Current congestion control algorithms ignore the application-layer delay, where the untransmitted data waiting at source nodes degrades the delay performance of services. Moreover, differentiated priorities are necessary for the application services with various delay requirements, especially for mission-critical services. Different from the existing works only considering the network delay, in this paper, by adding the flow queueing delay at source nodes, we formulate the network utility maximization (NUM) problem with additional deterministic service delay constraints. We propose a general TCP-based two-timescale congestion window control (TCWC) framework with delay-aware priority to enhance traditional algorithms. Specifically, to handle the obstacle of new delay constraints, we transform them to the time-average stability of virtual queues. By solving the new NUM problem via Lyapunov optimization, we design a short-term congestion window adjustment strategy in each time slot. To further guarantee the service delay, we apply extreme value theory (EVT) to evaluate the priorities of different flows, and determine the long-term control of window update rates. We deploy the proposed framework in three classic algorithms including NewReno, Vegas and DCTCP. In addition, simulation results show that our TCWC framework can significantly reduce the average service delay and provide deterministic guarantees compared with time-aware TCP congestion control algorithms such as TIMELY and BBRv2.

Performance measurement is one of the key enablers towards the realization of future Smart Grids (SGs). However, balancing the trade-off among measurement completeness, accuracy, and timeliness under constrained resources, while providing targeted support for communication-intensive areas consisting of critical infrastructure remains a challenge. In this paper, we propose a hybrid network Performance Measurement (PM) framework for deterministic SGs in two steps. 1) To obtain PM information for the whole network without incurring overhead, passive measurement with missing data imputation is tailored for meeting the divergence of packet loss types of large-scale SGs based on the proposed Temporal-Spatial based Distinguishable Generative Adversarial Imputation Network (STD-GAIN), which explores and exploits both the spatial-temporal correlation and higher-order local correlation for refined imputation performance. 2) Regarding the circumstance that the reliability of the imputed data does not meet the requirement, active measurement is conducted at a finer granularity, i.e., along a specified path, where All-Pair Shortest Path (APSP) model is utilized to find the best paths. Finally, the simulation results show that the proposed framework could achieve complete, accurate and real-time network performance measurement for SGs.

Delivering high-quality 360-degree VR video challenges the communication system on low latency and high quality transmission, which can be resolved by tile-based transmission technology. However, existing works do not take into account unexpected events and consider field-of-view prediction and bandwidth allocation separately, resulting in the inability to match dynamic transmission conditions and further resulting in significant performance loss. This paper proposes a multimodal fusion-based framework to enhance viewpoint prediction which is jointly optimized with adaptive streaming. The proposed approach presents a neural network model to predict the viewpoint of the user based on multimodal fusion technique, which takes unexpected events into consideration for refined accuracy. After the prediction, we assign weights to the tiles based on the results and further allocate bandwidth and rate to optimize the user experience. Experimental results on real-world datasets demonstrate that the proposed method outperforms existing approaches, significantly enhancing user experience by 16% on average in adaptive VR streaming.

Mobile Edge Computing (MEC) has been successful in proving solid support for delay-sensitive and computation-intensive applications, while invoking channel forecast enlightens a new dimension to further improve the performance. However, separately considering channel forecast and resource management fails in fully exploiting the merit of channel forecast. In this paper, we proceed in two steps. 1) By incorporating channel forecast, we extend the conventional Lyapunov optimization into multi-step-ahead Lyapunov optimization to minimize the queueing delay for non-causal scenario. 2) Based on the obtained insights, we tailor the conventional Long Short Term Memory (LSTM) into Differentiated Randomly Connected LSTM (DR-CLSTM) to obtain a desired trade-off between model complexity and forecast accuracy for the sake of delay minimization. Our simulation results highlight the performance gain of the proposed framework in terms of the system delay.

Millimeter-wave (mmWave) vehicular communication brings new technical challenges on wireless resource management due to the sensitivity to blockages and the directionality property, as conventional beam alignment techniques suffer from large communication overhead. To enable fast base station (BS) association and beam alignment, we propose a lightweight online learning framework by embracing sparse representation (SR) and Gaussian process (GP). To obtain preliminary information of the transmission environment, fingerprint-based method is advocated for static scenarios, while its performance degrades in dynamic scenarios. To incorporate the influence of vehicle motion, we innovatively propose the idea of trajectory-aware fingerprint, which further triggers the following two designs: 1) Trajectory Prediction: We utilize GP to predict the trajectory of moving vehicles. Noticing the utility of the forecast information drops fast with the computational complexity, we propose a differentiated prediction framework to balance accuracy and model complexity to maximize such utility and 2) Fingerprint Adaptation: As the existence of infinite number of trajectories, we approximate a trajectory using grayscale image, and prove the influence of such approximation on throughput is limited. Then, given a predicted trajectory, SR is invoked to perform robust fingerprint adaptation that facilitating resource management. Finally, the simulation results demonstrate the superiority of the proposed framework.

Erasure codes(EC) have been widely adopted to provide high data reliability with low storage costs in clusters. Due to the deletion and out-of-place update operations, some data blocks are invalid, which unfortunately arouses the tediousgarbage collection(GC) problem. Several limitations still plague existing designs: substantial network traffic, unbalanced traffic load, and low read/write performance after GC. This paper proposes FastGC, a fast garbage collection method that merges the old stripes into a new stripe and reclaims invalid blocks. FastGC quickly generates an efficient merge solution by stripe grouping and bit sequences operations to minimize network traffic and maintains data block distributions of the same stripe to ensure read performance. It carefully allocates the storage space for new stripes during merging to eliminate the discontinuous free spaces that affect write performance. Furthermore, to accelerate the parity updates after merging, FastGC greedily schedules the transmission links for multi-stripe updates to balance the traffic load across nodes and adopts a maximum flow algorithm to saturate the bandwidth utilization. Comprehensive evaluation results show via simulations and Alibaba ECS experiments that FastGC can significantly reduce 10.36%-81.22% of the network traffic and 34.25%-72.36% of the GC time while maintaining read/write performance after GC.

While the average delay is traditionally an importance metric for system performance, the emerging technologies have given birth to a variety of critical applications, for which the deterministic delay guarantee is highly desired. When optimizing the average delay objective is embraced with satisfying the deterministic delay constraint, it leads to complicated coupling and brings new challenges to resource allocation. In this paper, we propose a renewal framework for multi-user power control and subband allocation to improve the comprehensive delay performance. The average delay objective is optimized under the Markov decision process (MDP) problem, while the deterministic delay constraint is satisfied through Lyapunov optimization with virtual queues. Due to the conflict of the inter-slot influence in MDP and the i.i.d. state requirement in Lyapunov approach, we exploit the recurrent property of queue states and construct a renewal system. By solving the equivalent infinite-horizon MDP in the renewal framework, we propose a resource allocation algorithm, which is proved to be asymptotically optimal. Finally, the simulation results demonstrate that the proposed scheme meets the deterministic delay constraint and achieves better average delay performance than existing baselines.

Deterministic demand of mission-critical applications is essential in edge computing systems for realizing Industry 4.0. However, the conventional average-based network slicing schemes incur unexpected long-tail delay, resulting in the failure to meet strict deterministic delay guarantee. To resolve this issue, in this paper, we construct a two-scale TNS-Net architecture for the URLLC slice under the network slicing paradigm, aiming to meet deterministic end-to-end (E2E) delay requirements of multiple users with minimal resource usage. We consider multi-access edge computing (MEC) and model it as a many-to-one cascade queue, which includes the offloading queues at the user equipments (UEs) and a computation queue at the server. To analyze the delay performance, we decompose the offloading process into transmission and vacation periods, and employ the weighted approximation to address the multi-UE coupling in the computation process to derive the closed-form approximate E2E delay distribution. Based on the derived delay distribution, we propose an iterative two-scale network slicing (TNS) algorithm to guarantee deterministic delay, and construct a TNS-based deep-unfolding neural network, called TNS-Net, to improve the solution in presence of inaccurate channel statistics. Moreover, for the training of TNS-Net with deterministic delay as the network input, we apply extreme value theory (EVT) to analyze the distribution characteristic of delay bound violation. Finally, simulation results demonstrate that our theoretical analysis provides a relatively accurate estimate and the proposed TNS-Net ensures better delay guarantee with lower resource consumption.

Split Federated Learning (SFL) improves scalability of Split Learning (SL) by enabling parallel computing of the learning tasks on multiple clients. However, state-of-the-art SFL schemes neglect the effects of heterogeneity in the clients’ computation and communication performance as well as the computation time for the tasks offloaded to the cloud server. In this paper, we propose a fine-grained parallelization framework, called PipeSFL, to accelerate SFL on heterogeneous clients. PipeSFL is based on two key novel ideas. First, we design a server-side priority scheduling mechanism to minimize per-iteration time. Second, we propose a hybrid training mode to reduce per-round time, which employs asynchronous training within rounds and synchronous training between rounds. We theoretically prove the optimality of the proposed priority scheduling mechanism within one round and analyze the total time per round for PipeSFL, SFL and SL. We implement PipeSFL on PyTorch. Extensive experiments on seven 64-client clusters with different heterogeneity demonstrate that at training speed, PipeSFL achieves up to 1.65x and 1.93x speedup compared to EPSL and SFL, respectively. At energy consumption, PipeSFL saves up to 30.8% and 43.4% of the energy consumed within each training round compared to EPSL and SFL, respectively.

A significant drawback of erasure-coding is suffering from the expensive update traffic. The analysis of real-world-production traces shows that partial updates, including partial-block-updates and partial-stripe-updates, are both common. Existing schemes cannot work adequately for partial updates. Raid-based scheme coordinates multiple updated entire blocks to update parity, yet it incurs significant network traffic for partial-block-updates. Delta-based scheme transmits the updated parts and independently updates parity, yet it cannot share computed-delta parts for partial-stripe-updates. We propose CoRD, which optimally combines Raid-based and Delta-based schemes to minimize the update traffic. It exploits the offset address intersections between multiple updated blocks and only transmits the updated parts to coordinate in parity updates. CoRD further address cross-block update scenarios by flipping some dedicated blocks to improve the performance. Comprehensive evaluations verify the effectiveness of CoRD for the latest traces, with the update traffic reduction of 37.02%-87.19% and the performance improvement of 36.54%-231.92% compared to state-of-the-art.

In this paper, we propose a blockchain-based energy trading mechanism for electric vehicle (EV) charging and discharging, which aims to provide a user-friendly, secure, and efficient energy trading platform for EV users. Firstly, EV users are required to calculate an evaluation function through the Road Side Unit (RSU) decision-making mechanism and select the RSU that maximizes their benefits to participate in vehicle-to-vehicle (V2V) energy trading. Secondly, this paper develops a mixed pricing strategy for V2V energy trading based on the Bayesian game. The strategy, combined with the real-time micro-market supply and demand status, promptly adjusts the bids of both parties of the energy transaction. After determining the transaction price, the mechanism further derives the energy trading volume that maximizes social welfare. Finally, the simulation results show that in the V2V energy transaction under the blockchain environment, adopting the RSU decision-making mechanism proposed in this paper can reduce the buyer's cost and improve the seller's revenue. The mixed pricing strategy can effectively improve the price satisfaction of energy transaction users and the success rate of transaction matching. It can protect user privacy and reduce communication loss, demonstrating blockchain's broad application prospects in the future energy transaction market.

Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.

For ultra-reliable low-latency communication (URLLC), retransmission data should be scheduled with different priorities due to the urgency, which brings new challenges in delay-oriented optimization, especially with deterministic delay constraint. In this paper, we propose an adaptive modulation and coding (AMC) scheme for both initial transmissions and retransmissions in separate data queues. Different from most of the existing works focusing on average delay, we jointly consider delay performance and deterministic delay requirement by combining the Markov decision process (MDP) with Lyapunov optimization technique. To overcome the coupling in the objective and the deterministic constraint, we transform this problem into an infinite horizon MDP by constructing a renewal system with sampling. Based on this, we propose a delay-optimal modulation and coding scheme (MCS) selection policy using reinforcement learning. Simulation results show that the proposed scheme achieves better delay performance than the conventional AMC schemes.

With the wide application of deep learning (DL) across various fields, deep joint source–channel coding (DeepJSCC) schemes have emerged as a new coding approach for image transmission. Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two‐view distributed cooperative DeepJSCC (two‐view‐DC‐DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two‐view‐DC‐DeepJSCC with information disentanglement (two‐view‐DC‐DeepJSCC‐D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two‐view‐DC‐DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two‐view‐DC‐DeepJSCC‐D effectively captures both common and private information from two‐view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two‐view‐DC‐DeepJSCC‐D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two‐view‐DC‐DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two‐view‐DC‐DeepJSCC‐D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two‐view‐DC‐DeepJSCC‐D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two‐view‐DC‐DeepJSCC‐D exhibits stronger robustness across various signal‐to‐noise ratios.

Caching sheds a light on reducing long-distance data transmissions over networks, while raising significant privacy concerns. Moving one step ahead, collaborative edge caching is proposed to facilitate preserving user privacy via reducing the external data exposure. However, it still fails to avert the risk of privacy leakage from nearby edge devices. To tackle this issue, we develop an analytical framework for privacy preserving joint communication and content allocation algorithm for distributed collaborative edge caching systems, in which edge devices collaboratively cache and share the content items based on the dummy-based privacy preservation mechanism. Specifically, we define the system request uncertainty criterion from the perspective of information entropy to measure the privacy preservation performance. Consequently, the closed-form relationship between the system request uncertainty and the resource allocation decisions on both communication resources and content items can be derived. Then, we decompose the NP-hard resource management problem into two parts, and propose 1) an optimal dummy request allocation strategy through investigating special properties of the maximal allocation reward gain and 2) an asymptotically optimal content item allocation strategy with low complexity based on the extract penalty method (EPM), which are iterated to obtain a viable solution, followed by the proof of convergence and asymptotic monotone property. Finally, the performance improvements are verified by simulations.

The expansion of the Internet of Things (IoT) has led to a significant surge in data flow over edge networks, posing substantial challenges to data mining and management. While federated edge learning (FEEL) effectively accomplishes global integration and local training based on the decentralized data sets, its deployment across expansive IoT networks introduces additional challenges. The primary issues stem from managing the interaction between the communication load and learning effectiveness. The communication loads driven by recurrent data exchanges between the user equipment (UE) and central servers exacerbate network congestion and latency issues. Moreover, the learning efficacy is undermined due to the typically nonindependent and identically distributed (non-IID) characteristics of real-world IoT data. In this article, a novel communication-efficient hierarchical FEEL framework is proposed to tackle these challenges. Specifically, UEs are adaptively clustered according to their link conditions, geographic locations, and data distributions. Small base stations (SBSs) collect local model updates from the UEs in their clusters and communicate with a macro base station (MBS) for the global model aggregation. To jointly maximize the communication gain (in terms of reducing latency) and the learning gain (in terms of improving accuracy), a clustering and resource allocation optimization problem is formulated, and a cross entropy-based method with low computational complexity is proposed. Numerical experiments validate that the proposed hierarchical FEEL system achieves fast convergence and significantly improves the system efficiency for various learning tasks and the system settings.

Content popularity forecast is a key enabler toward the realization of proactive content caching, contributing to significant reduction of content fetching delay. Different from most of the existing literature that concentrating on enhancing the forecast accuracy, we tailor the popularity forecast and user clustering algorithms for improving the caching performance. Specifically, through analyzing the caching performance drop incurred by inaccurate popularity forecast from the Bayesian perspective, we obtain two critical insights, which trigger the following designs: 1) as the utility of forecast varies according to the content rank, we propose a content-caching-oriented popularity forecast algorithm based on Gaussian process (GP), where more computational resource is allocated to forecast the popularity of prioritized contents and 2) to alleviate the influence of forecast error on the rank of prioritized contents, we propose a content-caching-oriented user clustering algorithm based on the K-means algorithm. Since the involved optimization problem is NP-hard, we propose an iterative algorithm, whose convergence property in terms of region stability is proved, as the objective function may vary before a local minima is reached. Finally, the simulation results demonstrate the superiority of the proposed framework.

Intelligent reflecting surface (IRS) usually consists of a large number of passive elements, for which the element-grouping strategies can be adopted to group adjacent elements into a sub-surface for lower computational complexity. For the grouped elements of a sub-surface, the linear gradient phase shift configuration can achieve directional IRS reflect beam towards the intended receiver. In this paper, we propose a practical scalable optimization framework for element-grouping IRS by adopting the amplitude-dependent phase-gradient directional beamforming, which induces a new amplitude-phase coupling to the reflected signal. Specifically, by deriving the phase-gradient condition from Fermat’s principle, we propose a practical phase-gradient IRS reflection model. Under this practical model, the amplitude-phase coupling becomes complicated, which brings technical challenges to the IRS beamforming optimization. We study a joint transmit and reflect beamforming optimization problem to minimize the transmit power. By designing a trigonometric transformation to deal with the complicated amplitude-phase coupling, we propose a penalty-based phase control strategy under given element grouping. Subsequently, to solve the element-grouping combinatorial problem with performance guarantee, we propose a low-complexity IRS reflect beamforming algorithm based on Markov approximation. Simulation results demonstrate that the proposed algorithm achieves substantial performance gains compared to conventional schemes.

In-network aggregation is a fundamental operation for massive packets in the Internet of Things (IoT). By aggregating ultra-small packets, the energy consumption for data transmission is not related to the packet number, while the average delay performance depends on the delay of all packets even if they are aggregated. In this paper, we propose a low-delay ultra-small packet transmission scheme with in-network aggregation in energy-harvesting multi-hop networks, where each device periodically transmits packets in a collect-wait-forward relaying manner. Considering the resulting extra waiting time during relaying, we first drive the tractable form of the average end-to-end delay by problem transformation. By characterizing the two-dimensional evolution property from the perspective of both hops and time, the delay minimization problem is reformulated as an infinite-horizon average-cost Markov decision process with a two-dimensional optimality equation. To deal with the curse of dimensionality, we decompose the global Bellman equation into several per-device local relay selection problems. Based on the problem decomposition, we propose a distributed ultra-Small Packet Aggregation Relay SElection (SPARSE) algorithm via stochastic learning. The convergence is further proved theoretically and verified by simulation. Simulation results reveal that the proposed scheme achieves significant performance gain over the baselines for ultra-small packets.

Network slicing is now widely used to provide advanced services in terms of sliced resources. It is urgent for Video Streaming Service Providers (VSSPs) to guarantee the Quality of Experience (QoE) via the sliced resources from Network Service Providers (NSPs). In this paper, we propose a bandwidth-cache pricing-based network slicing approach for video streaming delivery. Specifically, the NSP slices the bandwidth and caching space jointly, and then reorganizes these resources flexibly to various VSSPs to maximize the QoE of all users. We design a bandwidth-cache pricing policy to solve the slicing problem, in which there is a trade-off between the bandwidth-cache resources in terms of QoE. We first quantitatively analyze the QoE for various resource bundles by adopting the diffusion approximation. Based on the derived QoE, we propose the heterogeneous auction, a framework for bandwidth-cache slicing with dynamic ascending prices. Specifically, the auction increases the prices of the demanded resource bundles submitted by active bidders. Later, we prove that optimal social surplus and incentive compatible can be realized. Finally, simulation results show that the proposed auction achieves better performance than conventional homogeneous auctions.

Deterministic Networking (DetNet) aims to provide an end-to-end ultra-reliable data network with ultra-low latency and jitter. However, implementing DetNet in wireless networks, particularly in the air interface, still faces the challenge of guaranteeing bounded delay. This paper proposes a delay-guaranteed three-layer scheduling framework for DetNet, named Deterministic Guarantee Scheduling (DGS). The top layer calculates the amount of new data entering the queue in each scheduling period and timestamps the data to track its arrival time. Based on the remaining waiting time of each flow’s data volume, the middle layer proposes a scheduling algorithm based on urgency, prioritizing the scheduling of data volumes with the shortest remaining queuing time. The lower layer fine-tunes the scheduling results obtained by the middle layer for actual transmission. We implemented the DGS framework on the 5G-air-simulator platform. Simulation results demonstrate that DGS outperforms all other mechanisms by guaranteeing delay for a larger number of deterministic flows and achieving better throughput performance.

Ultra-reliable low-latency communication (URLLC) is an indispensable element towards supporting various latency-sensitive and reliability-critical applications. To optimize the average delay while satisfying the deterministic delay constraint, initial transmission and retransmission should be handled with different priorities due to the differentiated urgency, which creates complex interdependency and brings new technical challenges to delay-oriented optimization. In this paper, we propose a retransmission-aware adaptive modulation and coding (RAMC) scheme to improve the delay performance in URLLC scenarios. Specifically, we first establish a cascaded queue system, including an initial transmission queue and a retransmission queue. The deterministic delay constraint is satisfied through Lyapunov optimization, where we transform the Lyapunov drift-plus-penalty problem into an infinite horizon Markov decision process (MDP) by constructing a renewal system with sampling to overcome the challenge brought by queue coupling. Next, we propose the delay-optimal RAMC scheme by solving the associated Bellman equation by improved reinforcement learning, which is proved to be asymptotically optimal. Finally, the superiority of the proposed RAMC scheme is verified through simulations.

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication (ISAC) devices to sense the targets and offload the data to the base station (BS) for further processing. However, as the number of ISAC devices grows, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which models the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.

In large-scale multi-access edge computing (MEC) networks, each device should make the computation offloading decision distributively. In this paper, we target on a delay-optimal computation offloading problem in large-scale MEC systems, where each task has two properties: data size and computation amount. Because the detailed state information of massive devices are huge in large-scale systems, we propose a distributed computation offloading algorithm using the mean field game (MFG). To design the distributed computation offloading algorithm, we first formulate the delay-optimal computation offloading problem as a Markov decision process (MDP) and derive the Hamilton-Jaccobi-Bellman (HJB) equation with the unknown task allocation proportion, where the combined influence from other devices and MEC servers should be estimated. Based on MFG, we obtain the Fokker-Planck-Kolmogorov (FPK) equation to describe the evolution of the system’s collective behavior, with the influence from other devices and MEC servers formulated as the mean field. To solve the large-scale problem with the unknown allocation proportion, we propose a optimal computation offloading algorithm based on the generative adversarial networks (GAN) structure. For the generator, we generate the unknown task allocation proportion due to its non-calculability and insufficient dataset. For the discriminator, we train the value function, and propose a water-filling algorithm to prioritize the task offloading. Finally, the simulation results evaluate the performance of the proposed algorithm and show the performance gain compared to conventional algorithms.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are gaining great attention for their ability to achieve full-space coverage. In this paper, the queue-aware STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure system stability. To tackle the challenge of infinite time periods for stability, the long-term stability-oriented problem is reformulated as a per-slot queue-weighted sum rate (QWSR) maximization problem using Lyapunov drift theory. Particularly, the allocated rate weight for each user is determined by the corresponding data queue at the base station (BS). By jointly optimizing the NOMA decoding order, the active beamforming coefficients at the BS, and the passive transmission and reflection coefficients at the STAR-RIS, three STAR-RIS operating protocols are considered, namely energy splitting (ES), mode switching (MS), and time switching (TS). An equivalent-combined channel gain based scheme is proposed to obtain the desired decoding order. For ES, the highly coupled and non-convex problem is solved iteratively and alternatively by invoking the blocked coordinate descent and the successive convex approximation methods. This approach is further expanded to a penalty-based two-loop algorithm to solve the binary amplitude constrained problem for MS. For TS, the problem is decomposed into two subproblems, each of which is solved similarly as ES. Simulation results show that: i) our proposed STAR-RIS assisted NOMA communication achieves superior performance to the conventional schemes; ii) the reformulated QWSR maximization problem is proven to ensure the system stability; and iii) TS performs best in both the QWSR and the average queue length.

Coded distributed computing (CDC) can efficiently improve the performance of Map Reduce by mitigating the effects of straggling servers and decreasing the communication load simultaneously. In this paper, for the latency-constrained computation tasks, we use hybrid coded MapReduce and optimize the hybrid coded scheme to minimize the total latency, including the computation latency in Map phase and the communication latency in Shuffle phase. For the practical scenarios where the number of straggling servers is relatively small compared with the number of all servers, we reformulate the original optimization problem into a non-convex optimization problem. Then we use the successive convex approximation (SCA) algorithm to approximate the non-convex optimization problem into the iterations of a convex optimization problem. We prove the effectiveness and convergence of the proposed iterative SCA algorithm. Besides, the proposed SCA-based algorithm shows the effective convergence and outperforms the enumeration algorithm of the original problem with the approximated total latency and the short execution time via numerical experiments. Furthermore, the hybrid coded scheme achieves significant performance improvement in terms of decreasing the total latency compared with baseline schemes.

Modern clustered storage systems have commonly used erasure coding to maintain data durability against failures, yet it introduces significant update overhead for partial updates (e.g., only part of a block is updated). Recent studies propose the append-commit and buffer-logging techniques, which append multiple updated data to buffer-log and cache the corresponding old data from disk into memory, to reduce the update costs when committing the updates to parity. However, caching the entire old data block will introduce additional disk reads and memory overhead because caching the old part that not be updated, caching the partial old data block will incur the significant disk seeks for frequent partial updates. Our real-cloud experiments show that the unbalanced disk I/O may cause the bottleneck for updating, which is unfortunately overlooked by existing studies.This paper proposes LASC, a locality-aware speculative cache scheme for partial updates. LASC perceives the update locality of a data block from an update request stream within a period and speculatively caches the old data from the disk. It caches the entire old data block with high update locality to reduce the disk seeks. For a series of update requests to the same data block, LASC only performs one disk seek. Otherwise, it caches the old partial data to migrate the disk reads and memory overhead. We evaluate LASC via trace-driven simulations and Alibaba ECS experiments for two of the largest and latest public block-level I/O traces and show that LASC can effectively balance the disk I/O and improve the update performance while keeping memory overhead low.

Edge learning is a promising enabler to leverage the distributed local data for powering the artificial intelligence at the edge network. Moreover, incorporating the external domain knowledge into purely data-driven learning models can further enhance the performance. In this paper, by taking both the benefits of edge learning and knowledge fusion, we propose a novel distributed knowledge-aware edge learning framework, in which the edge devices individually train the learning models with the assistance of the local knowledge bases at the edge devices and the global knowledge base at the edge server. Due to the limited cache capability, the edge device can only cache a small-scale local knowledge base, which restricts the performance gain by local knowledge fusion. Meanwhile, uploading local data from multiple edge devices for global knowledge fusion may lead to the air-interface congestion. To overcome these issues, we first formulate the global loss decay maximization problem with transmission scheduling decisions. Specifically, we derive the closed-form relationship between transmission scheduling and the learning performance. Then, we depict the implicit relationship between the knowledge fusion and the global loss decay via establishing a specific multi-armed bandit (MAB) framework, and derive an asymptotically-optimal solution accordingly. Extensive simulations demonstrate that the proposed policies outperform the state-of-art policies.

Network traffic monitoring plays a crucial role in maintaining the security and reliability of the communication networks. Although Machine Learning (ML) assisted abnormal traffic detection has been emerged as a promising paradigm, the existing data-driven learning-based approaches are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. To this end, we establish an online learning framework for abnormality traffic detection by embracing Gaussian Process (GP) and Sparse Representation (SR). The contributions of this paper are two-fold: 1). We utilize a special kernel, i.e., mixture of Gaussian, to better explore and exploit the evolving traffic characteristics, so as to more accurately model network traffic. 2). To combat noise and modeling error, we formulate a feature vector based on Kullback-Leibler (KL) divergence to measure the difference between normal and abnormal traffic, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.

To alleviate the local computation demands from the ever-increasing computation-intensive mobile applications, Mobile Edge Computing (MEC) has proved promising. Especially, by opportunistically offloading these computation tasks to the MEC server, the delay of computing could be significantly improved through communication. In this paper, we develop an analytical framework for joint communication and computation resources allocation for multi-user MEC systems. Specifically, to retrieve the combined effect of communication and computation capabilities, we establish a dual queue system, including a data queue sub-system and a computation queue sub-system. To address the associated stochastic resource optimization problem, we propose a low-complexity resource allocation algorithm by Lyapunov optimization to stabilize all the sub-queue systems. As the practical buffers are finite, the conventional delay analysis of Lyapunov optimization becomes inaccurate. Alternatively, we model the stochastic queue lengthes as discrete time controlled random walk processes, which are transformed to continuous time Stochastic Differential Equations (SDEs) with reflections by strong approximation. According to the steady state analysis on the SDEs, we derive closed-form steady state distributions of the queue lengths, and then obtain the average delay performance with finite buffers. Finally, the accuracy of the proposed delay analysis is verified through simulation.

The forecast of network traffic with arbitrary predicting horizon is a key enabler of smart management in next-generation networks, as sufficient amount of time can be provided for the proactive manipulation of network resources to maintain high quality transmission. Nevertheless, the evolving characteristic of network traffic challenges the current learning-based and data-driven algorithms on both prediction accuracy and computational complexity. In this work, we explore special properties of network traffic, which are further encoded into the Gaussian Process (GP)-based online learning framework, so as to better comprehend and predict future network traffic from a Bayesian perspective. Specifically, we proceed by three steps, 1). Observing network traffic is evolving, to explore and exploit the dynamic traffic patterns at different times and time-scales, we try to approximate the optimal kernel function of GP by utilizing a mixture of Gaussian to encode the dominant and several nondominant patterns. 2). As network traffic at different time-scales share several common patterns, we adopt Process Convolution (PConv) to fully exploit correlations among multiple subsequent time-slots, so as to facilitate network traffic forecast with large predicting horizon. 3). To promote the tracking capability of the proposed GP-PConv framework without significantly increasing the number of hyper-parameters to train, we slightly modify the GP-based prediction through Lyapunov optimization, which brings performance improvements both in terms of accuracy and computational complexity. Finally, we demonstrate the superiority of the proposed algorithm through simulation.

Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have received great attention due to the capability of achieving full-space coverage. In this paper, the queue-stable STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure the stability of queueing systems. To tackle the challenge of infinite time slots required for stability, the long-term stability-oriented problem is reformulated to maximize the per-slot queue-weighted sum rate (QWSR) of users. In particular, the rate weight allocated for each user is determined by the length of a data queue, which is maintained at the base station (BS) and pending to be delivered to each user. Then, the QWSR is maximized by jointly optimizing the NOMA decoding order, the active beamforming coefficients (ABCs) at the BS, and the passive transmission and reflection coefficients (PTRCs) at the STAR-RIS. To handle the highly-coupled and non-convex problem, the blocked coordinate descent and the successive convex approximation methods are invoked to iteratively and alternatively optimize the problem. Simulation results revel that: i) our proposed STAR-RIS assisted NOMA communication achieves better performance than the conventional schemes; ii) the reformulated per-slot QWSR maximization problem is proven to ensure the system stability.

Increasing demands of multimedia contents brings a great profit to the content providers, but also a challenge of how to efficiently delivery contents to make users have a good Quality of Experience (QoE). Take the advantage of owning wireless infrastructures, the telco operator is motivated to build a content platform for entering the market of content. In this paper, we consider a content platform belonging to the telco operator, which can provide periodically-updated contents with small Age of Information (AoI), namely, fresh contents. The content update consumes radio resource resulting in a trade-off between the AoI and the latency. We adopt the contract theory to monetize contents considering the above two factors in a realistic asymmetric information scenario. Necessary and sufficient conditions are derived to ensure the feasibility of the contract. We further propose the optimal update schemes and the corresponding fees, which maximizes the utility of the operator. Simulation reveals that the proposed contract enables the users, who attach importance to the freshness, to obtain frequently updating contents.

Caching popular contents in close proximity to the users can effectively reduce the latency in communication systems. Considering the imperfect content fetching from the original server, the caching management at the edge node should be determined according to not only the content popularity but also how difficult to fetch these content objects from their corresponding original servers. In this paper, we study the edge caching for minimizing the long-term average delay, where the fetching delay of the uncached contents is introduced. We construct a decision-theoretic framework for this delay-optimal content-caching problem, where the main obstacle is that the consideration of the imperfect content fetching breaks the Markovian property in the decision-theoretic framework. To overcome this obstacle, by analyzing the queue dynamics within the content fetching delay after the content object is removed from the cache, we transform the problem for meeting the Markovian property, and model it as an infinite horizon semi-Markov decision process (SMDP). Achieving the delay optimality of the caching problem needs to solve the Bellman equation of the SMDP, but it leads to the curse of dimensionality. We decompose the global optimality equation into several per-content optimality equations, and propose a low-complexity delay-optimal content caching algorithm by stochastic learning for each content. Finally, the simulation results show that our proposed algorithm achieves significantly lower delay than conventional caching algorithms.

Intelligent reflecting surface (IRS) is a promising solution for enabling the control of wireless environments. Most of existing works consider that an IRS consists of a large number of reconfigurable passive elements and each of them can reflect the signal independently. However, it is difficult and costly in practice to manufacture this kind of large-scale IRS. In this paper, we propose a practical electromagnetic-compliant reflection coefficient model for element-grouping IRS, which configures eachsub-surface(including multiple homogeneous elements) but not each single element. Under this model, the reflection amplitude and the phase shift are coupled, which brings new challenges for optimizing the IRS configuration. We study a phase shift optimization problem to maximize the received power in an IRS-aided wireless system. As this problem is difficult to solve due to the complicated coupling in the practical model, we transform it into avector compositionproblem equivalently. By constructing a geometry-based phase analysis model, we analyze the relationship of the vectors in a two-dimensional space to maximize the length of the sum vector. By exploiting the geometrical properties, we propose a low-complexity phase control algorithm to find the optimal phase shifts of IRS. Simulation results reveal that substantial performance gains are achieved by the proposed algorithm compared to the conventional schemes.

The conventional federated learning (FL) framework usually assumes synchronous reception and fusion of all the local models at the central aggregator and synchronous updating and training of the global model at all the agents as well. However, in a wireless network, due to limited radio resource, inevitable transmission failures and heterogeneous computing capacity, it is very hard to realize strict synchronization among all the involved user equipments (UEs). In this paper, we propose a novel asynchronous FL framework, which well adapts to the heterogeneity of users, communication environments and learning tasks, by considering both the possible delays in training and uploading the local models and the resultant staleness among the received models that has heavy impact on the global model fusion. A novel centralized fusion algorithm is designed to determine the fusion weight during the global update, which aims to make full use of the fresh information contained in the uploaded local models while avoiding the biased convergence by enforcing the impact of each UE’s local dataset to be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve fast and smooth convergence and enhance the training efficiency significantly.

To meet the ever-increasing demand for mobile video services, one of the effective solutions is caching some popular videos in edge nodes. In this paper, we propose an online stochastic learning algorithm with two time scales for joint caching and transmission optimization in the video frame level. To overcome the drift distortion caused by the dependency among video frames, the transmission process is formulated as an infinite horizon Markov decision process (MDP). We derive the equivalent Bellman equation and design the online value iteration algorithm via stochastic approximation for transmission. Due to the lack of the expression between the system performance and the caching policy, we design a gradient-free stochastic optimization algorithm to update the caching policy. Finally, simulation results show that our proposed algorithm achieves better performance than conventional caching algorithms.

We formulate and analyze a generic sequential resource access problem arising in a variety of engineering fields, where a user disposes a number of heterogeneous computing, communication, or storage resources, each characterized by the probability of successfully executing the user's task and the related access delay and cost, and seeks an optimal access strategy to maximize her utility within a given time horizon, defined as the expected reward minus the access cost. We develop an algorithmic framework on the (near-)optimal sequential resource access strategy. We first prove that the problem of finding an optimal strategy is NP-hard in general. Given the hardness result, we present a greedy strategy implementable in linear time, and establish the closed-form sufficient condition for its optimality. We then develop a series of polynomial-time approximation algorithms achieving (ϵ, δ)-optimality. The key components in our design include a pruning process eliminating dominated strategies, thus maintaining polynomial time and space overhead, and a comprehensive scheme allowing flexibly trading-off time and space overhead against performance guarantee.

By caching popular contents at edge nodes, the content can be delivered via device-to-device (D2D) communications to reduce the delay significantly. In this paper, we study cooperative content delivery from multiple edge nodes with duplicate caching, where the content caching and the cooperative transmission algorithms are designed jointly to minimize the average delay. Specifically, we decompose the delay optimization problem as short time-scale transmission subproblems for each time slot and a long time-scale caching master problem by primal decomposition. The transmission subproblem is modelled as a finite horizon Markov decision problem (MDP), and we propose a link selection algorithm based on the value function in the MDP model. For the caching master problem, we propose a caching algorithm by the simultaneous perturbation stochastic approximation (SPSA) method. Simulation results show that our proposed transmission and caching algorithms achieve lower delay than the conventional schemes.

The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to alleviate the heavy network load by enhancing the traffic offloading. This, in turn, has called for the optimization of content pushing strategy while considering both the transmission and caching resources, which jointly result in the complicated coupling among pushing decisions and lead to high computational complexity. Unlike most existing approaches which simplify the pushing problem via bypassing the complicated coupling, in this paper, we propose a multicast content pushing strategy to maximize the offloaded traffic with the cost on content caching based on structured deep learning. Specifically, we design the convolution stage to extract the spatio-temporal correlations of one content item between different pushing decisions, and construct the fully-connected stage to capture the spatial coupling among the decisions of pushing different content items to different user devices. Moreover, to address the absence of the ground truth on multicast content pushing, we relax the transmission constraint to derive a performance upper bound for guiding the training direction. This relaxed problem is solved based on dynamic programming in a bottom-up manner. Compared to the state-of-the-art baselines including both the traditional model-based and the general neural network-based strategies, the proposed pushing strategy achieves significant performance gain in both the random-generated dataset and the real LastFM dataset. In addition, it is also shown that the proposed strategy is robust to the uncertainty of user request information.

In this article, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system. Explicitly, in the proposed algorithm, each node interacts with only part of its neighboring nodes, the number of which is progressively determined according to a heuristic searching procedure, which takes into account both the predicted convergence rate and the communication and computation costs at each iteration, resulting in a trade-off between communication and computation. Then the node chooses its neighboring nodes according to an importance sampling distribution derived theoretically to minimize the variance with the latest information it locally stores. Finally, the node updates its local information with a new update rule which adapts to the number of communication nodes. We prove the convergence of the proposed algorithm and provide an upper bound of the convergence variance brought by randomness. Extensive simulations validate the excellent performances of the proposed algorithm in terms of convergence rate and variance, the overall communication and computation cost, the impact of network topology as well as the time for evaluation, in comparison with the traditional counterparts.

Edge caching is an efficient technique for mitigating redundant data transmissions over backhaul links. Contents are constantly evolving, and thus the cached contents should be updated timely to guarantee freshness. Information freshness is captured by the Age of Information (AoI) metric. In this paper, we explore a content resale problem, where a Network Service Provider (NSP) purchases contents from Content Providers (CPs) then sells contents to users. We model the problem as a three-stage sequential problem. Then we decompose the problem into two sub-problems on deciding the contents to be purchased and cached, and the respective prices. We solve the sub-problems through the framework of Stackelberg game and auction respectively. For the content purchase, we consider four different cases to design the auction mechanisms. In the auctions, the NSP has to reveal its private information due to the AoI characteristics. Furthermore, we prove that such disclosure will not bring the loss of the NSP’s utility. Finally, numerical results in different cases are provided to show that the NSP can efficiently exploit the benefit from the knowledge about CPs’ production costs and the competition among CPs under our proposed mechanism.

Content popularity prediction plays an important role on proactive content caching. Different to most of the existing works which focus on improving the popularity prediction accuracy, in this article, we consider the content caching oriented popularity prediction through a weighted clustering approach in order to improve the caching performance. We formulate the loss of the cache hit ratio as the system regret to indicate the caching performance, and construct a clustering-based popularity prediction framework for overcoming the user request sparsity with considering the similarity of popularity evolution trends. For depicting the explicit relationship between the caching performance and the popularity prediction accuracy, we derive the popularity prediction error distribution of each content, and design the caching threshold. By extracting the insights in the relationship between the popularity prediction accuracy and the user clustering strategy, we develop a weighted clustering-based popularity prediction algorithm, which takes the caching regret probability of files as the weights. Based on two real-world datasets, the simulation results demonstrate that the proposed popularity prediction scheme achieves better caching performance than the state-of-the-art schemes.

In distributed random access (RA), each device should consider its own information as well as the influence from others, which is difficult to obtain with diverse capabilities of devices. In this paper, we study the RA problem for massive devices with heterogeneous device capabilities in large-scale energy harvesting IoT networks. To deal with the overload issue for massive devices with different capabilities, we propose an optimal RA policy by improving the conventional mean field games (MFG) via exchanging the mean field terms (MFT) among devices. Specifically, we formulate the delay optimal problem as a two-dimensional Markov Decision Process (MDP) problem involving both energy and data states. For distributed deployment of massive RA, we divide the MDP into multiple per-device subproblems, and propose the distributed RA scheme by solving the Hamilton-Jacobi-Bellman (HJB) equation using stochastic learning. Considering the deviation of MFT estimation induced by heterogeneous device capabilities, we design an MFT consensus scheme based on stochastic approximation by information exchange among neighbor devices. For reducing the state space and exchanging the MFT efficiently, we adopt the number of simultaneous access devices instead of the conventional MFT. Furthermore, we prove the convergence of the proposed scheme with coupling MFT and Q-factor. Finally, simulation results demonstrate that the proposed RA scheme outperforms baseline schemes on delay performance, especially in the heavy traffic load regime.

Leveraging the content caching technology, popular contents can be in close proximity to the users, which reduces the delay effectively. In this paper, we dedicate to minimizing the long-term average delay for the cache-enabled wireless networks, in which the fetching delay is considered when the uncached contents are retrieved from remote servers. We construct a decision-theoretic framework for this delay-optimal content-caching problem. The most challenging obstacle is that the consideration of imperfect content fetching breaks the Markovian property in the decision-theoretic framework, which makes the problem difficult to solve. To overcome this obstacle, we propose an equivalent transformation by analyzing the queue dynamics during the content fetching delay, so that the transformed problem can be modeled as an infinite horizon semi-Markov decision process (SMDP). To handle the curse of dimensionality for solving the SMDP, we decompose the global optimality equation into several per-content equations, and propose a low-complexity delay-optimal content caching algorithm. Simulation results show that the proposed algorithm achieves significantly lower delay than conventional caching algorithms.

The random access (RA) decision of a device should depend on both its own state and the influence of others for avoiding the overload. However, the heterogeneous characteristics of massive devices lead to the difficult for estimating the influence. In this paper, we consider the RA problem for the large-scale energy harvesting IoT networks. To deal with the overload issue for massive heterogeneous devices, we propose a delay-optimal RA strategy by improving the conventional mean field games (MFG) via exchanging the mean field terms (MFT) among devices. Specifically, we formulate the delay-optimal problem as a two-dimensional Markov decision process (MDP) problem. For distributed deployment of massive random access, we divide the MDP into multiple per-device subproblems. With the given influence of other devices, i.e., MFT, we solve the per-device MDP and propose the optimal RA scheme via Hamilton-Jacobi-Bellman (HJB) equation. To obtain optimal access strategy, we adopt an online learning scheme to estimate the influence from others, where we transform MFT into the number of simultaneous access devices in order to reduce the state space significantly. Considering that the heterogeneous devices will cause the deviation of MFT estimation, we design a consensus scheme for the MFT based on stochastic approximation by information exchange among neighbor devices. Finally, simulation results show that the proposed RA scheme achieves a good delay performance comparing with other baselines.

Currently, it is still challenging for Video Streaming Service Providers (VSSPs) to develop advanced video delivery in wireless communications due to the limitation of network resources. In this paper, we propose a pricing strategy to effectively allocate heterogeneous resources, including transmission bandwidth and caching space, to improve users' Quality of Experience (QoE). Due to the properties of the two-hop video streaming model we propose, a video can be partially cached in the local storages to provide low waiting time and avoid playback interrupt. To design the pricing strategy for two heterogeneous resources, we first quantitatively analyzing the QoE with partial video caching by adopting diffusion approximation with stochastic data arrival. Based on the above QoE model, we develop an ascending auction framework for pricing heterogeneous resources. Furthermore, we prove that our proposed pricing strategy achieves asymptotically optimal social surplus and ε-incentive compatibility. Finally, simulation results show that our proposed pricing strategy can achieve better performance on the social surplus compared to the conventional pricing strategies.

Content caching should be updated according to the time-varying content popularity. However, with the consideration of the time-consuming cache replacement process, it is necessary to predict the content popularity and adjust the cached contents beforehand. In this paper, we propose a popularity prediction scheme for content caching. To overcome the request sparsity and exploit the diversity of popularity evolution trends, the users are grouped into non-overlapped clusters for predicting content popularity for each cluster respectively. Different to most of the existing works which focus on the accuracy of prediction, we consider the effect of the prediction error to content caching and adopt the loss of the cache hit ratio as the system regret. In the proposed clustered popularity prediction scheme, the system regret is estimated by analyzing its own prediction error distribution and obtaining the influence from those of other contents through an online learning framework. To achieve the optimal system performance, we design a K-mean clustering algorithm according to the expected regret and the popularity evolution trends. The simulation results show that the proposed clustered popularity prediction scheme achieves better caching performance than the state-of-the-art caching schemes.

With mobile edge computing (MEC), the data compression at the edge devices can effectively improve the communication efficiency by transmitting the compressed data. In this paper, we construct a joint data compression and transmission scheduling framework to optimize the system throughput with the limited transmission resources. Different to most of the existing works, we consider the interaction between the data compression and data transmission to achieve the optimal throughput. Specifically, to explore the effect of data compression, we construct a queue system through constructing the mapping between the original data queues and the compressed data queues under different compression schemes (including the uncompressed queues). We design the transmission scheduling algorithm based on Lyapunov optimization according to the original data queues. Due to the nature that the data compression does not change the original data queue length directly, we choose the optimal data compression scheme considering the achieved utilities when the compressed data are transmitted, which can be estimated via Q-learning. In addition, we theoretically prove the queue stability under our proposed joint data compression and transmission scheduling algorithm. The simulation results show that the proposed algorithm has better delay performance than the conventional schemes.

Considering the limitations on communication capability in the big data era, measurement selection plays an important role in obtaining the desired information by collecting only a part of data from the sensors. In this paper, we study the large-scale measurement selection problem, and propose a distributed algorithm exploiting the sparsity property extracted from the on-line data processing. Different to the existing works, we propose a mission-oriented framework to analyze the performance improvements for the specific mission of collecting new data. Specifically, a Bayesian hierarchical prior is adopted in order to quantify the importance of uncollected data by the on-line inference from the collected data. Based on the sparsity property obtained by on-line data processing, the sensors with important uncollected data will have high priority to access. Due to the massive number of sensors, the measurement selection algorithm is executed distributively at each device according to the common information broadcast by the fusion center. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes.

In this paper, we develop a dynamic communication-efficient computation load scheduling framework to complete the computation tasks with coded MapReduce considering arbitrary arrival and strict delay constraints over time-varying computing resource. Our goal is to minimize the communication load under the time-varying excess computing resources. We first reduce this problem to a task scheduling problem with exploiting the property of the computing repetition in the coded MapReduce framework. For the case that the full information about the available computing resource is known in advance, we obtain the optimal offline computation load scheduling algorithm. Guided by the optimal algorithm, we propose a dynamic online algorithm based on the predicted computing resource. Finally, our proposed algorithm is evaluated by simulation to demonstrate the superiority of the proposed algorithms over the conventional algorithms.

With the development of e-commerce and smart cities, utilizing unmanned vehicles to deliver packages has emerged as one of the most important methods to make customers receive packages efficiently and effectively. Hence, how to reasonably utilize multiple unmanned vehicles at the same time is a problem. Another main challenging issue is how to satisfy customers' personalized need. In this paper, we propose a novel context-aware multi-armed bandit-based online learning algorithm with active partition method for context space. To solve the massive injecting data flow problem, we utilize a tree-based structure expanding from top to bottom to choose different vehicles, which supports ever-increasing big metering datasets with historical and contextual information. We prove that our proposed context-aware online learning algorithm achieves sublinear regret performance. Experiment results show our proposal can enhance customers' satisfaction and reduce space cost tremendously.

We consider the online shortest path routing (SPR) of a network with stochastically time varying link states under potential adversarial attacks. Due to the denial of service (DoS) attacks, the distributions of link states could be stochastic (benign) or adversarial at different temporal and spatial locations. Without any a priori, designing an adaptive and optimal DoS-proof SPR protocol to thwart all possible adversarial attacks is a very challenging issue. In this paper, we present the first such integral solution based on the multi-armed bandit (MAB) theory, where jamming is the adversarial strategy. By introducing a novel control parameter into the exploration phase for each link, a martingale inequality is applied in our formulated combinatorial adversarial MAB framework. The proposed algorithm could automatically detect the specific jammed and un-jammed links within a unified framework. As a result, the adaptive online SPR strategies with near-optimal learning performance in all possible regimes are obtained. Moreover, we propose the accelerated algorithms by multi-path route probing and cooperative learning among multiple sources, and study their implementation issues. Comparing to existing works, our algorithm has the respective 30.3% and 87.1% improvements of network delay for oblivious jamming and adaptive jamming given a typical learning period and a 81.5% improvement of learning duration under a specified network delay on average, while it enjoys almost the same performance without jamming. Lastly, the accelerated algorithms can achieve a maximal of 150.2% improvement in network delay and a 431.3% improvement in learning duration.

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependences and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP) and approximate it to a virtual continuous time system (VCTS) with reflections. Different from most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closed-form approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). Furthermore, we discuss the extension of our proposed scheme to multi-MT multi-server scenarios. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we construct a Markov decision process (MDP) framework to optimize the delay performance in computation-constrained mobile edge networks. Different to most of the existing works, we consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are mutually strongly coupled in a cascade manner, which creates complex interdependence and brings new technical challenges. To address the challenge, the infinite horizon average cost MDP is reformulated to a virtual continuous time system (VCTS) with reflections. We derive the closed-form approximate priority functions for the MDP in different system scenarios with dynamic rate estimation. Based on the approximate priority function, we propose a multi-level water filling solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI) on the computation offloading policy in a closed form. Finally, the simulation results show that the proposed computation offloading scheme outperforms the conventional schemes.

Cooperative content caching has been demonstrated to achieve significant performance gain over the conventional content caching paradigm by exploiting content diversity through the participation of multiple cooperative nodes. Although cooperative content caching has the potential to increase the efficiency, an improper coalition formation may result in severe performance degradation. Therefore, the cooperative nodes should be carefully selected according to their interests in different content objects. In this paper, we develop an analytical framework for cooperative content caching from a coalitional game perspective. The cooperation issue for content caching among nodes is studied by the coalitional game theory, and the associated problems are analyzed in different cases that the utility transfer among nodes is allowed or not. If the utility transfer is allowed, by exploiting the properties of the coalitional costs, we derive the non-empty property of the core of a transferable utility coalitional game, and prove that the grand coalition is stable in spite of the presence of coalition costs. If the utility transfer is not allowed, we adopt a non-transferable utility coalitional game model. The grand coalition is not always stable in the presence of coalition costs. A merge and split algorithm is proposed to form the coalitional structure for iteratively improving the caching performance. Finally, the simulation results demonstrate the cooperation gains on both the sum and individual utilities in different scenarios.

Energy harvesting technology enables the devices to collect the energy from the surrounding environment. In energy harvesting networks, besides the coupling among different devices, the data transmission depends on the available energy of the devices as well, which leads to a complicated coupling and brings new technical challenges for delay optimization. In this paper, we study delay-optimal random access for large-scale energy harvesting networks. To overcome the challenges, we model a two-dimensional Markov decision process (MDP) with reflections to address the coupling between data and energy, and adopt the mean field game (MFG) theory to address the mutual coupling between devices by utilizing the large-scale property. Specifically, we decompose the optimization problem into two parts. First, to obtain the optimal access policy of each device, we derive the Hamilton-Jacobi-Bellman (HJB) equation which needs the statistical information of other devices. Second, to model the evolution of the state distribution in the system, we derive the Fokker-Planck-Kolmogorov (FPK) equation which needs the access policy of the devices. By solving these two coupled equations iteratively, we obtain the delay-optimal access solution by adopting the Lax-Friedrichs scheme and Lagrange relaxation method. Finally, the numerical results show that the proposed algorithm achieves significant performance gain compared to conventional algorithms.

Cooperative multicast has been demonstrated to achieve significant performance gain over the classic source-destination transmission paradigm by exploiting spatial diversity through the participation of multiple relay nodes. As a major technical challenge, the selection of relays for a multicast session has significant impact on the multicast performance. The challenge is even more pronounced when the number of channels is limited as the relay selection is in this context coupled with channel allocation. The goal of this paper is to design a fair multicast relay selection scheme with limited channel resources. Specifically, we establish an analytical framework for this joint relay selection and channel allocation problem and develop a lexicographic max-min multicast relay selection scheme. Our design consists of two technical steps. First, we consider the maximization of the minimal data rate. By decoupling relay selection and channel allocation, the problem is transformed to a max-min-max problem, which is difficult to solve. To make this problem tractable, we reformulate it as a convex optimization problem via relaxation and smoothing, and prove the asymptotic equivalence from a geometrical perspective. Second, we propose an adjustment algorithm based on the initial max-min solution, and prove that the proposed scheme achieves lexicographic optimality. Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes.

Spectrum aggregation (SA) across heterogeneous channels, including both dedicated and shared channels, provides the potential for improving spectrum utilization and fulfilling the requirement of broadband services. Heterogeneous SA brings new technical challenges on multisystem coexistence on shared channels and the resource allocation over heterogeneous channels. In this paper, we develop an analytical framework for heterogeneous SA from a queue stability perspective. To make all systems on the shared channels stable, we design a resource allocation algorithm for the coexistence of multiple systems. Specifically, we derive the closed-form modified water-filling power control for the single-pair case by Lyapunov optimization and prove that it achieves the queue stability for all systems. Based on the results, we propose a low-complexity suboptimal resource allocation algorithm for multipair SA, which is a NP-hard problem. We partition user pairs into groups by using graph coloring and allocate the shared channels to pair groups according to the maximal weight bipartite matching model. The simulation results verify the queue stability and show that the proposed schemes outperform the conventional schemes.

Cooperative caching is a promising technology for enhancing user experience and reducing redundant transmissions through the participation of multiple caching nodes. In this paper, we design a clustering algorithm for the sectionalized caching, in which each user divides its caching space into two parts and the contents cached in these two parts are determined according to the individual interest and the joint interest of all users in the same cluster respectively. Different to most of the existing works forming the clusters based on the interest similarity of all files, we adopt the piecewise interest similarity as the criterion of clustering, which takes advantage of the content diversity and contributes to the reduction of the transmission delay. We measure the gain of the cooperation between two users and obtain the piecewise interest similarity for two users accordingly. Since the gain of clustered caching highly depends on the formed cluster structure, we estimate the gain of the clustered caching based on the piecewise interest similarities by online learning and propose an affinity propagation (AP) based clustering algorithm. Finally, our proposed clustering algorithm is evaluated by simulation to show its superiority over the conventional clustering algorithms.

Centralized joint processing has been demonstrated to achieve significant performance gain over the conventional per-cell processing by avoiding the inter-cell interference in cloud radio access networks (C-RANs), but full scale cooperation over the entire network is infeasible due to its huge computational complexity. The goal of this paper is to design remote radio head (RRH) clustering and the associated power allocation algorithm under computational resource constraint for C-RANs, which is challenging due to the combinatorial clustering problem with non-convex constraints. To overcome the challenge, our design consists of two technical steps. 1) Considering the maximization of the sum data rate, we reformulate both the objective function and the computational resource constraint via relaxation and successive convex approximation (SCA) technique. 2) By exploiting the structure of this problem, we propose a low-complexity algorithm to decouple RRH clustering and power allocation and solve them iteratively. Furthermore, we prove the convergence property of the proposed iterative algorithm. Finally, our proposed algorithm is evaluated by simulation to show its superiority on throughput performance over conventional algorithms.

In this paper, we study the delay-aware access control of massive random access for machine-type communications (MTC). We model this stochastic optimization problem as an infinite horizon average cost Markov decision process. To deal with the distributive requirement and the exponential computational complexity, we first exploit the property of successful access probability to transform the coupling to the constraint on the number of MTC devices attempting to access. As a result, we decompose the Bellman equation into multiple fixed point equations for each MTC device by primal-dual decomposition. Based on the equivalent per-MTC fixed point equations, we propose the online hierarchical stochastic learning algorithm to estimate the local Q-factors and determine the access decision at the MTC devices separately with the assistance of the base station which broadcasts common control information only. Finally, the simulation result shows that the proposed hierarchical stochastic learning algorithm has significant performance gain over the baseline algorithm.

Massive number of machine-type communications (MTC) devices may lead to access overloading. In heterogeneous networks, the load balancing between macrocells and small cells and the backhaul capacity limitation bring new technical challenges due to their mutual coupling. In this paper, we adopt access class barring (ACB) for access control and jointly optimize access control, load balancing and preamble allocation in backhaul-constrained heterogeneous networks. Since this joint optimization problem is intractable due to its combinatorial nature, we decompose the problem and solve it in two steps. First, by introducing an auxiliary variable and exploiting the monotonicity property of objective function, we derive a closed- form solution for the optimal ACB parameter with the given cell load and number of preambles. Second, we propose an iterative algorithm to further optimize load balancing and preamble allocation with optimal ACB. Furthermore, we prove its convergence property by monotone convergence theorem. Simulation results show that the proposed massive access algorithm achieves significant performance gain compared to existing algorithms.

Cooperative multicast has been demonstrated to achieve significant performance gain over the classic source-destination transmission paradigm by exploiting spatial diversity through the participation of multiple relay nodes. As a major technical challenge, the selection of relays for a multicast session has significant impact on the multicast performance. The challenge is even more pronounced when the number of channels are limited as the relay selection is in this context coupled with channel allocation. We establish an analytical framework for joint relay selection and channel allocation problem and develop a lexicographic max-min multicast relay selection scheme. Our design consists of two technical steps. 1) We consider the maximization of the minimal data rate. By decoupling relay selection and channel allocation, the problem is transformed to a max-min-max problem, which is difficult to solve. To make this problem tractable, we reformulate it as a convex optimization problem via relaxation and smoothing, and prove the asymptotic equivalence from a geometrical perspective. 2) We propose an adjustment algorithm based on the initial max-min solution, and prove that the proposed scheme achieves lexicographic optimality.

This paper studies the problem of jamming-resistant spectrum aggregation and access (SAA) for energy-efficiency (EE) cognitive radio communications. We consider various jamming behaviors, where jammers may attack all available channels with arbitrarily changing strategies over time, attack a subset of the channels at certain time slots, or have different intelligence, i.e., oblivious or adaptive adversary, and so on. Without any priori knowledge about the channels and jammers, it is very challenging to design an efficient and practical jamming-resistant SAA algorithm to reach the optimal EE goal. In this paper, we utilize the advanced martingale concentration inequalities in an multi-armed bandits-based online learning framework to facilitate the optimal detection of various jamming behaviors. We first define a novel EE model for discontiguous orthogonal frequency division multiplexing to facilitate scalable SAA over distributed spectrum pools in practice. Then, the jamming-resistant dynamic channel access problem is formulated as a regret minimization problem. Meanwhile, an online stochastic gradient descent with bandit feedback procedure is adopted to allocate the transmit power. The proposed algorithm can autonomously detect the environmental features and find a near-optimal solution in each attacking scenario. Our algorithm is implemented with low complexity and with multiple users under some practical jamming scenarios. Extensive numerical studies show that under some practical jamming scenarios, our algorithm has an EE improvement of 45.3% over a fixed learning period, and an improvement of 82.5% in terms of learning duration compared with existing approaches.

In cloud radio access networks (C-RANs), the baseband units and radio units of base stations are separated, which requires high-capacity fronthaul links connecting both parts. In this paper, we consider the delay-aware fronthaul allocation problem for C-RANs. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity delay-aware fronthaul allocation algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for sufficiently small residual interference. Finally, the proposed fronthaul allocation algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

In this paper, we construct an analytical design framework for energy efficient scheduling for delay-constrained spectrum aggregation (ESSA), where the practical hardware limitations on SA capability bring various technical challenges. Specifically, the conventional water-filling power control cannot be adopted over all the channels, and the delay-aware scheduling solution should interact with the channel allocation. To overcome these challenges, we design the ESSA scheduling scheme in two steps. First, with given rate vector and channel allocation, we minimize the total power consumption for SA, including both the transmit power as well as the circuit power. Due to the properties of delay-constrained SA, we divide the scheduled users into conforming and nonconforming user sets, and design their water-filling power allocation strategies differentially. Second, based on the differentiated water-filling power control, we optimize the channel allocation and rate control iteratively via Lyapunov optimization to minimize the power consumption with the average delay constraint. The proposed ESSA scheme is finally evaluated by simulation results.

In this paper, we investigate energy cooperation among sustainable cooperative relay nodes, which adopt decode- and-forward (DF) relaying. The relay nodes work on either information decoding (ID) mode or energy harvesting (EH) mode when receiving the signal from the source. Due to different harvested energy at the relay nodes, effective energy cooperation can benefit the energy efficiency, but suffer from practical energy loss when achieving energy cooperation. By solving the problem using Lagrangian duality, we obtain the optimal energy cooperation policy for the relay nodes and further discuss its two-level waterfilling structure. Then we derive the optimal and low-complexity EH/ID mode selecting algorithm with the two-level waterfilling energy cooperation policy. Finally, we compare the throughput between the systems with and without energy cooperation to show the benefit of energy cooperation.

In wireless sensor networks, data harvesting using mobile data ferries has recently emerged as a promising alternative to the traditional multi-hop communication paradigm. The use of data ferries can significantly reduce energy consumption at sensor nodes and increase network lifetime. However, it usually incurs long data delivery latency as the data ferry needs to travel through the network to collect data, during which some delay-sensitive data may become obsolete. Therefore, it is important to optimize the trajectory of the data ferry with data delivery latency bound for this approach to be effective in practice. To address this problem, we formally define the time-constrained data harvesting problem, which seeks an optimal data harvesting path in a network to collect as much data as possible within a time duration. We then investigate the formulated data harvesting problem in the generic m-dimensional context, of which the cases of m=1, 2, 3 are particularly pertinent. We first characterize the performance bound given by the optimal data harvesting algorithm and show that the optimal algorithm significantly outperforms the random algorithm, especially when network scales. However, we mathematically prove that finding the optimal data harvesting path is NP-hard. We therefore devise an approximation algorithm and mathematically prove the output being a constant-factor approximation of the optimal solution. Our experimental results also demonstrate that our approximation algorithm significantly outperforms the random algorithm in a wide range of network settings.

In a wireless powered communication network, where user equipments (UEs) harvest radio frequency energy from an access point (AP) and send data to the AP, there exists the near-far problem with respect to energy harvesting efficiency due to UEs' random locations. In this paper, we introduce the concept of delay-aware energy balancing by minimizing the average transmission delay while taking into account the issue of unbalanced harvested energy distribution. In particular, we propose an adaptive harvest-then-cooperate protocol, where every UE first harvests the energy emitted by the AP and then sends data to the AP directly or via other UEs acting as relays in a time-division multiplexing manner. In this protocol, the AP selects the combination of transmission power and routing topology by matching load and energy distributions in the network while minimizing the average transmission delay. Furthermore, we develop a method generating scheduling schemes for this protocol to avoid data overflow in the UE relay. To determine the combination with minimum delay, we approximate the average delay as a Markov decision process and propose a low-complexity sample path-based algorithm to obtain a near-optimal solution. Simulation results demonstrate that the proposed protocol is able to balance the energy distribution while minimizing the transmission delay.

This paper investigates the ergodic achievable secrecy rate (EASR) of multiple-antenna amplify-and-forward relay systems, where one eavesdropper can wiretap the relay. To reveal the capability of the multiple-antenna relay in improving the secrecy performance, we derive new tight closed-form expressions of the EASR for three secure transmission schemes: artificial noise aided precoding (ANP), destination based jamming (DBJ) and eigen-beamforming (EB). We also derive the lower bounds of the EASR for ANP and DBJ with a large antenna array at the relay, and investigate their corresponding asymptotic performance in the high SNR and low SNR regimes to show valuable intrinsic insights as well. Based on the asymptotic analysis, we optimally allocate the power to the information signal and the artificial noise. Both the analysis and simulation results indicate that, in the moderate-to-high SNR regime, ANP achieves considerable performance gain over DBJ and EB, while in the low SNR regime, EB outperforms the other two schemes with equal power allocation. As SNR grows large, the EASR of EB approaches a constant independent of the first hop channel. Moreover, in the high SNR regime, it is optimal to allocate around half of total power to artificial noise for ANP and most of the power to artificial noise for DBJ.

In this paper, we study the data offloading via device-to-device (D2D) communications with proactive caching in mobile cellular networks. The problem is formulated as the optimal data caching problem, where mobile nodes have different mobility and limited cache capacities and data files have different popularity and content sizes, and is proved to be NP-hard. To deal with the problem, the contacts between mobile nodes are used to adjust the cached data files with a well-designed (1 + α)-approximation algorithm. Based on the analysis, we first propose a distributed infrastructure-assisted data offloading algorithm (IADOA), where the base station (BS) needs to provide information to mobile nodes. To further increase the flexibility, a fully-distributed data offloading algorithm (FDDOA) is designed, where mobile nodes exchange control information via D2D communications and make parameter estimations. Finally, simulation results show that our proposed algorithms have significant performance gains compared with two conventional baseline algorithms.

This paper investigates the ergodic secrecy capacity (ESC) of multiple-antenna amplify-and-forward relay systems, where one eavesdropper can wiretap the relay. To reveal the capability of the multiple- antenna relay in improving the secrecy performance, we derive new tight closed-form lower bounds of the ESC for two secure transmission schemes: artificial noise aided precoding (ANP) and eigen-beamforming (EB). We also derive the lower bound of the ESC for ANP with a large antenna array at the relay, and investigate their corresponding asymptotic performance in the high and low SNR regimes. Based on the asymptotic analysis, we optimally allocate the power to the information signal and the artificial noise. Both the analysis and simulation results indicate that, in the moderate-to-high SNR regime, ANP achieves considerable performance gain over EB, while in the low SNR regime, EB outperforms ANP with equal power allocation. As SNR grows large, the ESC of EB approaches a constant only related to the number of relay antennas. Moreover, in the high SNR regime, it is optimal to allocate around half of total power to artificial noise for ANP.

Inspired by the green communication trend of next-generation wireless networks, we propose an opportunistic wireless information and energy transfer relaying scheme for sustainable cooperative relaying, in which the relay nodes are powered only by their independent harvested energy to forward the desired information to the destination. Thus there exists an inherent tradeoff between the information decoding (ID) and the energy harvesting (EH) for forwarding. We first analyze the tradeoff and formulate an optimization problem on joint EH/ID receive mode selection and transmit power control for the relay nodes. By Lagrangian optimality, we obtain the optimal solution in opportunistic relay and power control. Due to the NP-hard property of the EH/ID mode selection, we propose a low-complexity algorithm by relaxation for EH/ID mode selection. The simulation results show that the performance of the low-complexity algorithm is close to the optimal solution, and outperforms the baselines.

In this paper, we consider the delay-optimal fronthaul allocation problem for cloud radio access networks (CRANs). The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity delay-optimal fronthaul allocation algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for sufficiently small cross link path gains. Finally, the proposed fronthaul allocation algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

Data harvesting using mobile data ferries has recently emerged as a promising alternative to the traditional multi-hop transmission paradigm. The use of data ferries can significantly reduce energy consumption at sensor nodes and increase network lifetime. However, it usually incurs longer data delivery latency as the data ferry needs to travel through the network to collect data, during which some delay-sensitive data may become obsolete. Therefore, optimizing the trajectory of the data ferry with data delivery latency bound is important for this approach to be effective in practice. To address this problem, we formally define the time-constrained data harvesting problem, which seeks an optimal data harvesting path in a network to collect as much data as possible within a time duration. We first characterise the performance bound given by the optimal data harvesting algorithm and show that the optimal algorithm significantly outperforms the random algorithm, especially when network scales. Motivated by the theoretical analysis and proving the NP-completeness of the time-constrained data harvesting problem, we then devise polynomial-time approximation schemes (PTAS) and mathematically prove the output being a constant-factor approximation of the optimal solution.

In this paper, we consider the dynamic power control for delay-aware D2D communications. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we utilize the interference filtering property of the CSMA-like MAC protocol and derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity power control algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for a sufficiently large carrier sensing distance. Finally, the proposed power control scheme is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

Sensing falsification is a key security threat in cooperative spectrum sensing in cognitive radio networks. Intelligent malicious users (IMUs) adjust their malicious behaviors according to their objectives and the network's defense schemes. Without long-term collection of information on users' reputation, the existing schemes fail to thwart such malicious behaviors. In this paper, we construct a joint spectrum sensing and access framework to thwart the malicious behaviors of both rational and irrational IMUs. Lack of reputation information makes the malicious behavior resistance degrade performance since the honest users may be misjudged as IMUs. Based on the moral hazard principal-agent model, we design an incentive compatible mechanism to provide a moderate punishment to IMUs. Our findings show that neither spectrum sensing nor spectrum access alone can prevent malicious behaviors without any information on users' reputation. According to the different properties of malicious behavior resistance by spectrum sensing and spectrum access, we employ joint spectrum sensing and access to optimally prevent the IMUs sensing falsification. The proposed malicious behavior resistance mechanism is shown to achieve almost the same performance as the ideal case with truthful sensing.

In this paper, we consider the probing order and stopping problem arising from the identification of spectrum holes in multi-channel cognitive radio networks, in which a secondary user (SU) seeks to maximize the probability of finding an available channel while minimizing the related probing cost within a long time horizon. This problem can be casted into a restless multi-armed bandit problem, which is proved to be PSPACE-hard. The key point of this problem is the trade-off between exploitation, in which the SU stops probing once an available channel is identified, and exploration, in which the SU continues to probe new channels even after identifying an available channel in order to learn the system state to reduce probing cost in the future. To strike a desirable balance between the two conflicting objectives, we develop a heuristic channel probing policy, termed the v-step lookahead policy, in which the SU makes its decision based on the prediction of system state within the future v steps, with v being a tunable parameter. We conduct an analytical study on the structure of the proposed v-step lookahead policy and demonstrate how the policy can be implemented with linear complexity with respect to the number of channels in the system via a detailed analysis on the 1-step lookahead policy. Numerical experiments between the v-step lookahead policy and myopic probing policy on two representative network scenarios demonstrate the effectiveness of the proposed v-step lookahead policy.

In this paper, we investigate the random access for an energy harvesting secondary user (SU) in a cognitive radio system, in which the SU harvests energy from radio frequency (RF) radiation of the primary user (PU). With multipacket reception channel model, the SU can increase its throughput through not only utilizing the idle periods of the PU, but also opportunistically sharing the PU spectrum with some probability when the PU is active. By choosing the appropriate random access probability, we maximize the throughput of the SU under the constraint of the primary queue stability. We also define the energy-limited region and spectrum-limited region to specify the tradeoff between the SU performance and the PU activity. Furthermore, we investigate the effect of the primary queueing delay constraint on the throughput performance of the SU.

Wireless information and energy transfer (WIET) is a prominent technology to prolong the lifetime of battery-charging wireless networks. In this paper, we exploit the benefit of massive MIMO for WIET under external interference, and propose the antenna partition for information decoding and energy harvesting. Considering the effects of the external interference, i.e., interfering the information reception and benefiting the energy harvesting, we analyze the tradeoff between the data rate and the harvested energy, and obtain the achievable rate-energy (R-E) region. Then, we propose a low-complexity receive antenna partition algoritinterference mitigationhm for WIET in massive MIMO systems with the consideration of interference mitigation. The algorithm maximizes the data rate while guaranteeing a minimum harvested energy. It is found that the SNR of the low-complexity algorithm is at least an approximable half of the optimal SNR. Simulation results verify our theoretical claims and show the effectiveness of the proposed low-complexity antenna partition algorithm.

Distributive service data storage at the caching-enabled base stations (BSs) can reduce the traffic load in future cellular networks. Taking the limited caching space into account, it is necessary for the BSs to adjust their caching data based on service popularity in order to achieve better caching efficiency. In this paper, we investigate the cache replacement strategy for BSs to minimize the transmission cost between BSs in cellular networks. The cache replacement problem is modelled as a Markov Decision Process (MDP). Without extra information exchange about caching data between the BSs, we propose a distributed cache replacement strategy based on Q-learning. Especially, we calculate the transmission cost for possible cache replacement actions according to the previous data request and transmission between BSs. The convergence of the proposed distributed cache replacement strategy is proved by sequential stage game model. Simulation results verify the convergence of the proposed cache replacement strategy and show its performance gain compared to conventional strategies.

Opportunistic forwarding assisted by mobile relays is an effective way of improving network capacity and packet delivery ratio in delay tolerant networks (DTNs). However, such performance gain comes at the price of increased energy consumption due to the duplicated transmissions at relays. In this paper, we investigate how energy harvesting, a promising technique of enabling sustainable communications, can be exploited to improve the performance of opportunistic forwarding in mobile DTNs. Specifically, we formulate the problem using a Markov Decision Process (MDP) framework in which each source should strike a balance between exploitation, by forwarding the packet to the relay currently in contact, and exploration, by waiting for possible better relays in the future, given the harvested energy constraint. The formulated MDP having exponential complexity, we devise a heuristic relay-assisted opportunistic forwarding scheme, termed as adaptive M-step lookahead scheme, to alleviate the computation complexity, where M can be adjusted adaptively according to both the current energy and the energy that might be harvested in the future. Simulation results show that our proposed algorithm can use the harvested energy more efficiently, especially for the circumstance where the energy harvesting rate is low.

Mobile relay-assisted forwarding can improve the network capacity, but meanwhile increase the energy consumption. In this paper, we propose two distance-based energy-efficient opportunistic forwarding (DEEOF) schemes in mobile delay tolerant networks (DTNs). The proposed schemes strike a balance between energy consumption and network performance by maximizing the energy efficiency while maintaining a high packet delivery ratio from two different angles. Specifically, in the developed algorithms, we introduce the forwarding equivalent energy-efficiency distance (FEED) to quantify the transmission distances achieving the same energy efficiency at different time instances. The expected energy efficiency can thus be estimated based on the FEED. Furthermore, the distribution of the greatest forwarding energy efficiency in the predicted period is investigated to provide more accurate prediction for the energy efficiency. The forwarding decision in the algorithms is made by comparing the current energy efficiency and the estimated future expectation. The performance improvement of the proposed algorithms is also demonstrated by simulation, especially for systems where the source has very limited battery reserves.

The increase of antennas can greatly improve the performance of MIMO systems, in the meanwhile, the precise channel state information is difficult to obtain since the channel matrix of massive MIMO is much more complex than the traditional one. In this paper, we propose a limited feedback strategy named hybrid limited feedback with selective eigenvalue information (HLFSEI), which jointly considers the conventional quantized feedback and codebook based feedback. In HLFSEI, because of the large amount of the elements of channel matrix, the quantized information of only selective eigenvalue elements is fed back from the receiver to the transmitter with feedback-link capacity constraint. We study the optimal feedback bit allocation of both feedback methods to maximize the throughput by theoretic deduction. Finally, we evaluate the performance of the proposed limited feedback scheme by simulation and show its performance gain compared to conventional feedback strategies.

Sensing falsification is a key security problem in cooperative spectrum sensing for cognitive radio networks. Most previous approaches assume that malicious users only cheat in their sensing reports following a predefined rule. However, some malicious users usually act intelligently to strategically adjust their malicious behavior according to their objectives and the network's defense schemes. The existing schemes cannot resist the malicious behaviors of intelligent malicious users (IMUs) without long-term collection of information on their reputation. In this paper, we construct a moral hazard principal-agent framework and design an incentive compatible mechanism to thwart the malicious behaviors of rational and irrational IMUs. We find that neither spectrum sensing nor spectrum access alone can prevent the malicious behavior without any information on users' reputation. According to the analysis of malicious behavior resistance methods, we propose a joint spectrum sensing and access mechanism to optimally prevent the IMUs from sensing falsification. Our evaluation results show that the proposed mechanism achieves almost the same performance as the ideal case with perfect sensing.

Instant Messaging (IM) Service is an integrated service composed by multiple types of subservices, which have diverse performance requirements and evaluation criteria. IM service has drawn a lot of attention, yet a suitable Quality of Experience (QoE) model for IM service can rarely be found. In this paper, we propose a Representative Service (RS) based QoE modeling criterion for IM service. We discover that the QoE of IM service users mainly depends on the Representative Service Quality (RSQ) by exploiting the patterns of the IM service user behaviors. In the proposed RS based QoE model, the Normalized Quality (NQ) is defined for different subservices with various performance evaluation criteria to unify their qualities, and the Attention Factor (AF) is proposed to estimate the subservice that a user is focusing on. By performing a small-scale but illustrative subjective test, we verify the proposed QoE model and indicate the effectiveness of the model in tracing the IM service user behaviors.

In this paper, to address network congestion stemmed from traffic generated by advanced user equipment, we propose a novel network resource allocation strategy, i.e., time-frequency resource conversion (TFRC), via exploiting user behavior, a specific kind of context information. The key idea is to use radio resources mainly on the traffic/connection to which a user pays attention. The TFRC withdraws spectrum resources strategically from connection(s) not focused on by the user, providing reuseable spectrum called “virtual spectrum hole”. Considering an LTE-type cellular network, a double-threshold guard channel policy is proposed to facilitate the implementation of TFRC. An analytical model is established to study benefits of exploiting TFRC in terms of call-level performance, including new call blocking, handoff call dropping, and recovering call dropping probabilities. Numerical results demonstrate the effectiveness of the proposed approach, in increasing the cell capacity (maximum user number per cell) while limiting potential service quality degradation introduced by the newly proposed technique.

In cellular networks, the proactive storage at caching-enable BSs is an efficient way to reduce traffic load of backhaul links. In this paper, we investigate the storage allocation problem with network coding. By decomposing the NP-hard problem, we propose a low-complexity storage allocation to solve two subproblems in an iterative way. Combining a heuristic initial allocation scheme and the iterative process, we can obtain the storage allocation result very close to the optimal solution. The convergence of the proposed algorithm is proved and its computation complexity is analyzed. The simulation results evaluate the performance of the proposed algorithm, which has graceful performance degradation on total storage with much lower complexity.

The finite-SNR Diversity-Multiplexing Tradeoff (DMT) problem is studied in the context of spectrum aggregation, where there exist multiple noncontiguous (discrete) spectrum segments each of which has its own power limit. Firstly, we give an optimal power allocation strategy to achieve the ergodic capacity under Rayleigh fading. Secondly, two different coding schemes, i.e., coding without across sub-channels and coding across, are presented to achieve the lower and upper bound of diversity gain of this system under the same multiplexing gain. Then the finite-SNR DMT under i.i.d Rayleigh fading is derived, and its accurate expression for the coding-without-across scheme and the upper bound for the coding-across scheme are achieved respectively. The asymptotic performances of both schemes are also obtained. Under the equal sub-channel bandwidth setting, for infinite SNR, the coding-across scheme achieves M (the number of sub-channels) times diversity gain against the without-coding-across scheme. The loss of diversity gain caused by non-identical bandwidth and power limit is also analyzed.

Two‐path successive relaying (TPSR) is an effective way to reduce the multiplex loss induced by the half‐duplex operation of the relay node in a conventional relay network. One crucial issue in TPSR network is that the listening relay always suffers inevitable inter‐relay interference (IRI), which degrades detection performance at the destination. In this study, a concatenated channel‐and‐network coding approach is proposed to solve the problem. In particular, a highly flexible channel code, namely, rateless code, is employed at the source to provide resilience to the residual IRI and reduce the retransmissions, which might break the system steady state of successive relaying. Then recognising the special interference structure, physical‐layer network coding is incorporated into the forwarding scheme of the relay nodes to exploit network diversity and improve system efficiency. By extrinsic information transfer analysis, the minimum number of required code symbols for successful data recovery are calculated, and degree distribution of the rateless code is optimised.

In this paper, we propose a three-stage rateless coded protocol for a half-duplex time-division two-way relay system, where two terminals send messages to each other through a relay between them. In the protocol, each terminal takes one of the first two stages respectively to encode its message using rateless code and broadcast the result until the relay acknowledges successful decoding. During the third stage, the relay combines and re-encodes both messages with a joint network-channel coding scheme based on rateless coding which provides incremental redundancy. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. The degree profiles of the specific rateless codes, i.e., Raptor codes, implemented at both terminals and the relay, are jointly optimized for both the AWGN channel and the Rayleigh block fading channel through solving a set of linear programming problems. Simulation results show that, the system throughput as well as the error rate achieved by the optimized degree profiles always outperforms those achieved by the conventional degree profile optimized for Binary Erasure Channel (BEC) and the previous network coding scheme with rateless codes.

The practical hardware limitations bring technical challenges to cognitive radio, e.g. limited capability of spectrum sensing and certain frequency range of spectrum access. In this paper, we propose a rollout-based joint spectrum sensing and access policy incorporating the hardware limitations of both sensing capability and spectrum aggregation, in which the optimal policy is shown to be PSPACE-hard. Two heuristic policies are proposed to serve as base policies, based on which the developed rollout-based policy approximates the value function and determines the appropriate spectrum sensing and access actions. We establish mathematically that the rollout-based policy achieves better performance than the base policy. We also demonstrate that the low-complexity rollout-based policy leads to only slight performance loss compared with the optimal policy.

In this paper, we design a joint channel-network coding scheme based on rateless code for the three-stage two-way relay system, where two terminals send messages to each other through a relay between them. Each terminal takes one of the first two stages to encode its message using a Raptor Code and then broadcasts the result into the air, respectively. In the third stage, upon successfully decoding the corresponding messages, the relay node re-encodes them with the new Raptor Codes, and then XORs the outputs and broadcasts the result to both terminals. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. Here, the degree profiles of the Raptor Codes used at each node are jointly optimized through solving a set of linear programming problems. Simulations show that, the system throughput achieved by the optimized degree profiles always outperforms the one with conventional degree profile optimized for binary erasure channel (BEC) and the conventional network coding scheme with rateless coding.

In this paper, we investigate the TCP throughput performance enhancement for cognitive radio networks (CRNs) through lower-layer configurations. There is an interaction between TCP and the lower-layer operations. The TCP sending rate at the transport layer determines the packet arrival rate of the lower-layer buffer, and meanwhile it is influenced by the average round trip time and packet loss rate, which are determined by the lower-layer mechanisms and configurations. Therefore, an iteration process is employed to investigate the TCP throughput under given channel condition and lower-layer configurations. For each iteration, queueing analysis is done to derive the packet loss rate and average delay on wireless link, which are then used to calculated the TCP throughput. Through derivations and numerical evaluations, the impacts of lower-layer parameters, primary user (PU) activities and channel conditions on the TCP throughput are discussed. Moreover, the way how these factors influence the TCP throughput is tracked. By using the proposed analytical method, TCP throughput enhancement can be achieved through appropriately setting lower-layer configurations.

Geometry-based optimal power control was proposed in [14] to transform the power-control problem to a new geometrical problem on the position relationship between a line and some points. This scheme provides a novel visual perspective and lowers the complexity of optimization. We generalize this scheme to a larger class of power-control optimization problems so as to maximize the network utility with multiple average and peak power constraints in wireless networks. To facilitate the handling of the geometrical model, we define a subset of geometrical models with specified characteristics, called a regular geometrical model, and derive the type of power-control problems eligible for the regular geometrical model. For such a type of problems, two strategies are proposed for the construction of the regular geometrical model. Utilizing geometrical properties, we propose a novel geometry-based optimization scheme for the general power-control problem. Its computational complexity is significantly lower than the conventional algorithms. We also provide a further discussion on irregular geometrical model cases. Finally, we provide two examples of deploying the proposed geometry-based power-control scheme.

In this paper, the queue dynamics of secondary users (SUs) in a multi-SU and multi-channel cognitive radio network is analyzed to obtain the expressions of quality of service (QoS) metrics. Specially, in the analysis, we take several lower-layer mechanisms and settings into account, including automatic repeat request (ARQ), finite-size buffer, adaptive modulation and coding (AMC) and non-ignorable spectrum sensing errors. By modeling the queue dynamics as a Markov chain, we derive the analytical expressions of queue length, packet dropping rate and packet collision rate. Based on these expressions, the QoS metrics including delay, packet loss rate and throughput are calculated further. Through simulation, our queueing analysis is verified and the QoS metrics are investigated.

In cognitive radio networks, multiple discontinuous spectrum opportunities are detected by spectrum sensing and utilized together to satisfy the service requirement by spectrum aggregation. In this paper, we develop an analytical framework for joint spectrum sensing and access scheme based on Partially Observable Markov Decision Process (POMDP). Considering the hardware limitations, the spectrum aggregation range is restricted and only a part of channels can be sensed. For obtaining the reward function of POMDP, the channel switch probability is estimated by theoretic deduction. Under this POMDP framework, an optimal spectrum sensing and access scheme is proposed to minimize the channel switch times. Simulation results show that our proposed optimal scheme reduces the times of channel switches significantly.

Cognitive radio (CR) allows unlicensed users to access the licensed spectrum opportunistically (i.e., when the spectrum is left unused by the licensed users) to enhance the spectrum utilization efficiency. In this paper, the problem of allocating resources (channels and transmission power) in multihop CR networks is modeled as a multicommodity flow problem with the dynamic link capacity resulting from dynamic resource allocation, which is in sharp contrast with existing flow-control approaches that assume fixed link capacity. Based on queue-balancing network flow control that is ideally suited for handling dynamically changing spectrum availability in CR networks, we propose a distributed scheme (installed and operational in each node) for optimal resource allocation without exchanging spectrum dynamics information between remote nodes. Considering the power masks, each node makes resource-allocation decisions based on current or past local information from neighboring nodes to satisfy the throughput requirement of each flow. Parameters of these proposed schemes are configured to maintain the network stability. The performance of the proposed scheme for both asynchronous and synchronous scenarios is analyzed comparatively. Both cases of sufficient and insufficient network capacity are considered.

Spectrum sensing is adopted to detect the presence of primary users in cognitive radio networks. Considering the sensing overhead, it is not always a good choice that the secondary user senses the channel all the time. In this paper, we propose an opportunistic spectrum sensing decision method according to the primary user's traffic. First, the traffic of primary user is observed and estimated. For estimating the parameters of the primary traffic, the Maximum Likelihood estimation is adopted and the confidence interval is calculated based on finite observed samples. Then, according to the estimated primary traffic information, a decision criterion is proposed for determining whether to sense the channel. The simulation results show that the performance of the proposed opportunistic sensing scheme is much better than that when sensing at every timeslots and very close to that in the ideal case in which the primary traffic information is obtained perfectly.

We propose a new framework of joint spectrum allocation and power control to utilize open spectrum bands in cognitive radio networks (CRNs) by considering both interference temperature constraints and spectrum dynamics. We first address a simpler problem for the case of a single flow. A TDM-based power-control strategy is adopted to achieve maximum end-to-end throughput by choosing an appropriate multihop route and spectrum combination for each single flow. Then, the simpler solution is extended to the multiflow case in which interflow interference and cumulative interference temperature must be considered. Considering the overhead of switching route and spectrum, the optimal waiting time before making a switch is derived. Our in-depth simulation study has shown that the proposed algorithms utilize spectrum more efficiently than other existing algorithms.

In cognitive radio networks, multiple spectrum opportunities can be used together to satisfy the service requirement by spectrum aggregation. In this paper, an admission control algorithm and a spectrum assignment strategy are proposed in order for both increasing the spectrum aggregation aware access capacity and decreasing the channel switch times when the channel states change. Considering different bandwidth requirement of secondary users, the proposed greedy admission algorithm takes limited aggregation capability into account. The channel switch times of secondary users at sensing moments is minimized based on the prediction of primary activities and the corresponding channel state transitions. The concept of outage probability is introduced into the scheme to indicate the probability of channel switch. The numerical results show the performance improvement of the proposed algorithms.

In this paper, an optimal resource allocation scheme is proposed for multi-user multi-channel cognitive radio networks under imperfect spectrum sensing. The channel dynamic model and the sensing errors are considered together to derive the metric of mean delay for each user-channel combination based on the vacation queueing model. Finally, the optimal resource allocation is determined according to the average system delay by bipartite graph matching. The simulation results indicate that the proposed mean delay metric can represent the transmission performance successfully.

In this letter, a power-control scheme for maximum sum-rate is proposed for the fading multiple access channels by considering the presence of primary users. Both the average transmit-power constraints and the peak interference-temperature constraints are considered. The interference caused by cognitive users must be under a pre-specified threshold for protecting primary users. The power-control optimization is considered as a novel geometrical problem which investigates the relationship of positions of a line and a few points. At most two users transmit simultaneously for optimality and the corresponding conditions are provided for both cases. Based on the analysis, the optimal power control is given for each specific fading state. For lowering computational complexity, the power-control optimization problem is divided into two categories according to different tight constraints. Simulation results are provided for the optimal power-control performance.

Cognitive radio makes it possible for an unlicensed user to access a licensed spectrum opportunistically on the basis of non-interfering. This paper addresses the problem of joint route selection and resource allocation in OFDMA-based multihop cognitive radio networks, in the objective of optimizing different types of end-to-end performance. Aiming to solve it optimally, we first show that this problem of optimal resource allocation can be formulated as a convex optimization problem and identify its necessary and sufficient conditions. Based on this conclusion, we propose an iterative algorithm that can be implemented in a distributed manner. This algorithm applies Lagrangian duality theory and the Frank-Wolfe method. The scheme thus converges to a globally optimal solution. We present numerical results from using the algorithm to provide insight into the optimal cross-layer design, e.g., the relationship between bottleneck throughput and hops, and the effect of interference temperature constraints.

In this paper, we investigated the end-to-end throughput of a chain in Cognitive Radio Networks (CRNs). We found that, the end-to-end throughput is dependant on both the primary usage patterns and the transmission scheduling scheme being used. In addition, to increase the end-to-end throughput of a Cognitive Radio (CR) chain, the scheduling scheme should be adjusted according to the primary usage patterns of the CR links in the chain. In the paper, firstly, we proposed an algorithm to approximate the achievable end-to-end throughput considering the primary usage patterns by abstraction and iteration. Then, a novel layered packets transmission scheduling scheme was proposed in attempt to realize the approximated end-to-end throughput. Finally, extensive simulations were conducted and results showed that, using proposed transmission scheduling scheme, the achievable end-to-end throughput of a CR chain is increased by considering the primary usage patterns and the final end-to-end throughput is close to the approximation.

Cognitive radio makes it possible for an unlicensed user to access a licensed spectrum opportunistically on the basis of non-interfering. This paper addresses the problem of resource allocation for multiaccess channel (MAC) of OFDMA-based cognitive radio networks, taking into account of the interference temperature constraints. The objective is to maximize the system utility, which is used to quantify different quality-of-service (QoS) requirements of different users. Firstly, a theoretical framework is provided, where necessary and sufficient conditions for optimal subcarrier assignment and power allocation are presented under certain constraints. Then, an effective algorithm is devised for more practical conditions based on Lagrangian duality theory, where subgradient/ellipsoid method is applied for Lagrangian multipliers iteration. With polynomial time complexities, the proposed resource allocation algorithm is proved to achieve optimal system performance by numerical results.

In cognitive radio networks, the unlicensed users can utilize the unoccupied licensed spectrum opportunistically. In this paper, we propose a joint power and end-to-end rate control algorithm considering restricting the interference to licensed users. By duality theory, the optimal resource allocation solution is given for the unlicensed users while satisfying the interference temperature limits. An asynchronous algorithm is proposed to be implemented in practical networks. Finally, we give a discussion to the proposed algorithm's performance on fairness. Numerical results show that the proposed algorithm can limit the interference to licensed user under a predefined threshold while maintaining a satisfied data rate fairly.

Cognitive radio makes it possible for an unlicensed user to access a spectrum opportunistically. This paper addresses the problem of power control in cognitive radio networks, to maximize the system utility in the presence of interference temperature constraint. Penalty function is applied in the problem formulation to take into account the transmit power budget and interference temperature constraints, which is solved efficiently by geometric programming. In the proposed distributed power control scheme, users exchange "price" messages which indicate the negative effect of interference at the receivers. Given this set of prices, each transmitter updates power levels on multiple channels using gradient descent method. It is proved that the proposed algorithm converges to the global optimum when operated in an totally asynchronous manner. The performance of the proposed scheme is investigated by numerical results in the end.

Current deployed emergency communications systems are only available to the rescuing workers. In this paper, a framework of wireless emergency communications is proposed for common communications in the disasters based on relaying and cognitive radio. In this framework, relaying provides small coverage expansion and high capacity for common communications. On the other hand, cognitive radio based frequency lowering provides large coverage expansion and low system capacity for special number communications. The coverage performance is evaluated by the calculation and simulation. To balance the tradeoff between coverage and capacity, both two-hop relaying and cognitive radio are adopted appropriately to satisfy the requirements of emergency communications according to their characteristics. The performance of the proposed framework is also investigated in this paper.

In cognitive radio network, power control is necessary to not only decrease the interference among the unlicensed users, but also avoid the negative effect to the licensed users. In this paper, a noncooperative power control model is proposed for the unlicensed users using game theory. In order to restrict the interference to the licensed users, an exponential part indicating the effect to the licensed users is added into the pricing function. Through game theoretic deduction, it is obtained that a unique Nash equilibrium solution exists under appropriating parameter value of the payoff function. Further, the Nash equilibrium solution of the proposed power control game with exponential pricing is Pareto optimality and achieves maximum total throughput under strict constraint of the interference temperature limitation. The appropriate value of the new parameter in the pricing function is discussed. The performance of the proposed power control algorithm is investigated by numeral results.

In cognitive radio network, the interference of the unlicensed users to the licensed users should be limited under interference temperature constraints. In this paper, the optimal power control scheme of a network is analyzed without interference temperature constraints firstly. Based on this, considering interference temperature constraints, the optimal power control in cognitive radio network is modeled as a concave minimization problem. Some useful properties of the power control optimization problem are exploited. According to these properties, an improved branch and bound algorithm which is more efficient than the general branch and bound algorithm is proposed for optimal power control optimization problem in cognitive radio network.