The rapid expansion of the Internet of Things (IoT) demands effective communication protocols that accommodate mobile and static end devices (EDs). LoRaWAN (Long Range Wide Area Network), a pioneering low-power wide-area network (LPWAN) technology, uses Adaptive Data Rate (ADR) approaches to optimize resource allocation, particularly for static EDs. However, traditional ADR approaches are ineffective in mobile contexts as they struggle to adapt to changing network conditions, resulting in significant packet loss and higher retransmission rates. Although innovative technologies, such as the Blind ADR (BADR), have been devised to improve the performance of mobile EDs, they still fall short of dealing with the unpredictable nature of mobile EDs. To address these challenges, this paper presents a novel HybridQ-ADR mechanism suitable for static and mobile EDs. This approach addresses the constraints of BADR and related methods in mobile scenarios. In particular, it provides a more efficient solution to reduce packet loss and collisions in dense and dynamic LoRa-based IoT networks. This is achieved by allocating Spreading Factors (SF) using signal orthogonality to minimize interference and provide reliable communication. Furthermore, the proposed HybridQ-ADR mechanism provides a new clustering technique based on estimated path loss. Specifically, it divides EDs into clusters and assigns different channels to each cluster, improving SF allocation and data transmission speeds. The proposed HybridQ-ADR mechanism includes a mobility-aware, Doppler-constrained SF allocation strategy, limiting each ED’s maximum SF based on its Doppler/mobility load to maintain reliable performance at high speeds. Performance evaluations using simulations and testbed implementations show that HybridQ-ADR improves latency, packet success rate, power consumption, and throughput for both static and mobile EDs.

This paper proposes a quantum-enabled gradient-based coverage optimization (QEGCO) framework for dynamic antenna configuration in future wireless networks. The optimization task of adjusting antenna azimuth and tilt to maximize soft coverage is formulated using a differentiable surrogate objective, enabling the application of gradient-based methods. A parameterized quantum circuit (PQC) encodes the control variables, and the parameter-shift rule is employed to compute exact gradients efficiently, independent of spatial sampling resolution. Entanglement is introduced via CNOT gates to enhance circuit expressivity and model interactions between antennas. Simulation results demonstrate that QEGCO achieves faster convergence, higher final coverage ratios, and superior computational scalability compared to classical stochastic gradient descent (SGD) and finite gradient descent (FGD) baselines. Complexity analysis highlights that QEGCO reduces computational complexity from ${\mathcal{O}}(2\cdot a\cdot T)$ in classical methods to ${\mathcal{O}}(2\cdot a\cdot\log (T))$, where a is the number of antennas, and T is the total time required for computation of gradients and coverage metrics. These findings illustrate the potential of quantum-assisted optimization techniques for scalable and efficient wireless network self-organization. Future directions include extending QEGCO to cooperative multi-cell networks and implementing hardware-efficient quantum circuits for near-term quantum devices.

This article proposes a holographic active reconfigurable intelligent surface (HARIS)-aided near-field (NF)-driven uplink non-orthogonal multiple access (NOMA) secure communication system under imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). To ensure efficient resource utilization, a sum secrecy rate (SSR) maximization problem is formulated by jointly optimizing the combined vector at the base station (BS), the power allocation for each uplink user, and the HARIS phase profile under strict quality of service (QoS) requirements and limited power budgets at both the uplink users and HARIS. Due to the non-convex nature of the problem, an alternating optimization (AO)-based algorithm is proposed, which uses semidefinite programming (SDP), convex upper bound approximation, and semidefinite relaxation (SDR) techniques to optimize all variables iteratively. Extensive simulations validate the performance and convergence of the proposed algorithm. In addition, the effects of system parameters, such as the number of HARIS elements, minimum QoS per user, maximum BS receive power, and the HARIS amplification factor, are investigated.

This paper investigates the effectiveness of employing multiple reconfigurable intelligent surfaces (RIS) for simultaneous wireless information and power transfer (SWIPT) in a vehicle-to-infrastructure (V2I) system. The optimal RIS is selected for transmission based on instantaneous signal-to-noise ratio (SNR) values, with the objective of optimizing the SWIPT system employing the power-splitting (PS) protocol and nonlinear energy harvesting (NL-EH). A unified objective is proposed to maximize information rate and harvested energy via joint optimization of transmit power and power splitting factor. Nonconvexity is addressed via an iterative algorithm, supported by closed-form expressions obtained through Karush-Kuhn-Tucker (KKT) conditions. Monte-Carlo simulations are performed to validate the accuracy of the analytical expressions. Additionally, a deep neural network (DNN) framework is introduced for realtime optimization prediction, achieving superior SWIPT performance over single RIS configurations with reduced complexity and faster execution.

This article investigates a near-field secure simultaneous wireless information and power transfer (SWIPT) network employing a non-orthogonal multiple access (NOMA) scheme. In this network, a base station equipped with an extremely largescale array antenna (ELAA) communicates with and transfers power to multiple single-antenna zero-energy devices (ZEDs) within the near-field region, while an eavesdropper attempts to wiretap the communication by intercepting the information signals. Specifically, we aim to maximize the overall secrecy sum rate of the ZEDs while ensuring a minimum energy harvesting criterion at the ZEDs. Consequently, a non-convex resource optimization problem is formulated and solved using an iterative approach, leveraging weighted sum-rate maximization via minorization-maximization (WSR-MM) with second-order cone programming (SOCP) transformation and general convex approximations. Finally, numerical results are presented to demonstrate the performance of the proposed near-field secure SWIPT system under varying network parameters.

Efficient resource allocation is essential for optimizing various tasks in wireless networks, which are usually formulated as generalized assignment problems (GAP). GAP, as a generalized version of the linear sum assignment problem, involves both equality and inequality constraints that add computational challenges. In this work, we present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address GAP in vehicular networks (VNets). Our approach leverages a hybrid quantum-classical structure, integrating a tailored cost function that balances both objective and constraint-specific penalties to improve solution quality and stability. Using the CVaR-VQE model, we handle the GAP efficiently by focusing optimization on the lower tail of the solution space, enhancing both convergence and resilience on noisy intermediate-scale quantum (NISQ) devices. We apply this framework to a user-association problem in VNets, where our method achieves 23.5% improvement compared to the deep neural network (DNN) approach.

With the rapid expansion of internet-of-things (IoT) networks, ensuring efficient data collection has become a key challenge, especially in remote and hard-to-reach areas. Unmanned aerial vehicles (UAVs) offer a flexible solution for IoT networks. However, UAV communications face challenges such as signal attenuation, interference, and line-of-sight (LoS) constraints. To address these limitations, reconfigurable intelligent surfaces (RISs) are proposed as a promising solution to enhance UAV communications. In this paper, we propose a novel federated learning (FL)-based framework for optimising RIS-assisted UAV networks. Our approach integrates deep reinforcement learning (DRL) for UAV trajectory planning and IoT device scheduling while leveraging FL to train UAV models collaboratively without sharing raw data. Additionally, a block coordinate descent (BCD) algorithm is employed to optimise RIS phase shifts. This framework enhances communication reliability, energy efficiency, and scalability, making UAV-assisted IoT networks more adaptive to dynamic environments.

As Beyond 5thGeneration (B5G) networks expand, securing communication links remains a challenge. Physical Layer Security (PLS) is a promising paradigm, and this work explores wireless channel-based Secret Key Generation (SKG) by proposing an Extended Huffman Coding (EHC) framework to enhance quantization stage. While non-uniform coding is common in Analog-to-Digital (A2D) conversion, it remains largely unexplored in SKG due to key mismatch risks. To address this, we propose a Huffman Coding (HC)-inspired framework that generates binary codes using reversed HC principles and extends them by sequentially altering the Least Significant Bit (LSB), ensuring equal-length unique codes for quantization levels while enhancing generation rate, agreement, and randomness. Additionally, histogram/ Probability Density Function (PDF) matching aligns quantization level probabilities at legitimate nodes to minimize mismatches while exchanging minimal statistical information without compromising secrecy. Unlike conventional quantization, which relies on predetermined codes vulnerable to eavesdropping, our scheme dynamically computes quantization codes at runtime, adding an extra layer of security. Moreover, while conventional HC avoids codes of a level to be a subcode of another level, SKG benefits when one code is a subcode of another, making detection harder. Instead of compression, as desired in conventional A2D conversion, SKG requires longer and random keys that reliably match at legitimate nodes, which our scheme ensures. Simulations are conducted under Rice-fading conditions that evaluate the impact of variations in the Rician K-factor on SKG performance. The proposed method is assessed using key SKG metrics named: Key Agreement Rate (KAR), Key Generation Rate (KGR), and Key Randomness Rate (KRR) (employing the NIST test suite). The obtained results demonstrate the potential of our proposed scheme for secure and scalable SKG solutions in future networks.

Smart wireless communications enabled by reconfigurable intelligent surfaces (RISs) have gained significant research interest in the areas of localization and sensing over the past few years. This paper investigates an unconventional approach for 3D direction-of-arrival (DoA) estimation of multiple aerial user targets using an RIS-based communication architecture. In particular, the measurements required for DOA estimation at the receivers are optimized through a deep reinforcement learning framework. The core of the proposed method lies in formulating the DoA estimation problem as a Markov decision process (MDP), which is optimized via a proximal policy optimization algorithm for its optimization. Considering a practical RIS setup with 2-bit states at each unit element, we demonstrate significant improvements in DoA estimation accuracy, in terms of reduced root mean squared error (RMSE) for various simulation scenarios of the system.

In recent years, space–air–ground integrated networks (SAGIN) have attracted considerable attention for their potential to support various applications in future 6G systems. However, the diversity of tasks generated by different network participants and the heterogeneous distribution of resources in SAGIN pose significant challenges in matching task demands with available resources. To tackle this issue, we propose a multiagent deep deterministic policy gradient (MADDPG) algorithm with centralized training and decentralized execution (CTDE) that jointly addresses offloading decisions and heterogeneous resource allocation. Tasks are categorized into three distinct types with task priorities considered. Low Earth orbit (LEO) satellites equipped with edge computing capabilities are modeled as agents. Each agent makes decisions regarding heterogeneous resource allocation and offloading based on its own observations, while global action and state information are used to update deep neural networks. The CTDE-MADDPG algorithm enables low-latency distributed decision making in complex networks without the need for time-consuming state synchronization and centralized decision making. The simulation results demonstrate that the proposed scheme effectively minimizes energy consumption while ensuring tasks are completed within latency and resource constraints, achieving superior performance compared to the baseline algorithms.

This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and by optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.

To meet rising data rate demands, cellular systems are expected to evolve towards higher carrier frequencies and larger antenna arrays, but conventional phased arrays face challenges in supporting such a prospection due to their excessive power consumption induced by numerous phase shifters required. Reconfigurable Refractive Surface (RRS) is an energy efficient solution to address this issue without relying on phase shifters. However, the increased radiation aperture size extends the range of the Fresnel region, leading the users to lie in the near-field zone. Moreover, given the wideband communications in higher frequency bands, we cannot ignore the frequency selectivity of the RRS. These two effects collectively exacerbate the beam split issue, where different frequency components fail to converge on the user simultaneously, and finally result in a degradation of the data rate. In this paper, we investigate a RRS-based wideband near-field multi-user communication system. Unlike most existing studies on wideband communications, which consider the beam split effect only with the near-field condition, we study the beam split effect under the influence of both the near-field condition and the frequency selectivity of the RRS. To mitigate the beam split effect, we propose a Delayed-RRS structure, based on which a beamforming scheme is proposed to optimize the user’s data rate. Through theoretical analysis and simulation results, we analyze the influence of the RRS’s frequency selectivity, demonstrate the effectiveness of the proposed beamforming scheme, and reveal the importance of jointly considering the near-field condition and the frequency selectivity of RRS.

Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may be distributed in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead.

This paper addresses the challenge of balancing conflicting goals, namely data rate and energy harvesting (EH) in Simultaneous Wireless Information and Power Transfer (SWIPT) systems, while incorporating Reconfigurable Intelligent Surface (RIS) technology. We formulate a weighted optimization objective to address this issue, seeking to simultaneously maximize data rate, EH, and minimize transmit power utilization. The proposed approach involves optimizing time switching (TS) ratios and transmit power using a practical phase-dependent amplitude model for each RIS element’s reflectivity. To address this complex optimization problem involving ratio of concave-convex problem, the paper introduces fractional programming-based modified Dinkelbach Algorithm providing upper and lower bounds, which are then compared with Quadratic transform-related algorithms and solutions based on Karush-Kuhn-Tucker (KKT) conditions. Numerical findings highlight the effectiveness of the proposed algorithms in enhancing the overall performance of SWIPT systems with RIS technology.

In reconfigurable intelligent surface (RIS) aided communication implementing space-shift keying at the transmitter for index modulation, the RIS is proposed to perform the dual function of selecting the phases of the reflecting elements optimally according to the phases of the channel gains, and acting as a modulator by transmitting$M$-ary phase-shift keying (PSK) modulated symbols via reflection phase modulation. The target antenna at the receiver is chosen by utilizing the optimal maximum likelihood (ML) detection rule and a hardware- and energy-efficient greedy detector, which performs detection based on the maximum received energy. For these detection scenarios, closed-form expressions for the probability of erroneous detection (PED) of the target antenna are derived, considering Nakagami-$m$channel fading. Numerical results supporting the analytical framework show the role of the rotation of the$M$-PSKconstellation to improve the reliability of the system. In particular,$\pi/M{-}$rotated$M$-PSK constellation is optimal for the greedy detector, whereas the ML detector is not dependent on such rotation to yield minimum PED.

Backscatter communication and non-orthogonal multiple access (NOMA) represent emerging paradigms in wireless communication, offering promising solutions to boost spectral and energy efficiency for energy-constrained devices in 6G networks. This work introduces a tailored symbiotic radio system designed for Internet of Things (IoT) devices, proposing an innovative framework that integrates backscatter-based IoT devices into power-domain NOMA networks. In this system, the primary base station employs NOMA principles to serve both far and near users simultaneously, while IoT devices equipped with backscattering technology transmit their data over the primary signal. The IoT transmitter not only serves the IoT receiver but also enhances the performance of the far user. The paper thoroughly examines outage probabilities for both NOMA and IoT networks. It delves explicitly into the outage probabilities of IoT receivers, considering both perfect and imperfect direct link interference cancellation. The results demonstrate significant performance improvements over traditional orthogonal multiple-access methods. Furthermore, Monte Carlo simulations are employed to validate the accuracy of the theoretical analysis.

Unmanned aerial vehicle (UAV)-assisted communication is becoming a streamlined technology in providing improved coverage to the internet-of-things (IoT) based devices. Rapid deployment, portability, and flexibility are some of the fundamental characteristics of UAVs, which make them ideal for effectively managing emergency-based IoT applications. This paper studies a UAV-assisted wireless IoT network relying on non-orthogonal multiple access (NOMA) to facilitate uplink connectivity for devices spread over a disaster region. The UAV setup is capable of relaying the information to the cellular base station (BS) using decode and forward relay protocol. By jointly utilizing the concepts of unsupervised machine learning (ML) and solving the resulting non-convex problem, we can maximize the total energy efficiency (EE) of IoT devices spread over a disaster region. Our proposed approach uses a combination of k-medoids and Silhouette analysis to perform resource allocation, whereas, power optimization is performed using iterative methods. In comparison to the exhaustive search method, our proposed scheme solves the EE maximization problem with much lower complexity and at the same time improves the overall energy consumption of the IoT devices. Moreover, in comparison to a modified version of greedy algorithm, our proposed approach improves the total EE of the system by $19 \%$ for a fixed 50 k target number of bits.

In this work, we investigate the performance of an active reconfigurable intelligent surface (RIS)-aided multi-user uplink secure private industrial network. With an aim to provide a more sophisticated and consolidated framework towards the robust transmission design, we formulate a sum secrecy rate maximization while ensuring a minimum performance at each user within available resource constraints considering the norm-bounded imperfect channel state information (CSI) at Eavesdropper (Eve). To tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the equalizer, beamforming at the RIS, and power allocation at each user. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of active RIS, compared to passive RIS, towards a robust uplink secure private network is demonstrated. Finally, we discuss the impact of key parameters such as maximum power budget at each user and RIS, and CSI error on the secrecy performance of the considered network.

Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.

Index modulation (IM) schemes for reconfigurable intelligent surfaces (RIS)-assisted systems are envisioned as promising technologies for fifth-generation-advanced and sixth-generation (6G) wireless communication systems to enhance various system capabilities such as coverage area and network capacity. In this paper, we consider a receive diversity RIS-assisted wireless communication system employing IM schemes, namely, space-shift keying (SSK) with binary modulation and spatial modulation (SM) with M-ary modulation for data transmission. The RIS lies in close proximity to the transmitter, and the transmitted data is subjected to a fading environment with a prominent line-of-sight component statistically modeled by a Rician distribution. A receiver structure based on a greedy detection rule is employed to select the receive diversity branch with the highest received signal energy for demodulation. The performance of the considered system is evaluated by obtaining a series-form expression for the probability of erroneous index detection (PED) of the considered target antenna using a characteristic function approach. In addition, closed-form and asymptotic expressions at high and low signal-to-noise ratios (SNRs) for the bit error rate (BER) of the SSK-based system, and the SM-based system employing M-ary phase-shift keying and M-ary quadrature amplitude modulation schemes are derived. The dependencies of the system performance on various parameters are corroborated via numerical results. The asymptotic expressions and results of PED and BER at high and low SNR values lead to the observation of a performance saturation and the presence of an SNR value as a point of inflection, which is attributed to the greedy detector.

Joint radar-communications (JRC) is considered to be a vital technology in deploying the next generation systems, since its useful in decongestion of the radio frequency (RF) spectrum and utilising the same hardware resources for dual functions. Using JRC systems for dual function generates interference between both the operations which needs to be addressed in future standardization. Furthermore, JRC systems can be advanced by deploying hybrid beamforming which implements fewer number of RF chains than the number of transmit antennas. This paper designs a robust hybrid beamformer for minimizing the interference of a JRC transmitter via RF chain selection resulting into mutual information maximization. We consider a weighted mutual information for the dual function JRC system and implement a common analog beamformer for both the operations. The mutual information maximization problem is formulated which is non-convex and difficult to solve. The problem is simplified to convex form and solved using Dinkelbach approximation abased fractional programming. The performance of the optimal RF selection based proposed approach is evaluated, compared with baselines and its effectiveness is inferred via numerical results.

Reconfigurable intelligent surfaces (RISs) play a crucial role in improving the coverage and efficiency of millimeter-wave (mmWave) communication systems for Internet of Things (IoT) networks by enhancing signal strength and reducing interference. However, to fully exploit their potential, mathematical models and optimization algorithms are needed to optimize the RIS configuration and control. In this work, we present a mathematical model and optimization algorithm for RIS-aided mmWave communication systems. This paper proposes a mathematical model to capture the effects of RISs on mmWave communication channels, including equations for channel estimation and prediction. We also develop an opti-mization problem to maximize the system throughput, along with a model for optimizing the RIS phase shift using the alternating projection phase shift algorithm. The power allocation algorithm for the mmWave base station is presented using the Karush-Kuhn-Tucker (KKT) approach. Finally, we perform simulations to validate the proposed solution and compare it with ideal and random phase shift solutions. The results show that our proposed solution performs close to the ideal solution, demonstrating the effectiveness of the proposed mathematical model and optimization algorithm.

Joint radar-communications (JRC) benefits from multi-functionality of radar and communication operations using same hardware and radio frequency (RF) spectrum resources. Thus, JRC systems possess very high potential to be employed into the sixth generation (6G) standards. Besides, intelligent reflecting surfaces have attracted wide attention in communication systems, due to low complexity of implementation. This paper designs a dynamic beamformer for reconfigurable intelligent surfaces (RIS) which maximizes spectral efficiency (SE). We jointly express the mutual information rate for communication and radar entities including a weighting factor which depicts the dominance of one operation over the other. The joint-SE based proposed method optimally selects the RIS subset. Furthermore, when the communication operation takes place the proposed method takes into account the interference occurring from the radar operation and vice-versa. Fractional programming based selection procedure is used for solving the problem of subset selection. Simulation results are presented and compared with different baselines to show effectiveness of the proposed method.

Vehicular edge computing has emerged as a promising paradigm by offloading computation-intensive latency-sensitive tasks to mobile-edge computing (MEC) servers. However, it is difficult to provide users with excellent Quality-of-Service (QoS) by relying only on these server resources. Therefore, in this article, we propose to formulate the computation offloading policy based on deep reinforcement learning (DRL) in a vehicle-assisted vehicular edge computing network (VAEN) where idle resources of vehicles are deemed as edge resources. Specifically, each task is represented by a directed acyclic graph (DAG) and offloaded to edge nodes according to our proposed subtask scheduling priority algorithm. Further, we formalize the computation offloading problem under the constraints of candidate service vehicle models, which aims to minimize the long-term system cost, including delay and energy consumption. To this end, we propose a distributed computation offloading algorithm based on multiagent DRL (DCOM), where an improved actor–critic network (IACN) is devised to extract features, and a joint mechanism of prioritized experience replay and adaptive$n$-step learning (JMPA) is proposed to enhance learning efficiency. The numerical simulations demonstrate that, in VAEN scenario, DCOM achieves significant decrements in the latency and energy consumption compared with other advanced benchmark algorithms.

Enabling a satellite network with edge computing capabilities can complement the advantages further of a single terrestrial network and provide users with a full range of computing service. Satellite edge computing is a potentially indispensable technology for future satellite-terrestrial integrated networks. In this article, a three-tier edge computing architecture consisting of the terminal–satellite–cloud is proposed, where tasks can be processed at three planes and intersatellites can cooperate to achieve on-board load balancing. Facing varying and random task queues with different service requirements, we formulate the objective problem of minimizing the system energy consumption under the delay and resource constraints, and jointly optimize the offloading decision, communication, and computing resource allocation variables. Moreover, the distribution of resources is based on the reservation mechanism to ensure the stability of the satellite-terrestrial link and the reliability of computation process. To adapt to the dynamic environment, we propose an intelligent computation offloading scheme based on the deep deterministic policy gradient (DDPG) algorithm, which consists of several different deep neural networks (DNNs) to output both discrete and continuous variables. Additionally, by setting the selection process of legal actions, the simultaneous decisions on offloading locations and allocating resources under multitask concurrency is realized. The simulation results show that the proposed scheme can effectively reduce the total energy consumption of the system by ensuring that the task is completed on demand, and outperform the benchmark algorithms.

This paper develops an efficient index modulation (IM) approach for the joint radar-communication (JRC) system based on a multi-carrier multiple-input multiple-output (MIMO) radar. The communication information is embedded into the transmitted radar pulses by selecting the corresponding indices of the carrier frequencies and antenna allocations, providing two degrees of freedom. Our contribution involves the development of a novel codebook based minimum Euclidean distance (MED) maximization and a constellation randomization pre-scaling (CRPS) scheme for efficient IM-JRC transmission. It can be inferred that the IM approach integrating the CRPS scheme followed by the codebook design maximizes the signal-to-noise ratio gain. The reuse of hardware and spectral resources in JRC system, and efficient index modulation for JRC leads to greener approach than existing method. The numerical results support the effectiveness of the proposed approach and show enhanced bit error rate performance when compared to existing baseline.

This paper considers a multiple-input multiple-output (MIMO) joint radar-communication (JRC) transmission with hybrid precoding and low resolution digital to analog converters (DACs). An energy efficient radio frequency (RF) chain and DAC bit selection approach is presented for a sub-arrayed hybrid MIMO JRC system. We introduce a weighting formulation to represent the combined radar-communications information rate. The presented selection mechanism is incorporated with fractional programming to solve an energy efficiency maximization problem for JRC which selects the optimal number of RF chains and DAC bit resolution. Subsequently, a weighted minimization problem to compute the precoding matrices is formulated, which is solved using an alternating minimization approach. The numerical results show the effectiveness of the proposed method in terms of high energy efficiency whilst maintaining good rate and desirable radar beampattern performance.

Abstract The joint radar‐communication (JRC) system is envisioned as an emerging sixth generation (6G) technology to tackle spectral congestion and hardware limitations by jointly implementing the communication and radar sensing on the same hardware platform and using the common radio frequency (RF) resources. Joint radar‐communication systems with a multi‐antenna setup leads to higher degrees of freedom, and hybrid beamforming can be exploited to achieve lower hardware complexity than conventional fully digital systems. This paper aims to design a spectral efficiency maximisation approach for a 6G inclined JRC system with hybrid beamforming and multi‐antenna setup while considering the interference between communication and radar operations and hardware impairments in the system. The rate expressions for communication and radar operations are defined, and the joint spectral efficiency is maximised via optimising the number of RF chains using an efficient selection algorithm taking into account the interference of one operation to the other and system hardware distortion. The simulation results are shown to support the effectiveness of the proposed approach, and they are compared with that of existing fully digital and hybrid beamforming based baseline methods with fixed number of RF chains. The proposed approach also exhibits a desirable communication‐radar trade‐off in terms of spectral efficiency gains.

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from an IoT user can be divided into several parts and cooperatively accomplished by multiple satellites to improve the overall operational efficiency of satellite networks. Network function virtualization (NFV) is viewed as a new paradigm in allocating network resources on-demand. Satellite edge computing combined with the NFV technology is becoming an emerging topic. In this paper, we propose a potential game approach for virtual network function (VNF) placement in satellite edge computing. The VNF placement problem aims to minimize the deployment cost for each user request, furthermore, we consider that a satellite network should provide computing services for as many user requests as possible. We formulate the VNF placement problem as a potential game to maximize the overall network payoff and analyze the problem by a game-theoretical approach. We implement a decentralized resource allocation algorithm based on a potential game (PGRA) to tackle the VNF placement problem by finding a Nash equilibrium. Finally, we conduct the experiments to evaluate the performance of the proposed PGRA algorithm. The simulation results show that the proposed PGRA algorithm can effectively address the VNF placement problem in satellite edge computing.

In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain selection mechanism is implemented in the baseband processing and the energy efficiency (EE) maximization problem is solved using fractional programming to obtain the optimal number of RF chains at the current channel state. Subsequently hybrid precoders are computed employing a sub-arrayed MIMO structure for EE maximization with weighted formulation of the communication and radar metrics, and the solution is based on alternating minimization. The simulation results show that the proposed method with minimum hardware achieves the best EE while maintaining the rate performance, and an efficient trade-off between sensing and communication.

In this paper, we investigate the service chaining placement problem for user requests in satellite ground station networks with minimum energy cost, which consists of server energy, switch energy, and link energy. We build the Server-Switch-Link energy model and formulate the energy optimization problem as an integer nonlinear programming problem. To address this problem, we implement a prediction-aided Greedy (PA-Greedy) algorithm depending on satellite mission planning in satellite control centers. We conduct the experiments to evaluate the proposed energy model and PA-Greedy algorithm in Fat-Tree networks, and compare the performance with the baseline Greedy algorithm and two energy models of Server-Link and Server. In a Fat-Tree network with 16 servers, the proposed Server-Switch-Link energy model with the PA-Greedy algorithm can reduce energy consumption by 17.28% when compared with the baseline Greedy algorithm, and outperform these Server-Link and Server energy models by 47.10% and 62.91%, respectively.

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this article, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs, and available bandwidth. This article proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. The results demonstrate that the HMAO algorithm is more effective than two baseline algorithms of genetic algorithm (GA) and fast elitist non-dominated sorting genetic algorithm (NSGA-II) in solving the large-scale optimization problem of resource allocation. Furthermore, we provide the performance comparison of the HMAO algorithm with two heuristic Greedy and Viterbi algorithms in on-line resource allocation.

To achieve an efficient and reliable data transmission in time-varying conditions, a novel non-orthogonal multiple access (NOMA) transmission scheme based on rateless codes (NOMA-RC) is proposed in the multicast system in this paper. Using rateless codes at the packet level, the system can generate enough encoded data packets according to users’ requirements to cope with adverse environments. The performance of the NOMA-RC multicast system with delay constraints is analyzed over Rayleigh fading channels. The closed-form expressions for the frame error ratio and the average transmission time are derived for two cases which are a broadcast communication scenario (Scenario 1) and a relay communication scenario (Scenario 2). Under the condition that the quality of service for the edge user is satisfied, an optimization model of power allocation is established to maximize the sum rate. Simulation results show that Scenario 2 can provide better block error ratio performance and exhibit less transmission time than Scenario 1. When compared with orthogonal multiple access (OMA) with rateless codes system, the proposed system can save on the transmission time and improve the system throughput.

To achieve efficient and reliable data transmission in a communication system in time varying conditions, a downlink non-orthogonal multiple access system based on rateless codes (NOMA-RC) is proposed in this paper. The NOMA-RC system can continuously send superimposed signals to the users according to the decoding results within a limited time. After a user decodes the signals successfully, it will send an acknowledgement to the transmitter. Then the system may adjust the message to be transmitted to improve the decoding probability for the remaining users. The performance of the NOMA-RC system with delay constrains is analyzed over Nakagami-m fading channels. The simulation results show that the NOMA-RC system is capable of reducing the transmission time and improving system efficiency compared to orthogonal multiple access system based on rateless codes.

Rateless spinal codes can achieve reliable transmission with high throughput performance, which is required by some power‐constrained applications, such as internet of things (IoT). In this study, the cyclic redundancy check (CRC) is divided into segments. We design the segmented CRC‐aided (SCA) spinal codes and propose a novel hybrid decoding algorithm. The decoder can terminate the decoding process earlier when an error decoding is detected in any segment. Moreover, a more targeted symbol transmission strategy after decoding errors occur is provided and we call it as the transmitting redundant symbols for specific segments (RSSS) strategy. The RSSS strategy saves the transmitting symbols by transmitting a variable number of symbols, thus improving the throughput of the system. Furthermore, we design a new tail‐biting structure for SCA‐spinal codes to compensate for the disadvantage of poor error detection ability for short segment CRC bits. The simulation results show that the proposed SCA‐spinal codes can reduce the decoding complexity and improve the throughput of the system. The transmission delay can also be reduced by dividing the information bits and CRC bits into an appropriate number of segments.

Low resolution analog-to-digital converters (ADCs) can be employed to improve the energy efficiency (EE) of a wireless receiver since the power consumption of each ADC is exponentially related to its sampling resolution and the hardware complexity. In this paper, we aim to jointly optimize the sampling resolution, i.e., the number of ADC bits, and analog/digital hybrid combiner matrices which provides highly energy efficient solutions for millimeter wave multiple-input multiple-output systems. A novel decomposition of the hybrid combiner to three parts is introduced: the analog combiner matrix, the bit resolution matrix and the baseband combiner matrix. The unknown matrices are computed as the solution to a matrix factorization problem where the optimal, fully digital combiner is approximated by the product of these matrices. An efficient solution based on the alternating direction method of multipliers is proposed to solve this problem. The simulation results show that the proposed solution achieves high EE performance when compared with existing benchmark techniques that use fixed ADC resolutions.

This paper proposes an energy efficient millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) beamformer with low resolution digital to analog converters (DACs) at the transmitter. We consider the case where all DACs have the same sampling resolution for each radio frequency (RF) chain and select the best subset of the active RF chains and the DAC resolution. A novel technique based on the Dinkelbach method and subset selection optimization is proposed to maximize the energy efficiency (EE) given a predefined power budget for transmission. We also implement an exhaustive search approach to serve as an upper bound on the EE performance and show the performance trade-offs. The simulation results verify that the proposed technique exhibits EE performance similar to the optimal exhaustive search technique while requiring lower computational complexity.

In this letter, we propose a segmented CRC-aided spinal code (SCA-spinal code) where an encoding structure is designed based on a novel sliding window decoding algorithm. The proposed algorithm reduces the decoding computation significantly by terminating the decoding process earlier when the segment decoding results fail to pass the corresponding CRC check. The decoding results are checked once a segment decoding is complete. Furthermore, we propose a transmission strategy after the decoding process fails to save the transmitting symbols and therefore improve the spectral efficiency. The numerical results show that our proposed SCA-spinal code reduces decoding complexity and improves spectral efficiency.

This paper proposes a non-orthogonal multiple access system based on rateless codes to improve anti-jamming performance. Firstly, each user's data is encoded by rateless codes. Then all the data is superimposed to form some composite signals, which are broadcast to every user. The power factors of each user and the superimposed signals are adjusted by the transmitter according to feedback provided by the users. This paper also analyzes the packet loss ratio, the frame error rate (FER) and the decoding time. The simulation results show that the proposed scheme not only reduces the FER, but also improves the system throughput.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems have recently been proposed to meet the needs of the future wireless communication standards. The efficient use of low resolution digital-to-analog converters (DACs) and hybrid architecture could significantly reduce the high power consumption associated with the mmWave MIMO system components. This paper designs an energy efficient transmitter with low resolution DACs for mmWave massive MIMO systems. An optimization problem is formulated and solved to find the optimal number of radio-frequency (RF) chains to be used at the transmitter to minimize the power consumption. This problem is constrained by the information loss which introduces the reduction of the number of the RF chains, expressed in terms of the system capacity. Rate and energy performance are compared with different beamforming techniques and architectures for various DAC resolutions.