With the rapid advancement of artificial intelligence (AI), deep learning (DL) has been extensively applied in channel modeling to capture complex nonlinear relationships and uncover underlying signal propagation mechanisms. However, current DL-based channel modeling methods typically rely on extensive training data, which limits their performance and adaptability in dynamic and changing environments. To address this imperative, this paper proposes a DL-enhanced channel parameter prediction model based on adaptive meta mask region-based convolutional neural networks (AMM-RCNN). First, an optimized Mask RCNN network is designed to extract the key environmental information from satellite images. The extracted scatterer feature maps and propagation statistics are used as the multimodal input of network, so as to capture the correlation between propagation environment and channel characteristics. Second, the meta-learning algorithm is applied to improve the prediction accuracy of the network with sparse training samples. The method utilizes measurement data from multiple routes as meta-learning tasks, thereby enhancing the generalization ability of the model in new scenarios. Finally, the proposed model is trained and validated with using real-world channel measurement data collected from a university campus. Simulation results demonstrate that the proposed model can accurately predict channel parameters across diverse routes in campus scenarios.

With the proliferation of sensitive information transmitted in Internet of Things (IoT), physical layer security (PLS) has emerged as a key technique to ensure the data confidentiality in complex cellular IoT environments. However, the massive IoT devices (IoTDs) organized in multiple cells pose challenges to secure IoT such as severe inter-cell interference and limited spectrum resources. In this article, we focus on the secure communication and interference management in multi-cell multi-carrier multiple-input single-output non-orthogonal multiple access (MISO-NOMA) IoT networks with imperfect channel state information (CSI). To maximize the secrecy sum rate (SSR), a resource optimization problem is formulated by jointly designing subchannel assignment, secure beamforming and artificial noise (AN) injection, while guaranteeing the quality-of-service (QoS) requirements, power budget, and subchannel assignment constraints. To solve the non-convex problem, an interference-aware secure resource allocation scheme is proposed, which decomposes the problem into two joint optimization subproblems. For the first subproblem regarding subchannel assignment, we develop an improved matching algorithm based on adaptive interference hypergraph (AIHG). The second involves secure beamforming and AN injection, which is designed by using accurate surrogate functions and alternating optimization (AO) algorithm. Simulation results validate the framework’s robustness and convergence, demonstrating a significant improvement in secrecy performance over benchmarks.

The age of information (AoI) as a new metric in vehicular communication system has begun to attract public attention. This paper considers a muti-user vehicular communication network, where the base station (BS) transmits with vehicle users by adaptively selecting orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA) schemes. The cognitive radio (CR) technology has been performed in NOMA system to improve the spectrum resources utilization. The adaptive CR-NOMA/OMA scheduling scheme is proposed based on user matching algorithm to reduce the user interference of inter-groups and intra-group. And the scheduling schemes for primary user and secondary user of CR-NOMA system using the generate-at-will (GAW) and the generate-at-request (GAW) data generation models are proposed. Then the nonlinear average AoI of CR-NOMA and OMA systems are formulized. Based on GAW and GAR models, the minimizing average AoI are derived. The quality of service (QoS) as to average AoI and achievable rate with different vehicle speeds, signal to interference plus noise ratio (SINR), transmit power and outage probability are analyzed. The simulation results are compared with validated analysis and better performances of vehicular systems can be achieved.

By leveraging the inherent ability of reconfigurable intelligence surface (RIS) to enhance wireless communication channels, the integration of RIS into simultaneous wireless information and power transfer (SWIPT) enabled vehicle-to-vehicle (V2V) systems presents a promising solution to jointly enhance communication performance and energy harvesting efficiency. Building on this potential, an RIS-assisted V2V dual-hop relay system is constructed, which deploys RIS on the gate of relay vehicle user equipment (VUE), enabling efficient signal refraction from source to relay VUEs. To address the critical challenge of information freshness in such RIS-assisted systems, age of information (AoI) is adopted as the key metric. And the AoI optimization problem is formulated that jointly considers energy/data buffer capacity limitations, relay sustainability, and real-time packet freshness. To effectively resolve this optimization problem under dynamic vehicular conditions, an prioritized experience replay – dueling double deep Q network (PER-D3QN) scheme based on deep reinforcement learning (DRL) is proposed to make the optimal relay decision for AoI minimization. Numerical results demonstrate that the average AoI using the proposed PER-D3QN scheme is reduced by 20 percent compared with the existing schemes.

The exploitation of low-altitude (LA) airspace is advancing globally, boosting the sensing demands for heterogenous flying aircraft. To fulfill an accurate and intelligent sensing, the future 6G base station (BS) requires to aggregate features of multiple sensors’ views, then perform edge inference via loading the artificial intelligence (AI) model. However, this process confronts communication and computation bottlenecks owing to high-dimensional feature uploading as well as frequent memory access. To overcome these bottlenecks, we propose a multi-task over-the-air edge inference system for LA airspace, where feature aggregation is efficiently achieved employing over-the-air computation, and multiple inference tasks arriving at the BS are processed in batches to reduce memory access. Under this arrangement, we formulate a joint batching and beamforming design problem to maximize the number of completed tasks, constrained by completion latency and inference accuracy requirements. To address this intractable problem, we first examine the case with synchronous task arrivals and single batch. A spatial correlation-aware beamforming design approach is proposed to effectively suppress feature aggregation error and ensure inference accuracy. Next, we delve into the general asynchronous task arrival case. An AI-generated online policy is developed, which innovatively utilizes diffusion model to output batching decisions, thereby adapting to the dynamic and uncertain nature of task arrivals. Simulation results obtained on real-world dataset corroborate the importance of capturing the spatial correlation among sensors. In addition, the proposed approach realizes outstanding performance compared to benchmark batching, beamforming, and learning methods, and the completed task amount is close to offline optimization with prior task information.

This work proposes deploying stacked intelligent metasurface (SIM) on individual Internet of Things (IoT) devices to enhance the uplink transmission capability under a finite blocklength (FBL) regime. Aiming to maximize the achievable sum rate, a joint transmit power allocation, SIM phase shifts, and receiving beamforming design optimization problem is formulated. By decomposing the original problem into three sub-problems, reducing the intractable quadratic fraction of signal-to-interference-plus-noise ratio (SINR), the nonconvex channel dispersion function, and the constant modulus constraints to linear forms, we propose an iterative algorithm to obtain the solutions. Numerical results demonstrate that in a multi-user uplink FBL network, the incorporation of SIM yields approximately a 40% enhancement in the sum rate. The optimization of phase shifts leads to an improvement of nearly 70% in the sum rate compared to a random phase setting scheme, highlighting the crucial role of proper phase shift configuration in realizing the significant performance gains offered by SIM. The performance of the proposed algorithm is validated to be close to the slack upper bound. Furthermore, with the same total number of metasurface elements, the multi-layer SIM performs better than the traditional single-layer RIS, which reveals the advantages of multi-layer structure.

Mobile edge computing (MEC) can effectively cope with compute-intensive applications, but its full play is limited by the communication environment. Intelligent reflecting surface (IRS), as one of the key technologies of 6G, has the ability to improve the transmission environment and effectively improve the performance of the communication system. In this article, the IRS-assisted MEC system with high-mobile users (H-MEC) is considered. Based on the system model, the minimizing total energy consumption is investigated. Partial offload mode is adopted to jointly optimize computation and communication for H-MEC system. The minimizing total energy consumption is a complex nonconvex problem and the variables are coupled to each other, which is decomposed into five subproblems by the method of block coordinate descent (BCD) and the corresponding algorithms are proposed. Specifically, the quadratic transformation is used to deal with the fractional term, and the Lagrange dual transformation is used to deal with the logarithmic term. A low-complexity iterative algorithm is then used to alternately optimize the computation and communication settings. Finally, the effects of system parameters, such as the number of users and antennas, the computing task size, and the bandwidth of the system on the total energy consumption, are investigated. The results show that the proposed algorithms for IRS assisted H-MEC system can effectively improve the system performance as to energy consumption.

Accompanied by the construction of new power system with renewable energy, like the offshore wind power and desert photovoltaic power, terrestrial ground 5G is no longer able to fulfill the communication requirement of industrial power IoT (IPIoT) with tremendous equipment in remote areas. Under this circumstances, we put forward the NOMA-enabled space–air–ground integrated IPIoT network (SAGIN-IPIoT) model, with a satellite to achieve wide coverage, and multiple UAVs to strengthen hot spot communication. NOMA is leveraged to improve system throughput by allowing the common frequency resource shared among multiple users. An energy-efficient (EE) maximization problem is formulated to jointly optimize subchannel and terminal power. However, since the objective involves both continuous and binary variables, it is a mixed integer nonlinear programming (MINLP) issue. Thus, we decompose it into two subproblems, which are respectively solved by matching game and Lagrange dual method with low complexity. According to the theoretical analysis and simulations, we can conclude that the method has better performance than the benchmark method.

With the rapid development of 5G and Internet of Vehicles (IoV) technologies, vehicles have evolved from mere transportation devices to mobile living spaces for humans. Thus, the complexity of data processing is growing along with the increasing of vehicle applications, which makes relying solely on on-board processing capabilities insufficient. Traditional Roadside Units (RSUs)-based edge computing has limitations such as high deployment cost and finite coverage. To address those issues, we construct an Air-Ground Vehicular Cooperative Computing Network (AVC$^{\textbf{2}}$N) that introduces Cooperative Vehicles (CVs) to reduce deployment cost while incorporating Unmanned Aerial Vehicles (UAVs) to expand communication coverage. We aim to balance offloading efficiency and environmental sustainability by proposing a system cost minimization problem with weighted sum of delay and energy consumption. However, it faces new challenges such as the lack of Global State Information (GSI), and the Ultra-Reliable Low-Latency Communications (URLLC) queue delay constraints, rendering traditional methods inadequate. To address the coupling between immediate decision and long-term queuing constraints, we employ Lyapunov optimization to partition the initial problem into two distinct sub-problems. The first focuses on optimizing the system transmission cost, which is tackled using a collaborative Deep Reinforcement Learning (DRL) framework. Specifically, we design an algorithm based on Multi Agent Deep Deterministic Policy Gradient (MADDPG), which effectively addresses GSI uncertainty and ensures URLLC awareness. The second sub-problem addresses server-side computing resource optimization, and we propose a greedy algorithm to tackle it. Experimental results showcase the effectiveness of our approach, demonstrating notable achievement in terms of learning convergence speed, overall system cost, queue delay, and queue backlog.

Through deploying satellites and unmanned aerial vehicles (UAVs) with onboard processing capability, the space-air-ground edge computing network (SAGECN) is poised to support ubiquitous access and computation offloading for Internet of Things (IoT) terminals deployed in remote areas. However, the current SAGECN faces several challenges in realizing its full potential, such as scarce spectrum resources, diverse computational demands, and dynamic network circumstances. To meet these challenges, we propose a cluster-non-orthogonal multiple access (C-NOMA)-enabled SAGECN model, where a satellite and multiple UAVs act as collaborative edge servers to execute tasks from IoT terminals. Since each offloaded task should be processed via a specific program, the edge servers carry out program caching, whilst transfer the tasks that do not match the cached programs to another server in a multi-hop manner. Considering the delay-sensitive requirements of computation tasks, we formulate a joint task offloading, communication-computation-cache resource assignment, and routing plan problem, aimed at minimizing the average system latency. To cope with this challenging issue, we partition it into three subproblems. First, a multi-agent learning-based approach is developed to collaboratively train the task offloading, flight trajectory, and program caching. As a step further, two optimization subroutines are embedded to perform routing plan, subchannel allocation, and power control, thereby rendering the overall solution. Experimental results reveal that our approach achieves outstanding performance in terms of system delay and spectrum efficiency.

Since the existing terrestrial fifth generation (5G) network has limited coverage, it is difficult to meet the growing demand for seamless network connection. Meanwhile, current network resource allocation methods mainly research on how to improve system performance only from the perspective of resource utilization, but rarely take users’ specific needs for network content into consideration. This brings severe challenges to efficient network service and flexible resource allocation. Therefore, we construct the content service-oriented resource allocation model for space–air–ground integrated sixth generation networks (SAGIN 6G), and formulate the three-sided matching issue among the space–air–ground integrated network equipment (SAGINE), content sources, and users. In this model, users request to establish connection with SAGIN which forwards users’ request to content service provider (CSP). CSP manages the creation of content data, and finally returns the requested content to users through SAGIN. For the content service-oriented resource allocation in SAGIN, finding the optimal stable three-sided matching with the largest cardinality is an NP-complete problem. Therefore, to efficiently solve the above issue, we design some reasonable restrictions and convert it to a restricted three-sided matching problem with size and cycle preferences. We further develop the content-oriented resource allocation algorithm (COR2A) and the user-oriented resource allocation algorithm (UOR2A) in a distributed manner. Extensive simulations verify our approach outperforms traditional benchmark resource allocation schemes in terms of system throughput, CSP revenue, and user experience.

With the help of unmanned aerial vehicle (UAV), remote terminals that out of wireless coverage can be connected to the Internet of Things (IoT) networks. Currently, the IoT relies on a large number of low-cost wireless sensors with limited energy supply to realize ubiquitous monitoring and intelligent control. The hybrid-powered networks composed of wireless-powered communication (WPC) terminal and solar-powered UAV can solve the energy supply problem of the IoT networks, and the nonorthogonal multiple access (NOMA) technique can solve the massive access problem of IoT terminals. Exploiting these benefits, we investigate joint UAV 3-D trajectory design and time allocation for aerial data collection in hybrid-powered NOMA-IoT networks. To maximize the total fair network throughput, we jointly consider energy limitation, Quality of Service (QoS) requirements, and flight conditions. The problem is nonconvex and time-dimension coupled which is intractable to solve by traditional optimization methods. Therefore, we develop a deep reinforcement learning (DRL) algorithm called fair communication is accomplished by trajectory design and time allocation (FC-TDTA), which uses the deep deterministic policy gradient (DDPG) as its basis. Simulation results show that our proposed algorithm performs better than benchmarks in fair throughput maximization. The proposed FC-TDTA algorithm can make the UAV: 1) fly in appropriate direction and speed, so that the UAV can arrive at the charging station before the energy runs out and 2) conduct WPC energy transmission and data collection to achieve fair communication.

UAV-based air-ground integrated computing networks (AGIN) have gained significant traction in remote areas for the Power Internet of Things (PIoT). This paper considers an AGIN-PIoT, where computing tasks generated by ground PIoT devices are offloaded to aerial UAVs that perform edge computing. Jointly optimizing task offloading and UAV trajectory poses challenges such as many decision variables, information uncertainty, and long-term queue delay constraints. Due to the limited battery capacity of PIoT devices and UAVs, our objective is to minimize system energy consumption under long-term queue delay constraints by jointly optimizing task offloading, trajectory planning, and computing resource assignment. In light of Lyapunov optimization, we decompose the original challenging optimization problem into two sub-problems: (1) task offloading and UAV trajectory planning and (2) aerial edge resource allocation. Accordingly, we develop a multi-agent deep reinforcement learning-based algorithm called AGIN-MADDPG for the former to achieve the maximum accumulative reward and propose a greedy solution for the latter. Extensive experiments and numerical results demonstrate that our approach can avoid the problem of gradient vanishing and outperforms other benchmark methods in terms of power consumption, task backlog, queue delay, and system throughput.

Abstract In order to overcome the disadvantages of traditional channel simulation approaches and achieve more accurate millimetre‐wave (mmWave) channel simulation under the condition of limited measured data, a novel Long Short‐term Memory Networks (LSTM) based hybrid mmWave channel simulation approach is proposed in this work. The proposed hybrid approach takes advantages of LSTM‐based time‐varying model of path loss and large‐scale channel parameters, statistical model of intra‐cluster multipath parameters and ray tracing, which are able to accurately simulate path loss, large‐scale channel parameters, multipath parameters and channel impulse responses, respectively. Based on the mmWave channel data measured in the waiting hall of Qingdaobei Railway Station, it is verified that the simulation results of our proposed hybrid approach are in good agreement with the measured data and better than that of existing channel simulation approaches. The hybrid channel simulation approach proposed in this work has important application value for channel modelling and simulation in the case of small amount of data.

As the fast development of vehicular network, the layout of the roadside unit (RSU) is indispensable. Due to the shackles of factors, such as coverage and cost, there is an urgent need for effective solution to solve the contradiction that RSU cannot be deployed on large scale. Parked cars provide a feasible solution for replacing RSUs and effectively reducing the arrangement of edge nodes. Inspired by this, parked cars as RSUs (P-RSUs) are leveraged to support cities’ vehicular network in this article. We first construct the P-RSU-based cellular-V2V heterogeneous networks (C-V2V HetNets) system model, and then formulate an optimization problem to maximize the energy efficiency (EE) of C-V2V HetNets with parked cars. Since the proposed issue is an NP-hard mixed-integer nonlinear programming (MINLP) problem coupled with P-RSU incentive, we reformulate it into two subproblems, which are the P-RSU recruitment and the joint resource allocation. For the first subproblem, an effective reverse auction-based mechanism is given to encourage parked cars participate and become P-RSUs. For the second subproblem, nonlinear fractional programming is used to optimize transmission power, and many-to-one matching is utilized to effectively obtain channel reusing scheme constrained by QoS. Moreover, a multihop-based transmission strategy is given to further expand vehicular network coverage. Algorithms are evaluated based on real-world scenarios using SUMO. Numerical results demonstrate that the proposed approach can both effectively recruit P-RSUs with low cost and achieve excellent system performance in terms of EE, spectrum efficiency, and network coverage compared to other benchmark algorithms.

In this article, with the aim to enhance the secure transmission and improve the utilization of spectrum resources in Internet of Things (IoT), a multicarrier nonorthogonal multiple access (MC-NOMA)-assisted full duplex (FD) network is investigated, in which nonorthogonal multiple access (NOMA) is implemented in both uplink and downlink transmissions. The lightweight and low-power physical layer security (PLS) technology is employed to protect the information from eavesdropping. Taking the imperfect channel state information (CSI) into account, we formulate a problem to optimize the beamforming vector, artificial noise (AN), transmit power, and subcarrier assignment policy aiming to maximize the worst case sum secrecy rate under the Quality of Service (QoS) and power consumption constraints. Since the formulated problem is nonconvex and difficult to be solved, we decompose it into two joint optimization subproblems. The first is resource allocation with given subcarrier assignment, which is solved by using the block coordinate descent (BCD) approach. The second is subcarrier assignment solved by the matching theory. Our simulation shows that the proposed scheme is robust against the CSI imperfectness of the eavesdropping and self-interference channels, while providing significant sum secrecy rate improvement compared with the orthogonal multiple access (OMA), half duplex (HD) systems, and other benchmark schemes.

In this paper, an active-user cooperative scheme for overlay cognitive radio (OCR) vehicle-to-vehicle (V2V) communication system based on three-dimensional (3D) channel model is proposed. Based on the proposed cooperative scheme, the achievable rate regions of the primary users (PU) and secondary users (SU) with outdated channel state information (CSI) are analyzed. According to the tradeoff between the achievable rates of PU and SU, three power allocation schemes are proposed using outdated CSI. The performance of PU and SU in terms of outage events are analyzed. Based on the analytical framework, the simulation results for achievable rate and outage probabilities are provided. And the impact of power allocation coefficient of PU and SU, outdated CSI, signal-to-noise ratio(SNR) and the codes cross-correlation on the proposed active-user cooperation is analyzed. The simulation results are compared with validated analysis to confirm the theoretical analysis.

By combining information communication technology with power grid, the smart grid-oriented Power Internet of Things (PIoT) has become a critical technology to guarantee the safe and reliable power grid operation and improve system energy efficiency. Nevertheless, PIoT devices have only limited communication and computing resources since they are mostly deployed in remote areas that may be out of service coverage of existing terrestrial 5G networks. To overcome the resource limitation, we leverage Air-Ground Integrated C-NOMA Heterogeneous PIoT Networks (PAGIC HetNets), and study the core challenges in PAGIC HetNets. As PIoT devices are normally powered by battery, we aim at minimizing the energy consumption of PIoT devices and thoroughly investigate the problem of task offloading and resource allocation with minimal energy consumption. This problem belongs to a mixed integer nonlinear programming (MINLP) with extra difficulty that the long-term queuing delay and short-term constraints are coupled. To tackle the difficulty, we use Lyapunov optimization to transform this hard problem into three subproblems. The first subproblem is task splitting and local computing resource assignment at the PAGIC user side, which we solve with the Lagrangian multiplier method. The second subproblem is queue-aware channel reusing, and matching theory is adopted to solve it. The third subproblem is optimizing the aerial server resource allocation, for which we propose a greedy-based solution. Numerical simulations demonstrate that our approach can obtain excellent performance in terms of energy consumption, spectrum efficiency, task backlog, and queuing delay with lower complexity compared with several benchmark methods.

Mobile edge computing (MEC) has been regarded as a promising technology to liberate the resource-limited users from computation-intensive and latency-sensitive tasks by computation offloading. Furthermore, implementing non-orthogonal multiple access (NOMA) technology in heterogeneous networks (HetNets) has become a trend to improve system throughput and spectrum efficiency. Exploiting these benefits, we investigate the joint task offloading and resource allocation problem for MEC in NOMA-based HetNets. To minimize the energy consumption of all users, we jointly consider task offloading decision, local CPU frequency scheduling, power control, computation resource and subchannel resource allocation. The optimization problem is challenging due to the strong coupling between offloading decision and resource allocation. We thus decouple the problem into two sub-problems of offloading decision and resource allocation, and propose an efficient approach to find the joint solution by solving these two sub-problems iteratively. Simulation results show that the proposed approach can efficiently lower energy consumption of users compared to other benchmark schemes with an acceptable complexity.

Abstract In this work, an artificial neural network (ANN) based time‐varying channel modeling framework is proposed, including a playback model and a prediction model. The purpose of the ANN‐based modeling framework is to playback 5G measured radio channels at certain measurement positions, and further predict large scale channel parameters (LSCPs) at unmeasured positions with limited amount of measurement data. 28 GHz channel measurements were also conducted at a high‐voltage substation for the first time worldwide to meet with 5G radio system deployment for China Energy Internet. Meanwhile, the performance of the playback channels is evaluated by comparison with the measurements and traditional geometry based stochastic modeling (GBSM) simulated channels. An optimized radial basis function (ORBF) ANN is applied in the prediction model, and the predicted LSCPs are compared with the other approaches, which shows that the ORBF has the best performance. This work offers a solution to predict radio channels and parameters in case of big measured or simulated channel datasets.

Abstract In this paper, grey genetic optimization model (GGOM) is proposed for predicting insufficient channel parameters without increasing the amount of measurement data. Based on the millimetre wave 28 GHz indoor measurement data for both LOS and NLOS scenarios, the GGOM model is compared with traditional back propagation (BP) and grey model (GM) to analyse channel parameters like delay spread, excess delay and azimuth spread. Results show that the fitness of GGOM is better than the grey model in improving the stability of system. It works well with insufficient data (size less than 30) in most cases as it is set regardless of the specific scene and measurement data. This is verified by QuaDRiGa platform by generating uniformly distributed and interpolated data between the experimental measurement data. GGOM fits best with the measurement data compared with other prediction methods in channel characterization. Moreover, the mean absolute percentage error (MAPE) for GGOM is the least compared with GM and BP methods. The proposed GGOM model has good performance in modeling insufficient data of propagation channel, practically.

Space-air-ground-integrated power Internet of Things (SAG-PIoT) can provide ubiquitous communication and computing services for PIoT devices deployed in remote areas. In SAG-PIoT, the tasks can be either processed locally by PIoT devices, offloaded to edge servers through unmanned aerial vehicles (UAVs), or offloaded to cloud servers through satellites. However, the joint optimization of task offloading and computational resource allocation faces several challenges, such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this article, we propose a learning-based queue-aware task offloading and resource allocation algorithm (QUARTER). Specifically, the joint optimization problem is decomposed into three deterministic subproblems: 1) device-side task splitting and resource allocation; 2) task offloading; and 3) server-side resource allocation. The first subproblem is solved by the Lagrange dual decomposition. For the second subproblem, we propose a queue-aware actor-critic-based task offloading algorithm to cope with dimensionality curse. A greedy-based low-complexity algorithm is developed to solve the third subproblem. Compared with existing algorithms, simulation results demonstrate that QUARTER has superior performances in energy consumption, queuing delay, and convergence.

In the Internet of Health Things based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the ultra-reliable and low-latency communication (URLLC) constraints, and the adversarial competition among IoHT devices have imposed new challenges for task offloading optimization. In this paper, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability of queuing delays and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose an energy-aware and URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. Guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. The effectiveness and reliability of UTO-EXP3 are validated through simulation results.

Low‐voltage broadband power line communication (PLC) system transmits its information by the power line on the existed infrastructure. However, PLC attenuation and its harsh environment make it difficult to establish a reliable communication between the source and destination nodes, and relay‐aided (RA) PLC technology can be used to solve this issue to increase the transmission rate and coverage. In this study, an indoor three‐hop RA PLC system is under investigation for the first time. Based on a simple PLC attenuation model, the optimal relay node positions and transmitted power distribution of each node are studied to get the best system performance in theory. Then, a criterion of selection optimal relay node positions in a practical power‐line network is derived by solving an optimisation problem. Finally, for a specific power‐line network, the criterion for the selection of optimal relay node positions at the existed sockets between the source and destination nodes is verified. The theoretical work in this study could be useful in the design of broadband multi‐hop RA PLC systems and their engineering applications.

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

In this article, a simultaneous wireless information and power transfer (SWIPT) system with full-duplex (FD) Internet of Things (IoT) nodes is considered and investigated. We induce the adaptive interference decoding (AID) strategy as well as the switch and inverter structure with antenna selection (SIAS)-based hybrid precoding scheme to the SWIPT system. A joint optimization of hybrid precoder, decoding rule, and power splitting (PS) ratio problem is formulated to minimize the total transmission power, while satisfying the data rate and harvested energy constraints. As it is a nonconvex problem, we propose a suboptimal solution with three stages. In the first stage, a search algorithm which can reduce the complexity of exhaustive search is proposed to decide the sending mode of each node. In the second stage, we utilize the semidefinite relaxation (SDR) approach to find the optimal digital precoder and PS ratios. In the last stage, we propose an alternate minimization algorithm to obtain the hybrid precoding vectors. The simulation results show that our proposed suboptimal solutions can achieve a better performance in terms of power consumption and outage probability compared with those for treating interference as noise (IAN) systems and the digital precoding schemes. Both AID strategy and SIAS-based hybrid precoding are beneficial to the FD SWIPT system. Moreover, the self-interference causes little effect on the system performance as long as it can be eliminated up to 30 dB, which can be easily achieved by the antenna separation technique.

In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it's also able to be applied in future channel predictions in case of large amount of measured data available.

Vehicular fog computing (VFC) has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize stable and reliable task offloading under information uncertainty remains a critical challenge. In this paper, we propose a matching-learning-based task offloading algorithm to address this challenge. First, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Second, we extend the work to the scenario of information uncertainty, and develop a matching-learning-based task offloading algorithm by combining matching theory and upper confidence bound (UCB) algorithm. Simulation results demonstrate that the proposed algorithm can achieve bounded deviation from the optimal performance without the global information.

Based on outdoor microcellular measurements at 26 and 32 GHz, the path loss models are modified by a proposed dynamic rain model to see the difference of the path loss models between clear and rainy air with respect to rainfall intensities and transceiver distances. Moreover, a dynamic rain cell model for a multi‐user system is developed to investigate the total rain attenuation. The results show that the parameters for floating‐intercept (FI) model have a bigger change with different rainfall intensities than close‐in (CI) model. When considering the dynamic rain model with a different radius and moving speeds, the larger rain cell radius, the larger path loss exponents in CI and FI models, the smaller path loss intercept in FI model and system capacity will be achieved. For a fixed rain cell radius, the larger moving speed of the rain cell, the shorter effective time on the path loss and system capacity will be achieved. In addition, the rain cell has a bigger effect on the path loss exponent, intercept and system capacity at higher frequency band with respect to its size, moving speed, and rainfall intensity.

In this study, the performances of commonly used precoding schemes are evaluated based on channel measurements carried out at 28 GHz in a railway station for a massive antenna system configuration. And a hybrid precoding algorithm for a fifth generation (5G) multi‐user antenna system is proposed and its performance is evaluated based on real measured channels. Specifically, An upper bound for achievable sum‐rate of phased zero forcing (PZF) algorithm is derived and a precoding technique by reduction of phase shifters (PSs) named reduced‐PZF (RPZF) is proposed, which can obviously reduce power consumption in hybrid precoding systems. In addition, the authors give the closed‐form expression for achievable sum‐rate when RPZF is used. There are no requirements in the authors’ proposed scheme with complicated matrix decomposition and optimisation techniques. The simulation results show that it is possible to reduce 30 and 50% of the PSs by using the proposed hybrid precoder with better system performance in the line‐of‐sight (LoS) and non‐LoS scenarios, respectively. In addition, the digital PSs with 3 bits low resolution can achieve the similar performance when using analogue ones.

This study is to construct the autoregressive models for the low‐voltage broadband power line communication (PLC) channel noise by two machine learning algorithms, namely the least square support vector machine (LS‐SVM) and wavelet neural networks. The main work is to compare the two classical machine learning algorithms and also compare them with the traditional Markovian–Gaussian method. To verify their availability and ability to adapt to the time‐variant PLC channels, noise measurements for low‐voltage PLC channels in indoor and outdoor scenarios are carried out. The accuracy and efficiency of the two models are studied and compared based on a large amount of measurement data. The results show that both of the noise models can simulate and adapt to the time‐variant low‐voltage broadband PLC channels very well. The LS‐SVM model is found to have shorter simulation time and higher accuracy. Moreover, the proposed noise models are also compared with the traditional Markovian–Gaussian model. The results show that both the proposed noise models exhibit higher accuracy and lower complexity, especially that the LS‐SVM is more appropriate to be applied as a noise generator in PLC link and network level simulations instead of the current Markovian–Gaussian model.

Machine-to-machine communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet applications such as industrial automation. In this paper, we develop a two-stage access control and resource allocation algorithm. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without prior knowledge of channel states. Particularly, the joint power allocation and channel selection problem is formulated as a two-dimensional matching problem, and solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified under various simulation scenarios.

In this paper, diffraction fields generated by 10°, 60°and 90°lossy wedges illuminated by TM (electric field parallel to edge) and TE (magnetic field parallel to edge) waves in TD-LTE 230MHz electric wireless private networks are simulated by using a heuristic diffraction coefficient. The influence of lossy wedge angle on diffraction field by changing diffraction angle, incidence angle and incidence distance is studied. The results show that when the incidence angle is larger, the influence of lossy wedge angle on diffraction field is larger. When the incidence angle is 45°, changes of diffraction fields generated by different lossy wedges with diffraction angle are almost the same, but when the incidence angle is 135°, changes of diffraction fields generated by different lossy wedges with diffraction angle make a big difference. Moreover, a conclusion that the lossy wedges have larger influence on the diffraction field with perpendicular polarization is drawn by simulations.

In this paper, based on 28 GHz indoor MIMO channel measurements, a cluster model based on Euclidean distance is analyzed. Moreover, according to QuaDRiGa (Quasi-Deterministic Radio Channel Generator) simulation platform, large-scale parameters like delay spread, ASA (Azimuth Spread of Arrival), Ricean K-factor and time evolution characteristics are simulated and validated. Results show that the cluster model describes the multipath environment physically. The QuaDRiGa simulation results fit quite well with measurement data. This verifies the applicability of the QuaDRiGa simulation model in millimeter wave bands. Received power and delay spread modeled as a function of distance in both LOS and NLOS cases, and the QuaDRiGa has better space-time continuity. The provided results are useful for design of 5G millimeter wave communication systems.

Broadband power line is an important way of communication in low‐voltage distribution networks because a power line is everywhere nowadays. However, due to the heterogeneity of a practical power‐line network and various loads connected to its termination points, modelling and simulation of power‐line communication (PLC) channels are difficult and complicated issues. In this work, the authors propose a novel PLC channel modelling approach using graph theory for the first time when knowing the power‐line network topologies and cable distributed parameters. The approach utilises simple and effective recursive operations in solving the k ‐shortest paths to estimate the channel transfer function for a specific power‐line network, it overcomes the complicated analytical derivation in computing the channel transfer function by the traditional two‐port network in the bottom‐up model and avoids to measure the actual channel in a top‐down model. Their proposed approach is validated by transmission‐line (TL) theory as well as measurement results, and it is also compared with TL theory. The results show that their proposed PLC channel modelling approach herein is simple to be implemented, it is expected to have extensive applications in broadband PLC systems for performance evaluations.

Multiuser multiple input multiple output (MU‐MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam‐vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal‐to‐leakage plus noise ratio (SLNR), we analyze the ergodic sum‐rate capacity using statistical Eigen‐mode (SE) and zero‐forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum‐rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum‐rate capacity.

This paper presents wideband millimeter-wave channel characterization based on measurements at 26GHz with 1GHz bandwidth carried out in an open office at KeySight Beijing, China, which is a representative of an indoor hotspot scenario. In the time domain measurements, an omni-directional biconical horn is used as the transmitter, while at the receiver a 26dBi horn is applied and rotated with 5o angular step in the whole azimuth plane, and from -10o - 30o in the elevation plane with 10o angular step. Based on the line-of-sight (LOS) measured channel impulse responses at 39 locations, this paper investigates the directional and mean path-loss models and their relevant shadow fading. The channel parameters such as root mean square (RMS) delay spread, RMS angular spread at the azimuth and elevation planes, Ricean factor and power angular profiles (PAPs) etc. are analyzed for the purpose of channel simulations and propagation mechanism studies at 26GHz.

5G, the new generation of mobile networks, attracts lots of interests and attentions in wireless communication with its aggressive performance goals. To be able to provide much higher data rate than today, 5G mobile networks will operate at frequencies higher than the crowded frequency bands used today, with broader bandwidth. Understanding the propagation characteristics of the wireless channel under the new frequency bands is essential for the research of 5G mobile networks. This requires a channel sounder (CS) system for channel measurement and modeling. To meet the 5G performance goals, 5G channel sounder is required to support mmWave frequency bands, ultra-broad bandwidth and massive multiple-input/multiple-output (MIMO). These requirements can be very challenging for design and implementation. In this paper, we provide a novel mmWave channel sounder architecture which can use commercial Off-The-Shelf (COTS) instruments to set up the system with high performance and reliability to meet 5G channel sounder requirements. We show validation test results to prove the mmWave channel sounder system performance. 1GHz bandwidth SISO channel measurement campaign in open office has been done at 26GHz.

Based on the indoor wideband multiple‐input multiple‐output (MIMO) device‐to‐device (D2D) measurements at 5 GHz with 100 MHz bandwidth carried out in University of Oulu, Finland, this study investigates the characteristics of indoor D2D radio channels with the same antenna height when both of the transceivers are moving or one end is fixed. The received power for the D2D channel is investigated by using double‐ and multiple‐Rayleigh distributions. It turned out that the received powers follow joint Rayleigh and double‐Rayleigh distribution as well as approximately Rayleigh distribution in non‐line‐of‐sight and line‐of‐sight scenarios, respectively. The angular distributions and their correlation of the angle‐of‐arrival and angle‐of‐departure in the azimuth and elevation planes are investigated in order to build three‐dimension model of Doppler power spectra and compare with the measurements. The channel parameters are derived from the measurements and compared with WINNER A1 scenario to find the differences between the D2D and conventional fixed‐to‐mobile (CF2M) channels. From the results, one can find the differences between the D2D and CF2M radio channels and better understand the physics for the D2D channel. Meanwhile, the channel parameters derived herein are important in the link and system level simulations for indoor D2D radio systems.

A new geometry-based stochastic scattering model (GBSSM) for wideband multiple-input-multiple-output vehicle-to-vehicle (V2V) channels is proposed in this paper. The proposed GBSSM with cross-polarized antennas combines three-dimensional two cylinders to model the stationary scatterers and two-dimensional multirings to imitate the moving scatterers. The channel realization by using the channel matrix in this paper is much more straightforward and concise to study the channel characteristics compared with the too complicated analytical solutions available so far. Because V2V propagation channels are nonstationary, the time-varying channel properties and parameters are further investigated based on the proposed GBSSM and realized channels, which can be used in the link and system-level simulations in V2V radio systems.

In this paper, a non-stationary geometry-based scattering model for street wideband multi-input multi-output (MIMO) vehicle-to-vehicle (V2V) fading channel is proposed. It is assumed that the static scatterers on the both roadside are uniformly distributed on time-varying ellipses, and the mobile scatterers are uniformly distributed in time-varying segment of the road. To avoid complicated procedure in deriving the analytical expressions of the channel parameters and functions, the channel is realized first, then the time-varying channel properties and parameters are investigated. In this work, we extend the proposed narrowband model to wideband and also introduce the carrrier frequency and bandwidth into the model.

Based on a ring, a disk and an elliptical scattering models, the power spectrum densities (PSDs) are derived and investigated for fixed-to-fixed (F2F) propagation scenarios where a local scatterer is moving in any direction with random velocity at the predefined geometries of the models. The velocity distributions of the scatterers are assumed to follow uniform, exponential and mixed Gaussian, and the transceiver vehicles are moving either with low- and high-speed. The results show that the one-ring, disk scattering model and the model in [15] are very close in describing the PSDs for the F2F radio channels. Moreover, different shape factors have little effect on the PSDs in the disk model. As the shape factor is large enough, the disk model tends to be the same as the one-ring model. The PSDs derived from the elliptical model are different from the one-ring and disk models because the scatterers are distributed not only close to the transceiver ends, but also between the transceiver.

Effects of noise cut for extracting wideband channel parameters were investigated by 15, 20, 25, and 30 dB fixed levels, and a method of dynamic noise cut. The comparisons are given by deriving the power delay profiles (PDPs), the rms delay spread (DS), number of paths (NoPs) and Ricean K-factors based on indoor-to-outdoor wideband measurements at 5.25 GHz with RF bandwidth of 100 MHz. The conclusion is that the traditional method using fixed level noise cut is in general under estimate the DS and NoPs. K- factors are not sensitive to noise irrespective of what kind of method is applied and how deep of the fixed levels. The PDPs are not very sensitive to fixed level noise cut, however, obvious changes can be observed by dynamic noise cut. The dynamic noise cut is preferred in case of clear noise floors for the measured IRs.

This paper is summarizing and comparing properties of channel models used for Beyond-3G (B3G) MIMO simulations: 3GPP spatial channel model (SCM), its extension (SCME), and models developed by WINNER. Compared models are offering complete channel model description in a sense of large-scale as well as small-scale effects in MIMO radio-channel. WINNER targeted model was supposed to provide reliable tool for estimation of system performance, covering frequencies up to 5 GHz and bandwidths of 100 MHz in different types of environment. Since SCM was originally proposed for 2 GHz range and 5 MHz bandwidth, certain extensions (SCME) were necessary. However, SCME performance was restricted since it has been design as backward compatible with SCM. That was the motivation to start using the new WINNER generic channel model, where model parameters are extracted from channel-sounding measurements covering targeted frequency range and bandwidth. This paper describes all important differences and compares features and performances of the models.

A new area of digital television broadcasting has emerged in the from of hand held reception. This has created a need for new standard channel models, which describe more accurately the conditions in portable reception. Hence, the Finnish partners of the multinational CELTIC Wing-TV project performed a comprehensive measurement campaign during autumn 2005. The analysis of the measurement results show that in most scenarios a strong specular or line-of-sight component is present and clearly dominating. This is noted from the very large values of Rician K-factor. The SFN characteristics of the DVB-H reception is clearly visible while considering the RMS delay spreads, total excess delays and variations of the number of taps. Based on the analysis of the measured data this contribution presents novel tapped delay line (TDL) channel models suitable for DVB-H testing

Based on the wideband measurements for indoor, rural and suburban environments at 5.25 GHz with 100 MHz bandwidth, the basic required channel parameters and models such as the rms delay spread, number of clusters, path delays, and tapped delay line models were derived for beyond 3G (B3G) wireless communications. The results derived in the paper could be the necessary models and parameters for B3G system level and link level simulations, and also for the planning B3G radio systems

The modeling results from wideband radio channel measurements are presented for two environments: rural and indoor. The paper focuses on the average and instantaneous characteristics of received power including: path loss, shadow fading, power delay profiles, and rms delay spreads. Furthermore, the cross correlation between shadowing of the path loss and the delay spread is investigated and found to be large. Doppler measurement campaigns were conducted in both environments, at 2.45 GHz and 5.25 GHz along the same measurement routes, with 100 MHz bandwidth. In this paper it is shown that we may apply 2.45 GHz band small scale models into 5.25 GHz band. It can be also seen, as for the measured scenarios, on average, the difference in terms of path loss between 2.45 GHz and 5.25 GHz in line-of-sight indoor environment, where waveguide effect is encountered, is c.a. 8 dB, instead in rural outdoor the mentioned difference is c.a. 6 dB.

In this paper, empirical channel models and parameters are derived from the wideband measured data at 5.3 GHz in outdoor mobile communications. The path loss exponents and intercepts are obtained by using the least square method. The mean excess delay and mean root-mean-square (rms) delay spread are within 29-102 ns and 22-88 ns, respectively. The correlation distances and bandwidths are within 1-11 /spl lambda/ and 1.2-11.5 MHz, respectively, when the envelope correlation coefficients equal 0.7 in line-of-sight cases. These correlation values depend strongly on the base station antenna heights. The window length for averaging out the fast fading components is about 1-2 m for microcells and picocells. The multipath number distributions follow both Poisson's and Gao's distributions, but Gao's distribution is better in the high probability region. Large excess delays up to 1.2 /spl mu/s and rms delay spread about 0.42 /spl mu/s are found in the urban rotation measurements, where the receiver is close to a large open square.