Intelligent reflecting surface (IRS) has been considered a promising technology to be applied to mobile edge computing (MEC) systems, especially when offloading links are blocked or weak. However, most existing works are restricted to narrow-band channel and ideal IRS reflection model, which is not practical and may lead to significant performance degradation. Thus, we consider an IRS-enhanced wideband MEC system with practical IRS reflection model. Our objective is to minimize the weighted latency of all devices by jointly optimizing the offloading data volume, edge computing resources, BS receiving vector, and IRS basic phase shift (BPS). Since the formulated problem is non-convex, we employ the block coordinate descent (BCD) technique to decouple it into two subproblems to alternatively optimize computing and communication resources. In particular, the computing resource optimization subproblem is solved based on Karush-Kuhn-Tucker (KKT) conditions and bisection search method. While the communication resource optimization subproblem is first transformed into a weighted sum-rate maximization problem based on LDR technique and KKT conditions. Then leveraging the equivalence between sum-rate maximization and MSE minimization, it is converted into a multi-variable problem that can be effectively solved using BCD technique. Simulation results show that the proposed schemes can reduce latency by 16% compared to baseline schemes when the number of IRS elements is 100, confirming the effectiveness of considering practical IRS reflection model for wideband MEC systems.

ABSTRACT This paper considers a downlink multiple input single output (MISO) non‐orthogonal multiple access (NOMA) system over Nakagami‐m fading channels, where a multi‐antenna base station (BS) serves several single‐antenna users with the statistical channel state information (CSI) of each user. We propose a novel low‐complexity transmit antenna selection by head user (TAS‐head) strategy for the first time to exploit the spatial diversity of multiple antennas. Based on our proposed TAS‐head strategy, we derive a closed‐form expression of the exact outage probability (OP). We further analyse the asymptotic OP and diversity order in high signal‐to‐noise ratio (SNR) regime. Finally, we formulate a power allocation optimization problem to maximize sum throughput under outage constraints. We also design an Adam algorithm in combination with numerical differentiation method to obtain a suboptimal solution. Monte Carlo (MC) simulations verify the accuracy of our derived exact OP. Results show that our proposed TAS‐head strategy is more effective than its benchmarks (TAS‐near/far and TAS‐maj). Furthermore, we prove that PA‐TDR criterion achieves better performance than PA‐ACG in scenarios where the descending order of target data rate is the same with that of channel condition. Our designed Adam algorithm turns out to be more effective in comparison with genetic algorithm (GA) in multi‐user case. Results indicate that our proposed TAS‐head strategy is an efficient method to meet users' QoS requirements, especially in low SNR (or transmit power) regime.

This paper examines cooperative relay networks for physical layer security, utilising amplify-and-forward (AF) and decode-and-forward (DF) protocols in the system. We looked at how likely it is that the hybrid decode and forward (HYDF) wireless cooperative relay network would go down and discovered that the proposed DPSBR scheme does better than the SPSBR scheme in terms of both OP and SOP. The relay nodes serve as reliable and collaborative entities that enhance the security and dependability of the communication system. Mathematical models precisely predict outage probability based on system parameters, including transmit power, channel conditions, and the number of relay nodes. The analytical results provide valuable insights into the security performance of the cooperative relay network, with a focus on physical layer security. However, in a single relay environment, we incorporate distance parameters and path loss exponents. Our proposed model outperforms the AF-DF. OP-SOP schemes concerning, secrecy capacity and analysis have been verified for accuracy through Monte Carlo simulations.

The global expansion of 5G networks has led to a significant increase in network traffic. In data centers, virtual machines (VMs) must be allocated on Physical Machines (PMs) according to a specific topology. Each VM requires specific network resources to function correctly. Consolidated VM deployment can help reduce traffic consumption and prevent bandwidth-related bottlenecks, while loose deployment can minimize VM failure rates and guarantee availability during PM and switch failures. A reasonable VM deployment plan is vital to improve availability and minimize network bandwidth consumption. This paper presents four typical data center architectures, network topologies, and cost matrices extending to generality. A joint optimization model is proposed to measure Virtual Cluster (VC) risk with global availability constraints. A heuristic algorithm is then introduced to minimize the value of the constrained optimization function. The evaluation results indicate that the proposed method is effective and improves performance over the benchmarks.

Transmissive reconfigurable intelligent surface (RIS) is considered as a promising technology for future wireless networks due to its low power consumption and low cost. To guarantee the user's fairness, in this paper, we propose a group-transmissive RIS scheme and formulate a max-min fairness problem via a joint optimization of the RIS transmission coefficient and power allocation. To solve it, we develop an alternating optimization algorithm to divide the original optimization problem into two subproblems. The semidefinite relaxation and successive convex approximation are used to solve each one. Then two subproblems are solved alternately until convergence and the final solutions are obtained. Finally, we present simulation results to validate the efficacy of our proposed scheme.

In this paper, we investigate a millimeter wave integrated sensing and communication system with aid of the hybrid reconfigurable intelligent surface (HRIS), where the dual-function radar and communication station (DFBS) applies the hybrid precoding structure. On this basis, we consider the sensing and communication performance, respectively, and formulate two optimization problems. One is to maximize the worst-case illumination power while ensuring the communication quality, and another is to maximize the total achievable rate while satisfying the sensing performance. To solve them, we first decouple each nonconvex problem into three subproblems via the alternative optimization technique. For the former one, we transform DFBS and HRIS beamforming optimization subproblems into the convex ones by the quadratic constrained quadratic programming (QCQP) and semidefinite program relaxation (SDR) techniques, and obtain the solutions by standard convex optimization technique. For the later one, fractional programming is applied to decouple the objective function, and then we transform DFBS and HRIS beamforming design subproblems into the convex ones by QCQP and Taylor expansion techniques, and obtain the solutions by the alternating direction method of multipliers (ADMM). For the hybrid precoding design subproblems of DFBS in both problems, a manifold optimization-alternating minimization (MO-AltMin) algorithm based on minimizing the Euclidean distance is used to obtain the solutions. Simulation results show the effectiveness of the proposed schemes.

Mobile edge computing (MEC) is expected to provide low-latency computation service for wireless devices (WDs). However, when WDs are located at cell edge or communication links between base stations (BSs) and WDs are blocked, the offloading latency will be large. To address this issue, we propose an intelligent reflecting surface (IRS)-assisted cell-free MEC system consisting of multiple BSs and IRSs for improving the transmission environment. Consequently, we formulate a min–max latency optimization problem by jointly designing multiuser detection (MUD) matrices, IRSs’ reflecting beamforming vectors, WDs’ offloading data size and edge computing resource, subject to constraints on edge computing capability and IRSs phase shifts. To solve it, an alternating optimization algorithm based on the block coordinate descent (BCD) technique is proposed, in which the original nonconvex problem is decoupled into two subproblems for alternately optimizing computing and communication parameters. In particular, we optimize the MUD matrix based on the second-order cone programming (SOCP) technique, and then develop two efficient algorithms to optimize IRSs’ reflecting vectors based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques, respectively. Numerical results show that employing IRSs in cell-free MEC systems outperforms conventional MEC systems, resulting in up to about 60% latency reduction can be attained. Moreover, numerical results confirm that our proposed algorithms enjoy a fast convergence, which is beneficial for practical implementation.

Wireless powered mobile edge computing (WPMEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for lowpower wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming, and uplink multiuser detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penaltybased optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

Collaborative cloud computing (CCC) has emerged as a promising paradigm to support computation-intensive and delay-sensitive applications by leveraging MEC and MCC technologies. However, the coupling between multiple variables and subtask dependencies within an application poses significant challenges to the computation offloading mechanism. To address this, we investigate the computation offloading problem for CCC by jointly optimizing offloading decisions, resource allocation, and subtask scheduling across a multi-core edge server. First, we exploit latency to design a subtask dependency model within the application. Next, we formulate a System Energy-Time Cost ($SETC$) minimization problem that considers the trade-off between time and energy consumption while satisfying subtask dependencies. Due to the complexity of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Maximum Local Searching Offloading (MLSO) and Sequential Searching Offloading (SSO), to jointly optimize offloading decisions and resource allocation. We then model dependent subtask scheduling across the multi-core edge server as a Job-Shop Scheduling Problem (JSSP) and propose a Genetic-based Task Scheduling (GTS) algorithm to achieve optimal dependent subtask scheduling on the multi-core edge server. Finally, our simulation results demonstrate the effectiveness of the proposed MLSO, SSO, and GTS algorithms under different parameter settings.

Abstract The design of an optimal non‐orthogonal multiple access (NOMA) transmission scheme with finite alphabet inputs for a typical two‐user uplink wireless communication system is investigated in which each terminal is equipped with a single antenna. Each of the two users utilises a four quadrature amplitude modulation (4‐QAM) constellation to transmit information data to a common base station, and the receiver employs a maximum likelihood (ML) detector to jointly estimate both transmitted signals. Assuming the availability of channel state information at both the transmitters and the receiver, it is aimed to design a pair of scalar beamformers for the two users such that the minimum Euclidean distance between elements of the received sum‐constellation is maximised subject to the power constraints on the users. A thorough consideration of all the different conditions results in the derivation of a closed‐form optimal beamformer design. As well, examination of the construction of sum‐constellation resulted from the optimum design directly leads to the unique decoding of the original transmitted signal of each user. To facilitate practical implementation, a fast decoding procedure of the optimum NOMA scheme is further developed. The corresponding theoretic probability of ML detection error is also derived. The theoretical development of an optimum sum‐constellation for the basic 2‐user 4‐QAM system provides a solid platform for the derivation of an optimum sum‐constellation for a K ‐user and/or M ‐QAM system. Indeed, a simple development of the basic sum‐constellation map facilitates such extensions. Numerical simulations not only demonstrate that the performance of the fast decoder agrees closely with the theoretical analysis, but also verify that it is superior in performance to other existing NOMA designs for the same system under high signal‐to‐noise ratio.

Low altitude platform (LAP) unmanned aerial vehicles (UAVs), also called drones, are currently being exploited by Edge computing (EC) systems to execute complex resource-hungry use cases, such as virtual reality, smart cities, autonomous vehicles, etc., by attaching portable edge devices on them. However, a typical drone has limited flight time, coupled with the resource-constrained attached edge device, which can jeopardize aerial computing missions if they are not holistically taking into consideration. Moreover, the fundamental challenge is how to co-schedule multi-drone among multi-location where EC services are needed, such that drones are scheduled to maximize the utility from the activities while meeting computing resource and flight time constraints. Therefore, for a given fleet of drones and tasks across disjointed target locations in a city, we derive a machine learning (ML) linear regression model that estimates these tasks resource requirement and execution time. Leveraging this estimation values, we jointly consider each drone’s flight time availability and its attached edge device resource capacity, and formulate a novel Multi-Location Capacitated Mission Scheduling Problem (MLCMSP) that selects suitable drones and co-schedules their flight routes with the least total distance to visit and execute tasks at the target locations. Then, we show that faster scheduling and execution of complex tasks at each location, while considering the inter-task dependencies is important to achieve effective solution for our MLCMSP. Hence, we further propose EdgeDrones, a variant bin-packing optimization approach through gang-scheduling of inter-dependent tasks that co-schedules and co-locates tasks tightly so as to achieve faster execution time, as well as to fully utilize available resources. Extensive experiments on Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) show that EdgeDrones achieves up to 73% higher resource utilization, up to 17.6 times faster executions, and up to 2.87 times faster flight travel time compared to the baseline approaches.

Internet of Vehicles (IoV) enables a wealth of modern vehicular applications, such as pedestrian detection, real-time video analytics, etc., that can help to improve traffic efficiency and driving safety. However, these applications impose significant resource demands on the in-vehicle resource-constrained Edge Computing (EC) device installation. In this article, we study the problem of resource-aware offloading of these computation-intensive applications to the Closest roadside units (RSUs) or telecommunication base stations (BSs), where on-site EC devices with larger resource capacities are deployed, and mobility of vehicles are considered at the same time. Specifically, we propose an Integrated EC framework, which can keep edge resources running across various in-vehicles, RSUs and BSs in a single pool, such that these resources can be holistically monitored from a single control plane (CP). Through the CP, individual in-vehicle, RSU or BS edge resource availability can be obtained, hence applications can be offloaded concerning their resource demands. This approach can avoid execution delays due to resource unavailability or insufficient resource availability at any EC deployment. This research further extends the state-of-the-art by providing intelligent multi-task scheduling, by considering both task dependencies and heterogeneous resource demands at the same time. To achieve this, we propose FedEdge, a variant Bin-Packing optimization approach through Gang-Scheduling of multi-dependent tasks that co-schedules and co-locates multi-task tightly on nodes to fully utilize available resources. Extensive experiments on real-world data trace from the recent Alibaba cluster trace, with information on task dependencies and resource demands, show the effectiveness, faster executions, and resource efficiency of our approach compared to the existing approaches.

To realize high-quality group gaming services, players’ gaming demands and computing tasks can be offloaded to edge servers through mobile edge computing, which can reduce response latency. However, how to achieve fairness in group gaming is an open problem. In this paper, we propose a solution to the above problem for a typical group gaming genre, namely Multi-player Online Battle Arena (MOBA). Firstly, we formulate the edge user allocation problem which needs to develop reasonable allocation strategies for players and offload their computing tasks to the appropriate edge server to improve their gaming experience. Secondly, we design a user allocation algorithm called MOBA-EUA. It’s a two-phase heuristic approach. In the first phase, players will be allocated to available edge servers that satisfy multiple constraints based on the principle of minimizing system responsiveness. Then they proactively perform reallocations to achieve inter-group fairness, until the grouping delay difference cannot be smaller. Finally, experimental results prove the effectiveness of our proposed algorithm.

Mobile edge computing (MEC) offloads tasks to the MEC server located at the edge of the network, which can not only solve intensive computing but also can ensure computation with low latency. In the research of MEC, there are few research on user mobility and inter-user relevance. In this paper, we consider the task computing of relevant users in the mobile process. We combine MEC with local computing to minimize the weighted sum of user’s delay and energy consumption. First, we propose a joint optimization problem of offloading strategy and resource allocation. Then, we design an iterative algorithm based on the one-time offloading principle and delay constraints, according to the inter-user relevance and user mobility. We adopt a dichotomy to achieve resource allocation and obtain the optimal solution of the objective function. The experimental results show that the proposed iterative offloading algorithm can effectively reduce the delay and energy consumption when considering the relevance and mobility of users.

Although the Internet of Things (IoT) devices have been widely used for data collection in various applications, the observed data of an object from each IoT device (i.e., source) may vary from the ground truth due to the different qualities of IoT devices and sensing environments. Truth discovery has become a promising technology to extract the truth among multiple conflicting pieces of data from different sources. Existing methods usually assume the quality of source (source reliability) is unknowna prioriand will be estimated as the weight for calculating the truth during each truth discovery task. However, in a long-term data observation scenario, the quality of source can be accumulated and utilized in the future truth discovery process. Aiming to take the long-term quality of source into consideration, in this article, we propose a reputation-based truth discovery method to derive the truth from the conflicting data. Specifically, we propose a generalized formulation with linear constraint for the truth discovery problem, which can cope with different regulations on the source reliability. Instead of directly using weight as the source reliability, we also define the reliability of a source by its contribution to the loss function. Then, we propose a novel reputation model to quantify the newly defined source reliability, which will be accumulated as the long-term source quality. Finally, we propose a reputation-based truth discovery model, where initial weights are assigned based on source reputations. Experiments conducted on real-weather conditions and GPS data sets demonstrate that our reputation-based truth discovery can reduce the number of iterations during truth discovery and achieve high accuracy.

The novel fog computing can save substantial resources for resource-constrained mobile users with computation offloading. However, in the existing on-demand schemes, the fog node cannot satisfy the users' demands during peak time due to its limited resources. Therefore, an efficientallocation scheme is desirable, in which the users' priority is evaluated and sorted based on their features, e.g., trustworthiness. In terms of the trust, allocating resources and motivating users to behave cooperatively are important for network efficiency and fairness. In this paper, a long-term trust-based offloading scheme (LTOS) is proposed with a resource allocation scheme and a non-cooperative game. Firstly, a long-term trust evaluation scheme is designed considering the users' behaviors in the task assignment process. In the offloading process, the computation and transmission resources are allocated to users based on their trust values. In addition, a non-cooperative offloading game is formulated to maximize users' utilities, which considers the joint optimization of energy cost and delay. The simulation results demonstrate that our proposed long-term trust-based offloading scheme is more efficient than existing on-demand methods in terms of energy cost and delay. Moreover, the task acceptance and trust level of users can be improved with the proposed LTOS.

In Internet of Vehicles (IoV), vehicle-based crowd-sensing provides various significant data, such as weather condition and GPS data, etc. However, due to different source quality, the sensed data of vehicles may vary from the ground truth. Truth discovery is usually used to analyze conflicting data, and it traditionally estimates source quality only from the current task. Aiming to take the long-term reputation into consideration, in this paper, we propose a trust-aware model. Specifically, we first propose a new model to define vehicle reliability. Instead of using weight as reliability, we define source reliability by its contribution to the loss function. Then, we propose a novel trust-aware model to accumulate long-term reputation of vehicle from its history tasks. Finally, based on vehicle reputation, initial weights are assigned for the future task. Experiments conducted on weather conditions and GPS datasets demonstrate that our trust-aware model can save execution time and achieve higher accuracy.

To make full use of the resources of multi-core CPU and improve the system performance, most processors adopt multithreaded technology. However, how to achieve the energy-efficient offloading strategy for multithreaded computing remains an open problem. In this paper, we provide a collaborative cloud and edge computing offloading strategy to reduce energy consumption. Firstly, we formulate a joint optimizing offloading decision and computation resource allocation problem. Then, we design a collaborative cloud and edge computing search offloading (CCESO) algorithm. Based on this, the energy consumption minimization problem of a single thread application is transformed into a convex optimization problem through the time allocation strategy to achieve the optimal solution of the objective function. Secondly, for multithreaded applications, the cooperation scheme and offloading strategy between multi-thread are given to reduce the energy consumption of multithreaded. Experimental data show that the proposed collaborative cloud and edge computing offloading strategy can effectively reduce energy consumption.

With the increasing development of cloud computing and wireless technology, mobile cloud computing has been developed to alleviate the limitation of battery capacity and computing capability of the mobile device by offloading some computation‐intensive tasks onto the cloud. However, the extra consumption for transmission from the mobile device to the remote cloud may lead to degradation of performance. To this end, the authors develop a Markov decision process‐based computation offloading (MDPCO) algorithm to minimise the energy efficiency cost (EEC) from a global perspective by jointly optimising the resource allocation and offloading decisions. Firstly, they formulate an EEC minimisation problem for a single‐chain application with M tasks. Due to the difficulty to directly solve the formulated problem, they decompose it into multiple subproblems and preferentially optimise the local computing frequency and transmission power by distributed algorithm under hard time constraints. Based on this, they proposed the Markov decision process‐based offloading algorithm to preschedule the computing side for each task from a global perspective to minimise the EEC further. The simulation results show that the performance of the MDPCO algorithm is significantly superior to that of the other algorithms under different parameters.

Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three‐layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed‐form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption.

Nowadays, advanced smart mobile devices equipped with multi‐core central processing units for handling multithreaded (MT) applications. However, existing research mainly uses single‐thread (ST) computing to deal with applications, which limits the performance of mobile computing. To make full use of multi‐core resources, this study proposes a fine‐grained MT offloading strategy to solve the offloading problem of MT application. The strategy jointly schedules cloud computing resources, as well as local multi‐core computing and communication resources. Precisely, the authors first formulate the minimum energy consumption problem for ST offloading. Then, they prove that the problem is convex and solve it by standard convex optimisation technique. Thirdly, they extend the optimisation goals from ST applications to MT applications, and design calculation rules for MT applications to reduce computing costs. Finally, based on these calculation rules and the optimal solution for ST offloading, they develop a MT offloading strategy to solve the computation offloading problem of MT applications. Simulation results show that the proposed fine‐grained MT offloading strategy effectively reduces the minimum delay requirement of mobile computing.

Modern multi‐core mobile devices are the main application objects of mobile cloud computing (MCC). In previous works, researchers have formulated various heuristic algorithms to solve the NP problem. This work combines MCC with multi‐threaded computing (MTC) of multi‐core mobile devices to avoid NP problems and proposes an MTC‐based MCC offloading strategy. First, the authors design an MTC strategy for the application model of cloud computing. Then, they use the data transmission scheme that is dynamically adjusted according to the fading channel state. Finally, based on the MTC strategy and the optimal data transmission scheme, they obtain the MTC‐based MCC offloading strategy through a linear time searching algorithm. Simulation results show that compared with the local MTC strategy and the single‐threaded MCC offloading strategy, the MTC‐based MCC offloading strategy can significantly reduce energy consumption and improve the computing ability in multi‐threaded applications.

The number of wireless devices is growing rapidly on a daily basis echoing the increasing number of applications of the Internet of Thing. Facing massive connections and unavoidable interference, how to provide a green communication is a concerning matter. In this regard, nonorthogonal multiple-access (NOMA) is a natural communications technology that can scale with the massive number of simultaneous connections for a limited bandwidth. In this paper, we aim to maximize the energy efficiency (EE) for an NOMA-based cloud radio access network, where sub-6 GHz and millimeter wave bands are used in fronthaul and access links, respectively. In particular, we formulate the power optimization problem to maximize the EE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form. A two-layer algorithm is then proposed. In the outer loop, the ℓ1-norm technique is adopted to transform the nonconvex fronthaul capacity constraint into a convex one, whereas in the inner loop, the weighted minimum mean square error approach is applied. Simulation results indicate that the proposed NOMA scheme can obtain higher EE as well as throughput when compared with orthogonal multiple-access methods.

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

This paper presents a mobile computation offloading strategy, a novel framework which combines the static information and the dynamic partition to achieve low latency and energy cost. Previous works can lead to either high resource cost or inaccurate offloading decisions. In the proposed method, the static information is introduced into the strategy establishment process. In static information extraction, two offline strategies are established with the best and worst predicted communication quality. Then, strategies are compared with each other to find the same decisions, and every component is labeled as non-removable, removal or removable; in dynamic partition, the removable components are allocated into mobile terminal and cloud with the practical communication condition. Additionally, a linear time search method is proposed to find the optimal partition of application. To evaluate the strategy performance, three applications are used to test the efficiency of the strategy. The experiment demonstrates that the proposed strategy enables more resource saving in energy cost and latency than existing methods.

The effects of a primary user's (PU) multiple random arrivals and departures on the throughput of the sensing-based spectrum sharing cognitive radio networks are investigated in this paper. We give a comprehensive study on the probability that the PU change its state n times during the whole frame time, and propose a new model to describe the PU traffic. Based on this proposed model, an optimal power allocation strategy with four-level is presented, to maximize the achievable throughput of the SU. Simulation results show that the throughput of the SU can be improved considerably by our scheme.

A non-cooperative game approach is employed to optimize the energy efficiency (EE) for multi-user cognitive radio over multi-input-multi-output (MIMO) interference channels (ICs). Both the per-secondary-user (SU) power constraints and the total interference threshold are taken into consideration in the problem formulation. Although optimizing EE for the formulated multi-constraint fractional problem is non-convex and multi-objective, we show that it can be reformulated as an equivalent multi-objective unconstrained non-fractional problem. A distributed iterative EE optimization algorithm (DIEEOA) for multi-user cognitive radio over MIMO ICs is proposed to achieve the Nash Equilibrium of the non-cooperative game. Effectiveness of the algorithm is validated through computer simulation, and system parameters' impact on the EE is discussed.

Multicast technology will play a very important role in the future multimedia communication application. In this paper, a new transmission mechanism called multi-group multicast (MGMC) based on inter-group and inner-group cooperation is proposed for cognitive networks. The new transmission mechanism sets multiply multicast groups as a pair, and then these multicast groups transmit information in a cooperative way by using the same frequency resource. We formulate a weighted overall rate optimization problem with interference constraints to the primary user (PU) and peak power constraint at each user and obtain the optimal solution by theoretical analysis in this new transmission model. Numerical results show the impact of user number on rate for every multicast group.

This study investigates sensing‐based spectrum sharing access (SSSA) and sensing‐based spectrum opportunistic access (SSOA) schemes in cooperative orthogonal frequency division multiplexing (OFDM)‐based cognitive radio networks with imperfect spectrum sensing. The optimal resource allocation strategy, including sensing time and transmit power, is designed to maximise the ergodic throughput of the secondary system. For a two‐hop cooperative communication in the secondary system, the authors adopt the amplify‐and‐forward relay protocol and enable the source and relay to sense the state of the primary user jointly. To protect the PU effectively from harmful interference, they consider the average interference power constraint in each hop. The total average transmit power constraint of the source and relay is considered. Two simplified versions for the SSSA and SSOA schemes are employed because of the complexity of the problem. They then propose two algorithms that acquire the optimal sensing time and power allocation for both schemes. Finally, simulation results are presented to compare the performance of the two schemes.

SUMMARY Because of the presence of incumbents in cognitive radio networks, the unused spectrum in the TV bands, popularly referred to as ‘white spaces’, are fragmented with the size of each fragment varying from one TV channel to several TV channels. What is more, because the secondary transmissions adjust their spectrum usage over time, white spaces become increasingly partitioned into a collection of discrete fragments, which decreases the spectral utilization. To improve throughputs, most of the prior researches focused on selecting the best transmission channel in the context of spectrum fragmentation but have rarely involved aggregating the fragmentation to a contiguous channel. In this paper, we present two adaptive spectrum access strategies, both of which not only select the best transmission channel but also efficiently solve the fragmentation problem. The first strategy involves one‐agile radios that build a transmission using single fragment of frequency, which partially remedy the fragmentation problem using higher‐layer solutions. The second strategy suppresses the impact of spectrum fragmentation successfully at the physical layer by combining k spectrum fragments to form a single transmission. The simulation results show that both of the strategies bring larger throughputs compared with the prior solutions. Copyright © 2012 John Wiley & Sons, Ltd.

The extraction of rotation invariant representation is important for many signal processing problems such as image analysis, computer vision, and pattern recognition. In this paper, we present a systematic analysis of the Two-Dimensional Fractional Fourier Transform (2D-FRFT), and show that under certain conditions, the 2D-FRFT technique possesses the attractive property of rotation invariance. Based on our analysis, we proposed a novel digital image watermarking method which combines 2D chirp signal with the addition and rotation invariant properties of 2D-FRFT to achieve improved robustness and security. The effectiveness of the proposed solution is demonstrated through experiments.

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.