In the case of extreme natural disasters like typhoons, earthquakes, and forest fires, the terrestrial communication infrastructure often suffers from severe damage, which seriously undermines the effectiveness of emergency response efforts, leading to critical challenges, such as the timely assessment of disasters, the quick emergency response strategy development, and the rapid implementation of reconnaissance and search-and-rescue operations. To address these challenge issues, autonomous aerial vehicles (AAVs)-based mobile edge computing (MEC) techniques had attracted research attention to effectively support emergency communication, disaster assessment, and rescue strategy decisions. In order to characterize the time-critical requirements of many emergency rescue applications, the concept of “Age of Task” (AoT) was introduced in this article as a metric for assessing the timeliness of task, and the minimization of the weighted AoT across all the terrestrial user equipments (UEs) was formulated. By leveraging the Lyapunov optimization analysis framework, the problem of minimizing the time-averaged weighted AoT was transformed into a series of real-time subproblems that involve task offloading scheduling decision, computational resource allocation, UE transmit power control, and AAV flight trajectory planning, all of which enable an AoT-aware AAV-based MEC network for emergency rescue applications. To highlight the effectiveness, four benchmark schemes were included for comparison to show the advantages of the AoT-aware adaptive AAV-based MEC algorithm (AAAUMA) in terms of the realized task freshness performance, lower energy consumption by the AAV, and smaller data buffer backlog sizes at all ground source nodes.

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.

In this article, we focus on a wireless multiuser uplink network consisting of a single antenna access point (AP) and multiple single antenna users, in which each user transmits time-sensitive confidential message to the AP in a time-division multiple access (TDMA) manner. When a user is scheduled to transmit, the other users will be regarded as potential eavesdroppers. In practical Internet of Things (IoT) applications, different users may have different requirements for throughput, and the timeliness of information needs to be guaranteed. In order to effectively adapt to these heterogeneous application requirements, the average weighted sum Age of Information (AoI) minimization problem is formulated under the premise of satisfying the minimum sampling rate requirement, power allocation constraint, and user scheduling constraint. In order to solve this problem, we first propose two stationary randomized scheduling policies, which are modeled as D/Geom/1 and Geom/Geom/1 queueing systems, respectively, and design two algorithms to find the optimal sampling period of D/Geom/1 system, the sampling probability of Geom/Geom/1 system, the power ratio allocated to confidential information, and the user scheduling probability. Second, an AoI-aware adaptive secure transmission scheme (AASTS) is proposed under Lyapunov optimization framework by transforming the original time-average weighted sum AoI minimization problem into a real-time optimization problem related to data queue state and AoI evolution of every time slot. Numerical results show that the proposed AASTS scheme can achieve better average AoI performance, and the D/Geom/1 system is superior to the Geom/Geom/1 one.

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.

A wireless multi-user uplink heterogeneous network is investigated in this paper, which comprises an access point and two distinct user groups including throughput-oriented users and age of information (AoI)-oriented users, in which throughput-oriented users prioritize achieving as high throughput as possible, while AoI-oriented users emphasize timely transmission of information. It is assumed that the transmitted information needs to be kept strictly confidential to unintended users, and the time-division multiple access (TDMA) approach is adopted to transmit confidential information of each user. For such a network, all users who are not scheduled for transmission will be treated as potential eavesdroppers. The objective of our work is to maximize the average achievable secrecy rate of throughput-oriented users while minimizing the average AoI of AoI-oriented users subject to the data queue causality and stability constraints, the sampling rate requirements of AoI-oriented users, the time-averaged and peak transmission power constraints, and the user scheduling constraint. We propose using Lyapunov optimization to convert the original time-averaged optimization problem into a sequence of real-time ones associated with both queue sizes and AoI involved in the current time slot. On this basis, an adaptive heterogeneous traffic security transmission (AHTST) strategy is proposed to determine the optimal strategies for the flow control of throughput-oriented users, the sampling rate control of AoI-oriented users, the power allocation, as well as the user scheduling. Numerical results demonstrate that the AHTST strategy surpasses the considered benchmark schemes in both achievable average secrecy rate and average AoI.

Existing load-balancing methods used in data center networks involve some shortcomings such as excessively large decision delays during reactions to microbursts and large overheads involved in active probing. Programmable data planes have provided new opportunities for local decision-making on switches to address these issues. We observe that queue behavior (i.e., queue occupancy, queuing trend, and dequeue time interval) in switches can reflect the current or future congestion degree on a network. Furthermore, following data-driven experiments, we found an accurate fitting function of congestion degree to queue behavior. Thus, we propose an in-network load-balancing scheme based on a programmable switch, called queue-behavior-aware localized load balancing (QALL). In QALL, each switch independently selects egress ports probabilistically according to fine-grained-measured local queue behavior. The key concept of QALL is to take account the evolutionary process of reaching the current queue state into its decision basis for load balancing. Experimental results under actual DCN workloads (including web search and data mining workloads) demonstrate the effectiveness of QALL. In terms of flow completion time, decision delay, network shock, load sharing accuracy, and packet reordering, QALL outperformed recent perpacket (DRILL), per-flowlet (LetFlow and CONGA), and per-flow (ECMP) load balancers, particularly under heavy load. For example, under asymmetrical topology with 90% load level, the flow completion time of QALL was lower than that of ECMP, LetFlow, CONGA, and DRILL by up to 54.7%, 46.5%, 38.9%, and 18.9%, respectively.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS’s transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

In datacenter networks, bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline coexist. They compete with each other for limited network resources, and the effective scheduling of such mix-flows is extremely challenging. We propose a deep reinforcement learning with private link approach (DRL-PLink), which combines the software-defined network and deep reinforcement learning (DRL) to schedule mix-flows. DRL-PLink divides the link bandwidth and establishes some corresponding private-links for different types of flows to isolate them such that the competition among different types of flows can decrease accordingly. DRL is used to adaptively and intelligently allocate bandwidth resources for these private-links. Furthermore, to improve the scheduling policy, DRL-PLink introduces the novel clipped double Q-learning, exploration with noise, and prioritized experience replay technology for DDPG to address function approximation error, to induce lager and more randomness for exploration, as well as more effective and efficient experience replay in DRL respectively. The experiment results under actual datacenter network workloads (including Web search and data mining workload) indicate that DRL-PLink can effectively schedule mix-flows at a small system overhead. Compared with ECMP, pFabric, and Karuna, the average flow completion time of DRL-PLink decreased by 77.79%, 65.61%, and 23.34% respectively, when the deadline meet rate is increased by 16.27%, 0.02%, and 0.836% respectively. Additionally, DRL-PLink can also well achieve load balance between paths.

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.

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.

In this paper, we investigate a secure relaying network with simultaneous wireless information and power transfer (SWIPT). It is assumed that Alice wants to send confidential information to Bob under the existence of a passive eavesdropper (Eve), while the relay is equipped with a data buffer and an energy storage device. More specifically, we aim at achieving higher secrecy throughput, while retaining the stability of the data and energy queues. To achieve this with acceptable complexity, we transform the original long-term stochastic optimization problem into a series of online subproblems using the framework of Lyapunov optimization. The proposed scheme shows that the optimal time switching factor is 0 or 1, which is different from the conventional secure relaying network with SWIPT. In addition, simulation results verify that the proposed scheme can improve the secrecy throughput compared with the baseline scheme.

The multi‐input multi‐output (MIMO) transmission against a smart attacker has recently been formulated as a non‐collaborative game, in which the reinforcement learning‐based transmitter is devised to suppress the attack motivation of the malicious attacker for more secure transmission. Basically, large enough transmission power is needed to obtain the Nash equilibrium in favour of the MIMO transmitter. When energy efficiency is required, a secure MIMO transmission design with reasonable energy efficiency will become highly desirable. This study focuses on how to make the MIMO transmitter not only resist malicious attacker but also realise reasonable energy efficiency. To this end, a new game between the MIMO transmitter and the malicious attacker is formulated to take into account the energy efficiency of the MIMO transmitter. Since the energy efficiency requirement tends to reduce the transmission power by the MIMO transmitter, which may degrade the realised security performance in an adverse environment, the probabilistic transmission strategy was employed to further enhance the secure transmission. The analysis results unveil that the proposed energy‐efficient secure MIMO transmission design can not only ensure reasonable security performance but also noticeably improve energy efficiency performance.

In this article, we aim at maximizing the data gathering performance of the rechargeable wireless sensor network, where a mobile sink moves along the predefined path to charge sensor nodes through a wireless energy transfer technique and gather data from them. First, we show how to transform the original time-average optimization problem into a queue stability one by using the Lyapunov optimization framework, then we show how to decompose it into multiple subproblems by using the optimization decomposition. A distributed speed control and routing algorithm was proposed to reduce the computing load of the mobile sink and to obtain the near-optimal solution for data collection. Our analysis shows that there is an inherent tradeoff between the network utility and the average data queuing size, and the proposed adaptive transmission scheme can achieve the near-optimal network utility when a certain queueing delay can be tolerated.

In this paper, we consider a wireless powered cooperative non-orthogonal multiple access (NOMA) relay network, in which one source is supposed to send independent messages to two users with the assistance of one energy-constrained relay that harvests energy from the source. Firstly, we study the minimum power consumption at the source node to fulfill the least required transmission rates by two users in both time switching relaying (TSR) strategy and power splitting relaying (PSR) one. Secondly, when the relay is provisioned with data buffer and energy storage, the long-term average power consumption minimization problem is formulated to take into account of the data and energy queue causality, peak transmit power constraint, and transmission mode selection. By using Lyapunov optimization framework, a novel buffer-aided transmission scheme (BATS) is proposed to asymptotically approach the optimal solution. Our analysis shows that, the PSR outperforms the TSR in terms of the realized energy efficiency, and BATS can be utilized to further improve the energy efficiency. It is disclosed that, there is an inherent trade-off between the long-term power consumption and the average queuing delay. In addition, larger user rates or less power consumption can be realized if a larger delay can be tolerated.

Coded caching scheme provides us an effective framework to realize additional coded multicasting gain by exploiting coding into multiple transmitted signals. The goal of coded caching design is to jointly optimize the placement and delivery scheme so as to minimize the amount of transmitted coded signals. However, few research efforts consider multiple-round coded caching design problem, in which fixed and mobile users may coexist in one network, namely different number of active users present in successive rounds. Obviously, such dynamic network configurations may lead to undesired frequent placement caching content updating at user sides, if we assume coded caching scheme for all users in each round separately. Thus how to tailor the coded caching design, such that the frequent caching content updating in the placement phase can be avoided, and simultaneously retaining full caching gain in delivery phase in multiple rounds will become highly desirable. In this paper, by carefully examining the bipartite graph representation of the coded caching scheme, a dynamic centralized coded caching design is proposed on the basis of the concatenating-based placement and the saturating matching based delivery scheme. Our analysis unveils that, the proposed dynamic coded caching scheme can realize the flexible coded multicast, and is order optimal.

In this paper, we consider a buffer-aided multiuser multiple-input single-output (MISO) network consisting of one multi-antenna access point (AP) and multiple single-antenna users, in which the AP is provisioned with data buffers for temporarily storing the data for each user either from the upper layer applications or the message by the AP. The data for one specific user must be kept confidential from all other unintended users. For such a system, we aim at maximizing the long-term average achievable secrecy rate region by carefully designing the flow control, the information signal and artificial noise beamforming, as well as the user selection. To address this issue, we first transform the time average optimization problem into a real-time one by using the Lyapunov optimization framework. Then it is proposed to decompose the optimization problem into several sub-problems by using the optimization decomposition technique. Although the information signal and artificial noise beamforming sub-problem is non-convex, we show that it can be decomposed into a two-stage optimization problem to effectively solve it by using the exact line search and DC (difference of two convex functions) algorithms. Moreover, we extend the average secrecy rate region maximization problem to the worst-case scenario, in which all unintended users are colluding in eavesdropping. Our analysis discloses that, there exists an inherent tradeoff between the average achievable secrecy rate region and the average queueing length. It is shown that a better average secrecy rate region can be realized by fully taking advantage of the buffer-aided transmission potentials in the MISO network, if a certain queueing delay is tolerable.

The MIMO transmission against a smart attacker has recently been formulated as a noncollaborative game, in which both the MIMO transmitter and the malicious attacker try to maximize their predefined utilities. In this paper, by carefully analyzing the Nash Equilibrium (NE), we focus on the conditions, in which the gaming results incline to the malicious attacker instead of the MIMO transmitter. In this adverse case, it is highly desirable to develop an effective mechanism to suppress the attack intention by the attacker for better secure communication. Motivated by this, an adaptive secure MIMO transmission scheme was proposed to make the MIMO transmitter better resist malicious attackers in adverse channel conditions. Compared with the existing gaming-based strategy, not only the transmit power of the MIMO transmitter but also the transmission probability will be adjusted in the proposed adaptive secure transmission scheme. Our analysis results show that the proposed scheme can be regarded as a generalized adaptive transmission one, i.e., when the adaptive transmit power policy is enough to suppress the attack motivation, the proposed scheme will be reduced to the adaptive power control scheme; otherwise, both the adaptive transmit power and the adaptive probabilistic transmission can be employed to suppress the attack motivation. The analysis results confirm us that the proposed adaptive transmission scheme provides us a choice to enhance the secure MIMO transmission performance in adverse conditions.

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.

Legacy TCP/IP protocol has gradually revealed many deficiencies, such as poor mobility and scalability. The distribution of information based on the content has a possible number of practical applications in the vehicular environment, such as congestion detection, collision avoidance, emergency announcements, parking notifications, and advertising. In this study, we have presented a technique for the distribution of information based on the content in a hybrid vehicle-to- everything (V2X) environment. The COC-V2X technique relies on contents that each vehicle having by communicating with each other, and has the potential to solve some of the problems that present in IP-based VANETs. It can also gain edge of both the cellular eNodeB if there exists the decentralized vehicle-to-vehicle communication technologies. We evaluate the performance of the proposed approach via realistic VANETs traces based scenarios. The preliminary conclusions illustrate that our proposed novel approach is superior to current legacy TCP/IP in terms of message delivery, low latency, and low overhead.

In this study, the authors study secure multiuser transmission in a massive multiple‐input multiple‐output (MIMO) relaying system, where a base station sends its messages to multiusers via an amplify‐and‐forward relay station. Beamforming matrices of maximum ratio combining/maximum ratio transmission (MRC/MRT) and zero‐forcing reception/zero‐forcing transmission (ZFR/ZFT) at relay are considered. The authors derive exact and asymptotic expressions for user rate and outage secrecy rate for a predetermined secure outage probability of eavesdropper links with channel state information imperfection. They have shown that ZFR/ZFT is a better choice than MRC/MRT from the perspectives of secrecy performance. Furthermore, the asymptotic analysis is proposed to show that almost the eavesdropper rates in all cases are degraded as a number of relay antennas goes to infinity, and simultaneously the transmit power at the base station and the relay can be scaled down significantly. Finally, numerical results provide more insightful validity of the proposed analysis.

In this paper, the access point (AP) in a wireless network is assumed to provide energy supply via wireless energy transfer to multiple terminals in the downlink, and all the terminals use the harvested energy to transmit their collected data to the AP in the uplink in a time division multiple access (TDMA) manner. Each terminal is provisioned with a finite energy storage and a finite data buffer to store the harvested energy and to buffer the arrived data traffic, respectively. Due to the limited data buffer and energy storage size, there might be data loss due to either data buffer overflow or energy storage depletion. Firstly, we aim at maximizing the long-term weighted sum-rate through energy beamforming vector design, power allocations, rate control, time allocations, and transmission mode selection subject to average transmit power, peak transmit power, data loss ratio requirements, practical data buffer as well as energy storage constraints. Secondly, the weighted max-min scheduling scheme is proposed to guarantee the fair access requirement by multiple terminals. Numerical analyses are presented to show that, the proposed adaptive design can substantially improve the average achievable rate region, while the proposed weighted max-min fair scheduling can effectively ensure the fair access requirements.

Recently, placement delivery array (PDA) was formulated to describe the placement and delivery phases with a single array for centralized coded caching scheme in an error-free shared link. In this paper, we explore PDA characterizations for two other models: device-to-device (D2D) network and distributed computing system. The inherent connections between these systems and the shared link caching system are displayed through PDA, which allows us to transform the PDA based schemes originally designated for shared link to those networks. As a result, combining with existing constructions, we can obtain schemes requiring low subpacketization level for D2D network or smaller number of files for distributed computing system.

In this paper, we consider the wireless powered communication network, in which K terminals are supplied by one common power station (PS) via the radio frequency (RF) energy harvesting technology to transmit their independent data to the corresponding K receivers. In addition, each terminal is assumed to be equipped with an energy storage to store the collected energy and one data buffer to cache the message to be delivered. We formulate an optimization problem to minimize the average power consumption of the PS subject to the given data arrival rates, the energy sustainability and the data buffer stability constraints at all K terminals. By using Lyapunov framework, an adaptive transmission scheme is proposed to determine the energy beamforming, the transmit power allocation and transmit mode selection based on the energy storage and data buffer status. Our analysis unveils that, there exists an inherent tradeoff between the average power consumption and the average queuing delay, and the requested information rates by multiple energy-constrained wireless powered terminals can be effectively supported with less power consumption if a certain transmission delay is tolerable.

Caching is a promising solution to satisfy the ever-increasing demands for the multi-media traffics. In caching networks, coded caching is a recently proposed technique that achieves significant performance gains over the uncoded caching schemes. However, to implement the coded caching schemes, each file has to be split into F packets, which usually increases exponentially with the number of users K. Thus, designing caching schemes that decrease the order of F is meaningful for practical implementations. In this paper, by reviewing the Ali-Niesen caching scheme, the placement delivery array (PDA) design problem is first formulated to characterize the placement issue and the delivery issue with a single array. Moreover, we show that, through designing appropriate PDA, new centralized coded caching schemes can be discovered. Second, it is shown that the Ali-Niesen scheme corresponds to a special class of PDA, which realizes the best coding gain with the least F. Third, we present a new construction of PDA for the centralized coded caching system, wherein the cache size M at each user (identical cache size is assumed at all users) and the number of files N satisfies M/N = 1/q or (q - 1)/q (q is an integer, such that q ≥ 2). The new construction can decrease the required F from the order O(eK·((M/N) ln(N/M)+(1-(M/N)) ln (N/(N-M))) of Ali-Niesen scheme to O(eK·(M/N) ln(N/M)) or O(eK·(1-(M/N)) ln(N/(N-M))), respectively, while the coding gain loss is only 1.

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error, and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatial-temporal-domain doubly random due to the target mobility, environment dynamics, and different surroundings at different reference nodes. First, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Second, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Third, the convergence conditions, the convergence properties, and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme Finally, numerical simulations are presented to corroborate the EP analysis.

Cooperative network localization plays an important role in wireless sensor network (WSN), wherein neighboring sensor nodes will help each other to calibrate their locations. However, due to the dynamic wireless propagation environment and different surroundings, the measurement accuracy at different network nodes is different and varies overtime. In this paper, the uncertainties in both measurement accuracy and reference node locations are considered to account for the impact of different surrounding environments and the initial node location errors on the cooperative network localization. A mean-field variational inference-based positioning (VIP) algorithm is proposed for cooperative network localization. The mechanism of the proposed VIP algorithm, the convergence properties, implementation complexity, and the parallel implementation structure are presented to show that the VIP algorithm provides an effective mechanism to incorporate and share the localization information among all network nodes for an improved localization performance. Finally, a concise Cramer-Rao lower bound (CRLB) is derived to reveal the principle of localization error propagation. It is disclosed that the localization error propagation principle is similar to the Ohm's Law in circuit theory, which provides a new insight into the impact of the measurement accuracy, the reference node location errors and the number of reference nodes on the cooperative network localization performance.

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.

Resource virtualization is considered as a potential technique to cope with the mushroomed multimedia services. With resource virtualization, physical resources are characterized as virtual resource shared by various Service Providers (SPs). In this paper, we focus on the virtual resource allocation problem for multimedia service in 5G network. The system rewards is maximized with the considerations of the system virtual resource consumption and the incomes generated from mobile users. To address this issue, a Multimedia Service Admission Model (MSAM) based on Semi-Markov Decision Process (SMDP) is proposed to characterize the system rewards. Then the steady-state probability and service request blocking probability are derived. Numerical results show that theoretic results are consistent with the simulation results.

The channel estimate and carrier frequency offset (CFO) acquisition are two essential bases for multiple-input-multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques. In high-mobility scenario, the fast time varying channel and the serious Doppler frequency shift will make the channel tracking and CFO estimation much more challenging. In this paper, the achievable limits of joint basis expansion-model (BEM)-based channel tracking and CFO estimation are derived in terms of the Bayesian Cramer-Rao lower bound (BCRLB) to reveal the impact of dependent factors, like the pilot and BEM modeling error. The BCRLB analysis shows that, the measurement reliability, BEM modeling error, channel correlation and the statistical channel dynamics will dominate the achievable limits. And smaller pilot space seems to be an imperative choice to improve the joint estimate performance.

A novel sparse domain-based information hiding framework is proposed in this paper to attach the watermarking signal to the most significant sparse components of the host signal over the pre-defined overcomplete dictionary. The adaptive sparse domain can be utilized to embed watermarking logo with better security and robustness. This can be realized owing to the fact that, not only the sparse domain can be customized from the given samples, but also the sparse transform coefficients of the original watermarking signal can be embedded, which provides inherent privacy. This paper provides two kinds of methods that embed watermark directly and embed the sparse representation coefficients of watermarking logo, and analyzes the condition of uniqueness of the sparse solution. Experimental results demonstrate the superiority of the proposed sparse domain digital watermarking technique over the traditional frequency domain or spatial domain schemes.

The wireless cooperative localization plays a key role in location-aware service. However, its objective function, e.g., the posteriori probability function, is commonly nonconvex due to nonlinear measurement function and/or non-Gaussian system disturbance. Moreover, due to the unavoidable reference node location error, the associated objective function is commonly intractable, which further complicates the cooperative localization. In this paper, a novel particle-assisted stochastic search (PASS) algorithm is proposed to realize the cooperative localization. Given a nonconvex objective function, the proposed PASS method can find out the global optimum in probability, assisted with its search particles, detection particles, and proposal particles. In addition, the PASS algorithm can harness the reference node location uncertainties in cooperative localization, by employing its proposal particles. The associated Cramer-Rao lower bound (CRLB), localization error propagation, computational complexity, and convergence properties are also presented to assess the proposed PASS-based cooperative localization. Finally, received signal strength-based localization is simulated to validate the effectiveness of the proposed PASS approach.

This paper considers a multi-input multi-output (MIMO) wireless relay network, where the source S communicates wirelessly to the destination D via the help of a nonregenerative relay R. Previous studies are almost exclusively based on the assumptions of equal time-duration phases for S-R and R-D transmission and equal number of multiple antennas for every node, and the results concerning arbitrary time scheduling and arbitrary antenna arrays are not yet known. To achieve the best end-to-end data rate, this paper considers joint source and relay beamforming design combined with time scheduling and subchannel pairing, in accordance to respective link conditions. We first propose a practical beamforming framework that is inspired by the optimal MIMO relay beamforming structure. It is shown that in this framework, after subchannel pairing, the joint beamforming design problem can be transformed to a joint optimal power allocation problem. Since the overall optimization problem is nevertheless very difficult, involving both continuous and integer variables as well as a non-convex objective function make, we solve it by enumerating integer variables, and with each given integer values, performing alternative optimization and decomposing the problem into convex subproblems. Numerical results confirm the effectiveness of our approach, and it is shown that relaxing the equal time-duration constraint and pairing subchannels optimally can significantly improve the end-to-end capacity.

This paper investigates the general achievable rate region (ARR) for a wireless multi-way relay (MWR) network with full message exchange and decode-and-forward (DF) cooperative strategy. The MWR network consists of K users, each equipped with M antennas, and a single relay equipped with N antennas; and there is no user-user direct connectivity. Since different schedules would result in different ARRs, the general ARR must subsume all possible schedules; and this result is not previously known. We first present a cut set outer bound, and then derive and analyze the general ARR for DF by considering all meaningful schedules. We show that the general ARR is a convex hull expanded from a set of ARRs corresponding to special schedules. We further show that the ARR of a specific schedule can be expanded by its extreme points via the Krein-Milman theorem. In this, we have shown that the general ARR (of a MWR network using DF) is obtained by identifying and expanding these extreme points. We demonstrate the numerical results via exemplary cases.

This study analyses the delay‐limited throughput performance of an uplink cellular relay network utilising hybrid automatic repeat request (HARQ) protocol in which one mobile station (MS) communicates with one base station (BS) with the aid of one amplify‐and‐forward relay station (RS). The authors’ analysis allows for a practical fading scenario where MS → RS and RS → BS channels undergo independent Rayleigh and Rician fading, respectively. Delay‐limited throughput expressions for two HARQ protocols are derived, respectively, namely Repetition Time Diversity and Incremental Redundancy. Furthermore, the impact of different parameters on the achievable throughput, such as the maximum HARQ round number L , line‐of‐sight signal K , information rate R etc., is analysed. The authors’ analysis unveils that, for the presumed dual‐hop cooperation model with the specified tolerable delay requirement and given channel condition, there exists an optimal information rate R * that achieves the maximum delay‐limited throughput for the two HARQ protocols. Moreover, it is disclosed that, both HARQ protocols with the adaptive optimised information rate outperform the counterpart with the fixed information rate in low and moderate SNR regions, suggesting that an adaptive transmission scheme should be employed to achieve a better throughput performance.

The optimized weight spectrum sequence (OWSS) is utilized as a design criterion in this paper to determine the periodic puncturing pattern and the non-periodic puncturing pattern of partially systematic RCPT (PS-RCPT) codes for LTE systems. It is shown that the OWSS-criterion based PS-RCPT codes outperform the pseudo-random puncturing (PRP) based PS-RCPT codes. Meanwhile, it is unveiled that, the puncturing ratio of information bits should be carefully determined in PS-RCPT codes generation to achieve reasonable tradeoff between the waterfall region and the error floor region performance.

Orthogonal frequency division multiplexing (OFDM) technique has gained increasing popularity in both wired and wireless communication systems. However, in the conventional OFDM systems the insertion of a cyclic prefix (CP) and the transmission of periodic training sequences for purpose of channel estimation decrease the system's spectral efficiency. As an alternative to OFDM, isotropic orthogonal transform algorithm (IOTA)-based multicarrier system adopts a proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. In this study, the authors propose linearly precoded IOTA-based multicarrier systems to achieve blind channel estimation by utilising the structure of auto-correlation and cross-correlation matrices introduced by precoding. The results show that the proposed IOTA-based multicarrier systems achieve better power and spectral efficiency compared with the conventional OFDM systems.

Using cyclic prefix (CP), the transmission schemes, orthogonal frequency-division multiplexing (OFDM), single carrier block transmission, and time reversal, can be unified as linear block precoding. Considering frequency-selective channels, this paper studies linear block precoding for CP-based multi-point transmission with per-antenna power constraints (PAPCs) under capacity maximization and mean-square-error (MSE) minimization criteria. We show that, the optimal precoders for both criteria could be, but not necessarily, in the form of OFDM transmission (i.e., an inverse discrete fourier transformation (IDFT) matrix multiplying a complex diagonal matrix). Based on the optimal precoder structure, the two problems are simplified to two matrix-free optimization problems for which we prove strong duality holds. Moreover, it is shown that the dual problems can be equivalent to two unconstrained convex optimization problems. Efficient optimum precoding algorithms are proposed for both problems. Simulation results show that the maximum capacity (or minimum MSE) in the PAPC case almost coincides with that in the sum power constraint (SPC) case when the power budgets for each antenna are equal, but a capacity (or MSE) gap exists between the two power constraint cases when the power budgets for each antenna are different.

The foundation of compressed sensing (CS) is the sparse representation of signals. Over-complete dictionaries could be utilized to map signals into their sparse representation over the dictionary. And iterative subspace identification (ISI) is an effective algorithm to determine the over-complete dictionary from signal samples. In this paper, the QR decomposition is proposed to be employed in the ISI scheme so as to obtain the adaptive over-complete dictionary. It is shown that the QR-ISI outperforms the ISI in terms of the recovered PSNR. Finally, the QR-ISI method could be applied to image denoising. Experiment results are presented to show that the QR-ISI offers a feasible method for image denoising with reasonable performance.

In this paper, we investigate the service coverage for interweaving cognitive radio networks with cooperative relays in shadowed areas within the metropolitan region. We highlight the influence of relays on the primary and secondary systems in severe shadow fading channels. The outage probability is utilized as a comprehensive performance metric to characterize the coverage quality for the cognitive radio system with relays. We show that cooperative spectrum sensing among the secondary transmitter and relays can improve the vacant spectrum detection probability and the false alarm probability, thereby making the additionally introduced interference by relays to be constrained within a tolerable limit. Overall, our analysis unveils that both the spectrum utilization rate and the spatial coverage can be significantly improved through the use of the relays, and there exists the spatial selectivity phenomena for both the primary and secondary systems.

In this paper, an exponential model is proposed to approximate the error performance of Turbo codes within low and intermediate SNR region. Our analysis unveils that, the proposed exponential model offers a simple but effective method to approximate the waterfall region performance of Turbo codes, which is generally intricate for analytical bounding techniques. By combining the proposed exponential model with the well known union bound estimate, the paper derives a combined performance model to extend the performance estimate within moderate to high SNR region. Moreover, it is shown that, only the free distance term of the weight spectrum is not always enough for a reasonable estimate of the error floor region performance, especially when the free distance multiplicity is small. Numerical results are presented to validate the applicability of the proposed performance model.

In this paper, based on a belief-propagation decoding strategy, a class of generalized parity-check codes called complex rotary codes is investigated. It is shown that, by using iterative sum-product decoding, the complex rotary codes have a much lower decoding complexity than Turbo codes, but have almost the same performance for the high code rate and short frame case (frame length< 500 bits). It is also shown that the prime block size of complex rotary codes is essential to achieve better performance because of its uniform checking characteristic.

In this correspondence, several errors related to the distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks in (B. Krishnamachari et al., 2004) are spotted and corrected.

In this paper, the performance of type III hybrid ARQ with RCPC codes is considered. The combining of multiple retransmitted RCPC codes in type III hybrid ARQ scheme leads to the so-called composite code combining. A new concept, i.e. combined puncturing pattern, is proposed to illuminate its effect. Due to the composite combining, different code symbol may exhibit different effective SNR owing to the accumulative effect, which can be described by the combined puncturing pattern. The closed-form of the pairwise error probability (PEP) is derived for the combined RCPC codes. It is shown that the PEP is dependent on the sum of the symbols' SNR of the incorrect path. Accordingly, the upper and lower bounds, along with the approximation of the symbols' SNR, are proposed to evaluate the residual error rate of the combined RCPC codes in type III hybrid ARQ scheme. By using the state-diagram analysis, we obtain the bounds and approximations of the performance of type III hybrid ARQ with RCPC codes. Finally, the applicability of both the proposed bounds and the approximation are validated through numerical analysis and computer simulations.

By using multiple repeated signal replicas to formulate the accumulative observed noisy signal sequence (AONSS) in the hybrid ARQ system, a novel data-aided maximum likelihood (DA ML) SNR estimation is proposed for the AWGN channel. Based on the AONSS, new lower hounds, i.e., the generalized deterministic and random Cramer-Rao lower bounds (GCRLB's), which include traditional Cramer-Rao lower bounds (CRLB'') as special cases, are derived. It is indicated that the conventional DA ML estimate is a special case of the novel DA ML estimate. An alternative differential observed noisy signal sequence (DONSS) is also proposed, yielding a blind ML SNR estimation technique. It is shown by numerical analysis and simulation results that both the proposed DA ML and the proposed blind ML SNR estimation techniques can offer satisfaction SNR estimation.

Abstract The hybrid automatic repeats request (ARQ) scheme, together with code combining, is an effective way to provide reliable transmission of data packets over noisy channel. In this paper, it is shown that the application of rate‐compatible punctured convolutional (RCPC) codes in truncated type III hybrid ARQ scheme leads to the so‐called composite code combining. In order to illuminate the effect of the involved composite code combining, a new concept, i.e. combined puncturing pattern, is introduced. By state‐diagram analysis, it is revealed that the overall performance of the truncated type III hybrid ARQ with code combining is dependent on both the maximum transmission number and the residual error probability after code combining. By extending the analysis to the convolutional codes whose symbols exhibit different effective signal‐to‐noise ratio owing to the composite code combining, a closed form of the residual error probability of the combined RCPC codes is derived. Based on the combined puncturing patterns, upper and lower bounds on the error performance are derived, together with an approximation method which is shown to be quite satisfactory by numerical and simulation results. Copyright © 2003 John Wiley & Sons, Ltd.