To address the waste of spectrum resources caused by the separate operation of integrated sensing and communication (ISAC) and mobile edge computing (MEC), the integrated sensing, communication, and computing (ISCC) paradigm has been proposed. However ISCC systems are still facing serious channel fading and obstacle occlusion problems, which are expected to be solved by the emerging simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In this paper, we propose a STAR-RIS-assisted ISCC framework, where the computational tasks from users are offloaded to a base station (BS) for processing with the assistance of a STAR-RIS, and the results are subsequently downloaded back to the corresponding users. Notably, target sensing operation is executed concurrently throughout the process of result downloading. To enhance communication efficiency, the non-orthogonal multiple access (NOMA) scheme is incorporated. The number of offloading bits of users is maximized by optimizing the uplink-downlink resource of the network, including the received beamforming vector, active beamforming vector, time slot, phase shift of STAR-RIS, and computational resource. In order to solve the highly complex non-convex problem, we employ the block coordinate descent (BCD) framework to decompose the proposed problem into three subproblems. The convex-concave procedure (CCCP) method is used to deal with the nonconvex constraints. For the rank-1 constraints, the semidefinite relaxation (SDR) approach and the penalty method are invoked. The numerical results show that: 1) the application of STAR-RIS significantly improves the offloading capability of the network, 2) the NOMA scheme significantly outperforms the orthogonal multiple access (OMA) scheme, and 3) comparing with other benchmark schemes, the proposed algorithm significantly improves the overall network performance.

A reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system is investigated, where the transmitter (Alice) is a dual-functional radar-communication (DFRC) base station (BS) that aims to sense the location of a potential warden (Willie), while simultaneously transmitting public and covert signals to the legitimate users, Carol and Bob, respectively. Both cases of known and unknown Willie locations are considered. For the known-location case, assuming perfect channel state information (CSI) at Willie, a covert rate maximization is formulated with the joint optimization of active and passive beamforming, which is solved using successive convex approximation (SCA), penalty method, and semidefinite relaxation (SDR). For the unknown-location case, we propose to estimate Willie’s location via radar sensing and develop a sensing-based imperfect CSI model. In particular, the CSI error uncertainty is bounded by the sensing accuracy, which is characterized by the Cramér-Rao bound (CRB). Subsequently, a robust communication rate maximization problem is formulated under the constraints on quality-of-service (QoS) of Carol, sensing accuracy, and covertness level. The Schur complement and S-procedure are employed to handle the non-convex constraints. Numerical results compare the system performance under the two cases, and demonstrate the significant covert performance superiority of the sensing-based imperfect CSI model and NOMA over the general norm-bounded imperfect CSI model and the orthogonal multiple access scheme. Furthermore, the dual yet contradictory effects of sensing on covert communications are revealed. It is also found that Alice primarily utilizes Carol’s signal for sensing, while allocating almost all of Bob’s signal for communication.

This article studies the energy efficiency optimization of cargo uncrewed aerial vehicle (UAV) pickup systems, with constraints on on-board energy and load capacity. In the UAV-enabled cargo pickup system, minimizing the total energy consumption and ensuring the safe flight of the cargo UAV is a problem to be solved. However, due to building blockages, the channel between the UAV and ground base stations (GBSs) frequently switches between line-of-sight (LoS) and non-line-of-sight (NLoS), thereby affecting the UAV’s communication quality. This effect is further aggravated by environmental noise interference. Moreover, limited by the on-board energy, it is unrealistic for the UAV to pick up all the cargo in a single flight without charging or replacing the battery. To address the above-mentioned challenges, we propose a UAV pickup system energy efficiency optimization (UPSEEO) framework. In this framework, the UAV’s trajectory between any two pickup points is optimized via the A${}^{*}$algorithm to ensure the stability of the UAV communication link. Next, we employ the particle swarm optimization (PSO) algorithm to optimize both task allocation and flight speed to minimize the total energy consumption, subject to constraints on UAV on-board energy limits and payload capacity. Numerical results show that the proposed framework can ensure the UAV’s communication quality in any spatial topology, with an improvement in energy efficiency of approximately 5% to 50% compared to the comparison experiment.

Emerging multimodal systems present new requirements for mobile edge networks to handle multimodal data. In this paper, a novel multimodal mobile edge computing (MEC) framework is proposed, which synchronizes the multimodal data acquisition, communication, and computation to ensure both consistency and efficiency. The key objective is to simultaneously maximize multimodal data throughput and minimize the energy consumption of mobile terminals (MTs) under synchronization and resource constraints. A multi-objective optimization (MOP) is formulated, where the sensor activation time, computation offloading, and resource allocation are jointly optimized. To solve this nonconvex problem, a dual-layer Lagrangian multiplier method (D-LMM) is developed. It decouples the optimization into an upper-level throughput maximization and a lower-level energy minimization. The former is converted into a convex problem via quadratic transformation, yielding a stationary solution for sensor activation times, while the latter is solved by alternating optimization. The D-LMM algorithm is proven to converge to a local optimum. Simulation results verify that the proposed framework significantly improves throughput and reduces MT energy consumption. The synchronization-aware multimodal coordination further ensures sufficient data collection and robust performance across varying network scales and resource conditions, enabling reliable downstream operations.

Mobile edge computing (MEC) facilitates high reliability and low-latency applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for classification intelligence tasks (CITs) in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum utility of our system. We focus on CITs and formulate an optimization problem that considers task characteristics including the accuracy requirements and the parallel computing capabilities in MEC systems. To solve the proposed problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation and compression offloading. We use successive convex approximation and convex optimization method to derive optimized feasible solutions for the subcarrier allocation, offloading variable, computing capacity allocation, and compression ratio. Based on our solutions, we design an efficient joint computing offloading and resource allocation algorithm for CITs in MEC systems. Our simulation demonstrates that the proposed algorithm significantly improves the performance by 16.4% on average and achieves a flexible trade-off between system revenue and cost considering CITs compared with benchmarks.

An extremely large-scale intelligent reflecting surface (XL-IRS) aided communication system is studied. Although XL-IRS can effectively combat the double path-loss attenuation, the large aperture size introduces significant near-field effects. The complex near-field propagation and large number of XL-IRS elements can lead to optimality and complexity challenges in beam focusing design. Focusing on a spectral efficiency (SE) maximization problem, two unsupervised learning based algorithms are conceived for the joint optimization of base station and XL-IRS beam focusing, which operate without pre-training and exhibit strong robustness. Specifically, a dense-connected dilated autoencoder meta learning (DDAML) algorithm is proposed to achieve high SE by utilizing dense connections, while reasonably designing an autoencoder to reduce computational complexity. Furthermore, considering a need for low execution time in practical applications, a convolutional dilated autoencoder meta learning (CDAML) algorithm is also proposed to further reduce computational complexity. Simulation results show that the proposed DDAML algorithm achieves the highest SE, while the proposed CDAML algorithm significantly reduces computational complexity at the cost of limited SE loss. Moreover, the two proposed algorithms also demonstrate remarkable robustness in XL-IRS-aided near-field communications.

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multiuser multiserver MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy tradeoff cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy tradeoff cost and its adaptability for various applications.

In this paper, a cellular-connected uncrewed aerial vehicles (UAVs) pickup and delivery system is studied in which the multiple UAVs are served by ground base station (GBS). These UAVs commence operations from the hangar, systematically execute a sequence of pickups and deliveries before returning, all while maintaining a continuous and reliable communication link with the GBS throughout their missions. Owing to the limited onboard energy resources of UAVs and the influence of task sequence and payload characteristics on UAV’s energy consumption, this study focuses on minimizing energy usage through the optimization of task sequences and flight trajectories. Firstly, a radio map of the operational area is constructed to identify flight zones that ensure reliable communication, an improved Dijkstra algorithm is then proposed to compute the shortest viable path between any two access points that satisfy reliable communication criteria. Furthermore, a chromosome structure tailored for a hybrid genetic algorithm (HGA) is devised to address the complexities of multi-UAV task allocation. With the aid of a developed distance matrix, the HGA is utilized to determine the optimal delivery sequence, thereby achieving the objectives of minimizing the maximum energy consumption (MME) and minimizing the sum energy consumption (MSE) respectively. Finally, simulation analyses demonstrate that the proposed MME and MSE optimization strategies enable approximately a 50% reduction in peak flight energy consumption and around a 40% decrease in total energy consumption, compared to multi-UAV pickup and delivery systems without energy optimization.

In this paper, we consider a cargo autonomous aerial vehicle (AAV)-aided multi-parcel pick-up and delivery network, where the communication ability of the AAV is provided by the ground base stations (GBSs). For such a system setup, our goal is to optimize the trajectory of the cargo AAV while minimizing the combined impact of total energy consumption and total outage time. Simultaneously, we aim to maximize overall user satisfaction throughout the entire flight duration. More specifically, we propose a pick-up and delivery of AAV (PDU) framework to address this problem and this framework consists of two parts. First, a simulated annealing (SA) algorithm is used to obtain the pick-up and delivery (P&D) order of parcels. On the basis of obtaining the P&D order through SA, we further use deep reinforcement learning (DRL) to optimize the flight trajectory of the AAV to ensure the expected communication quality between the AAV and GBSs. To verify the effectiveness of our proposed algorithms, we design three baseline strategies for comparison, and also investigate the effect of using the PDU framework with different weights. Finally, numerical results show that the performance of PDU strategy is improved by about 5%-30% compared with other strategies in solving the performance tradeoff of AAV energy consumption, communication quality, and user satisfaction.

The evolution towards Internet of Things (IoT) in the forthcoming sixth generation (6 G) is facing massive amounts of transmitted data and harsh wireless transmission environment, which severely degrade the quality of communication. To overcome these difficulties, a novel multiple intelligent reflecting surfaces (IRSs) assisted unmanned aerial vehicle (UAV) network framework with non-orthogonal multiple access (NOMA) is proposed in this article, where the UAV applies the NOMA scheme to deliver the information to the ground users assisted by multiple IRSs. We aim to maximize the achievable rate of the considered network while guaranteeing the minimum communication rate of each user, by jointly optimizing the multi-IRS phase shifts, UAV transmit power, UAV trajectory, and NOMA decoding order. To handle the coupled variables and integer constraints, we decompose the original problem into three subproblems based on the block coordinate descent (BCD) framework. Specifically, we first obtain the multi-IRS phase shifts by applying the semidefinite relaxation (SDR) technique. Next, the UAV transmit power allocation is derived by exploiting the concave convex procedure (CCCP) method. The UAV trajectory and NOMA decoding order are finally obtained by invoking the penalty-based method and the successive convex approximation (SCA) technique. Based on these, an alternating optimization algorithm is proposed. The numerical results show that: 1) the NOMA scheme enhances the utilization of the spectrum and enhances the access capacity of the communication system; 2) the multi-IRS cooperative structure increases the reflective channels and effectively improves the air-ground transmission environment, thus enhancing the system achievable rate; 3) the proposed multi-IRS assisted UAV NOMA algorithm achieves a significant network rate improvement compared to other benchmark schemes.

Mobile edge computing (MEC) enables the provision of high-reliability and low-latency applications by offering computation and storage resources in close proximity to end-users. Different from traditional computation task offloading in MEC systems, the large data volume and complex task computation of artificial intelligence involved intelligent computation task offloading have increased greatly. To address this challenge, we propose a MEC system for multiple base stations and multiple terminals, which exploits semantic transmission and early exit of inference. Based on this, we investigate a joint semantic transmission and resource allocation problem for maximizing system reward combined with analysis of semantic transmission and intelligent computation process. To solve the formulated problem, we decompose it into communication resource allocation subproblem, semantic transmission subproblem, and computation capacity allocation subproblem. Then, we use 3D matching and convex optimization method to solve subproblems based on the block coordinate descent (BCD) framework. The optimized feasible solutions are derived from an efficient BCD based joint semantic transmission and resource allocation algorithm in MEC systems. Our simulation demonstrates that: 1) The proposed algorithm significantly improves the delay performance for MEC systems compared with benchmarks; 2) The design of transmission mode and early exit of inference greatly increases system reward during offloading; and 3) Our proposed system achieves efficient utilization of resources from the perspective of system reward in the intelligent scenario.

Differential privacy is a promising framework for computing on sensitive data while preserving privacy. However, balancing privacy and accuracy remains a significant challenge. In this paper, the DPAdaMod_AGC algorithm is proposed to address the above challenge, which uses adaptive gradient clipping to improve the accuracy of deep learning models without compromising privacy. By reducing the inclusion of nonessential noise during training, the proposed algorithm combines stochastic gradient descent with gradient clipping striking an effective trade-off between privacy and accuracy. Simulation analysis shows that compared with the DPAdaMod algorithm, the algorithm proposed in this paper not only improves the classification accuracy by 2.3%, but also consumes less privacy budget while protecting privacy. The results demonstrate the superiority of the DPAdaMod_AGC algorithm in achieving both privacy and accuracy goals.

Personalised learning seeks to provide a tailored and highly effective learning experience, to maximize the unique potential of individual learners. However, despite the potential benefits, the implementation of personalised learning has not significantly materialized within the current structure of higher education institutions. In this paper, we propose a Guided Personalised Learning (GPL) model, specifically designed to facilitate effective interactions between educators and learners. The GPL model empowers learners to develop their tailored learning plans, while enabling educators to adapt their teaching and embrace student-centred pedagogy to address the diverse learning needs of students in the same classroom. We developed prototypes for the practical implementation of the GPL model in two undergraduate engineering courses and conducted initial evaluations of their effectiveness.

The advent of mobile edge computing (MEC) technology offers new prospects for executing demanding applications close to the user. However, complex applications like intelligent transportation and autonomous driving pose modeling and problem-solving challenges due to inter-user service logic correlations. Therefore, we construct a model that considers the terminal's mobility, time-varying channel status, and inter-user task dependencies and formulate a problem aiming to optimize the task completion delay and the energy consumption weighted cost in a dynamic MEC scenario. To resolve this problem, a Double Deep Q Network (DDQN)-based algorithm is developed for task offloading, while integrated sub channel allocation and transmit power control constitute part of the interaction with the dynamic environment to generate the reward signal, optimizing the long-term system performance. Comprehensive simulations verify that the proposed algorithm outperforms the comparative methods in terms of reducing the cost, and its adaptability in different scenarios has also been validated and analyzed.

Applying millimeter wave (mmWave) in mobile edge computing (MEC) networks helps to improve task offloading performance from both communication and computing aspects. In this paper, we propose a MEC-enabled heterogeneous network in which mmWave small cells are deployed overlaid sub-6GHz macro cells. Base stations in two tiers are equipped with MEC servers characterized by different computing capacity and computing buffer. An association strategy based on the MEC service area is designed. In order to analyze the offloading performance, we derive the successful offloading probability and offloading latency with the tools of stochastic geometry and queueing theory. Simulation results demonstrate that there exist optimal values of small base station density and MEC service radius to obtain the maximum successful offloading probability and the minimum offloading latency, which provides useful guidance for the practical deployment.

This paper proposes a joint sensing, communication and computation (JSCC) framework in unmanned aerial vehicle (UAV)-assisted systems, where multi-functional terminal devices (TDs) can perform high-accuracy radar sensing as well as offload computation data to an airborne mobile edge computing (MEC) server over the same frequency band. The key objective of the JSCC framework is to simultaneously minimize the transmitted sensing beampattern matching error whilst maximizing the minimum computation efficiency of TDs. This problem is formulated as a multi-objective optimization problem (MOOP) that jointly optimizes the transmit beampattern, computation offloading, and UAV trajectory. To achieve the computation-sensing trade-off region, we first transform the MOOP into a single-objective optimization problem (SOOP) via the 1-constraint method. To make it more tractable, a generalized Dinkelbach’s and successive convex approximation (GD-SCA) algorithm is proposed. Specifically, GD-SCA transfers the non-convex max-min fractional programming in the resultant SOOP by introducing a general auxiliary polynomial via generalized Dinkelbach’s algorithm. Thereafter, the transmit beampattern, computation offloading, and UAV trajectory optimization are decoupled into two nested subproblems, which can be iteratively solved by invoking successive convex approximation (SCA) method to handle the remaining non-convex components. The proposed GD-SCA can obtain high-quality suboptimal solutions of the original MOOP. We validate the effectiveness of the proposed algorithm by considering two multiple access techniques, i.e., non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA). Simulation results demonstrate that the proposed algorithm can achieve an improved computation-sensing trade-off region compared to conventional schemes especially when exploiting NOMA. Moreover, the multi-functional performance can be significantly improved while stringently guaranteeing both radar sensing and computation offloading requirements.

This paper proposes a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) emergency communication network. Multiple STAR-RISs are deployed to provide additional and intelligent transmission links between trapped users and UAV-mounted base station (BS). Each user selects the nearest STAR-RIS for uploading data, and NOMA is employed for users located at the same side of the same STAR-RIS. Considering practical requirements of post-disaster emergency communications, we formulate a throughput maximization problem subject to constraints on minimum average rate and maximum energy consumption, where the UAV trajectory, STAR-RIS passive beamforming, and time and power allocation are jointly optimized. Furthermore, we propose a Lagrange based reward constrained proximal policy optimization (LRCPPO) algorithm, which provides an adaptive method for solving the long-term optimization problem with cumulative constraints. Specifically, using Lagrange relaxation, the original problem is transformed into an unconstrained problem with a two-layer structure. The inner layer is solved by penalized reward based proximal policy optimization (PPO) algorithm. In the outer layer, Lagrange multipliers are updated by gradient descent. Numerical results show the proposed algorithm can effectively improve network performance while satisfying the constraints well. It also demonstrates the superiority of the proposed STAR-RIS assisted UAV NOMA network architecture over the benchmark schemes employing reflecting-only RISs and orthogonal multiple access.

In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the cooperative computation among multiple UAVs can improve the overall computation service capability. Multi-UAV MEC systems can meet the quality of service requirements for computation intensive applications of ground terminals (GTs) in complex field environments, emergency disaster relief and other special scenarios. In this paper, a multi-UAV cooperative computation framework is proposed while taking the GT movement and random arrival of computation tasks into consideration. A long-term optimization problem is formulated for the joint optimization of UAV trajectory and resource allocation, subject to minimizing the total GT computation task completion time and the total system energy consumption. To solve this problem, a joint multiple time-scale optimization algorithm is proposed. In particular, the optimization problem is decomposed into a long time-scale multi-UAV trajectory planning subproblem and a short time-scale resource allocation subproblem. The proximal policy optimization algorithm is invoked to solve the long time-scale subproblem. The greedy algorithm and the successive convex approximation (SCA) method are employed to solve the short time-scale subproblem. Finally, a joint multiple time-scale optimization algorithm with a two-layer loop structure is proposed. Simulation results show that: 1) the proposed multi-UAV cooperative computation MEC system outperforms the conventional MEC system without collaboration among UAVs; and 2) the proposed algorithm can quickly adapt to different degrees of environmental dynamics and outperforms the benchmark algorithm for different network sizes, task requirements, and available resources.

In this paper, we consider an unmanned aerial vehicle (UAV)-based patrol inspection scenario, where the cellular-connected UAV traverses multiple pre-determined waypoints for data collection, and then offloads computation task to the ground base stations (GBSs). This paper aims to minimize the sum of the total energy consumption by jointly optimizing the task completion time, communication scheduling, computation resource allocation, and UAV's trajectory. First, we decompose the original problem into two trackable subproblems: 1) design the optimal traverse order among cruise points; and 2) determine the optimal transmission strategy between two consecutive cruise points. Then, by involving the communication rate performance and the topology construct among the GBSs and the cruise points, a novel weighted factor of the edge is proposed to design the traverse order, which can be compatible with the light and heavy task offloading scenarios. The successive convex approximation (SCA) technique and block coordinate descent (BCD) framework are adopted to optimize the UAV's trajectory and the wireless resource allocation. The numerical results finally indicate that our proposed transmission strategy solution decreases the total energy consumption in various scenarios and outperforms other benchmark schemes.

Hybrid precoding has been recognized as promising and effective for practical 5G communication. It is generally challenging to obtain the sample for deep learning-based hybrid precoding due to its need of massive precoding vector and channel matrix. To effectively solve this issue, a novel coordinated hybrid precoding algorithm based on unsupervised learning graph attention networks (CHP-ULGAT) is first developed by making full use of the underlying topology formed by the channel matrix. Subsequently, a more realistic situation of existing an ultra-low execution time is considered. A sub-optimal coordinated hybrid precoding based on unsupervised learning convolutional neural networks (CHP-ULCNN) is proposed to further reduce complexity. Moreover, we present effective ways to design the multi-matrix operation and the loss function to address the practicability of the algorithm. Extensive simulation results show that the proposed hybrid-precoding algorithms have obvious advantages in spectral efficiency (SE) and energy efficiency (EE) improvement with ultra-low computational complexity, considering the different number of RF chains and deployment scenarios.

Millimeter wave (mmWave) communication combined with massive multiple input multiple output (MIMO) system is one of the most promising technologies in future wireless networks due to the characteristics of high bandwidth and narrow beam. For the strong channel attenuation, the more accuracy channel state information (CSI) is needed to make sure that the signal is received accurately in mmWave system. In this paper, a hardware-friendly channel estimation algorithm, named modular image denoising approximation message passing (MIDAMP), is proposed by combining the real image denoising network (RIDNet) and the learning approximation message passing network. The gap between the estimated channel and the real channel can be greatly reduced through using the powerful denoising ability of MIDAMP. The results of simulation demonstrate that the proposed MIDAMP algorithm has better performance in estimation accuracy and achievable sum rate (ASR) compared with some existing algorithms.

Coded caching manages to release cellular network traffic by increasing transmission rate via satisfying multiple user requests simultaneously. Specific contents stored in the private cache memory are used as side information to decode individual requests from the coded broadcasting messages. Considering local content popularity could improve caching performance dramatically. However, in the mobility scenario, local content popularity varies with user movements. Even worse, contents in the cache memory might become outdated when the user location changes. In this paper, we propose a dynamic coded caching scheme that reduces the loss of coded caching gain due to the user movement and local content popularity dynamic changing. We quantify the relationship between user preference, local popularity, and user mobility. We formulate a metric to measure the performance of the proposed coded caching scheme and propose a reinforcement learning problem to obtain the cache replacement strategy in the mobility scenario. Numerical results verify that our obtained replacement policy significantly outperforms the popularity-based, least-frequently-used, and multilayer replacement policy in terms of traffic offloading.

Unmanned aerial vehicles (UAVs) are deployed in emergency disaster-relief operations to provide communication services as substitutes for damaged ground base stations (BSs), as well as to offload computational tasks for applications such as target recognition. In view of the limited computing power of a single UAV, we focus on the edge computing offloading problem with multiple-UAV cooperation. As a single UAV is not enough to offload massive delay-sensitive computing tasks in the emergency communication scenarios, we have built up a multi-UAV cooperation computing architecture. By exploring the multiple-UAV cooperation computing offloading capacity, we formulated an optimization problem of minimizing the total time slot size. Since the proposed problem is relevant to mixed integer nonlinear programming, it can be decomposed into two sub-problems: computing task scheduling and UAV trajectory. To handle the formulated problems, we developed a joint optimization algorithms by invoking the penalty method and successive convex approximation (SCA) method. The simulation results show that, compared with the benchmark algorithms, the proposed algorithm can significantly reduce the computation task delay and improve the execution efficiency of the UAVs.

This paper investigates the intelligent computing task-oriented computing offloading and semantic compression in mobile edge computing (MEC) systems. With the popularity of intelligent applications in various industries, terminals increasingly need to offload intelligent computing tasks with complex demands to MEC servers for computing, which is a great challenge for bandwidth and computing capacity allocation in MEC systems. Considering the accuracy requirement of intelligent computing tasks, we formulate an optimization problem of computing offloading and semantic compression. We jointly optimize the system utility which are represented as computing accuracy and task delay respectively to acquire the optimized system utility. To solve the proposed optimization problem, we decompose it into computing capacity allocation subproblem and compression offloading subproblem and obtain solutions through convex optimization and successive convex approximation. After that, the offloading decisions, computing capacity and compressed ratio are obtained in closed forms. We design the computing offloading and semantic compression algorithm for intelligent computing tasks in MEC systems then. Simulation results represent that our algorithm converges quickly and acquires better performance and resource utilization efficiency through the trend with total number of users and computing capacity compared with benchmarks.

Deep learning (DL) enabled semantic communications have been developed to improve the offline communication efficiently and intelligently by exploring the semantic information, while constraining their applications in real-time online scenarios. In this work, we propose SemAudio, the first DL-based streaming semantic communication system for real-time audio processing. To better extract the semantic features of the audio signal, SemAudio employs the Transformer-XL due to its potential to capture long-distance dependency. Moreover, the system works based on a chunk-based mask attention strategy to enable real-time streaming. By incorporating the novel Transformer-XL and chunk-wise approach, SemAudio can effectively learn and extract semantic features from real-time audio data. Furthermore, to alleviate the channel distortion and attenuation, the semantic and channel encoder/decoder are jointly designed by minimizing the mean error in both time and frequency domains rather than the merely time domain. The extensive experimental results suggest that our proposed SemAudio outperforms the traditional communications. Besides, the proposed SemAudio compromises the quality and latency to meet real-time requirements, which obtains satisfactory performance with significantly higher accuracy and lower latency under multiple channel conditions for real-time audio communication.

Power consumption caused by radio frequency (RF) chains in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can be solved by beam selection. However, the spectral efficiency of traditional beam selection algorithms is unsatisfactory due to the reduction in the number of RF chains and the multiuser interference. This work proposes a differential evolution (DE)-based beam selection algorithm and an improved QR precoder to reduce power consumption and increase the performance of the systems. The proposed algorithm selects the optimal beams with DE-based beam selection for each user, which reduces the power consumption and the interference among each beam. In addition, to greatly decrease the multiuser interference, we propose an improved QR precoder by equalizing diagonals and using Tomlison-Harashima (TH) theory which can greatly reduce the computational complexity and improve the performance. The simulation results show that the proposed scheme outperforms some existing algorithms in the aspects of energy efficiency and spectral efficiency.

This paper studies a mobile edge computing (MEC) framework for cellular-connected multiple unmanned aerial vehicles (UAVs), where the UAVs compute the tasks locally or offload them to ground base stations (GBSs). Considering the time-varying characteristics of the task arriving, we formulate a stochastic problem to minimize the system's average weighted sum energy consumption, by optimizing UAV-GBS association, communication and computation resource allocation, and UAVs' trajectories. We apply Lyapunov approach to convert the stochastic problem into a deterministic problem that is then solved by invoking Lagrange duality method and successive convex approximation technique, based on which an online joint optimization algorithm is proposed. Moreover, we design a velocity-triggered penalty term (VTPT) to reduce the UAVs' energy. Numerical results validate the effectiveness of the designed VTPT, and also demonstrate that our proposed algorithm not only decreases the energy consumption but also maintains the task queue stability.

In response to the ever-increasing demands of users for delay-sensitive applications, issues on shortening the task completion time in the mobile edge computing (MEC) system has aroused widespread concern. From the perspective of task execution order, this work provide a task scheduling scheme for multiple edge nodes (EN) while federated learning (FL) is utilized for the collaboration of the ENs in the MEC system. First, to acquire an efficient execution order for the pending computational tasks that dynamically generated on one edge node, a task scheduling algorithm based on deep Q network (DQN) is proposed, which reduces the average task completion time. Then, based on the federated learning, of which characteristic matches the edge system well, we integrate an aggregation mechanism to take advantage of every participating edge node to obtain a set of global parameter that optimizes task completion delay for all nodes in the MEC system. Simulations verify the effectiveness and the superiority of the proposed algorithm in processing delay-sensitive tasks and analyze the key factors that contributes to the system performance.

This article studies the collision avoidance and interference mitigation for unmanned aerial vehicle (UAV) swarm where many UAVs track a common target. The considered problem is formulated to jointly minimize the average interference and ensure the collision avoidance among UAVs. By exploiting the problem characteristics, our major contributions are summarized as follows. First, the singular case tolerance (SCT)-artificial potential field (APF) is proposed to overcome the failure of traditional APFs in collision avoidance, where the repulsive force gain coefficient among UAVs is dynamically controlled by the corresponding interferences. Second, the mean-field game (MFG) model is established to control communication power, where the instantaneous interferences among flying UAVs are represented by the mean-field approximation. Third, considering the tight coupling of trajectory and interference of UAVs, a cooperative control approach enabled by dual-fields is proposed to jointly adjust the trajectories and power of UAVs. Simulation results validate the significant performance improvement of the cooperative control approach enabled by dual-fields. Compared with separate APF and MFG, the proposed dual-field-cooperation approach can achieve about 117% throughput gain and 88% interference reduction when the UAV swarm is close to the target.

Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max–min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV’s trajectory. We develop a concave–convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max–min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement.

In Internet of Things (IoT) applications with multihop networking, not only traditional energy efficiency but also many distinct features should be considered when designing routing protocols, including different security requirements, heterogeneity, and scalability. In this article, an energy-efficient multilevel secure routing (EEMSR) protocol in IoT networks is proposed. Considering that clustering is a reasonable solution of conserving energy, a cluster-based multihop routing protocol is utilized to reduce the high communication overhead due to the scalability of IoT networks. In particular, more reasonable analytic hierarchy process and genetic algorithms are adopted to assign accurate weight and optimize intercluster routing in which heterogeneous IoT networks are considered to support large amount of heterogeneous IoT entities and services. Moreover, multiple trust levels are adopted to defend the different attacks by calculating the trust factor on the clustering and routing, including data perception trust, data fusion trust, and communication trust. It is shown that the proposed algorithm outperforms the existing algorithms in terms of network lifetime, throughput, packet delivery ratio, network energy balance, and adaptability.

In this paper, we consider a mobile edge computing (MEC) network where multiple cellular-connected unmanned aerial vehicles (UAVs) can offload their computation tasks to multiple ground base stations (GBSs). In practice, the UAVs are generally unable to master stochastic information of task arrival and channel changes in advance, which may cause a severe issue in terms of energy consumption. Therefore, we formulate a stochastic optimization problem with the goal of minimizing the average weighted sum energy consumption, by jointly optimizing UAV-GBS associations, communication and computation resource allocation, and three-dimensional (3D) UAV trajectories, during which a velocity-triggered penalty term (VTPT) is designed to suppress a large amount of the energy consumption of the UAVs. To handle the stochastic problem, we propose an online resource allocation and trajectory optimization algorithm with outer and inner structures. The outer structure transforms the original problem to a deterministic one by applying the Lyapunov-based optimization framework. The inner structure solves the obtained deterministic problem via the Lagrange duality method and the successive convex approximation technique, based on the block coordinate descent framework. Numerical results demonstrate that: 1) VTPT dramatically decreases the UAVs’ energy consumption, and 2) the proposed algorithm not only reduces the energy consumption but also ensures the computation queue stability compared with other benchmark schemes.

With the rapid growth of data traffic in cellular networks, edge caching has become an emerging technology for traffic offloading. We investigate the caching placement and content delivery in cache-enabling cellular networks. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay which considers both content transmission delay and content update delay. To solve this challenging problem, we decompose the optimization problem into two sub-problems, the user association sub-problem in a short time scale and the caching placement in a long time scale. Specifically, we propose a low complexity user association algorithm for a given caching placement in the short time scale. Then we develop a deep deterministic policy gradient based caching placement algorithm which involves the short time-scale user association decisions in the long time scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We illustrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively.

With the exponential growth of data traffic in wireless networks, edge caching has been regarded as a promising solution to offload data traffic and alleviate backhaul congestion, where the contents can be cached by an unmanned aerial vehicle (UAV) and user terminal (UT) with local data storage. In this article, a cooperative caching architecture of UAV and UTs with scalable video coding (SVC) is proposed, which provides the high transmission rate content delivery and personalized video viewing qualities in hotspot areas. In the proposed cache-enabling UAV-D2D networks, we formulate a joint optimization problem of UT caching placement, UAV trajectory, and UAV caching placement to maximize the cache utility. To solve this challenging mixed integer nonlinear programming problem, the optimization problem is decomposed into three sub-problems. Specifically, we obtain UT caching placement by a many-to-many swap matching algorithm, then obtain the UAV trajectory and UAV caching placement by approximate convex optimization and dynamic programming, respectively. Finally, we propose a low complexity iterative algorithm for the formulated optimization problem to improve the system capacity, fully utilize the cache space resource, and provide diverse delivery qualities for video traffic. Simulation results reveal that: i) the proposed cooperative caching architecture of UAV and UTs obtains larger cache utility than the cache-enabling UAV networks with same data storage capacity and radio resource; ii) compared with the benchmark algorithms, the proposed algorithm improves cache utility and reduces backhaul offloading ratio effectively.

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

Coordinated beamforming (CoBF) for multi-user massive multiple-input and multiple-output (MIMO) heterogeneous networks (HetNets) promises for capacity enhancement. However, challenges of energy efficiency (EE) and ultra-low latency are yet to be addressed due to the circuit power and calculation latency heavily depend on the number of transmit antennas. To solve these problems, a maximizing EE algorithm named coordinated beamforming based on convolutional neural networks (CoBFCNN) is proposed in which the advantages of convolutional neural networks and deep learning are fully exploited. Basing on the results of this study, an optimization problem of maximizing EE with lower complexity and lower calculation latency for the different constraints is formulated and exploited for multi-user massive MIMO HetNets. Simulation and analysis show that the proposed CoBFCNN algorithm can significantly satisfy the performance of maximizing EE for the multi-user massive MIMO HetNets with significantly lower complexity and ultra-low calculation latency, especially when the number of antennas is large.

In cache-enabling cellular networks, we investigate the caching placement and content delivery. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay. We decompose the optimization problem into two sub-problems, the user association sub-problem in short time-scale and the caching placement in long timescale. Specifically, we propose a low complexity belief propagation based user association algorithm in the short time-scale. Then we develop a deep deterministic policy gradient based caching placement algorithm in the long time-scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We demonstrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively.

The number of radio-frequency (RF) chains is limited in millimeter-wave massive multiple-input and multiple-output (MIMO) systems, which brings challenges to channel estimation. To solve this problem, we exploit generative adversarial networks (GAN) and a deep convolutional neural networks (CNN) for a three-dimensional (3D) lens millimeter-wave massive MIMO system, which could learn channel structure and obtain accurate channel estimation from the training data. A novel GAN-CNN blind denoiser (GCBD) based neural networks is proposed in this paper. Based on the analysis and simulations, the GCBD networks enjoys satisfying accuracy even in the low signal-to-noise (SNR) region and significantly outperforms existing algorithms with lower estimation error, including a support detection (SD)-based channel estimation and sparse non-informative parameter estimator-based cosparse analysis approximate message passing for imaging (SCAMPI), Non-Local Means (NLM), Block Method of 3-Dimension (BM3D) schemes. Moreover, we consider a typical blind denoising problem by removing the unknown noise from the noisy channel, which is different from the exiting scheme.

Unmanned aerial vehicle (UAV) has been widely applied in internet-of-things (IoT) scenarios while the security for UAV communications remains a challenging problem due to the broadcast nature of the line-of-sight (LoS) wireless channels. This article investigates the security problems for dual UAV-assisted mobile edge computing (MEC) systems, where one UAV is invoked to help the ground terminal devices (TDs) to compute the offloaded tasks and the other one acts as a jammer to suppress the vicious eavesdroppers. In our framework, minimum secure computing capacity maximization problems are proposed for both the time division multiple access (TDMA) scheme and non-orthogonal multiple access (NOMA) scheme by jointly optimizing the communication resources, computation resources, and UAVs' trajectories. The formulated problems are non-trivial and challenging to be solved due to the highly coupled variables. To tackle these problems, we first transform them into more tractable ones then a block coordinate descent based algorithm and a penalized block coordinate descent based algorithm are proposed to solve the problems for TDMA and NOMA schemes, respectively. Finally, numerical results show that the security computing capacity performance of the systems is enhanced by the proposed algorithms as compared with the benchmarks. Meanwhile, the NOMA scheme is superior to the TDMA scheme for security improvement.

With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV’s mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV’s trajectory, based on the Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.

The cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA) are investigated in this paper. The delivery of multi-media contents for the mixed augmented reality (AR) and normal multi-media application is assisted by multiple mobile UAV base stations, which cache popular contents for wireless backhaul link traffic offloading. To cope with the dynamic content requests and mobility of users in practical scenarios, the dynamic optimization problem for user association, caching placement of UAVs, real-time deployment of UAVs, and power allocation of NOMA is modeled as a stackelberg game to minimize the long-term content delivery delay. Specifically, the game is decomposed into a leader level problem and a number of follower level problems. A correction mechanism is added in deep reinforcement learning (DRL) to optimize the user association in leader level. A meta actor network is proposed in DRL to jointly optimize the UAVs caching placement, real-time UAVs deployment and power allocation of NOMA in follower level. Then, a dynamic caching placement and resource allocation algorithm based on multi-agent meta deep reinforcement learning is proposed to minimize the long-term content delivery delay. Finally, we demonstrate that the considerable gains are achieved by the proposed algorithm.

In this article, we investigate the content distribution in the hotspots area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and caching placement are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment and caching placement for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into two sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement by greedy algorithm. Finally, we propose a low complexity iteration algorithm for the joint UAV deployment and caching placement optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that the proposed algorithm obtains the near-optimal solution of exhaustive search, converges within several iterations and achieves better performance compared with the benchmark algorithms.

With the rapid explosion of data volume from mobile networks, edge caching has received significant attentions as an efficient approach to boost content delivery efficiency by bringing contents near users. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul are analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. Under the hybrid caching policy, new analytical results for successful content delivery probability, average successful delivery rate and energy efficiency are derived in the general scenario, the interference-limited scenario and the mean load scenario. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. The performance gain is dramatically improved when the content popularity is less skewed, the cache capacity is sufficient and the helper density is relatively large. Furthermore, it is confirmed that there exists an optimal helper density to maximize the energy efficiency of the cache-enabled HetNets.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with an MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things in a finite period. In this article, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling, and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization problem, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device- to-device (D2D) enabled caching cellular networks with time-varying user terminal (UT) movement and content popularity, we model these dynamic networks as a stochastic game to design a cooperative caching placement strategy. We consider the long-term caching placement reward of all UTs. Each UT becomes a learning agent and the caching placement strategy corresponds to the actions taken by the UTs. In an effort to solve the stochastic game problem, we propose a multi- agent cooperative alternating Q-learning (CAQL) caching placement algorithm. We discuss the convergence and complexity of CAQL, which can converge to a stable caching policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic environment.

In device-to-device (D2D) enabled caching cellular networks, user terminals (UTs) collaboratively store and share a large volume of popular contents from the base stations (BSs) for traffic offloading and delay reduction. In this paper, the multi-winner auction based caching placement in D2D-enabled caching cellular networks is investigated for UT edge caching incentive and content caching redundancy reduction. Firstly, a multi-winner once auction caching placement (MOAC) algorithm is proposed. The maximum social welfare problem is solved by semidefinite programming (SDP) relaxation to obtain a near optimal caching placement. Moreover, the pricing strategy of the auction is developed as a Nash bargaining game. We further propose a multi-winner repeated auction based caching placement (MRAC) algorithm, which can greatly reduce the computation complexity with tiny performance loss. Simulation results show that the proposed algorithms can reduce the traffic load and the average content access delay effectively compared with the existing caching placement algorithms.

Caching at base stations (BSs) has been proposed as a promising technology to address the challenges of the increasing traffic. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul is analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. By utilizing the hybrid caching policy, analytical expression of successful content delivery probability (SCDP) is derived. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. SCDP increases with the increasing backhaul capacity until converging, whose convergence rate increases with the increment of cache capacity.

In cache enabled D2D communication networks, the cache space in a mobile terminal is relatively small compared with the huge amounts of multimedia contents. As such, how to cache the diverse contents in the multiple cache enabled mobile users, namely, the caching deployment, has substantial impact on network performance. In this paper, we propose a preference-aware caching based on cooperative game to incentivize users to cache contents for others, and then maximize the total cache utility by allocating cache space. We formulate a preference-aware cooperative game for cache utility maximization problem, which considers the user preference, physical distance, cache placement cost and cache space allocation. We divide the game into caching decision sub-game and cache space allocation sub-game. In caching decision sub-game, we obtain the Nash equilibrium solution by a heuristic caching decision method to minimize the total cost of getting objective contents. In cache space allocation sub-game, we solve the cache space allocation problem by using Lagrange method to finally maximize the total cache utility of the whole network. The convergence of the proposed algorithm is validated by simulation results. Compared with the existing caching schemes, the proposed scheme can achieve significant improvement on cache utility, cache hit ratio and content access delay gains.

Caching at the edge has emerged as a promising technology to enhance the quality of service (QoS) of users and mitigate the backhaul load. Since the cache capacity is not arbitrarily large to store all contents, the backhaul can assist to fetch the contents from the core network. The network performance analysis of base station (BS) caching should consider the limited backhaul capacity. In this paper, we analyze the energy efficiency of the cache-enabled cellular networks considering the backhaul with stochastic geometry. First, the content coverage probability (CCP) based on the limited backhaul is analyzed. With the obtained CCP results, the expressions of throughput, power consumption and energy efficiency for a general case and a specific case with the mean load approximation are derived respectively. Simulation results confirm the accuracy of theoretical analysis and verify that BSs caching can dramatically improve energy efficiency on the condition that the content popularity is skewed and the backhaul capacity is relatively small.

This paper considers the resource allocation in cache-enabled energy-cooperative HetNets, where base stations (BSs) with different cache sizes are powered by both conventional grids and renewable energy sources, and energy can be shared between BSs via the smart grid. Simulation results demonstrate that the proposed joint user association and power control algorithm can significantly enhance the sum data rate and the energy efficiency of the whole network.

Caching in the cellular networks has been proposed as a promising technology for reducing the content delivery latency and backhaul cost. Since the backhaul capacity is limited in the practical scenario, the network performance analysis of base station (BS) caching should address the effects of the limited backhaul. This paper investigates the energy efficiency of the cache‐enabled cellular networks with the limited backhaul based on the stochastic geometry method. First, the successful content delivery probability (SCDP), which depends on the successful access delivery probability, successful backhaul delivery probability, and cache hit ratio, is analyzed under the limited backhaul. Based on the obtained SCDP results, we derive the analytical expressions of throughput, power consumption, and energy efficiency for various scenes including the general case, the interference‐limited case, and the mean load approximation case. The accuracy of theoretical analysis is verified by the Monte Carlo simulation. The simulation results show that BS caching can dramatically improve energy efficiency when the content popularity is skewed, the content library size is small, and the backhaul capacity is relatively small. Furthermore, it is confirmed that there exists an optimal BS density which maximizes the energy efficiency of the cache‐enabled cellular networks.

In cache enabled D2D communication networks, the cache space in a mobile terminal is relatively small compared with the huge amounts of multimedia contents. As such, a strategy for caching the diverse contents in a multiple cache-enabled mobile terminals, namely caching deployment, will have a substantial impact to network performance. In this paper, a user preference aware caching deployment algorithm is proposed for D2D caching networks. Firstly, based on the concept of the user preference, the definition of user interest similarity is given, in which it can be used to evaluate the similarity of user preferences. Then a content cache utility of a mobile terminal is defined by taking the communication coverage of this mobile terminal and the user interest similarity of its adjacent mobile terminals into consideration. The logarithmic utility maximization problem for caching deployment is formulated. Subsequently, we relax the logarithmic utility maximization problem, and obtain a low complexity near-optimal solution via dual decomposition method. The convergence of the proposed caching deployment algorithm is validated by simulation results. Compared with the existing caching placement methods, the proposed algorithm can achieve significant improvement on cache hit ratio, content access delay and traffic offloading gain.

Licensed-assisted access (LAA) adds great value to cellular networks by extending access to the unlicensed bands, which leads to increased capacity and spectrum efficiency. To fully take advantage of LAA systems and achieve fair coexistence with Wi-Fi networks, channel access mechanisms, such as the listen before talk (LBT), are required. However, due to the LBT mechanism, LAA systems may suffer from the interference of Wi-Fi hidden nodes. In this paper, a hidden node aware joint licensed and unlicensed resource allocation algorithm for LAA systems is proposed. By utilizing measurable medium access control (MAC) layer statistics, an adaptive unlicensed band weight factor is introduced to reflect the impact of Wi-Fi hidden nodes. A resource allocation optimization problem is formulated to maximize the throughput of LAA with quality of service (QoS) guarantee, then an iteration-based solution is obtained by optimizing subcarriers and power allocation in the licensed band and fraction of time allocation in the unlicensed band with fixed power. Extensive simulation results are given to evaluate the performance and validate the effectiveness of the proposed algorithm.

Heterogeneous network (HetNet) has been widely accepted as a viable way to deal with the increasing traffic demand. However, the deployment of small cell base stations (SBSs) inevitably triggers a tremendous escalation of energy consumption. In this paper, we study the energy efficient HetNet deployment problem taking the disparities of the traffic load distribution into account. The optimization task is to select a set of SBSs from the candidate sites to maximize the energy efficiency while satisfying the traffic load requirement of the traffic demand points (TDPs). A two- stage approximation algorithm is introduced to solve the optimization problem by decomposing the original problem into two sub problems and settling with the greedy algorithm and the modified matching algorithm respectively. Simulation results verify that the proposed deployment strategy can greatly improve the energy efficiency compared with the random deployment strategy and is suitable for the scene with a large number of TDPs in the serving area whose minimum traffic load is large and the traffic load disparities are great.

Content centric networking (CCN) is a promising architecture for the future networks. In-networking caching of CCN can significantly improve the content delivery efficiency. Content popularity is one of the key factors considered in the design of the caching strategy. However, the existing research ignores the timeliness of content popularity statistics, which makes the changing of popular contents cached in the network lag behind the changing of the user preference. In this paper, a content clustering and popularity prediction based caching strategy (CPC) is proposed to solve this problem. Firstly, the massive contents are divided into different content types using cluster analysis. Then, the popularity of different content types is predicted by the autoregressive integrate moving average (ARIMA) model. Finally, based on the predicted content popularity, the process of the caching placement decision is given. The proposed caching strategy is a distributed caching management method, which can be implemented without centralized controller. Simulation results show that the proposed caching strategy can achieve better performance in terms of cache replacement rate, cache hit ratio and average hop count.

To cancel phase noise which causes the bit error rate of OFDM systems degradation, a novel orthogonal-polarization-based phase noise self- cancellation (OP-PNSC) scheme in the polarization dependent loss (PDL) channel is proposed. In the proposed scheme, the orthogonal polarizations are utilized to transmitted orthogonally polarized signals which are added together at the receiver to cancel phase noise. These orthogonally polarized signals transmitted in the same-frequency channel enable that the proposed scheme has the advantages of more efficient in cancelling phase noise and the spectral efficiency (SE) improvement. Considering the realistic wireless channel, the distortion of PDL is investigated and a PDL pre-compensated OP- PNSC (PPC-OP-PNSC) scheme is proposed to mitigate the power imbalance caused by PDL. Then, the signal- to-interference-plus-noise ratio (SINR) and SE performances are analyzed to evaluate the performance of the OP-PNSC scheme. Finally, the numerical results show that the OP-PNSC scheme achieves performance close to that of OFDM system without phase noise in the PDL channel.

Benefit from the caching hits on the routers along the request routing path, in-network caching plays an important role in diminishing the distance between the consumers and their desired contents. A content caching model is proposed to intuitively analyze the small world characteristics of in-network caching in information centric networks. A directed long edge from a caching node to the provider of its stored content is created with the probability of the content replica's cache hit ratio. Simulation results validate that the information-centric networks demonstrate the small world characteristics due to the adoption of in-network caching.

Driven by the energy consumption concerns, renewable energy harvesting is introduced to reduce the energy demand from the traditional power grid. As a key technology of wireless communications, heterogeneous networks can achieve the spectrum and energy efficiency by deploying various low transmit power base stations, which provide an ideal scenario to utilize the renewable energy harvesting to supply power for the base stations. This paper investigates the user association problem in the heterogeneous networks with hybrid energy sources, where all the base stations are powered by grid and renewable energy. The user association problem is first formulated for utility proportional fairness, aiming to achieve the energy balancing. Then an iterative algorithm operated by the users and base stations is proposed, which can converge to the global optimal solution. Simulation results demonstrate that the proposed algorithm can reduce the on-grid power consumption and achieve a flexible energy balancing.

With the diversification of mobile services, mobile traffic exhibits new characteristics which is different from traditional voice traffic. In this paper, we investigate the optimal base station (BS) density in cellular networks by taking into account the self- similar characteristics of mobile traffic using stochastic geometry approach. Based on the traffic load distribution satisfying self-similar characteristics, traffic coverage probability is defined and its relation with BS density is derived. An optimization problem with the aim of minimizing the BS density is formulated under the constraint of traffic coverage probability. Using binary search algorithm, the optimal BS density and the bounds (both upper and lower bound) for simplified analysis is obtained. The simulation results confirm the accuracy of theoretical analysis and verify the impact of the self-similar characteristics on the optimal BS density.

Energy harvesting is becoming an attractive option of energy supply for wireless networks as it can effectively reduce capital expenditure (CAPEX) and operational expenditure (OPEX). In this paper, an energy efficient radio resource optimization algorithm is proposed for a two-tier heterogeneous cloud radio access network (H-CRAN) where macro cells are empowered by conventional grid power and remote radio heads (RRH) are empowered by renewable energy sources. The resource allocation optimization is firstly formulated as a mixed integer programming problem, which is NP-hard. Therefore, an equivalent green power utilization maximization problem is formulated, and solved by Lagrange dual decomposition method. Numerical results show that the proposed algorithm can increase the utilization of the green power harvested from the renewable energy sources. This, in turn, leads to reduced grid power consumption compared to the baseline algorithms.

This paper focuses on resource allocation in energy-cooperation enabled two-tier heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA), where base stations (BSs) are powered by both renewable energy sources and the conventional grid. Each BS can serve multiple users at the same time and frequency band. To deal with the fluctuation of renewable energy harvesting, we consider that renewable energy can be shared between BSs via the smart grid. In such networks, user association and power control need to be re-designed, since existing approaches are based on OMA. Therefore, we formulate a problem to find the optimum user association and power control schemes for maximizing the energy efficiency of the overall network, under quality-of-service constraints. To deal with this problem, we first propose a distributed algorithm to provide the optimal user association solution for the fixed transmit power. Furthermore, a joint user association and power control optimization algorithm is developed to determine the traffic load in energy-cooperation enabled NOMA HetNets, which achieves much higher energy efficiency performance than existing schemes. Our simulation results demonstrate the effectiveness of the proposed algorithm, and show that NOMA can achieve higher energy efficiency performance than OMA in the considered networks.

This paper considers a hybrid heterogeneous network (HetNet), where macro cells adopt massive multiple-input multiple-output (MIMO), and small cells adopt millimeter wave (mmWave) transmissions. We assume that all base stations (BSs) are solely powered by the renewable energy. The implementation of these emerging techniques has a substantial effect on the user association (UA). Motivated by this, we formulate a user association problem to maximize the network utility while the power cost of each BS does not exceed the harvested energy. To solve it, a low complexity distributed UA algorithm is proposed. The results demonstrate that the proposed algorithm achieves higher throughput than the max reference signal received power (RSRP) and max signal-to-interference-plus-noise ratio (SINR) UAs. It also shows that increasing the number of antennas at the macro cell BS with more power consumption, the throughput continues to increase by using the proposed algorithm, compared to the decrease in throughput by using the existing ones. Increasing the number of mmWave BSs, mmWave BS antennas or mmWave bandwidths can significantly improve the throughput. Compared with massive MIMO macro cells, mmWave small cells play a dominant role in enhancing the throughput of the networks due to the larger bandwidths.

This paper studies the nonlinear transceiver design in downlink coordinated multi-cell system where base stations (BSs) are powered by hybrid sources, including conventional grid and renewable energy. Renewable energy is collected by energy harvesting equipment deployed at BS. However, the harvested energy from different BSs varies in space and time, and the system gain will be limited by the least energy of the coordinated BSs. One of the methods to settle this drawback is energy cooperation among BSs. The nonlinear joint transceiver is formulated as an optimization problem to maximize the minimum signal-to-interference noise ratio of streams under the transmit power constraint. The closed-form solutions are derived by the proposed two-step algorithm. The proposed algorithm guarantees the performance balancing among all users and all streams of each user. Simulation results illustrate the performance improvement of the proposed algorithm.

In multiuser MIMO systems, the required feedback rate per user increases linearly with the number of transmit antennas in order to achieve full multiplexing gain. When it comes to massive MIMO systems, the feedback overhead grows unacceptable. This motivates us to explore a novel feedback reduction scheme based on principal component analysis (PCA). The proposed PCA based feedback scheme exploits the spatial correlation characteristics of massive MIMO channel model, since transmit antennas are deployed compactly at base station (BS). In the proposed scheme, mobile station (MS) utilizes compression matrix to compress spatially correlated high-dimensional channel state information (CSI) into low-dimensional one. Then the compressed low-dimensional CSI is fed back to BS instantaneously with reduced feedback overhead and codebook search complexity. The compression matrix is attained by operating PCA on CSI which is estimated over a long-term period by MS. In order to recover high-dimensional CSI at BS, compression matrix is refreshed and fed back from MS to BS every long-term period. Numerical results and feedback overhead analysis show that the proposed PCA based feedback scheme can offer a tradeoff between system performance and feedback overhead.

This paper formulates a joint base station operation and user association optimization problem in the cloud based heterogeneous cellular networks with hybrid energy sources, where all the base stations are connected to the cloud computing center and powered by both on-grid power and renewable energy. The combinatorial optimization problem is decomposed into two subproblems based on the time scale separation. For the base station operation problem, a simple algorithm is designed to determine the active base stations using a Lagrange multiplier as the threshold, which is inspired by the submodularity maximization problem. For the user association problem, an iterative algorithm implemented by the user and base station side is proposed to achieve load balancing. Simulation results demonstrate that the proposed algorithm can reduce energy consumption, and enable a flexible tradeoff between energy consumption and load balancing.

In the research of green networks, considering the base station (BS) density from the perspective of energy efficiency is very meaningful for both network deployment and BS sleeping based power saving. In this paper, we optimize the BS density for energy efficiency in cellular networks by the stochastic geometry theory. First, we model the distribution of base stations and user equipment (UE) as spatial Poisson point process (PPP). Based on such model, we derive the closed-form expressions of the average achievable data rate, the network energy consumption and the network energy efficiency with respect to the network load. Then, we optimize the BS density for network energy efficiency maximization by adopting the Newton iteration method. Our study reveals that we can improve the network energy efficiency by deploying the suitable amount of BSs or switching on/off proportion of the BSs according to the network load. The simulation results validate the theoretical analysis, and show that when the right amount of BSs is deployed according to the network load, the network energy efficiency can be maximized and the maximum energy efficiency is a fixed value once the network parameters are given.

In the heterogeneous networks (HetNets) powered by hybrid energy sources, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak-to-average on-grid energy consumption ratio, since the large peak-to-average on-grid energy consumption ratio will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we propose a joint user association and green energy allocation algorithm which aims to lexicographically minimize the on-grid energy consumption in HetNets, where all the base stations (BSs) are assumed to be powered by both the power grid and renewable energy sources. The optimization problem involves both the user association optimization in space dimension, and the green energy allocation in time dimension. The independence nature of this two-dimensional optimization allows us to decompose the problem into two sub-problems. We first formulate the user association optimization in space dimension as a convex optimization problem to minimize total energy consumption via balancing the traffic across different BSs in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. Simulation results indicate the proposed algorithm achieves significant on-grid energy saving, and substantially reduces peak-to-average on-grid energy consumption ratio.

Per-cell codebook based limited feedback technique in the coordinated multi-point transmission (CoMP) system has received more and more attention because of its flexibility and scalability. In practical CoMP systems, quantized channel state information (CSI) is subject to a feedback delay due to signal processing at the receiver, finite bandwidth of feedback links, and finite capacity of backhaul links. In this paper, we study the effect of different codeword selection schemes based on per-cell codebook on data rate in CoMP systems. The impact of CSI feedback delay is considered by using Gauss-Markov temporal correlation channel model. We derive the theoretical upper bounds of data rate for two codeword selection schemes, joint codeword selection (JCS) and independent codeword selection (ICS) respectively. Simulation results show that the practical data rate is close to the theoretical data rate upper bound both for JCS and ICS, which has validated the availability of the theoretical data rate upper bounds.

In green communications, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak on-grid energy consumption, since the large peak on-grid energy consumption will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we consider the two-dimensional optimization to lexicographically minimize the on-grid energy consumption in heterogeneous networks (HetNets). All the base stations (BSs) therein are envisioned to be powered by both power grid and renewable energy sources, and the harvested energy can be stored in rechargeable batteries. The lexicographic minimization of on-grid energy consumption involves the optimization in both the space and time dimensions, due to the temporal and spatial dynamics of mobile traffic and green energy generation. The reasonable assumption of time scale separation allows us to decompose the problem into two sub-optimization problems without loss of optimality of the original optimization problem. We first formulate the user association optimization in space dimension via convex optimization to minimize total energy consumption through distributing the traffic across different BSs appropriately in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. To solve the optimization problem, we propose a low complexity optimal offline algorithm with infinite battery capacity by assuming non-causal green energy and traffic information. The proposed optimal offline algorithm serves as performance upper bound for evaluating practical online algorithms. We further develop some heuristic online algorithms with finite battery capacity which require only causal green energy and traffic information. The performance of the proposed optimal offline and online algorithms is evaluated by simulations.

In this paper, a fair user association scheme is proposed for heterogeneous networks (HetNets), where the user association optimization is formulated as a Nash bargaining problem. The optimization objective is to maximize the sum of rate related utility, under users' minimal rate constrains, while considering user fairness and load balance between cells in different tiers. Nash bargaining solution and coalition are adopted to solve this optimization problem. Firstly, a two-player bargaining scheme is developed for two base stations (BSs) to bargain user association. Then this two-player scheme is extended to a multi-player bargaining scheme with the aid of Hungarian algorithm that optimally groups BSs into pairs. Simulation results show that the proposed scheme can effectively offload users from macrocells, improve user fairness, and also achieve comparable sum rate to the scheme that maximizes the sum rate without considering user fairness.

Multi-cell coordinated system is an attractive way of improving data rate, by serving one user through several base stations (BSs). BSs might share both data and their channel state information (CSI), but the demand on backhaul capacity makes it inflexible. In this paper, we consider the case with BSs sharing data dedicated to users but having only local CSI, and propose a decentralized nonlinear precoding algorithm for multi-cell coordinated system with multi-stream multi-antenna users. The proposed algorithm is designed to eliminate the inter-user interference and the inter-stream interference, and meanwhile to achieve equal performance for streams of each user. In the proposed algorithm, Tomlinson-Harashima precoding is applied to eliminate partial interference, and transmit space matrix is designed to eliminate the other interference. After the interference elimination, closed-form expression of the transmit and receive processing matrix are derived to maximize the minimum signal to interference plus noise ratio for streams of each user. Simulation results show that, compared with the decentralized linear precoding, the proposed algorithm achieves better performance.

The heterogeneous cellular networks (HCNs) will be the typical layout of the next generation mobile networks. Understanding the aggregate interference from multi-tier heterogeneous base stations (BSs) of HCNs is the key for research on network deployment and interference management. In this paper, we propose a statistical model for quantifying the aggregate interference in HCNs and evaluating its impact on system performance. We first model the distribution of multitier heterogeneous BSs as spatial Poisson point process and derive the characteristic function (CF) of the downlink aggregate interference for a specific target user. We review the CF of single-tier network interference and proof that the aggregate interference of HCNs follows the stable distribution, based on which, we derive statistical characterization of aggregate interference amplitude and power, respectively. Then, we propose an aggregate interference statistical model based on truncated-stable distribution. Finally, the users outage probability of the HCNs is analysed via the proposed model. The proposed model is validated with simulation. This work provides essential understanding of interference of HCNs and gives insights which can facilitate system performance analysis and interference management.

Base station (BS) sleeping has been proved to be an effective technique for saving energy consumption in cellular networks. However, BSs in sleeping mode might cause coverage holes, which have a negative impact on the connectivity of the network. In order to overcome this problem, we propose a combined coordinated multi-point (CoMP) transmission and BS sleeping scheme under the heterogeneous networks (HetNets) scenario. The proposed scheme aims at improving energy efficiency as well as increasing coverage probability. In this paper, stochastic geometry analysis is adopted for evaluating the performance of the proposed scheme, instead of the conventional hexagonal grid based approach. Impact of CoMP on energy efficiency in HetNets with a random on/off strategy applied at macro base stations (MBSs) is examined thoroughly. We derived two performance indicators which are coverage probability and energy efficiency in a two-tier HetNets scenario. Numerical results confirm that the combined CoMP and BS sleeping can improve the energy efficiency as well as increase the coverage probability compared with implementing BS sleeping only. The impact of the density ratio of MBSs to Pico BSs (PBSs) on energy efficiency and coverage probability is also quantified.

Coordinated multiple point transmission (CoMP) technology becomes popular due to its potential for improving the performance of cell-edge UEs who undergo strong inter-cell interference (ICI). To fully exploit the potential CoMP gain, the appropriate method to converge cells into cluster is a prerequisite. However, the majority of existing research are based on theoretical hexagonal cell grid model solely. In this paper, a CoMP multi-cell clustering scheme which considers randomly located cellular network is proposed. In randomly located cells model, the layout of cells and UEs are generated via a Poisson Point Process (PPP). The proposed clustering scheme takes full account of both ICI suffered by UEs and cells distribution, by mapping the CoMP network onto a bipartite network, in which two types of nodes represent cells and UEs respectively, while edges are quantified to denote the channel gain. Meanwhile a projection method is introduced to project the bipartite network onto a Euclidean space where distance between UEs and cells is easily calculated. Finally the efficient K-mean method is utilized to extract nearby UEs and cells out of the space and form the clusters. The proposed scheme hardly incurs extra overheads since only UE's large scale fading information is required. The simulation results demonstrate a considerable throughput gain attained by UEs over comparing algorithm.

In wireless networks, it is of great significance to balance power consumption and network quality of service (QoS). In this paper, we propose an optimal user association algorithm for delay and power consumption tradeoffs in HetNets with hybrid energy sources. In the considered HetNets, all the base stations (BSs) are assumed powered by a combination of power grid and renewable energy sources, in order to achieve both reliable and green communications. The proposed user association algorithm aims to enhance network QoS by minimizing the average traffic delay, as well as reduce on-grid power consumption by maximizing the utilization of green power harvested from renewable energy sources. To this end, a convex optimization problem is formulated to minimize the weighted sum of cost of average traffic delay and cost of on-grid power consumption. We have proved that the proposed user association algorithm converges to the global optimum which enables a flexible tradeoff between average traffic delay and on-grid power consumption. Simulation results indicate that the proposed user association algorithm substantially reduces on-grid power consumption with limited sacrifice on average traffic delay, compared with the existing user association algorithm which aims to minimize the average traffic delay.

In the open subscriber group (OSG) mode of heterogeneous network (HetNet), the inter macro-user interference is the main interference to macro user equipments (UEs), and the inter cell interference (ICI), containing the inter picocell interference and the cross-layer interference from macro base station (BS), is the main interference to pico UEs. In this paper, we propose an interference alignment (IA) scheme for such HetNet. The transceiver is designed as follows. Firstly, the precoding matrix for macro BS and the interference suppression matrices for macro UEs are used to eliminate the inter macrouser interference. Secondly, the precoding matrices of pico BSs are designed to align the inter picocell interference onto the space of the strongest cross-layer interference produced by macro BS. Finally, the aligned interference is removed by the interference suppression matrices of pico UEs. In addition, we introduce a power allocation scheme for the macro BS to further reduce the interference to pico UEs. The simulation results and the complexity analysis show that the proposed scheme can reduce the requirement of the receive antennas number and improve the system throughput of HetNet with lower complexity.

As traditional wireless cellular networks are facing a rapid growth of traffic demand, future networks may consist of a large number of small cells which are densely deployed. However, networks in dense environment is exposed to strong inter-cell interference problem which is critical to networks capacity. To solve the problem, we use the Graph Coloring Algorithm (GCA) to divide the small cells into different clusters to mitigate the serious inter-cell interference between the dense small cells, and accordingly reducing the transmission power of small cell BSs in some subframes based on the proposed utility function. The existence of the optimal solutions to the problem is discussed, then the Differential Evolution (DE) is applied to search the optimal transmission power which maximizes the proposed utility function. Intensive simulations show that the proposed scheme can significantly mitigate the co-tier interference and maximize networks capacity.

In heterogeneous networks (HetNets), due to transmit power disparity between macro and pico base stations (BSs), the conventional strongest downlink (DL) reference signal received power (RSRP) based user association results in high uplink (UL) interference. Such interference degrades the UL performance especially in terms of energy efficiency. In this paper, we propose Joint Uplink and Downlink User Association (JUDUA) that takes both the UL and DL energy efficiencies into consideration when deciding the serving BS for user equipments (UEs). JUDUA formulates user association optimization problem as a Nash bargaining problem aiming to maximize the sum of log-scale UL and DL energy efficiencies among all UEs. Simulation results demonstrate that JUDUA improves UL and DL energy efficiencies of UEs, which in turn boosts UL and DL system capacity, reduces UL transmit power compared with the conventional user association schemes.

Joint relay selection and spectrum allocation scheme with multiple source-destination pairs and multiple potential relays in cooperative relay networks is proposed in this paper. In cooperative relay networks, the selected relay nodes and spectrum resource allocated to each source-destination pair have great impact on the system performance. It is an optimization problem for which relay nodes are selected and which spectrum resources are assigned. The exhaustive search can solve this problem but the complexity will increase factorially with the network size (i.e., the number of source-destination pairs and the number of relays in the network) and exponentially with the number of available spectrum resources. This paper proposes a novel quantum particle swarm optimization (QPSO) based relay selection and spectrum allocation scheme which can maximize three definitions of system throughput but has less computational complexity. Simulation results show that QPSO based scheme has the ability to find global optimal solution compared with other schemes in the literature.

The access point sleeping scheme is an efficiency method for power saving in the cellular networks. This paper proposes a QoS-aware adaptive access point sleeping scheme designed for energy efficiency in the relay cellular networks. To achieve the best tradeoff between QoS guarantee of users and the power saving of the network, the optimization problem of the access point selection for each user is given, and a dynamic energy control (DEC) algorithm is presented, in which, a best access point (base station or relay station) is allocated to each user and the access point without user association will be switched off for energy saving. We evaluated the network performance of power consumption, system throughput and energy efficiency by simulation, which show that, compared other schemes, the proposed access point sleeping scheme with DEC algorithm can improve the energy efficiency with the QoS guarantee for relay cellular networks, especially in low traffic condition.

The long term evolution (LTE) networks provide high data rate access to support multi-media services at the expense of more power consumption in user equipment (UE). Discontinuous reception (DRX) mechanism is one of the promising technologies for energy saving in LTE networks, which extends UE battery lifetime effectively. The DRX parameters configuration should make a compromise between power saving and wake-up delay, which can be achieved by the DRX parameters configuration according to different traffic types with specific quality of service (QoS) requirement. In this paper, we model the DRX mechanism with web traffic using a semi-Markov process. Based on the proposed model, the performance of power saving and wake-up delay with various DRX parameters is analysed, which provides insight on effects of the DRX parameters on the power saving and wake-up delay performance. The analytical results are verified by the simulation, which shows that LTE DRX achieves much power saving gains at the cost of tolerable delay.

Per-cell codebook based precoding is an effective transmission method in coordinated multi-point (CoMP) systems with the limited feedback constraint. There are two codeword selection methods for per-cell codebook, one is joint codeword selection (JCS) and another is independent codeword selection (ICS). In this paper, the system throughput obtained by the JCS and the ICS is analyzed firstly, which shows that JCS has a better trade-off between system throughput and feedback overhead. Then a codebook compression scheme for the JCS is proposed, which decreases the selection complexity of JCS by reducing the size of the codebook. Simulation results show that the proposed scheme can decrease the selection complexity greatly with tiny loss on performance and no additional feedback overhead.

In heterogeneous network (HetNet), the combined cell range extension (CRE) and enhanced inter-cell interference coordination (elCIC) proposed by 3GPP is considered as the most effective user association scheme. However, this scheme requires strict frame synchronization between macrocells and small cells. In this paper, we propose a Nash bargaining solution (NBS) based user association scheme which does not require such synchronization between cells in different tiers. The proposed scheme formulates user association optimization problem as a Nash bargaining problem. The objective is to maximize the sum data rate related utility of all users in the overall system while guaranteeing user's minimal data rate and considering the user fairness. The simulation results show the proposed scheme can achieve higher sum rate of all users and better user fairness compared with the combined CRE and elCIC based user association scheme. Moreover, the proposed scheme has low computational complexity by applying Hungarian algorithm.

In the LTE networks, the multicast services can be transmitted by single frequency network (SFN) mode and point-to-multipoint (PTM) mode, and the unicast services are delivered with point-to-point (PTP) mode. To avoid the network congestion in the LTE networks with the multicast and unicast mixed services, an analytic hierarchy process (AHP) in load balancing is proposed to minimize the demanded radio resources of the maximum load cell. The system model of the demanded radio resources in the maximum load cell is based on AHP. For the same multicast service, the AHP calculates the weight of the demanded radio resources with the three transmission modes of SFN, PTM and PTP. Then the selecting problem of the transmission mode is solved by means of simulated annealing (SA) heuristics. It selects the optimal mode between SFN and PTM for the multicast services. Simulation results show that the proposed AHP in load balancing achieves less demanded radio resources of the maximum load cell than SFN mode for all the multicast services.

The optimum power allocation of the one-side interference channel with the non-cognitive relay node was studied. Assuming the orthogonal resources were used on the channels between the sources and relay node, we first derived a transmission scheme based on the dirty paper coding and the interference cancellation. Then with this transmission scheme the rates that was achievable in both the weak and strong interference regimes were given. A joint power allocation scheme among the sources and relay node was proposed, which maximized the sum-rate. The performance of the proposed power allocation scheme was proved. More explicit analysis investigated the effects of the noncognitive feature of the relay node on the power allocation and sum-rate. The relationship between the channel gains and the optimum joint power allocation had also been analyzed.

In this paper, the energy efficiency of cooperative ARQ transmission in Amplify-and-Forward (AF) relay networks is discussed. The average total energy consumed per bit for cooperative ARQ under Quality of Service (QoS) constraints is formulated. With numerical method, the objective is optimized over the transmission data rate, the transmit power of the source and the relay, given fixed maximum retransmission number. Then, performances of various maximum retransmission numbers are analyzed. Simulation results demonstrate that cooperative ARQ with large maximum retransmission number has solid capability to save energy when the transmission energy plays a dominant role compared to circuit energy. However, for energy saving, small maximum retransmission number should be adopted on condition that tight maximum average transmission delay constraint is imposed.

Future wireless multimedia services require a quality of service guarantee and ubiquitous high data rate for all users. In this paper a scheduling algorithm is proposed for multimedia services in relay-based cellular networks. The decode-and-forwarding relaying protocol with transparent frame structure is used. The scheduling priority takes into account the packet delay and maximum tolerance packet loss probability for both direct-link users and relay-link users. Since the relay-link users have two hops, the priority factor of the first hop of relay-link users is defined as the average priority factors of relay-link users in the second hop, and a match factor is proposed considering the capacity match between the first hop link and the second hop link for each relay. Simulation results show that the proposed algorithm achieves overwhelming significant performance gain on the average packet delay and packet loss rate, although suffering some throughput and fairness reduction.

Load balancing (LB) is an important function of the self-organizing network (SON) for coping with the uneven load distribution to achieve higher spectrum efficiency and lower operational expenditure. This paper proposes a cluster based self-organizing LB scheme, which employs a user-vote mechanism to avoid the ‘virtual partner’ problem experienced by current LB schemes with the load-based partner selection. The user-vote can assist the hot-spot base station (BS) to efficiently select partner BSs for constructing its cluster, and then shift the traffic to the partners within the cluster. Simulation results show that the proposed scheme can effectively solve the ‘virtual partner’ problem. Furthermore, it can reduce the call blocking rate via a small number of partner BSs.

This paper considers using wideband slope as the measure to compare the energy efficiency between different transmission schemes for wideband cooperative wireless relay networks. Two different scenarios are considered (relay with unlimited power supply and relay with limited power supply) and two different relay strategies (amplify and forward, decode and forward) with direct transmission being used as a benchmark. The condition for getting optimal energy efficiency is obtained using theoretical analysis and simulation. The results show that (i) the source-relay distance is the most important factor influencing the energy efficiency of the whole system and (ii) the conditions for optimum energy efficiency depend on the type of relay mode.

This paper investigates the performance of clustered base station (BS) coordination with limited feedback and channel state information (CSI) delay. Given imperfect CSI caused by the limited feedback and channel delay, the expression of data rate per cell is derived. Moreover, compared to the rate with perfect CSI, a rate loss upper bound is obtained. A optimal feedback bits expression are derived to minimize the rate loss caused by limited feedback and channel delay. The numerical results show that the rate loss reduces when the number of feedback bits increases and the channel delay decreases. Meanwhile, the rate loss value reaches to a fixed value when feedback bits increase gradually. On the other hand, when the channel delay increases gradually, the rate loss can be improved little by the increasing of feedback bits. Lastly, as SNR increases, the system performance with the imperfect CSI improves more slowly than that with the perfect CSI.

An adaptive distributed precoding scheme is proposed for coordinated multi-point transmission systems. The precoding vector used by coordinated base station (BS) is determined by adaptive gradient iteration according to the perturbation vector and adjustment factor. The user equipment only feeds one quantized adjustment factor back to each coordinated BS. The adjustment factor is selected based on the precoding vector of the coordinated BS in the previous frame and the perturbation vector predefined in this frame. The proposed scheme takes advantage of the spatial non-correlation and temporal correlation of the distributed MIMO channel. The design of the perturbation vector set is given. Simulation results show that the proposed scheme has a good trade-off between system performance and the system control feedback overhead.

In a distributed antenna system, the optimal antenna deployment design scheme is proposed, in which both the location and the number of the distributed antennas (called access point, AP) are considered for energy efficiency. By minimizing the average distance between users and the APs, the optimal AP distribution is given. The optimal number of APs can be obtained considering both the circuit power and the transmission power for each AP. The simulation results show that the transmission power can be reduced by optimal AP location design. The simulation also considered the trade-off between the circuit power and the transmission power consumption and the total system power can be reduced significantly.

In coordinated multi-point transmission (CoMP) systems, the optimal remote radio unit (RRU) location is analyzed theoretically and a RRU location design scheme for energy efficiency in practical scenarios is given. An average minimum access distance criterion is given for RRU location optimization. By minimizing the average distance between users and RRU, the optimal RRU distribution can be obtained when users are located uniformly in the cell. Taking into account the fact that user distribution will not be completely uniform in a practical environment, the k-means clustering algorithm is used to get the optimized RRU deployment in a practical user distribution. Simulation results show that the uplink transmission power can be greatly reduced with the RRU optimized location design in both the uniform and non-uniform user distribution.

The fundamental tradeoff between the transmission rate and the reliability of an one-side interference channel with relay is studied using the diversity-multiplexing tradeoff (DMT) as a measure. By analyzing the capacity and the outage probability, the DMT under strong interference condition is given. This paper concludes that the strength of the signal on interference channel will limit the maximum diversity and the strength of the signal forwarded by the relay will determine the diversity gain actually achieved. This paper also concludes that the power allocation ratio at the relay for the interference and the intended signal for one receiver does not affect the DMT.

In this paper, a concept of utility proportional fairness is defined, which achieves fairness of utility for different applications. The optimization problem of utility proportional fairness has been formulated meanwhile the optimal solution is presented. The utility proportional fairness is applied in the orthogonal frequency division multiplexing (OFDM) wireless networks, and the optimal subcarrier allocation for utility proportional fairness is deduced. An effective and practical simplified subcarrier allocation algorithm is proposed, in which the instantaneous data rate is substituted by average rate via exponentially weighted low pass time window. Simulation results illustrate that both the optimal and simplified subcarrier allocation algorithms guarantee the utility fairness between different applications well, and the simplified algorithm achieves a perfect tradeoff between utility fairness and system throughput and provides the quality of server (QoS) for multimedia applications.

Radio resource allocation is one of the key technologies in orthogonal frequency division multiplexing (OFDM) cellular systems, where subcarriers and power are schedulable resources. In this paper, we solve the fair resource allocation problem based on the idea of the Nash bargaining solution (NBS) from cooperative game theory, which not only provides the resource allocation of users that are Pareto optimal from the view of the whole system, but also are consistent with the fairness axioms of game theory. We develop a suboptimal solution of NBS via low-pass time window filter and first-order Taylor expansion, and then propose an efficient and practical dynamic subcarriers allocation algorithm. Simulation results show that the proposed dynamic subcarriers allocation algorithm providing utility fairness and improving system capacity.