this paper investigates an indoor wireless network that aggregates communication resources in visible light and radio-frequency (RF) bands, taking advantage of the complementary aspects of the two technologies. More specifically, a non-orthogonal multiple access (NOMA) scheme is introduced for the visible light communication (VLC) downlink, such that cell-edge users experiencing a weak VLC signal enhance their aggregated data rate with the aid of cooperative communications over RF sidelinks (i.e., device-to-device links). The optimal resource allocation strategy over both VLC and RF bands is derived aiming at EE maximization based on the Dinkelbach’s algorithm and successive convex approximation. Additionally, for the sake of flexibility, a weighted EE metric is proposed for the characterization of the aggregated VLC/RF network performance. Simulation results are provided to validate the proposed analysis, revealing the impact of various design and system parameters, such as the weighting factors, quality of service requirements, and channel conditions.

Recently, the use of reconfigurable intelligent surfaces (RIS) has gained popularity and is emerging as a promising technique to provide improved link reliability and enhanced coverage area. In this paper, we propose an optical reflecting surface (ORS)-assisted free space optics (FSO) communication system, which is based on optical space shift keying (OSSK) technique. Specifically, the closed-form expression for probability density function (PDF) of the ORS-assisted FSO channel is derived over Malaga turbulence model. Further, we have obtained the moment generating function (MGF) of the instantaneous signal-to-noise ratio (SNR) of the overall OSSK-based multiple-input multiple-output (MIMO)-FSO system. Using the derived channel statistics, an upper bound expression for the average bit error rate (BER) and a lower bound for the ergodic capacity are derived. Further, the asymptotic BER is utilized to calculate the diversity gain of the system. Numerical results are provided to corroborate the theoretical analysis of the system, along with insightful discussions. It is observed from the numerical results that the atmospheric turbulence and pointing errors have a negligible effect on the performance of the proposed system. Finally, a trade-off is noticed with respect to the average BER performance versus the spectral efficiency of the proposed system.

In the past few years, satellite communications have greatly affected our daily lives, and the integrated terrestrial-satellite network can combine the advantages of satellite and base stations (BSs) to provide wider coverage and lower cost. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods has become a major challenge. This paper proposes a framework for resource allocation of terrestrial-satellite network based on non-orthogonal multiple access (NOMA). Then, a deployment method of local cache pools is given to achieve lower time delay and maximize energy efficiency in terrestrial-satellite network. In the proposed framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association, power control, and cache design. The MADDPG algorithm is divided into two stages, users and BSs are set as agents to complete the optimization problem in the framework. Finally, the simulation results show that the proposed method has better optimized performance compared with the traditional single-agent deep reinforcement learning algorithm and can efficiently solve the problems of resource allocation and cache design in the integrated terrestrial-satellite network.

Data collection in massive Internet of Things networks requires novel and flexible methods. Unmanned aerial vehicles (UAVs) are foreseen as a means to collect data rapidly even in remote areas without static telecommunication infrastructure. In this direction, UAV-mounted reconfigurable intelligent surfaces (RISs) aid in reducing the hardware requirements and signal processing complexity at the UAV side, while increasing the network’s energy efficiency and coverage. Hence, in this paper, we propose the utilization of a UAV-mounted RIS for data collection and study the coverage probability in such networks. Additionally, we propose a novel medium access control protocol based on slotted ALOHA and Code Combining to handle the communication of multiple sensors. To account for the crucial energy issue in UAVs, we devise an energy model that considers both the UAV and the RIS weight, as well as the environmental conditions and the UAV’s velocity. Finally, we characterize the performance of the proposed data collection scheme by analyzing the average throughput and the average collected data per flight, while providing useful insights for the design of such networks.

Many real-time application scenarios are developed in 6G communications. Driven by the low-latency data processing requirements, multi-tier computing has become an important technology to improve user experience and reduce network overhead. In this paper, we consider a multi-tier computation offloading network structure for 6G applications, in which the cloud computing center and the nearby vehicle edge server (VES) are able to partially calculate the tasks offloaded from the user equipment (UE), and the remaining task is processed locally in the UE. By jointly optimizing user scheduling, cloud offloading ratio, VES offloading ratio, and VES mobility, the objective function is to minimize the delay of the system transmission and computation under the constraints of discrete variables and energy consumption. To solve the problem, a primal-dual deep deterministic policy gradient (PD-DDPG) algorithm based on multi-tier computation offloading is proposed. Simultaneously, compared with baseline algorithms, PD-DDPG algorithm has an obvious advantage in both the speed of convergence and the system delay.

Grant-Free (GF) access has been recognized as a promising candidate for Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF access, URLLC still may not effectively gain high reliability and millimeter-level latency, simultaneously. This is because the network load is typically time-varying and not known to the base station (BS), and thus, the resource allocated for GF access cannot well adapt to variations of the network load, resulting in low resource utilization efficiency under light network load and leading to severe collisions under heavy network load. To tackle this problem, we propose a multi-tier-driven computing framework and the associated algorithms for URLLC to support users with different QoS requirements. Especially, we concentrate on $K $ - repetition GF access in light of its simplicity and well-balanced performance for practical systems. In particular, our framework consists of three tiers of computation, namely network-load learning, network-load prediction, and adaptive resource allocation. In the first tier, the BS can learn the network-load information from the states (success, collision, and idle) of random-access resources in terms of resource blocks (RB) and time slots. In the second tier, the network-load variation is effectively predicted based on estimation results from the first tier. Finally, in the third tier, by deriving and weighing the failure probabilities of different groups of users, their QoS requirements, and the predicted network loads, the BS is able to dynamically allocate sufficient resources accommodating the varying network loads. Simulation results show that our proposed approach can estimate the network load more accurately compared with the baseline schemes. Moreover, with the assistance of network-load prediction, our adaptive resource allocation offers an effective way to enhance the QoS for different URLLC services, simultaneously.

Ultra-Reliable and Low-Latency Communication (URLLC) had been initially proposed as one of the three main application scenarios in the fifth generation of mobile telecommunications systems (5G). While URLLC is expected to support vehicular networking with low latency, current 5G infrastructures still cannot well assure about one-millisecond-level delay for various time-sensitive applications in vehicular networks. To better serve vehicular networks as well as other vertical application scenarios with stringent latency requirement, URLLC remains the hotspot for beyond-5G and 6G and is expected to dig deeper in optimization paradigm together with random-access control technologies. Model-based design principles integrating machine learning (ML) intelligence have been recognized as a competitive candidate to empower URLLC quickly into reality. In this article, we first anatomize the constitution of the end-to-end delay for URLLC towards vehicular networks and concentrate on the ways of how to apply model-ML integrated intelligence to reduce major delay components, including access delay, queuing delay, and transmission delay. Facing the challenging task, we derive an intelligent multi-tier-driven computing framework for access-delay reduction. We then introduce an efficient resource allocation approach driven by multi-deep-reinforcement-learning networks to jointly lower the queuing delay and transmission delay. Finally, we share the discussions about the open issues on latency control for future URLLC.

With the exponential increase of the number of devices in the communication ecosystem toward the upcoming sixth generation (6G) of wireless networks, more enabling technologies and potential wireless architectures are necessary to fulfill the networking requirements of high throughput, massive connectivity, ultra reliability, and heterogeneous quality of service (QoS). In this work, we consider an uplink network consisting of a primary user (PU) and a secondary user (SU) and, by integrating the concept of cognitive radio and multiple access, two protocols based on rate-splitting multiple access and non-orthogonal multiple access with successive interference cancellation are investigated in terms of ergodic rate. The considered protocols aim to serve the SU in a resource block which is originally allocated solely for the PU without negatively affecting the QoS of the PU. We extract the ergodic rate of the SU considering a specific QoS for the PU for the two protocols and perform a qualitative and quantitative comparison between the two protocols. In the numerical results, we validate the theoretical analysis and illustrate the superiority of the considered protocols over two benchmark schemes.

Future wireless networks beyond the fifth generation (5G) have been envisioned to increase many folds, in terms of key performance metrics such as latency, data rate, reliability, mobility, and user's quality of experience. However, one of the biggest difference between the sixth generation (6G) and its predecessor is the ability to support high speed connectivity of mobile services for global coverage, especially focusing on under-connected areas (e.g., forests, oceans, deserts, and mountains). Integrated satellite-terrestrial networks (ISTNs) provide the best solution to unravel the unconnected issues of isolated areas not only on the ground but also in the air and on the sea. Despite this premise, the research in ISTNs is still in its infancy with many open problems, associated with long delay communications, large coverage for massive number of devices, and highly demanding real-time services. To alleviate these problems, this article introduces optimal multi-beam design empowered by machine learning techniques for 6G ISTNs, with respect to the real-time constraints of mission-critical services. First, we give an overview of the multibeam design for 6G ISTNs with user clustering approaches, energy-efficiency radio resource allocation, and distributed computing. Then, the frameworks for intelligent multibeam based on machine learning, real-time optimisation, and game theory, are proposed. As case studies, we investigate the performance of machine learning-aided real-time optimisation for 6G ISTNs in disaster relief and real-time optimal multibeam in satellite-terrestrial IoT networks. Potential research directions incorporating digital twin and quantum-inspired optimisation are presented to further support optimal multibeam design in 6G ISTNs.

To address the inefficiency in terms of resource utilization of grant-free (GF) protocols, the fast uplink (FU) grant medium-access protocol has been proposed. In this letter, we design a proactive wireless power transfer (WPT) framework for FU grant zero-energy massive machine-type communication. Specifically, zero-energy devices (ZEDs) first harvest energy during the WPT phase and then transmit data based on the FU grant protocol. Moreover, a multi-arm bandit traffic prediction scheme is adopted. Then, considering that due to the FU grant protocol ZEDs may harvest energy for several WPT rounds prior to data transmission, we extract the ZEDs’ outage probability during uplink. Based on the derived expressions, a chance-constrained optimization problem is formulated, which minimizes the energy consumption for WPT, while guaranteeing an acceptable outage probability for the ZEDs during data transmission. Simulations validate the theoretical analysis and illustrate that the proposed scheme outperforms GF access.

In this paper, we study a relay-assisted federated edge learning (FEEL) network under latency and bandwidth constraints. In this network, $N$ users collaboratively train a global model assisted by $M$ intermediate relays and one edge server. We firstly propose partial aggregation and spectrum resource multiplexing at the relays in order to improve the communication of the relay-assisted FEEL system. Furthermore, we derive analytical and asymptotic expressions of the system outage probability and convergence rate. For the purpose of improving the system performance, we further optimize the relay-assisted FEEL network by maximizing the number of users who participate in each round of federated learning, through allocation of the wireless bandwidth among users and relays. Specifically, two bandwidth allocation (BA) schemes have been proposed, assuming either instantaneous or statistical channel state information (CSI). Simulations show the advantages of the proposed BA schemes over other benchmarks, regarding the accuracy and convergence rate of the considered relay-assisted FEEL network.

In this letter, a new two-parametric pulse that outperforms state-of-the-art pulses with the best performance in terms of BER is proposed, which is studied in terms of frequency and time domain characteristics. Specifically, the pulse is based on the inverse-hyperbolic functions acsch and asech which are used for the first time for an ISI-free pulse, and its design depends only on the roll-off factor and the timing jitter parameter (i.e., two-parametric design). Finally, the proposed pulse is shown to outperform most of the well-known pulses reported in the literature, since it presents lower error probability, smaller maximum distortion and wider eye-diagram.

In this work, the integration of multi-access edge computing (MEC) with wireless power transfer (WPT) and slotted-ALOHA (SA) is investigated. All aforementioned technologies are considered key enablers for future Internet of Things networks, therefore, their interaction is of great significance. To that end, an energy efficient medium access protocol is designed, where each wireless powered device (WPD) harvests energy in order to either locally process a task or to offload it to the MEC server, by using SA. A non-convex energy minimization problem is formulated, which with the aid of proper transformations and successive convex approximation (SCA) is transformed to an equivalent convex one. Finally, simulation results are presented which aid to extract various insights for the proposed architecture.

A key service of the sixth generation (6G) of wireless networks is envisioned to be native Artificial Intelligence, which calls for radical changes to the way the nodes communicate and perform computations, as well as the role of wireless environment. For this purpose, over-the-air computing (AirComp) is a promising technique for ultra low-latency wireless data aggregation, enabled by the waveform superposition properties of a multiple access channel. In this letter, the synergy of decentralized AirComp, reconfigurable intelligent surfaces (RISs) and machine learning is proposed, to transform the wireless environment to intelligent AirComp environment (IACE), i.e., with inherent and advanced capabilities to perform computations in a fully decentralized way at the physical layer. Specifically, we minimize the AirComp error, i.e., the average mean-square errors of devices with respect to a target function, by jointly optimizing the RIS phase-shift vector and the transmission and reception scaling factors of devices. Also, to solve this challenging problem, we propose an online deep neural network (DNN) optimization approach. Finally, simulation results validate the effectiveness of IACE and the proposed DNN approach.

To compensate for severe blockages and achieve flexible and pervasive network access, UAV-mounted RISs have recently attracted research interest. In this work, a solar-powered UAV-mounted RIS (SUR) system is investigated, where solar cells are utilized to provide supplementary propulsion power. To maximize the energy efficiency of the SUR system, the optimal joint active and passive beamforming, as well as the energy-constrained UAV trajectory, i.e., its velocity and placement, are derived in closed form. Furthermore, subject to the size, weight, and power limitations of the UAV, the number of reflecting elements, the flying and hovering time of the SUR system are also optimized. Finally, simulations are provided to demonstrate the effectiveness of the proposed SUR system.

Recently, federated learning (FL) has sparked widespread attention as a promising decentralized machine learning approach which provides privacy and low delay. However, communication bottleneck still constitutes an issue, that needs to be resolved for an efficient deployment of FL over wireless networks. In this paper, we aim to minimize the total convergence time of FL, by quantizing the local model parameters prior to uplink transmission. More specifically, the convergence analysis of the FL algorithm with stochastic quantization is firstly presented, which reveals the impact of the quantization error on the convergence rate. Following that, we jointly optimize the computing and communication resources as well as the number of quantization bits, in order to guarantee minimized convergence time, subject to energy and quantization error requirements. The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed. Moreover, the proposed method is shown to result in faster convergence compared with baseline schemes. Finally, useful insights for the selection of the quantization error tolerance are provided.

Coded caching is the distribution of content across a communication system using techniques from coding theory in order to create multicasting opportunities among the users receiving the content. This enables a multiplicative improvement over the classic uncoded caching with respect to the transmission rates required in the delivery phase of the content. Since its introduction, coded caching has eliceted significant research interest as a result of which several different schemes have been proposed over the last few years. This work focuses on the fundamental case of coded caching with uncoded prefetching. In this case, the users’ caches are filled with uncoded content during a prefetching phase in order to best serve the request made by each user during a subsequent delivery phase. This important case has recently received a complete information-theoretic characterization. However, reaching the information-theoretic optimality imposes a significant computational imbalance among the users. To mitigate this imbalance, we perform a complete computational analysis of the two major forms of coded caching with uncoded prefetching, namely centralized and decentralized. Furthermore, we propose a new information-theoretically optimal method for the delivery phase that achieves a significant computational improvement compared to the state of the art.

We investigate the performance of a downlink wireless system with a multiantenna base station under the effect of limited channel feedback. In order to inactivate transmit antennas with poor channel quality and, thus, reduce the feedback overhead, a channel gain threshold-based multiantenna selection (CGT-MAS) scheme is proposed without optimization (NOCGT-MAS) and the corresponding single-antenna scheme (NOCGT-SAS) is considered as a baseline. Considering that the number of selected antennas affects both data transmission rate and the feedback overhead, an effective throughput defined as the difference between the data transmission rate and feedback rate is employed for evaluating the system performance. We conduct an effective throughput analysis for both the NOCGT-SAS and NOCGT-MAS schemes and show that an optimal channel gain threshold exists to maximize the effective throughput of the proposed NOCGT-MAS scheme. As a consequence, we propose the optimal instantaneous CGT-MAS algorithm (OICGT-MAS) to maximize the instantaneous effective throughput. Besides, we derive a closed-form expression of an average effective throughput for the CGT-MAS scheme and propose an optimal average channel gain threshold-based multiantenna selection algorithm (OACGT-MAS) for maximizing the derived average effective throughput. Numerical results demonstrate that the effective throughput of the proposed NOCGT-MAS is substantially better than that of conventional NOCGT-SAS. Moreover, although the OICGT-MAS scheme has a slight advantage over OACGT-MAS in terms of the effective throughput, it has a much higher computational complexity than the latter method.

We investigate physical layer security of a downlink cooperative non-orthogonal multiple access system with an untrusted relay. A friendly jammer (FJ) is employed in order to improve the secrecy sum rate. Aiming at maximizing the secrecy sum rate by optimizing the power allocation at both the source and the FJ, an optimization problem is formulated and solved iteratively by using alternating optimization method. Specifically, the problem is decoupled into two sub-problems, each of which is optimally solved. Moreover, the secrecy sum rate of the proposed system with imperfect channel state information is studied. Finally, simulation results validate the effectiveness of the proposed FJ based schemes.

Reconfigurable intelligent surfaces (RIS) have been presented as a solution to realize the concept of smart radio environments, wherein uninterrupted coverage and extremely high quality of service can be ensured. In this letter, assuming that multiple RIS are deployed in the propagation environment, the performance of a cascaded RIS network affected by imperfect phase estimation is evaluated. Specifically, we derive closed-form expressions for the outage probability, the ergodic capacity and the average symbol error probability that can be utilized to evaluate the coverage of the proposed network, as well as the average capacity and the data transmission accuracy. Finally, we validate the derived expressions through simulations and show that by choosing the number of the participating RIS correctly, a cascaded RIS network can outperform a single RIS-aided system and extend the network’s coverage efficiently.

This paper introduces bit-interleaved polar coded modulation with iterative detection/decoding (BIPCM-ID). In order to enable the soft output of successive cancellation list (SCL) decoding, two types of re-encoders are proposed, namely the max-re-encoder and min-re-encoder, respectively. Regarding the iterative decoding, we analytically verify that the average mutual information (AMI) between the transmitted and decoded symbols can approach the Shannon bound with the proposed schemes. Moreover, bit error rate (BER) and block error rate (BLER) in single-user and multi-user scenarios are studied. Finally, simulation results show that the performance of BIPCM-ID outperforms other bit-interleaved coded modulation (BICM) systems with LDPC and Turbo codes, while also reducing the computational complexity.