Integrated sensing and communication (ISAC) is an emerging paradigm designed to support next-generation wireless services and applications. However, ISAC systems with limited computation capabilities are unable to handle computation-intensive and latency-sensitive tasks. This paper proposes a novel integrated sensing, communication, and computation (ISCC) network empowered by a reconfigurable intelligent surface (RIS) to mitigate the performance degradation caused by interference between radar sensing and uplink offloading. To effectively coordinate the cross-layer resource allocation among communication, sensing, and computation, we propose a resource scheduling problem. Specifically, we maximize the total computation rate while satisfying the sensing signal-to-noise ratio (SNR) requirement by jointly optimizing the energy allocation for local computing and offloading, the transmit and receive beamforming at the base station (BS), and the RIS reflective beamforming. To address this complex non-convex problem, we develop an efficient scheduling algorithm based on the block coordinate descent (BCD) framework. The iterative algorithm employs the fractional programming algorithm based on Lagrangian dual transform and quadratic transform, the generalized eigenvector methods, the convex relaxation techniques, and the successive convex approximation (SCA) algorithms to solve each subproblem separately. Experimental results demonstrate that the proposed scheme outperforms several baseline methods, confirming that RIS technology can effectively enhance system performance. In addition, we reveal the impact of various parameters on system performance.

This paper investigates a wireless powered internet of things (IoT) network, where a hybrid access point (HAP) performs both wireless energy transfer (WET) and wireless information transfer (WIT). Specifically, the HAP charges IoT devices via WET and collects their updated information via WIT. To ensure the information freshness, an adaptive cooperative transmission scheme is proposed, where information packets of devices are transmitted either directly or cooperatively with the assistance of another device. To this end, an expected weighted sum of age of information (EWSAoI) minimization problem is formulated to adaptively determine the best device pairs for cooperative transmissions, and schedule the corresponding transmission, i.e., WET, direct WIT, or cooperative WIT. Leveraging the Lyapunov optimization framework, a low-complexity adaptive scheduling scheme is proposed, wherein the device pairing or information transmissions are determined based on the instantaneous AoI and energy status of IoT devices. Furthermore, to reduce signalling overhead, a two-timescale scheduling scheme is proposed, wherein a matching algorithm is incorporated to pair devices for cooperative transmission in a large timescale while the adaptive transmission of energy and information are executed in a small timescale. Simulation results validate that adaptive cooperative scheduling scheme effectively reduces the AoI with low system overhead, and the gain approaches to 43.3%, compared with non-cooperative scheme.

Coded caching is an effective technology to reduce traffic load. While, existing works rarely consider secure coded caching scheduling, with concerning time-varying wireless channels and dynamic user's request. In this paper, we study the adaptive secure scheduling for coded caching system in a vehicle network, where a vehicular eavesdropper (VE) intends to intercept the signals transmitted by the base station (BS), and the BS serves legitimate vehicular users (VUs) by exploiting physical layer security (PLS) technique to avoid the information leakage. On the basis, we formulate a long-term averaged power minimization problem subject to the dynamic content requests of users and secure transmission requirement. By exploiting Lyapunov optimization framework, the formulated problem is transformed and decomposed into a series of online optimization problems for every time slot. By solving them, we obtain the adaptive coded caching scheme with adaptive scheduling and resource allocation, based on the instantaneous channel and buffer state information. Lastly, numerical results are presented to validate the effectiveness of the adaptive coded caching scheme, and to unveil the inherent tradeoff between the cache size and the power consumption.

This paper investigates a wireless-powered Internet of Things (IoT) network comprising a hybrid access point (HAP) and two devices. The HAP facilitates downlink wireless energy transfer (WET) for device charging and uplink wireless information transfer (WIT) to collect status updates from the devices. To keep the information fresh, concurrent WET and WIT are allowed, and orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are adaptively scheduled for WIT. Consequently, we formulate an expected weighted sum age of information (EWSAoI) minimization problem to adaptively schedule the transmission scheme, choosing from WET, OMA, NOMA, and WET+OMA, and to allocate transmit power. To address this, we reformulate the problem as a Markov decision process (MDP) and develop an optimal policy based on instantaneous AoI and remaining battery power to determine scheme selection and transmit power allocation. Extensive results demonstrate the effectiveness of the proposed policy, and the optimal policy has a distinct decision boundary-switching property, providing valuable insights for practical system design.

By controlling the propagation environment, intelligent reflecting surface (IRS) improve the channel quality, and becomes a promising technique. Meanwhile, movable antenna (MA) shows great potential to enhance the received signal-noise-ratio (SNR) by configuring antenna positions. In this paper, we exploit the advantages of both techniques, and study a MA and IRS enhanced wireless powered internet of things (IoT) network, wherein a hybrid access point (HAP) charges MA-enabled IoT devices via wireless energy transfer (WET) technology, and devices utilize the harvested energy to upload their information to the HAP. Basically, a network throughput maximization (NTM) problem is formulated to jointly optimize the IRS reflecting beamforming, the time allocation subject to total time constraint, and the MA position control subject to MA's feasible region constraints. Concerning the non-convexity of the NTM problem, we exploit the block coordinate ascent (BCA) approach to divide it into reflecting beamforming and time allocation sub-problem, and MA position control sub-problem, which are independently and iteratively solved until the solution of original problem is converged. For the reflecting beamforming and time allocation optimization sub-problem, the successive convex approximate (SCA) algorithm is used to transform it into a convex problem. For the MA position control sub-problem, we transform it into a convex mixed integer non-linear programming (MINLP) problem. Finally, extensive simulation results demonstrate the proposed approach for IRS-assisted wireless powered IoT network with MA can significantly improve the network throughput, where the performance gain is over 127%, compared with IRS-assisted wireless powered IoT networks.

In this paper, we aim to minimize the age of information (AoI) for a random access internet of things (IoT) network, where AoI is a metric to measure the freshness of information delivery. Since non-orthogonal multiple access (NOMA) can improve network throughput and connectivity, we exploit an AoI-oriented NOMA-based random access scheme, wherein devices simultaneously access wireless channel over multiple power levels with different access probabilities when their AoIs is not smaller than a threshold. We firstly study the comprehensive steady-state analysis of an AoI-independent NOMA-based random access scheme, which is a special case when the threshold is one. The AoI evolution is formulated as a markov chain based on the analyzed transmission success probability, and the probabilities of AoI states and the achieved AoI under generate-at-will are derived. Then, an AoI minimization algorithm is proposed to optimize the power access probabilities. Concerning stochastic-arrival, the steady-state probabilities of devices’ active state, successful transmission, and number of active devices, are derived to analyze the expected AoI. Finally, the steady-state probabilities of AoI states and the achieved AoI of AoI-dependent NOMA-based scheme are obtained. Simulation results validate our analysis, and demonstrate the significant performance improvement in terms of AoI. In specific, the proposed scheme can achieve AoI reduction by 65%, compared with random access without NOMA.

In this paper, we focus on buffer-aided wireless powered Internet of Things (IoTs) comprising of one wireless access point (AP) and multiple devices, where the AP provides energy to all devices via downlink radio frequency (RF) energy beams. All devices utilize the harvested energy to transmit their data to the AP in a time-division multiple access (TDMA) manner. Every device is assumed to be provisioned with energy storage and data buffer to store the collected energy from the AP and its data, respectively. The problem of minimizing the long-term average age of information (AoI) of the system is formulated in this paper. By solving the problem under the Lyapunov optimization framework, the AoI-aware adaptive transmission scheme is obtained, in which downlink RF energy beamforming, downlink energy transfer and uplink access, as well as transmit power and transmission rate by every device, will be jointly adjusted in order to minimize average weightede AoI according to the underlying channel state information (CSI), the buffer state information (BSI), the energy-consumption status information (ESI) of all terminals, as well as the AoI status information (ASI). Our analysis unveils that, the status update rate at devices has a significant impact on the achievable AoI performance, and the minimum average weighted AoI can only be realized at a reasonable status update rate, which is neither too high nor too low. Moreover, flexible AoI-aware scheme can be realized by adjusting either the AoI priority level or the AoI weighting coefficient.

We consider a mobile edge computing (MEC) assisted Industrial Internet of Things (IIoT) network, where multiple assembly processing lines in a smart factory are equipped with sensing devices. They sense raw products, generate and offload computing tasks, and finally process the raw products based on the computing results. In this scenario, different positions of the processing machines lead to different priorities and diverse Quality-of-Service (QoS) requirements of tasks. Therefore, how to schedule tasks and allocate the network resources becomes a critical and challenging issue. In this study, we introduce a novel batch-based hybrid nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission scheme. The selection between NOMA and OMA schemes is optimized based on the QoS requirements of tasks. Then, we formulate a production efficiency maximization problem with the objective of maximizing the speed of the assembly lines subject to the deadline constraints of offloading and computing procedures. To this end, a two-layer decomposition method is used to decompose the formulated problem into two subproblems. Furthermore, we utilize a bisection searching method to approximate the optimal solution, and propose an efficient method to determine the feasibility of the top-layer subproblem. Simulation results demonstrate the significant performance improvement of our proposed method. In specific, the production efficiency is enhanced by 525% in comparison with pure NOMA scheme.

Mobile edge computing (MEC) and digital twin (DT) technologies have been recognized as key enabling factors for the next generation of industrial Internet of Things (IoT) applications. In existing works, DT-assisted edge network resource optimization solutions mostly focus on short-term performance optimization, and long-term resource optimization has not been well studied. Thus, this paper introduces a digital twin-assisted heterogeneous edge network (DTHEN), aiming to minimize long-term energy consumption by jointly optimizing transmit power and computing resource. To solve the stochastic optimization problem, we propose a long-term queue-aware energy minimization (LQEM) scheme for joint communication and computing resource management. The proposed scheme uses Lyapunov optimization to transform the original problem with long-term time constraints into a deterministic upper bound problem for each time slot, decouples it into three independent sub-problems, and solves each sub-problem separately. We then theoretically prove the asymptotic optimality of the LQEM scheme and the tradeoff between system energy consumption and task queue backlog. Finally, experimental results verify the performance analysis of the LQEM scheme, demonstrating its superiority over several benchmark schemes, and reveal the impact of various parameters on the system.

As a main use case of 5G and Beyond wireless network, the ever-increasing machine type communications (MTC) devices pose critical challenges over MTC network in recent years. It is imperative to support massive MTC devices with limited resources. To this end, Non-orthogonal multiple access (NOMA) based random access network has been deemed as a prospective candidate for MTC network. In this paper, we propose a deep reinforcement learning (RL) based approach for NOMA-based random access network with truncated channel inversion power control. Specifically, each MTC device randomly selects a pre-defined power level with a certain probability for data transmission. Devices are using channel inversion power control yet subject to the upper bound of the transmission power. Due to the stochastic feature of the channel fading and the limited transmission power, devices with different achievable power levels have been categorized as different types of devices. In order to achieve high throughput with considering the fairness between all devices, two objective functions are formulated. One is to maximize the minimum long-term expected throughput of all MTC devices, the other is to maximize the geometric mean of the long-term expected throughput for all MTC devices. A Policy based deep reinforcement learning approach is further applied to tune the transmission probabilities of each device to solve the formulated optimization problems. Extensive simulations are conducted to show the merits of our proposed approach.

In this paper, we analytically study the performance of non-orthogonal multiple access (NOMA) transmissions in a random access network with truncated channel inversion power control. Specifically, in a slotted ALOHA network in support of NOMA transmissions, a wireless device randomly selects the transmission power with a certain probability, using channel inversion power control yet subject to the upper bound of the transmission power. Taking into consideration the stochastic nature of wireless fading channels, we first quantify two network areas such that devices in different areas have various choices of transmission powers for NOMA transmissions. An analytical model is developed to analyze the successful transmission probability and throughput of wireless devices located in different areas. Based on the analysis, two optimization problems are formulated to maximize the network throughput and the minimum throughput of wireless devices by tuning the transmission probabilities of each device. To solve the formulated combinatorial optimization problems, two heuristic algorithms are proposed. Extensive simulations are conducted to validate the analysis, and verify the efficiency of the proposed algorithm to attain the maximum network throughput and max-min fairness.

In this paper, non-orthogonal multiple access (NOMA) technology was exploited in a downlink wireless network to improve the averaged age of information (AoI) performance, where a source deployed with data buffers is supposed to send independent information to two users. A long-term average AoI minimization problem is formulated by taking into account of data and energy causality, peak and long-term average power constraints. Then, to fully explore the potential of data buffers, we used Lyapunov optimization framework and proposed a novel adaptive AoI-aware butter-aided transmission scheme (ABTS) to adjust the transmission rate and transmit power according to dynamic channel state information (CSI) and butter state information (BSI). Simulation results were presented to validate that the proposed ABTS scheme performs much better than those schemes with OMA transmission in terms of reducing AoI.

Mobile edge computing (MEC) harvests the computation capability at the network edge to perform the computation intensive tasks for diverse IoT applications. Meanwhile, the unmanned aerial vehicle (UAV) has a great potential to flexibly enlarge the coverage, and enhance the network performance. Accordingly, it has been a promising paradigm to use the UAV to provide the edge computing service for massive IoT devices. This paper studies the path planning problem of a UAV-assisted edge computing network, where an UAV is deployed with an edge server to execute the computing tasks offloaded from multiple devices. We consider the mobility of devices, where a GaussMarkov random movement model is adopted. By taking the energy consumed for the dynamic flying and executing the tasks at the UAV into account, we formulate a path planning problem that aims to maximize the amount of offloaded data bits by the devices while minimizing the energy consumption of the UAV. To deal with the dynamic change of the complex environment, we apply the deep reinforcement learning (DRL) method to develop an online path planning algorithm based on double deep Q-learning network (DDQN). Extensive simulation results validate the effectiveness of the proposed DRL-based path planning algorithm in terms of the convergence speed and the system reward.

In this article, a cybertwin-assisted nonorthogonal random access (RA) system is presented, where the cybertwins of the physical devices at the access point (AP) collect the devices’ information and decide the transmission parameters on behalf of the devices to achieve the maximum system performance. Specifically, the system performance of a$p$-persistent slotted CSMA system with nonorthogonal multiple access (NOMA) is analyzed, in which wireless devices transmit data to the ensure the received signal strength at the AP side is either high power or low power with certain probabilities. We first develop an analytical framework to quantify the successful transmission probability and the sum data rate as a function of the above probabilities. Accordingly, the feasible region of the number of high-power and low-power devices to ensure successful transmission is derived. With the analysis, nonconvex optimization problems are then formulated to maximize successful transmission probability and the sum data rate, respectively. To tackle the nonconvexity, an effective and fast-convergent iterative algorithm is designed to obtain the optimal transmission probabilities for the devices. Extensive simulations are conducted to validate our analytical results and demonstrate the benefits of NOMA in RA networks.

In this article, we exploit nonorthogonal multiple access (NOMA) for simultaneous energy and information transfer in a wireless-powered Internet-of-Things (IoT) network. As double near-far problem causes severe unfairness, we propose a fairness-aware NOMA-based scheduling scheme to enhance the max-min fairness. Specifically, according to the channel conditions, we divide IoT devices into the interference and noninterference groups with relatively good and poor channel qualities, respectively. Energy transfer is concurrently scheduled with data transmissions of devices with good channels. Thus, devices can harvest more energy to achieve higher rates at the cost of reduced rates of devices with good channels due to the interfering energy signals. We then apply order statistics to theoretically analyze the achievable rates of ordered devices. Based on the analysis, devices are optimally categorized into the interference and noninterference groups to achieve the max-min fairness, i.e., the minimum rate of devices in both groups is maximized. An adaptive power allocation algorithm is also proposed to further improve the network fairness when the transmission power of the energy transmitter is controllable. Throughput-aware NOMA-based scheduling is also presented and compared with the fairness-aware NOMA-based scheduling to illustrate the performance tradeoff between the throughput and fairness. The simulation results validate that the proposed NOMA-based scheduling schemes significantly improve the fairness and throughput performance of wireless-powered IoT networks, compared with the existing solutions.

This paper considered an energy-harvesting based secure two-way relay (EH-STWR) network, where two users exchanged information with the assistance of one buffer-aided relay that harvested energy from two users. To realize the confidential message exchange between two users in the presence of a potential eavesdropper, a secure bidirectional relaying scheme based on time division broadcast (TDBC) was proposed, where one user sent artificial noise to suppress the eavesdropper and another user transmitted data to the relay. A secure sum-rate maximization problem was formulated subject to average and peak transmit power constraints, data buffer and energy storage causality, and transmission mode constraints. By employing the Lyapunov optimization framework, a security-aware adaptive transmission scheme was proposed to jointly adapt transmission mode selection, power allocation, and security rate allocation according to channel/buffer/energy state information (CSI/BSI/ESI). Analysis results showed that the average achievable secrecy rate region can be significantly improved and there exists an inherent trade-off among transmission delay, requirement of transmit power consumption, and achievable secure sum-rate. Moreover, the channel condition between the energy-constrained relay and the potential eavesdropper is a critical factor on the achievable long-term average secrecy rate performance.

Non-orthogonal multiple access (NOMA) has been considered as a promising solution for improving the spectrum efficiency of next-generation wireless networks. In this paper, the performance of a p-persistent slotted ALOHA system in support of NOMA transmissions is investigated. Specifically, wireless users can choose to use high or low power for data transmissions with certain probabilities. To achieve the maximum network throughput, an analytical framework is developed to analyze the successful transmission probability of NOMA and long term average throughput of users involved in the non-orthogonal transmissions. The feasible region of the maximum number of concurrent users using high and low power to ensure successful NOMA transmissions are quantified. Based on analysis, an algorithm is proposed to find the optimal transmission probabilities for users to choose high and low power to achieve the maximum system throughput. In addition, the impact of power settings on the network performance is further investigated. Simulations are conducted to validate the analysis.

In this paper, resource allocation is studied for a fully sustainable cooperative IoT network, in which a relay powered by renewable energy forwards data to a destination while charging multiple IoT nodes by radio-frequency (RF) signals. An optimal joint time and power allocation problem is formulated to maximize the long-term sum-throughput of IoT nodes, taking into consideration the bounded transmit power of IoT nodes, the stochastic characteristic of energy harvesting (EH) process, and dynamic wireless channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different network settings. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Then, we extend our solution to a general network with asymmetric links. By employing Lyapunov optimization, an online stochastic resource allocation algorithm is proposed to obtain the maximum network throughput. It has been shown that the proposed algorithm can achieve close-to-optimal network throughput while maintaining the stability of the system. Finally, extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

This paper proposes a transmission protocol for massive Internet of Things (IoTs) networks with energy harvesting (EH). Specifically, the IoT devices harvest energy from the renewable natural sources, such as solar and wind, and use the harvested energy to transmit data to a base station (BS). Due to the massive number of IoT devices in the network, it is very challenging, if not impossible, to schedule data transmissions of IoT devices with variable energy supplies. To this end, a hierarchical chain based transmission model is proposed to attain high transmission efficiency of massive IoT devices, considering the stochastic nature of EH and large number of IoT devices. Specially, massive IoT devices are grouped based on their geographic locations; and IoT in one geographic area form a transmission chain to relay the data to the BS. Based on the proposed model, we propose a random chain based transmission protocol, where IoT devices randomly select next hop receiver to relay the data to the BS. The probability density function (pdf) of the size of the random chain is derived, based on which the sustainable energy throughput can be obtained. Finally, extensive simulations validate the analysis and demonstrate that chain based transmission protocol significantly outperform the hierarchal cluster-based transmission protocols.

The near-far problem causes severe throughput unfairness in wireless powered communication networks (WPCN). In this paper, we exploit non-orthogonal multiple access (NOMA) technology and propose a fairness-aware NOMA-based scheduling scheme to mitigate the near-far effect and to enhance the max-min fairness. Specifically, we sort all users according to their channel conditions and divide them into two groups, the interference group with high channel gains and the noninterference group with low channel gains. The power station (PS) concurrently transmits energy signals with the data transmissions of the users in the interference group. Thus, the users in the noninterference group can harvest more energy and achieve a higher throughput, while the users in the interference group degrade their performance due to the interfering signals from the PS. We then apply order statistic theory to analyze the achievable rates of ordered users, based on which all users are appropriately grouped for NOMA transmission to achieve the max-min fairness of the system. Meanwhile, the optimal number of interfered users that determines the set of users in each group, is derived. Our simulation results validate the significant improvement of both network fairness and throughput via the fairness-aware NOMA-based scheduling scheme.

This paper studies resource allocation for a fully sustainable cooperative network, which consists of multiple Internet of Things (IoT) nodes powered by radio-frequency (RF) energy, one relay with renewable energy supplies, and one destination. Specifically, the relay forwards the data received from IoT nodes to the destination and charges the IoT nodes at the same time. A throughput maximization problem is formulated, which takes into consideration the upper bound of transmit power, stochastic energy harvesting (EH) process and channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different parameters. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

In this paper, we study a buffer-aided collaborative relaying framework for cooperative communication system composed of one source node, multiple half-duplex DF relays with buffers, and one destination node. A two-phase adaptive relaying scheme is proposed,i.e., the source transmits data and the relay buffers receive data in the first phase, and all relays collaboratively transmit the buffered data to the destination node in the second phase. To achieve higher temporal and spatial diversity gains, time slots are dynamically allocated according to the state information of wireless channel (CSI), each node’s energy consumption (ESI), and each relay’s buffer (BSI). Lyapunov optimization theory is utilized to maximize the average achievable throughput under buffer stability and power consumption constraints, and an online buffer-energy-aware adaptive (BEAA) scheduling scheme is proposed to jointly consider relay selection, power allocation, and time allocation. It is disclosed that the proposed BEAA scheduling scheme is able to achieve a higher average network throughput by adapting the transmissions according to the CSI, ESI, and BSI. Moreover, it is unveiled that there exists inherent tradeoff among the transmission delay, power consumption, and the achievable throughput. Extensive simulations are presented to validate the efficiency of the proposed adaptive collaborative relaying protocol.

In this paper, we consider wireless-powered relay network consisting of one source, one wireless powered relay and one sink, where the relay is provisioned with both data buffer and energy storage. Firstly, a novel time-switching transmission scheme is proposed to divide the transmission from source to sink into three phases, namely, the relay energy harvesting phase, the relay receiving phase and the relay forwarding phase. And the achievable end to end (E2E) throughput and optimal timeswitching parameter are derived. Secondly, the transmission scheme is reformulated as a stochastic optimization problem to take into considerations of all the dynamic characteristics of the energy harvesting status, the data buffer status and the underlying time-varying channel conditions. By employing the Lyapunov optimization theory, an online buffer-energy aware adaptive transmission scheme is proposed to adjust the transmission at both source and relay according to the channel state information(CSI), the data buffer state information (BSI) and the energy state information (ESI). Moreover, the tradeoff between the average E2E throughput and the transmission delay is presented to show the great potential of the proposed adaptive transmission design to improve the achievable transmission throughput. Finally, simulations are presented to validate the efficiency of the proposed relaying protocols.