Terahertz (THz) communication with its wide bandwidth resource and large channel capacity offers significant potential for data-intensive Internet of Things (IoT) applications through unmanned aerial vehicle (UAV) networks. As THz waves have high directionality and severe propagation loss, massive MIMO system with beamforming is usually applied to achieve high gain and mitigate transmission interference. However, with a large number of antennas and fast varying channels, it will cause unacceptable overhead. To further address the challenges of THz propagation, this paper proposes a dynamic beam management method to stabilize the high frequency directional network and optimize the beamforming in UAV-assisted high-low frequency IoT communications. In particular, a high-low frequency collaborative network with beam training and tracking is employed, utilizing out-of-band lower frequency for auxiliary information transmission. In addition, a novel hierarchical codebook design is explored to enhance beam training performance, followed by a simplified beam training procedure that utilizes low frequency information. Moreover, an effective beam tracking method using previous information further reduces the re-training complexity. The communication framework based on this procedure can quickly complete beam alignment and dynamically track the beams according to the real-time quality of service. Numerical results validate superior performance of our proposed dynamic beam management method compared to the benchmark methods.

Though Outphasing power amplifier (OPA) exhibits high efficiency across a wide dynamic power range, its frequency coverage is constrained, prompting novel design techniques to enable broadband or multi-band operation. This paper proposes a mode expansion methodology for OPAs that adopt a non-commensurate transmission line combiner (NCTLC). It is indicated in this paper that an NCTLC-based OPA has eight distinct operation modes by judiciously expanding the delta length and base length of the NCTLC, as well as reconfiguring the Outphasing angle. Theoretical analyses confirm that the performance advantages of the OPA can be preserved before and after mode expansion. It is precisely the multi-mode operation that enhances the design flexibility of NCTLC-based OPAs. Moreover, to efficiently design the NCTLC, a relationship between the back-off range of the OPA and the delta length of the NCTLC is established. For experimental validation, three prototypes are designed and fabricated using Wolfspeed CGH40010F devices. Among these, two single-band OPAs operate at 1.0 GHz and 1.2 GHz, and a dual-mode dual-band OPA operates at 1.96/2.48 GHz. The measurement results indicate that the saturation output power of all OPAs is greater than 43.1 dBm with drain efficiency (DE) of above 56%. In the meantime, the 6 dB back-off DEs of all OPAs exceed 50.5%.

The escalating demand for network services has prompted the evolution of Service Function Chaining (SFC) within 6G networks to deliver sophisticated, customized services while ensuring robust cybersecurity. This paper introduces an efficient and secure framework for SFC in Mobile Edge Computing (MEC) environments, termed the Federated Learning-based SFC (FL-SFC), which integrates SFC, MEC, and Federated Learning (FL) to enhance service policy decision-making and safeguard user privacy. The FL-SFC framework enables dynamic updating of service policies and optimizes communication efficiency. We propose an anomaly detection model, CNN-GRU, which combines Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to significantly improve anomaly detection performance at the network edge. Additionally, to address the high communication costs associated with service policy models, we have designed a model compression mechanism leveraging sparsification and quantization techniques, which substantially reduces communication overhead during model training. Simulation experiments demonstrated the superiority of the FL-SFC framework and the CNN-GRU model in detection performance over existing methods. Results indicate that our model excels in accuracy, precision, recall, and F1-score while significantly reducing the number of communication bits, thereby validating the effectiveness of our approach.

The rapid growth of the mobile Internet has led to an increasing demand for reliable transmission of various data types, particularly images shared among multiple users over wireless networks. Traditional communication systems face challenges in managing large-scale image transmissions. Semantic communication, focusing on conveying meaning rather than raw bits, offers a promising solution. For semantic communication, reliability hinges on two key factors: successful transmission and successful understanding. Taking image semantic communication (ISC) systems as an example, successful transmission ensures the physical delivery of the image, while successful understanding refers to the correct interpretation of its semantics. To address these requirements, we design a semantic extraction and reconstruction module, called STC-based on Swin Transformer and convolutional neural network that enables parallel semantic processing and incorporates enhanced channel-aware attention mechanism, which is then integrated with residual blocks to form a joint source-channel coding (JSCC) model for semantic extraction, compression, and reconstruction. To optimize ISC reliability, we design a system utility function integrating the impacts of successful transmission and comprehension. We formulate a utility maximization problem for joint semantic compression rate (SCR) selection and resource allocation, solved by a carefully-designed joint SCR selection and resource allocation (JSSRA) algorithm based on the hierarchical soft actor-critic method. Simulation results demonstrate that our JSCC model significantly improves image quality compared to other learning-based methods while maintaining computational efficiency. Meanwhile, the JSSRA algorithm enhances system utility by 15%-50% compared to existing resource allocation methods. These results validate the effectiveness and superiority of our proposed methods in multi-user ISC systems.

Advancements in Augmented Intelligence of Things (AIoT) have made vehicle trajectory anomaly detection essential for road safety and traffic efficiency. However, the privacy-sensitive nature of trajectory data results in regional data silos, limiting model generalization. Federated learning (FL) enables collaborative training without data sharing, but still struggles with data heterogeneity and synchronous update inefficiencies in real-world scenarios. To address these challenges, we propose a personalized FL scheme for trajectory anomaly detection, namedpFedVTAD. It introduces a mutual-distillation module that uses a messenger model to bidirectionally transfer knowledge between the global and personalized models, producing area-aligned personalized models via adaptive local distillation. It also presents a privacy-preserving asynchronous aggregation that combines differential privacy, a model bank, and threshold-triggered merging to balance privacy and communication efficiency under partial participation and asynchronous arrivals. Experiments on a public trajectory dataset show that under asynchronous updates, pFedVTAD yields a well-generalized global model and area-tailored personalized models, demonstrating strong deployability in dynamic cross-area AIoT settings.

Unmanned aerial vehicle (UAV) networks face critical challenges in dynamic environments where conventional approaches treat trajectory optimization and resource allocation as separate problems, failing to capture their intricate interdependencies and leading to suboptimal performance, excessive energy consumption, and processing delays. This paper addresses these limitations through a novel hybrid methodology that uniquely integrates deep reinforcement learning with convex optimization for joint optimization. Our innovation lies in two interdependent algorithms: Deep Reinforcement Learning (DRL)-based relay UAV trajectory optimization algorithm (DRL-RUTOA), which leverages Model-Agnostic Meta-Learning for rapid environmental adaptation, and computation-aware multi-UAV trajectory optimization algorithm (CA-MUTOA), which employs a benefit-cost prioritization mechanism for selective computational offloading. Unlike previous approaches, we formulate a unified multi-objective optimization framework that simultaneously balances network throughput, energy efficiency, and computational task management. Simulation results demonstrate that our integrated approach significantly outperforms conventional methods, achieving a 35% improvement in network throughput, 28% reduction in processing delay, and 42% reduction in energy consumption. Additionally, our framework exhibits superior convergence efficiency, requiring only 15 iterations compared to 32-42 iterations for conventional methods, confirming its practical viability for resource-constrained UAV operations in complex mission environments.

Network slicing is a key technology that can provide service assurance for the heterogeneous application scenarios emerging in the next-generation networks. However, the heterogeneity and complexity of virtualized end-to-end network slicing environments pose challenges for network security operations and management. In this paper, we propose a knowledge-driven cross-domain collaborative anomaly detection scheme for end-to-end network slicing, namely HFL-TranWGAN. Specifically, we first design a hierarchical management framework that performs three-tier hierarchical intelligent management of end-to-end network slices, while introducing a knowledge plane to assist the management plane in making intelligent decisions. Then, we develop a knowledge-driven sub-slice anomaly detection model, the conditional TranWGAN model, in which an encoder, a generator, and multiple discriminators perform adversarial learning simultaneously. Finally, taking the sub-slice anomaly detection model as the basic training model, we utilize hierarchical federated learning to achieve inter-slice and intra-slice collaborative anomaly detection. We calculate the anomaly scores through the discrimination error and reconstruction error to obtain the anomaly detection results. Simulation results on two real-world datasets show that the proposed HFL-TranWGAN scheme performs better in anomaly detection performance such as F1 score and precision compared to the benchmark methods. Specifically, HFL-TranWGAN improved precision by up to 8.53% and F1 score by up to 1.88% compared to benchmarks.

Holographic video creates an immersive experience for users with lifelike scene reconstruction, yet it comes with the trade-off of managing massive data volumes. Therefore, to maintain a consistent quality of experience (QoE) in wireless networks with fluctuating channel conditions, it is necessary to develop efficient and adaptive holographic video streaming methods. This paper proposes a coordinated multicast streaming framework for holographic video that integrates coordinated multipoint transmission with transcoding-enabled multicasting techniques. Our framework supports the simultaneous transmission of holographic video tiles at various bitrates from multiple multi-antenna base stations to different multicast groups, significantly enhancing the overall viewing experience. We formulate an optimization problem with the goals of improving the average video quality experienced by all users while reducing the energy consumption for transcoding, which is NP-hard. By employing the proximal policy optimization, a leading-edge deep reinforcement learning algorithm, and convex optimization techniques, we develop a dynamic algorithm for joint bitrate selection and resource allocation. Simulations confirm the effectiveness of our algorithm, showing marked improvements in QoE over existing baselines.

The rapid advancement in wireless communication technology enables the drones to offer flexible and resilient offloading services to the mobile users in the presence of limited terrestrial infrastructure. However, the interaction between users and drones being in an open environment has raised critical security and privacy concerns. In this paper, we propose integrating the proof-of-work (PoW)-based consensus blockchain technology into the network to tackle these challenges. Specially, the drones are tasked with economically incentivizing the edge computing nodes as the miners to compete for the block generation privilege by solving cryptographic puzzles. We formulate a two-stage Stackelberg differential game to allocate edge computing resources between the drones and miners, leveraging the average reputation of miners to solve for dynamic task pricing. Additionally, the drones and miners take on the roles of leaders and followers in this game, respectively. By solving the openloop Stackelberg equilibrium, we derive the evolving trend of the optimal strategies for the system in the dynamic pricing environment. Furthermore, the numerical results substantiate the effectiveness and feasibility of this proposed scheme.

Block propagation is a critical step in the consensus process, which determines the fork rate and transaction throughput of public blockchain systems. To accelerate block propagation, existing block relay protocols reduce the block size using transaction hashes, which requires the receiver to reconstruct the block based on the transactions in its mempool. Hence, their performance is highly affected by the number of transactions missed by mempools, especially in the P2P network with frequent arrival and departure of nodes. In this paper, we introduce Presync, a transaction synchronization protocol that can reduce the difference of transactions between the block and the mempool with controllable bandwidth overhead. It allows mining pool servers to synchronize the transactions in candidate blocks before the propagation of a valid block. Low-bandwidth mode provides a lightweight synchronization by identifying the unsynchronized transactions, so that the missing transactions can be detected with a low redundancy. High-bandwidth mode conducts a full synchronization of the candidate block using short hashes, and the Merkle root is utilized to match the valid block. We study the performance of Presync through stochastic modeling and experimental evaluations. The results illustrate that low and high-bandwidth modes can respectively reduce the end-to-end delay of compact block by 60% and 78% with bandwidth usages 25KB and 63KB, in a network with 5 active pool servers and 2/3 online probability of full nodes.

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the dilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation time or block size, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay. The analysis of capability-security trade-off can provide a theoretical guideline to manage blockchain networks based on the requirements of data sharing scenarios.

Recently, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a practical paradigm to enable low latency computing offloading for dispersed users in the fifth generation (5G) wireless networks. However, severe security and privacy concerns are associated with the open environment between the UAVs and edge computing nodes. In this paper, we address these challenges by integrating blockchain technology into UAV-enabled MEC networks. We present an innovative Delegated Proof of Stake (DPoS) consensus mechanism where the UAV is a primary node and verification nodes are edge computing nodes selected by the reputation mechanism. To enhance mobile users’ Quality of Service (QoS), edge computing resources need to be allocated among UAV and verification nodes. Based on this, we propose the trading mechanism for resource pricing and allocation based on the two-stage Stackelberg differential game. Meanwhile, dynamic states of user demands and verification node reputations are modeled using differential equations as constraints of the objective function at various stages to simulate adaptive service requests for users and incentivize active participation for verification nodes. Simulation results prove the effectiveness of the proposed resource trading scheme and demonstrate the equilibrium and convergence status of resource pricing and allocation for edge computing.

5G/B5G networks are expected to deliver huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

5G/B5G networks are expected to deliver a huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, the advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

In a D2D-enabled MEC system, devices cooperate in task computation by relaying tasks to servers or providing computation capabilities for users. We investigate how nodes choose the roles to join in the offloading process in a dynamic environment, where mobile devices forming a tree-like multihop network can play relays and intermediate executors earning corresponding economic utility. By mathematically modeling the multihop computation offloading, we formulate the task-flow constrained network-wide utility maximization problem as a potential game. Based on the properties of the potential game, we prove the existence of Nash equilibrium and propose two learning-based algorithms, i.e., myopic best response (MBR-CO) and stochastic learning-based computation offloading (SL-CO), to find the equilibrium point in a distributed manner. Theoretical and simulation results show that MBR-CO is dominant in static scenarios, and SL-CO achieves a high utility and stable performance in dynamic scenarios.

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the trilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation rate, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay.

Solid-state drives (SSDs) have been widely deployed in high-performance data center environments, where multiple tenants usually share the same hardware. However, traditional SSDs distribute the users’ incoming data uniformly across all SSD channels, which leads to numerous access conflicts. Meanwhile, SSDs that blindly allocate one or several channels to one tenant sacrifice device parallelism and capacity. When SSDs are shared by tenants with different access patterns, inappropriate channel allocation results in SSD performance degradation. In this article, we propose a self-adapting channel allocation mechanism, named SSDKeeper, for multiple tenants that share one SSD. SSDKeeper employs a machine learning-assisted algorithm to take full advantage of SSD parallelism while providing performance isolation. By collecting multitenant access patterns, SSDKeeper predicts an optimal channel allocation strategy for multiple tenants using the well-trained model. To further consume the blocks in different channels evenly, SSDKeeper equips with a novel channel swap scheme to prolong the SSD lifespan. Comparing with traditional SSDs, SSDKeeper reduces the overall latency of read and write by 12.6% and the lifespan is prolonged up to$3.7\times $.

This work investigates a remote status updating system where the transmission process is modeled as a multi-stream M/G/1/1 non-preemptive system. We derive the closed-form expression of the average AoI of each stream in a heterogeneous case, where the distributions of service time are different for streams. To obtain more insights, we apply the results in a homogeneous system, where the service time distributions are identical, and find that preemption of packets would not always lead to the reduction of AoI, especially when the variance coefficient of the service time is small. We further optimize the generation rate to minimize the sum of average AoI. The results in heterogeneous cases show that given the same average service time for all streams, a higher generation rate should be allocated to the stream with a small service time variance. For the homogeneous cases with different AoI urgency weights for each stream, a higher generation rate should be reserved for the stream with more urgent AoI requirements for timeliness improvement. Besides, a lower bound on sum of average AoI is also provided, which only depends on the service rate and the number of data streams in homogeneous systems. Numerical results validate our theoretical analysis.

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID, imbalanced (statistical heterogeneity) and distribution shifted training data of FL is distributed in the federated network, which will increase the divergences between the local models and the global model, further degrading performance. In this paper, we propose a flexible clustered federated learning (CFL) framework named FlexCFL, in which we 1) group the training of clients based on the similarities between the clients’ optimization directions for lower training divergence; 2) implement an efficient newcomer device cold start mechanism for framework scalability and practicality; 3) flexibly migrate clients to meet the challenge of client-level data distribution shift. FlexCFL can achieve improvements by dividing joint optimization into groups of sub-optimization and can strike a balance between accuracy and communication efficiency in the distribution shift environment. The convergence and complexity are analyzed to demonstrate the efficiency of FlexCFL. We also evaluate FlexCFL on several open datasets and made comparisons with related CFL frameworks. The results show that FlexCFL can significantly improve absolute test accuracy by$+10.6\%$on FEMNIST compared withFedAvg,$+3.5\%$on FashionMNIST compared withFedProx,$+8.4\%$on MNIST compared withFeSEM,$+4.7\%$on Sentiment140 compare withIFCA. The experiment results show that FlexCFL is also communication efficient in the distribution shift environment.

The age of information (AoI) becomes a fashion and effective measurement for evaluating the timeliness and freshness of state updates in Internet of Things (IoT). The majority of existing works provide abundant insights for optimizing age through packet management. In this paper, the average AoI of a remote data transmission system in which the transmission process is modeled as a multiple stream M/M/1/1 non-preemptive queue process is considered. We first derive the exact theoretical expression of the average AoI of multiple stream M/M/1/1 non-preemptive queue and then extend this result to more general M/G/1/1 queues. Results suggest that the M/G/1/1 non-preemptive queue strategy can effectively improve the system performance. The comparison of preemption strategy and non-preemption strategy under different service processes shows that preemption of packets does not always lead to reduction of AoI, especially for the system with small coefficient of variance of service time. Moreover, it is found that given the same average service time, the M/G/1/1 queue with small coefficient of variance of service time performs better.

Vehicular edge computing (VEC) has been envisioned as an important application of edge computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VEC. They cooperate with edge severs to process offloading tasks at the vehicular network edge, leading to a new paradigm called parked-vehicle assisted edge computing (PVEC) in the Internet of Vehicles (IoV). However, recent researchers mostly focus on how to optimize the total cost of requesting vehicle (RV), and rarely pay attention to the optimization of the utility of PVs that provide services, including the reward from RV and the overhead of executing task. In this paper, we study a task assignment problem with computing delay constraints for PVEC in IoV. Specially, extra performance loss caused by offloading subtasks to PVs is taken into the cost function of RV. The optimal task assignment problem is formulated and solved with the Stackelberg game framework and a ternary search-based algorithm to minimize the cost of RV and maximize the utility of PVs. Finally, extensive numerical results are provided to demonstrate that our scheme is more efficient in deducing the total cost of RV and increasing the reward for PVs than other two existing schemes.

The Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. The reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the information freshness in IoT systems. By modeling the IoT networks as M/M/1 and M/D/1 queuing systems, this work focuses on characterizing the violation probability of peak AoI and AoI in IoT systems, where a sensor delivers updates to a monitor under M/M/1 and M/D/1 queues with first-come-first-served policy. From a time-domain perspective, we explore the correlation between interdeparture time and system time, by which the closed-form expressions of peak AoI distribution and the violation probability for any AoI constraint are derived. The obtained results induce accurate characterizations for probability distribution functions of peak AoI and AoI. Consequently, accurate characterizations of average AoI and the variance of AoI are obtained. Then, for peak AoI and AoI, the optimal generation rate of the status update that induces the minimal violation probability is also found. The numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints. The theoretical findings and predictions are verified by numerical simulation results as well as provide guidance for the design of IoT networks.

Frequent-pattern mining is a common means to reveal the hidden trends behind data. However, most frequent-pattern mining algorithms are designed for dynamic random-access memory (DRAM), instead of nonvolatile memories (NVMs) which are preferred by energy-limited systems. Due to the huge differences between the characteristics of NVMs and those of DRAM, existing frequent-pattern mining algorithms encounter the issues of write amplification and energy waste when they are run on NVMs. Moreover, the design complexity is exaggerated when parallel computing architecture is introduced to speedup the mining process. A scalable, time-efficient, and energy-economic solution to the frequent-pattern mining problem is thus urgently needed. Based on the well-known frequent-pattern tree (FP-tree) approach to frequent-pattern mining, this article proposes parallel EvFP-tree (PevFP-tree), a parallel frequent-pattern mining solution for NVMs. By considering the NVM characteristics, PevFP-tree accelerates the mining process and enhances the energy efficiency, as compared to a straightforward design of FP-trees on the parallel architecture. Moreover, PevFP-tree offers superior scalability in terms of the degrees of parallelism of the mining algorithm and the branching factor of its tree structure. Observing that keys are often sparsely distributed in FP-trees, we also propose a compression technique to PevFP-tree, namely, compressed PevFP-tree (CpevFP-tree), which further enhances the time and energy efficiencies of PevFP-tree. The proposed PevFP-tree and CpevFP-tree are evaluated by a series of experiments based on realistic datasets from diversified application scenarios, where CpevFP-tree achieves 88.73% of performance improvements over a straightforward design of FP-trees in the parallel architecture, and 79.47% of performance improvements over PevFP-tree, on average.

Federated learning (FL) is a distributed deep learning method that enables multiple participants, such as mobile and IoT devices, to contribute a neural network while their private training data remains in local devices. This distributed approach is promising in the mobile systems where have a large corpus of decentralized data and require high privacy. However, unlike the common datasets, the data distribution of the mobile systems is imbalanced which will increase the bias of model. In this article, we demonstrate that the imbalanced distributed training data will cause an accuracy degradation of FL applications. To counter this problem, we build a self-balancing FL framework named Astraea, which alleviates the imbalances by 1) Z-score-based data augmentation, and 2) Mediator-based multi-client rescheduling. The proposed framework relieves global imbalance by adaptive data augmentation and downsampling, and for averaging the local imbalance, it creates the mediator to reschedule the training of clients based on Kullback-Leibler divergence (KLD) of their data distribution. Compared with FedAvg, the vanilla FL algorithm, Astraea shows +4.39 and +6.51 percent improvement of top-1 accuracy on the imbalanced EMNIST and imbalanced CINIC-10 datasets, respectively. Meanwhile, the communication traffic of Astraea is reduced by 75 percent compared to FedAvg.

As an important metric of transmission performance, the maximum overall throughput of two-hop half-duplex relaying (HDR) in Ultra-Reliable and Low-Latency Communications (URLLC) is still not fully understood. In particular, an expression which can be evaluated straightforwardly is not available, and the explicit blocklength (BL) and coding rate configurations of source and relay which achieve the maximum throughput are not known either. In this paper, first we derive a closed-form expression of optimal BL configuration, then a closed-form expression of suboptimal coding rates configuration is obtained, finally we derive the closed-form expression of maximum overall throughput. Numerical results validate our theoretical analysis, and show that the maximum throughput of two-hop HDR in URLLC is far superior to conventional relaying and close to the corresponding Shannon capacity.

Solid state drives (SSDs) have been widely deployed in high performance data center environments, where multiple tenants usually share the same hardware. However, traditional SSDs distribute the users' incoming data uniformly across all SSD channels, which leads to numerous access conflicts. Meanwhile, SSDs that statically allocate one or several channels to one tenant sacrifice device parallelism and capacity. When SSDs are shared by tenants with different access patterns, inappropriate channel allocation results in SSDs performance degradation. In this paper, we propose a self-adapting channel allocation mechanism, named SSDKeeper, for multiple tenants to share one SSD. SSDKeeper employs a machine learning assisted algorithm to take full advantage of SSD parallelism while providing performance isolation. By collecting multi-tenant access patterns and training a model, SSDKeeper selects an optimal channel allocation strategy for multiple tenants with the lowest overall response latency. Experimental results show that SSDKeeper improves the overall performance by 24% with negligible overhead.

Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. Reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the data freshness in IoT systems. This work focuses on characterizing the violation probability of AoI in IoT systems where a sensor delivers updates to a monitor under M/M/1 queue with first-come-first-served (FCFS) policy. By exploring the correlation between inter-departure time and system time, the closed-form expression of the violation probability for any AoI constraint is derived. The obtained result induces an accurate characterization of the probability distribution function of AoI. The optimal generation rate of the status update that induces the minimal violation probability is also found. Numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints.

We have witnessed the tremendous success of deep neural networks. However, this success comes with the considerable memory and computational costs which make it difficult to deploy these networks directly on resource-constrained embedded systems. To address this problem, we propose TaijiNet, a separable binary network, to reduce the storage and computational overhead while maintaining a comparable accuracy. Furthermore, we also introduce a strategy called partial binarized convolution which binarizes only unimportant kernels to efficiently balance network performance and accuracy. Our approach is evaluated on the CIFAR-10 and ImageNet datasets. The experimental results show that with the proposed TaijiNet, the separable binary versions of AlexNet and ResNet-18 can achieve 26× and 6.4× compression rates with comparable accuracy when comparing with the full-precision versions respectively. In addition, by adjusting the PCA threshold, the xnor version of Taiji-AlexNet improves accuracy by 4-8 percent comparing with other state-of-the-art methods.

In recent years, time-series data have emerged in a variety of application domains, such as wireless sensor networks and surveillance systems. To identify the similarity between time-series data, the Euclidean distance and its variations are common metrics that quantify the differences between time-series data. However, the Euclidean distance is limited by its inability to elastically shift with the time axis, which motivates the development of dynamic time warping (DTW) algorithms. While DTW algorithms have been proven very useful in diversified applications like speech recognition, their efficacy might be seriously affected by the resolution of the time-series data. However, high-resolution time-series data might take up a gigantic amount of main memory and storage space, which will slow down the DTW analysis procedure. This makes the upscaling of DTW analysis more challenging, especially for in-memory data analytics platforms with limited nonvolatile memory space. In this paper, we propose a strategy to downsample time-series data to significantly reduce their size without seriously affecting the precision of the results obtained by DTW algorithms (downsizing without downgrading). In other words, this paper proposes a technique to remove the unimportant details that are largely ignored by DTW algorithms. The efficacy of the proposed technique is verified by a series of experimental studies, where the results are quite encouraging.

Energy harvesting technology has been popularly adopted in embedded systems. However, unstable energy source results in unsteady operation. In this paper, we devise a long-term energy efficient task scheduling targeting for solar-powered sensor nodes. The proposed method exploits a reinforcement learning with a solar energy prediction method to maximize the energy efficiency, which finally enhances the long-term quality of services (QoS) of the sensor nodes. Experimental results show that the proposed scheduling improves the energy efficiency by 6.0%, on average and achieves the better QoS level by 54.0%, compared with a state-of-the-art task scheduling algorithm.

Caching popular contents at radio access networks is a promising approach to improve the content delivery efficiency. Most of the existing content delivery schemes focus on the perspective of content providers, paying less attention to the service demand of content requesters. In this paper, we investigate the content delivery policy of a mobile device with service delay constraint in a cache- enabled heterogeneous network (HetNet), where a macro base station (MBS) is overlaid with some small base stations (SBS) with caches. In the considered network, the mobile device needs to make content delivery decisions based on the time, cache state, and signal-to-interference-plus-noise ratio (SINR) state. The problem of solving an optimal content delivery policy is modeled as a Markov decision process (MDP), where the objective is to minimize the delivery cost of the mobile device under the constraint of content service deadline. In order to address this problem, we propose a reinforcement learning (RL) algorithm to learn the optimal policy. The simulation results demonstrate that our proposed RL-based policy achieves a significant improvement in content delivery cost compared with other benchmark solutions.

Caching popular contents at the mobile devices is a promising technique to alleviate the backhaul data rate demand. Since both file placement and data exchange among mobile devices consume energy, the energy status of devices has a significant effect on the caching utility of the whole system. This work considers the caching optimization in a cellular network where mobile devices are served by one base station (BS). As the devices can collect the file segments from the local storage, via device-to-device (D2D) links, and via a cellular link, we aim at minimizing the percentage of file segment that should be collected from the BS by optimizing the file placement scheme at devices to improve caching performance. Due to the difficulty of solving the optimal caching problem, we propose a residual energy-aware file placement algorithm based on the popularity distribution of contents and causality of energy arrival. Simulation results show that in comparison to other two conventional caching methods, the proposed algorithm can effectively reduce the percentage of file segments that collected from the BS.

Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while their private training data remains in local devices. This distributed approach is promising in the edge computing system where have a large corpus of decentralized data and require high privacy. However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications. In this paper, we demonstrate that the imbalanced distributed training data will cause accuracy degradation in FL. To counter this problem, we build a self-balancing federated learning framework call Astraea, which alleviates the imbalances by 1) Global data distribution based data augmentation, and 2) Mediator based multi-client rescheduling. The proposed framework relieves global imbalance by runtime data augmentation, and for averaging the local imbalance, it creates the mediator to reschedule the training of clients based on Kullback-Leibler divergence (KLD) of their data distribution. Compared with FedAvg, the state-of-the-art FL algorithm, Astraea shows +5.59% and +5.89% improvement of top-1 accuracy on the imbalanced EMNIST and imbalanced CINIC-10 datasets, respectively. Meanwhile, the communication traffic of Astraea can be 92% lower than that of FedAvg.

With the rapid growth of edge-cloud computing, emerging applications pose higher performance demand on the storage system for storing massive data that are generated from various sources. The multi-sourced data shows different properties in size, retention time, and read/write frequency. Hybrid storage system is promised to efficiently handle the data in edge-cloud computing environment satisfying different data demands. The key problem is how to place the data on the hybrid storage system according to the run-time status and the properties of both data and the storage systems. In this paper, we propose Archivist - a machine learning assisted data placement mechanism for hybrid storage systems to reduce file access latency. We first design a machine learning based approach for predicting the access patterns of the incoming data. Then, we present a data placement algorithm to optimize the data on the hybrid storage mediums by matching the properties of data and the features of storage mediums. Extensive experimental results show that Archivist can achieve up to 49% improvement of system performance for file accesses compared with baseline.

Checkpointing is a key enabler of hibernation, live migration and fault-tolerance for virtual machines (VMs) in mobile devices. However, checkpointing a VM is usually heavyweight: the VM's entire memory needs to be dumped to storage, which induces a significant amount of (slow) I/O operations, degrading system performance and user experience. In this paper, we propose FLIC, a fast and lightweight checkpointing machinery for virtualized mobile devices by taking advantages of recent byte-addressable, non-volatile memory (NVRAM). Instead of saving the VM's entire memory to storage, we store its working set pages in NVRAM, avoiding accessing slow flash memory (compared to server-grade SSDs). To further reduce the write activities to flash memory, we propose an energy-efficient data deduplication to eliminate redundant data in VM snapshot and save storage space. Experimental results based on an Exynos 5250 SoC show that our approach can effectively improve the performance of checkpointing in mobile virutalization and save energy.

Solar powered sensor nodes have been adopted in many applications, but unstable energy source and high energy loss are hindrances to their wide spreading. Storage-less and converter-less solar powered non-volatile sensor nodes reduce the energy loss to a great extent. However, without energy buffers, sensor nodes become more sensitive to solar variations. Making full use of harvested energy to provide better quality of services (QoS) to guarantee stable operations under this circumstance is crucial. In this paper, we devise an energy efficient task mapping strategy for storage-less and converter-less solar powered non-volatile sensor nodes. The proposed strategy, Puppet uses a reinforcement learning to make nodes achieve higher energy utilization and finally enhance the QoS. Experimental results show that the proposed strategy reduces the deadline miss ratio (DMR) in Puppet by 22% while increases energy utilization and effective energy utilization by 11% and 25%, on the average, respectively.

In future mobile networks, more and more machine-type communication (MTC) devices with different service requirements will be deployed. To meet the massive access needs of MTC, this paper develops a cluster-based congestion-mitigating access scheme (CCAS), with aim to mitigate the severe collision of MTC devices (MTCDs) that access to the base station (BS) concurrently. To this end, we first design a modified spectral clustering algorithm to group MTCDs into different clusters based on their locations and service requirements. Then, a device called MTC gateway (MTCG) is chosen by two steps to assist transmitting data for MTCDs in each cluster. In the data transmission process, MTCG is in charge of aggregating packets generated by MTCDs in a cluster and forwarding them to BS when the number of buffered packets reaches a certain threshold. To model the aggregation and forwarding process of each MTCG, we use queuing theory to analyze the access performance in terms of collision probability and access delay. In addition, we also implement simulations to further validate the accuracy of our analytical model and the effectiveness of CCAS. Numerical results, which are consistent with the theoretical values, show that the proposed CCAS can significantly decrease collision probability, and increase the number of successfully received packets of the system without increasing average access delay.

The large amount of data among billions of devices deployed for Industrial Internet of Things (IIoT) cause a massive energy consumption. Driven by the pursuit of green communication, this paper presents a space-reserved cooperative caching scheme for IIoT in the fifth generation mobile heterogeneous networks, where the cache space in a base station is divided into two parts, one is used to store the prefetched data from the servers ahead of the device request time and the other is reserved to store the temporarily buffering data in the wireless transmission queue at the device request time. With the constraint that the quality of service is guaranteed, we propose an algorithm to obtain the optimal proportion between the two parts of the cache space for the purpose of reducing the average energy consumption. Simulation results verified that the proposed caching scheme is more efficient than the conventional one with respect to the average energy consumption.

System fairness has been regarded as an important performance index related to qualities of services in mobile networks. Most of researches evaluate the fairness of a cellular system in terms of the cumulative distribution function (CDF) of user throughputs. However, it's difficult to treat the CDF as a parameter to set, adjust and compare. This paper proposes Gini coefficient, which is a primary measure of the inequality of income in economics, can be developed to represent the system fairness in mobile networks. Furthermore, we present a scheme with modified genetic algorithm (GA) to achieve certain level of the system fairness by adjusting the almost blank subframe (ABS) arrangement in LTE-Advanced heterogeneous networks (HetNets). Validations and numerical analysis on the relationship between the system throughput and fairness are performed by computer simulations.

Machine type communication (MTC) is deemed as one of the killer applications in 5G mobile communication networks, for which power saving is an important yet challenging issue. In current cellular networks, most proposed power-saving mechanisms are designed for human-to-human (H2H) communications, and thus not applicable to MTC. Hence, it is imperative to design effective power-saving mechanisms for MTC in 5G networks. 3GPP has proposed Discontinuous Reception (DRX) mechanism to allow users receive data at specified time slots and turn off the radio module at other time slots, aiming at reducing power consumption. In this paper we design an energy efficient coercive sleep mode (CSM) to reduce power consumption, based on the idea of DRX. In CSM, a metric “NUMBER” is introduced to record the number of packets in addition to inactivity timer in DRX. We use a semi-Markov process to model mechanism, for evaluating the power saving factor and wake up latency. We also employ simulation to examine the impact of DRX parameters on system performance. Numerical results show that CSM achieves significantly higher energy efficiency and is thus appropriate for low cost MTC, compared with standard DRX.

Recently network slicing has been identified as a promising network architectural technology for the next generation mobile cellular networks (5G) to address the challenges stemming from a wide range of applications. Especially, for machine type communication (MTC) application, it is widely recognized that traditional cellular network architecture is not adequate to meet the requirements in terms of massive connectivity and low latency. For exploiting network slicing, data aggregation (DA) can be adopted to effectively address the massive connectivity and latency requirement. In this paper, we propose an efficient network slicing data aggregation (NSDA) scheme for MTC applications. Different from conventional DA scheme where data aggregation is performed based on device locations, we perform DA according to latency requirement for MTC devices (MTCDs), with aim to exploit the benefits of network slicing and thus improve network access capacity and decrease access latency. We formulate the DA problem as a 0-1 Linear Programming and propose an efficient two-step algorithm to aggregate the MTC data for accessing a specific network slice. We examine the performance of our proposed NSDA in typical MTC scenarios via simulations. Numerical results reveal that NSDA significantly outperforms traditional MTC access schemes (without network slicing) in terms of network capacity, access congestion degree, latency, etc.

Frequent-pattern mining is a common means to reveal the hidden trends behind data. However, most frequent-pattern mining algorithms are designed for DRAM, instead of the energy-economic nonvolatile memories (NVMs). Due to the huge differences between the characteristics of NVMs and those of DRAM, existing frequent-pattern mining algorithms suffer from serious overheads of write amplification or energy consumption as used on NVMs. The design complexity is exaggerated when parallel computing is used to speedup the mining process. This paper proposes PevFP-tree, a parallel frequent-pattern mining solution for NVMs, e.g., phase-change memory (PCM). By considering the NVM characteristics, PevFP-tree accelerates the mining process and enhance the energy efficiency. Moreover, PevFP-tree offers superior scalability in terms of the degree of parallelism of the mining algorithm and the branching factor of its tree structure. The efficacy of PevFP-tree is evaluated by experiments based on realistic datasets.

Recently convolutional neural networks (CNNs) have essentially reached the state-of-the-art accuracies in image classification and recognition. CNNs are usually deployed in server side or cloud to handle tasks collected from mobile devices, such as smartphones, wearable devices, unmanned systems and so on. However, significant data transmission overhead and privacy issues have made it necessary to use CNNs directly in device side. Nevertheless, the statically trained model deployed on mobile devices cannot effectively handle the unknown data and objects in new environments, which could lead to low accuracy and unsatisfied user experience. Hence, it would be crucial to retrain a better model via future unknown data. However, with tremendous computing cost and memory usage, training a CNN on mobile devices with limited hardware resources is intolerable in practical. To solve this issue, by using the power of cloud is a promising solution to assist mobile devices to train a deep neural network. Therefore, this paper proposes a cloud-assisted lightweight CNN framework, named FitCNN, with incremental learning and low data transmission, to deploy CNNs on mobile devices and make them smarter. To reduce the data transmission during incremental learning, we propose a strategy to selectively upload the data with high learning value, and develop an extracting strategy to choose light weights of the new CNN model trained on the cloud to update the old one on devices. Experimental results show that the selectively uploading strategy can reduce 39.4% uploading transmission based on a certain dataset, and the extracting weights strategy reduces by more than 60% updating transmission with multiple CNNs and datasets.

It is a significant problem to efficiently identify the frequently occurring patterns in a given dataset, so as to unveil the trends hidden behind the dataset. This paper is motivated by the serious demands of a high-performance in-memory frequent-pattern mining strategy, with joint optimization over the mining performance and system durability. While the widely used frequent-pattern tree (FP-tree) serves as an efficient approach for frequent-pattern mining, its construction procedure often makes it unfriendly for nonvolatile memories (NVMs). In particular, the incremental construction of FP-tree could generate many unnecessary writes to the NVM and greatly degrade the energy efficiency, because NVM writes typically take more time and energy than reads. To overcome the drawbacks of FP-tree on NVMs, this paper proposes evergreen FP-tree (EvFP-tree), which includes a lazy counter and a minimum-bit-altered (MBA) encoding scheme to make FP-tree friendly for NVMs. The basic idea of the lazy counter is to greatly eliminate the redundant writes generated in FP-tree construction. On the other hand, the MBA encoding scheme is to complement existing wear-leveling techniques to evenly write each memory cell to extend the NVM lifetime. As verified by experiments, EvFP-tree greatly enhances the mining performance and system lifetime by 40.28% and 87.20% on average, respectively. And EvFP-tree reduces the energy consumption by 50.30% on average.

It is a significant problem to efficiently identifythe frequently-occurring patterns in a given dataset, so as tounveil the trends hidden behind the dataset. This work ismotivated by the serious demands of a high-performance inmemoryfrequent-pattern mining strategy, with joint optimizationover the mining performance and system durability. While thewidely-used frequent-pattern tree (FP-tree) serves as an efficientapproach for frequent-pattern mining, its construction procedureoften makes it unfriendly for nonvolatile memories (NVMs). Inparticular, the incremental construction of FP-tree could generatemany unnecessary writes to the NVM and greatly degrade theenergy efficiency, because NVM writes typically take more timeand energy than reads. To overcome the drawbacks of FP-treeon NVMs, this paper proposes evergreen FP-tree (EvFP-tree), which includes a lazy counter and a minimum-bit-altered (MBA) encoding scheme to make FP-tree friendly for NVMs. The basicidea of the lazy counter is to greatly eliminate the redundantwrites generated in FP-tree construction. On the other hand, theMBA encoding scheme is to complement existing wear-levelingtechniques to evenly write each memory cell to extend the NVMlifetime. As verified by experiments, EvFP-tree greatly enhancesthe mining performance and system lifetime by 28.01% and82.10% on average, respectively.

Virtualization offers significant benefits, such as better isolation and security for mobile systems. However, the limited amount of memory and virtualization's memory-demanding nature make it challenging to virtualize mobile systems efficiently. In this paper, we utilize morphable resistive memories to design a high-performance mobile system with an extensible memory space. With morphable resistive memories, a simple and effective page management technique, Balloonfish, is proposed to convert the memory cell state between multilevel and single-level for achieving a balance between performance and memory space. First, an application-specific page allocation is proposed for managing morphable resistive memories in virtualized mobile systems. Besides, we use a balloon-style algorithm to balance memory allocation among multiple virtual machines. Our evaluation based on the Samsung Exynos 5250 system-on-chip with various real Android applications shows that our system achieves 28.63% performance improvement compared with the baseline scheme.

Smartphones are becoming increasingly energy-hungry to support feature-rich applications, posing a lot of pressure on battery lifetime and making energy consumption a non-negligible issue. In particular, dynamic random access memory (DRAM)-based main memory subsystem is a major contributor to the energy consumption of mobile devices. In this paper, we propose direct read (DR). Swap, an energy-efficient in-memory paging design to reduce energy consumption in smartphones. In DR. Swap, we adopt emerging energy-efficient nonvolatile memory (NVM) and use it as the swap area. Utilizing NVMs byte-addressability, we propose DR which guarantees zero memory copy for read-only requests when accessing a page in swap area. To better understand the energy consumption of swapping, we build an energy model to analyze the energy consumption of different paging architectures. We evaluate DR. Swap based on the Google Nexus 5 smartphone, experimental results show that our technique can reduce more than 50% energy consumption compared to DRAM backed swapping.

The explosive growth of smart mobile user equipments (UEs) boosts the emerging of numerous mobile applications. Most of these applications require an always-online connectivity, which incurs overly-frequent Radio Resource Control (RRC) state transitions, leading to signaling storm and user access failure. To address this issue, many researches focus on avoiding frequent transitions between RRC states by maintaining UEs in the RRC connected state for longer time. However, these researches bring up substantial energy consumption. In this paper, we propose an energy-efficient system signaling control method, by which each UE adjusts its RRC release timer adaptively according to the traffic patterns of mobile applications. Numerical results show that in comparison to the conventional signaling control method, the proposed method can save 27.5% average energy consumption with well-controlled signaling load. Meanwhile, the disparity of user experience is significantly lower.

Macro-femto networks, which comprise a macrocell underlaid with multiple femtocells, have attracted much research attention due to its benefits for capacity increase and coverage extension. However, the mass deployment of femtocells with universal frequency reuse may cause severe interferences, and thus greatly deteriorate system performance. In this kind of systems, access control is an effective mechanism for interference management. In this paper, we investigate the case where the macro users may suffer severe interferences from femto base stations (FBS), and propose an access control based interference coordination scheme. By introducing auction model, FBSs decide the asking price for reserving their resources for macro users. Macro base station (MBS) bids for the access permission of macro users and selects a candidate FBS with the maximum access utility as the access point for a macro user. If an agreement for the transaction price of a macro user is reached, this user can change access point and thus the suffered interferences can be reduced. We conduct extensive simulations to validate the effectiveness of our scheme. Numerical results show that the proposed interference coordination approach operated in hybrid access mode can effectively mitigate interferences and improve system performance compared with the closed access mode.

Data transmission reliability and energy efficiency are most crucial for Wireless Body Area Network (WBAN) to perform healthcare monitoring. In this paper, we jointly consider adaptive power control and routing in multi-hop WBANs, and develop a low overhead energy-efficient routing scheme (EERS). The proposed EERS can establish an energy-efficient end-to-end path as well as adaptively choose transmission power for sensor nodes. We conduct extensive experiments on a MicaZ platform to compare the performance of the proposed EERS and the collection tree protocol (CTP) in terms of packet reception ratio (PRR), collection delay, energy consumption, and energy balancing. Experimental results show that EERS outperforms CTP in terms of reliability, delay and energy consumption. In particular, EERS reduces nearly 30% mean delay as compared to CTP, and saves 10% energy consumed by CTP at the default power (0dBm) while achieving at least 0.95 PRR.

Deployment of relay nodes (RNs) in cellular orthogonal frequency division multiple access (OFDMA) systems provides an effective solution to increase high data rate coverage and improve cell throughput. However, a challenging issue is that additional interferences caused by RNs may substantially compromise the performance gain if no measure is taken. In this study, the authors address the problem of interference coordination in relay-aided cellular OFDMA systems, aiming at exploiting the benefits of RNs while minimising the negative effects of interferences introduced. The authors first analyse the possible interference scenarios in multi-cell systems. Based on the insights into their analysis, the authors propose resource allocation with interference coordination (RAIC) scheme for cellular OFDMA systems. RAIC selectively perform one of three resource allocation algorithms according to the offered traffic load in the system, to mitigate interferences and thus enhance system throughput. The authors conduct intensive simulation experiments based on the model with realistic broadband channel propagation conditions. Numerical results demonstrate that their proposed RAIC can effectively improve system throughput compared with the resource allocation schemes without appropriate interference coordination.

This paper addresses the problem of interference coordination in relay-aided cellular OFDMA systems, aiming at exploiting the benefits of RNs while minimizing the negative effects of interferences introduced. We first analyze the possible interference scenarios in multi-cell OFDMA systems. Based on the insights in our analysis, we propose an Integrated Interference Coordination Scheme (IICS) for cellular OFDMA systems. IICS consists of two phases, each performing a resource allocation algorithm, to mitigate interferences and thus enhance system throughput. We conduct simulation experiments based on the model with realistic broadband channel propagation conditions. Numerical results show that our proposed IICS can effectively improve system throughput compared with the resource allocation schemes without adequate interference coordination.