Multi-domain multivariate time series (MTS) forecasting is increasingly important for large-scale and transferable time-series modeling. However, heterogeneous datasets usually contain different numbers of variables, making scalable inter-series dependency modeling challenging. Existing scalable forecasting paradigms often rely on channel-independent (CI) strategies to accommodate variable-dimensional datasets. Nevertheless, by sharing global parameters across independently processed channels, CI models may confuse heterogeneous dependency structures under multi-domain joint training, leading to the dependency confusion problem. In this paper, we argue that effective multi-domain MTS forecasting requires variable-number-agnostic channel-dependence modeling that can explicitly capture fine-grained inter-series dependencies (FID), including both local inter-series dependencies and cross-temporal inter-series dependencies. To this end, we propose the Fine-grained Inter-series Dependency Enhanced (FIDE) framework, a plug-and-play module for patch-based CI forecasters. FIDE introduces dependency prototypes to dynamically perceive input-specific inter-series dependency patterns and employs dependency transmission to propagate dependency information across temporal segments while remaining agnostic to the number of variables. Extensive experiments on eight real-world benchmarks with six representative CI forecasting models demonstrate that FIDE consistently improves forecasting accuracy, effectively mitigates dependency confusion, and achieves statistically significant gains over baseline methods.

With the progressive evolution of large language models (LLMs) and the increasing need of computing for$\mathbf{6 G}$, it becomes crucial for multiple network nodes with limited computing resources to share the need for large model inference. Model partition methods have been proposed to enable computation-intensive artificial intelligence (AI) services by splitting an AI model across multi-edge and cloud nodes. In this paper, a distributed inference optimization framework for transformer decoder-only based LLMs (DIO-LLMs) is proposed in edge-cloud collaborative networks. The partitioning and offloading strategy is determined based on the computing workload and network status. DIO-LLMs specifically accounts for the parallel execution capabilities of the transformer architecture. It employs a two-phase model partitioning strategy, comprising inter-layer and intra-layer partitions, to effectively distribute LLMs across edge and cloud nodes. Additionally, to mitigate inference latency under resource limitations, a Greedy Proximal Policy Optimization (GPPO) based algorithm has been developed to devise optimal strategies. Simulation results indicate that under memory constraints, the proposed algorithm can reduce inference latency more effectively than other baseline algorithms.

Diffusion-based denoising models have demonstrated impressive performance in probabilistic forecasting for multivariate time series (MTS). Nonetheless, existing approaches often model the entire data distribution, neglecting the variability in uncertainty across different components of the time series. This paper introduces a Diffusion-based Decoupled Deterministic and Uncertain ($\mathrm{D^3U}$) framework for probabilistic MTS forecasting. The framework integrates non-probabilistic forecasting with conditional diffusion generation, enabling both accurate point predictions and probabilistic forecasting. $\mathrm{D^3U}$ utilizes a point forecasting model to non-probabilistically model high-certainty components in the time series, generating embedded representations that are conditionally injected into a diffusion model. To better model high-uncertainty components, a patch-based denoising network (PatchDN) is designed in the conditional diffusion model. Designed as a plug-and-play framework, $\mathrm{D^3U}$ can be seamlessly integrated into existing point forecasting models to provide probabilistic forecasting capabilities. It can also be applied to other conditional diffusion methods that incorporate point forecasting models. Experiments on six real-world datasets demonstrate that our method achieves over a 20\% improvement in both point and probabilistic forecasting performance in MTS long-term forecasting compared to state-of-the-art (SOTA) probabilistic forecasting methods. Additionally, extensive ablation studies further validate the effectiveness of the $\mathrm{D^3U}$ framework.

Current time series forecasting models face significant challenges in addressing two key issues inherent in cloud cluster workload forecasting: high missing rates and high dimensionality. To tackle the missing data challenge, we propose a Channel Dependency Pre-learning Module (CD-Block) that combines wavelet decomposition with contrastive learning. This module extracts inter-channel dependencies through self-supervised learning, enhancing the model’s ability to interpret and process incomplete data. For the high dimensionality problem, we introduce a Fourier Graph Network (FGN) that reformulates convolutions in the frequency domain, significantly reducing computational complexity. FGN incorporates a time-frequency alignment loss to align pre-learned channel dependencies with the spectral representations of time series. Building on these innovations, we propose the Pre-learned Dependency Fourier Network (PefNet). Experimental results demonstrate that PefNet achieves superior performance on four high-dimensional benchmark datasets for forecasting tasks and achieves state-of-the-art (SOTA) performance on three real-world cloud cluster workload datasets with different missing data scenarios.

Long-term time series forecasting (LTSF) poses significant challenges due to the need to capture complex long-term dependencies over extended horizons. We revisit the temporal structure of time series data and categorize it into regular periodic pattern and irregular periodic pattern. We argue that a comprehensive modeling of long-term temporal dependencies must simultaneously account for both regular periodic pattern and the latent dependencies present in irregular periodic pattern. However, prior works either focus on regular periodic pattern from a single scale or neglect the rich dependencies within irregular periodic pattern. To address these limitations, we propose PerTime, a multi-scale periodic mixing model for long-term time series forecasting, which consists of two modules: the Periodic Broaden Module (PBM) and the Hierarchical Fusion Module (HFM). PBM enables the extraction of periodic features across multiple time scales, while HFM captures latent long-term dependencies in irregular periodic pattern and integrates information across scales. Extensive experiments on six long-term forecasting benchmark datasets demonstrate that PerTime outperforms existing methods.

Time series forecasting plays a pivotal role in edge intelligence. Current research predominantly focuses on exploring complex model architectures, such as Transformer and Graph Neural Network (GNN), which demonstrate remarkable advantages in capturing high-dimensional complex features. However, these models suffer from inherent limitations in computational efficiency and deployment resource, resulting in significant constraints in temporal efficiency and edge computing compatibility. To address these challenges, this paper proposes a lightweight linear model architecture termed ELinear. By introducing the Linear Channel Fusion module (LCF), the Frequency-domain Multi-Period Awareness mechanism (FMPA) and the Residual Period Fusion module (RPF), we enhance the prediction accuracy of linear models. Experimental results demonstrate that ELinear achieves a 4.7% reduction in Mean Absolute Error (MAE) and a 3.1× improvement in compute speed compared to state-of-the-art (SOTA) models on the widely adopted ETT benchmark dataset.

With the development of virtualization technology, serverless computing has been gaining significant attention in recent years, primarily due to its advantages in scalability and a pay-as-you-go pricing model. In edge networks, the deployment of fine-grained function instances to handle massive request data makes it more difficult to optimize the quality of user experience. This paper addresses the scheduling of heterogeneous function processing requests generated by users in serverless edge computing scenarios, considering the constraints of resources on edge nodes, and making decisions regarding the warm and cold start during the scheduling process. The problem is modeled as a constrained multi-objective optimization issue aimed at minimizing latency and energy consumption. A deep reinforcement learning strategy, grounded in Multi-Agent Proximal Policy Optimization (MAPPO), is introduced to address this challenge, with each user being represented as an autonomous agent. Simulations were conducted to assess the impacts of the learning rate, the request volume, and the size of the input data of the function. The experimental results indicate that, compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), under different scenarios of request scales, the average system delay is reduced by at most 31%, and the average energy consumption is reduced by at most 24%.

As Internet of Things (IoT)-based artificial intelligence (AI) applications grow, the surge in computational and communication demands has raised concerns about energy consumption, making it critical for 6G networks to address this challenge. This paper examines the joint optimization of AI service placement, task scheduling, and computing resource allocation in an edge-network-cloud system to minimize long-term energy consumption. These problems are interdependent: AI service placement determines service locations, influencing task scheduling, which in turn dictates computing resource allocation. The key challenge lies in the coupling of these variables and the two time-scale nature of the problem, involving long-term (AI service placement) and short-term (task scheduling and computing resource allocation) strategies. To address this, a Hierarchical Markov Decision Process (HMDP) framework is proposed for efficient and coordinated optimization across time scales. A Hierarchical Mean-Field Dueling Double Deep Q-Network (HMFD3QN) algorithm is developed within this framework, where the upper layer optimizes AI service placement, and the lower layer manages task scheduling and computing resource allocation. By integrating mean-field theory, the algorithm reduces the complexity of multi-agent interactions. The computing resource allocation problem is shown to be convex when other variables are fixed, and an optimal strategy is derived using Karush-Kuhn-Tucker (KKT) conditions to simplify the action space for reinforcement learning. Experimental results demonstrate that the proposed method can reduce energy consumption by up to 34% compared to baseline methods, significantly improve queue stability, and increase the proportion of tasks meeting QoS requirements.

In the forthcoming sixth-generation (6G) era, edge-network-cloud collaboration is needed to support artificial intelligence as a service (AIaaS) with a strong demand for computing power. However, how to guarantee the Quality of AI Service (QoAIS) and utilize the edge-network-cloud collaboration to enhance the performance of AI service is a big challenge. In this paper, we propose an AI service management and network resource scheduling architecture based on human-like networking. Considering the Quality of Service (QoS) requirements and AI tasks, we propose a joint AI agent placement with deep neural network (DNN) deployment and dynamic bandwidth resource allocation algorithm (JAAPD-D). JAAPD-D is proposed to solve the short-term and long-term joint resource allocation problem which includes communication, computation, and memory resources in the network. We adjust the agent placement, DNN deployment, and schedule routing path to ensure effective service transmission in the long time interval and dynamically allocate bandwidth resources in the short time interval. We use Lyapunov optimization to ensure the system stability of the whole network, meet the QoS requirements of various services, and minimize the average end-to-end delay of services. Simulation results show that JAAPD-D outperforms existing algorithms in terms of delay, traffic accepted rate, network system throughput, and cost.

In the future, 6G network is expected to achieve deep integration of communication and computation, where computation-centric services will be ubiquitous in the network. There are differences in data size, computing power types (CPU/GPU), model complexity, and Quality of Service (QoS) requirements among various CPU computing services and artificial intelligence (AI) services. By providing AI as a Service (AIaaS) in 6G network, the deployment of AI models and the scheduling of task and computing resources can be accelerated. The fundamental challenge lies in the effective amalgamation of the long-term strategy of the model placement problem and the short-term strategy of the task scheduling problem to attain dynamic scheduling and management of tasks and heterogeneous computing resources. A two-timescale optimization method for joint task offloading, computing resource allocation and model placement is proposed in this article. We present an edge-network-cloud framework that configures AIaaS functional units, taking into account the heterogeneous computing requirements and QoS demands of different services. A long-term problem to minimize latency and energy consumption is formulated. To work out the coupled optimization parameters, the problem is decomposed into short-term deterministic sub-problems using Lyapunov optimization. We propose low-complexity algorithms for joint task offloading strategy based on deferred acceptance algorithm, computing resource allocation strategy based on convex optimization, and model placement strategy based on multi-armed bandits. Experimental results demonstrate that our approach outperforms reinforcement learning and other popular optimization algorithms in terms of complexity and effectiveness.

Abstract Nowadays, wireless communication system is facing the problems of spectrum resource shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized to primary users to improve the spectrum utilization. In this paper, a cognitive network model based on hybrid overlay–underlay spectrum access mode is established. To solve the resource allocation problem, a multi‐agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network is proposed. DQN is used for action selection and graph attention network is used to obtain the information about neighbours, so as to achieve local cooperation. The proposed algorithm can adaptively optimize cognitive network throughput, spectrum efficiency, or power efficiency by controlling the transmission power and channel selection of cognitive users. To improve the information interaction efficiency, the agent's states are divided into two categories, whether it needs to interact with neighbours or not, which shortens training time and improves convergence speed. Simulation results show that the proposed algorithm can effectively improve the power efficiency of cognitive networks. Compared with Q‐learning, DQN and exiting graph convolutional reinforcement learning algorithm, the proposed algorithm has faster convergence speed and higher stability, and obtains higher network power efficiency.

Network slicing can provide logically isolated networks on the shared network infrastructure by invoking multiple technologies and administrative domains to fulfill end-to-end (E2E) service level agreements (SLAs). To guarantee the E2E service communication quality in the sliced network, an SLA-based cross-domain orchestration framework is proposed in this paper. The framework includes an E2E cross-domain coordination orchestrator at the upper layer and multiple subordinate domain controllers. Furthermore, we design two latency equalization policies applied to the upper layer orchestrator to divide the latency budget for each lower layer domain. Based on the reinforcement learning approach, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) and Pointer Network SFC Mapping (PN-SFC), intra-domain resource allocation/mapping algorithms are designed independently for the lower radio access network (RAN) and core network (CN) domain controllers, respectively. The above algorithms are used to jointly optimize the enhanced mobile broadband (eMBB) users service satisfaction level and maximize the number of E2E accessed users. Simulation results show that our proposed algorithm can effectively guarantee the eMBB users QoS and improve the network capacity.

Abstract Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among tenants. To achieve the aim that the infrastructure provider can fairly allocate virtual resources to tenants, a two‐layer resource allocation architecture in a heterogeneous radio access network (RAN) is proposed and it is formulated as a mathematical program with equilibrium constraints (MPEC). The existence of the solution in the lower layer is proved via the properties of the quasi‐variational problem, indicating that the MPEC is solvable. Combining the two‐layer architecture and successive convex approximation method, a fair algorithm is proposed, which provides fair resource allocation strategies for the infrastructure provider. Compared with the existing static slicing and social optimal methods, the analysis and simulation results confirm that the proposed algorithm weighs the utilities of the total network system and each tenant. In addition, regarding their utilities, the gap between the proposed method and the social optimal is within 5%, which outperforms static slicing.

The temperature and relative humidity (hereafter called humidity) in the indoor environment is closely related to the operation of its control system. The centralized control system, which has identical air inlets not only leads to uneven indoor temperatures and humidity but also highly controlled latency when interference occurs. To address this challenge and improve the precision and uniformity of temperatures and humidity in the indoor environment, we propose a distributed temperature and humidity control (DTHC) framework based on deep reinforcement learning (DRL). In this work, we use a constant temperature and humidity air-conditioning (CTHA) system for a museum as a case study to validate the optimization performance of the proposed controller. The state–action space, reward function, and DRL network structure are proposed. The air flow rate of multiple air inlets of CTHA is adjusted according to the feedback from the distributed temperature and humidity sensors. We develop a DRL-based computational fluid dynamics (CFD) experiment platform to evaluate the proposed mechanism. The experiment results show that our approach can improve the precision and uniformity of the temperature and humidity while enhancing the anti-interference capability of the control system. The adjustment time and energy consumed to reach the desired indoor air temperatures and humidity are reduced compared with rule-based methods.

Mobile network traffic forecasting is one of the key functions in daily network operation. A commercial mobile network is large, heterogeneous, complex and dynamic. These intrinsic features make mobile network traffic forecasting far from being solved even with recent advanced algorithms such as graph convolutional network-based prediction approaches and various attention mechanisms, which have been proved successful in vehicle traffic forecasting. In this paper, we cast the problem as a spatial-temporal sequence prediction task. We propose a novel deep learning network architecture, Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Networks (AMF-STGCN), to model the traffic dynamics of mobile base stations. AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting. Experiments on four real-world datasets from two different domains consistently show AMF-STGCN outperforms the state-of-the-art methods.

This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users’ requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. Then, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell–Hestenes–Rockafellar method and the branch and bound method, obtaining the optimal solution. The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users’ requirements change of two slices and get better spectral efficiency performance.

In this study, a deep reinforcement learning (DRL) method was employed to solve the joint optimization problem for user association, resource allocation, and power allocation in heterogeneous networks (HetNets), which is an NP-hard problem. Existing studies have taken various optimization objectives into account. The heterogeneous network-deep-Q- network frame-work (HetDQN) is proposed to solve this type of optimization problem in HetNets. Based on maximum spectral efficiency, we designed a 6- layer deep neural network. The state space, objective function, and reward function are presented. In comparison with the existing solution, HetDQN can achieve a higher spectral efficiency. The simulation results revealed that HetDQN has better performance in term of convergence.

The robust beamforming problem in multiple-input single-output (MISO) downlink networks of simultaneous wireless information and power transfer (SWIPT) is studied in this paper. Adopting the time switching fashion to perform energy harvesting and information decoding respectively, we aim at maximizing the sum rate under imperfect channel state information (CSI) and the chance constraints of users' harvested energy. In view of the fact that the constraints for minimal harvested energy is not necessary to meet from time to time, this paper adopts chance constraint to model it and uses the Bernstein inequality to transform it into deterministic constraints equivalently. Recognizing the maximum sum rate problem of imperfect CSI as nonconvex problem, we transform it into finding the expectation of minimum mean square error (MMSE) equivalently in this paper, and an alternative optimization (AO) algorithm is proposed to decompose the optimization problem into two sub- problems: the transmit beamformer design and the division of switching time. The simulation results show the performance gains compared to non-robust state of the art schemes.

In this paper, multi-cell power allocation approach is researched. Different from the traditional optimization decomposition method, Deep Reinforcement Learning (DRL) method is employed to solve the power allocation issue which is an NP-hard problem. The objective of our work is to maximize the overall capacity of the entire network in the scenario where the base stations are randomly and densely distributed. We propose a wireless resource mapping method and a deep neural network for multi-cell power allocation named as Deep-Q-Full-Connected-Network (DQFCNet). Compared with the water-filling power allocation and Q-learning method, DQFCNet can achieve a higher overall capacity. Furthermore, the simulation results show that DQFCNet has significant improvement in convergence speed and stability.

The asymmetry traffic between downlink (DL) and uplink (UL) in massive machine‐type communication (mMTC) systems is so prominent that it makes the traditionally fixed frame protocols insufficient to handle. Meanwhile, the dynamic time‐division duplexing (D‐TDD) is a promising and attractive technology since its number of time slots for the DL and UL can be asymmetric and adjusted dynamically. In the cellular and mMTC co‐existing network, to balance the discrepancy of the UL/DL ratio and alleviate the interference as well, in this study, the authors design a D‐TDD‐based transmission frame structure to first fulfil the basic transmission requirements of the human‐type communication (HTC) users with a low power almost blank subframe. Herein, the stochastic geometry methods are adopted to calculate spectral efficiencies of the HTC user equipment. Then, focusing on the worst queue state in mMTC, they devise the slot allocation problem with the min–max objective of the UL/DL queues and utilise the sub‐gradient descent (SGD) method for a solution. Simulation results show that the proposed traffic aware sub‐frame configuration is more appropriate for the dynamical asymmetry environment. Meanwhile, the adopted dynamic step size SGD algorithm can achieve a trade‐off between the worst‐case queue and the network throughput.

Considering the expensive radio frequency (RF) chain, huge training overhead, and feedback burden issues in massive MIMO, in this letter, we propose a mixed-timescale per-group hybrid precoding scheme under an adaptive partially connected antenna structure, where the RF precoder is implemented using an adaptive connection network (ACN) and M analog phase shifters (APSs), where M is the number of antennas at the base station. Exploiting the mixed time stage channel state information (CSI) structure, the joint-design of ACN, and APSs is formulated as a statistical signal-to-leakage-and-noise ratio maximization problem, and a heuristic group RF precoding algorithm is proposed to provide a near-optimal solution. Simulation results show that the proposed design advances at better energy efficiency and lower hardware cost, CSI signaling overhead and computational complexity than the conventional hybrid precoding schemes.

Energy efficiency is an important factor to optimize the multi-hop forwarding strategy. PD (Pairing-inspired Dijkstra) multi-hop data forwarding algorithm is proposed to share cellular spectrum resources with D2D (device-to-device) users. Candidate multiplexing channel model is established in our proposal. Based on this model, PD algorithm solves the issue on channel and path selection. PD algorithm includes two parts, KM dichotomous matching algorithm and multiple iterations for Dijkstra algorithm. Under energy efficiency and QoS (Quality of Service) constraint, PD algorithm selects optimal transmission path among D2D users. Furthermore, the energy efficiency and transmission delay are evaluated in simulation section under PD, Dijkstra and CD (Closest to Destination) algorithm. Simulation results indicate that PD has better performance on energy efficiency and E2E (End to End) delay.

Nowadays, with the increasing awareness of environmental and economic issues, energy efficiency has received an enormous amount of attention. In this paper, we investigate an energy-saving algorithm called Greedy Base Station Sleeping (G- BSS), based on clustering for Device-to-Device (D2D) communication in cellular network and derive the energy utility function to evaluate energy consumption of the implementation scenario. In addition, a novel user association scheme is developed for the first time to solve the communication problem of users in sleep cells, in which the D2D clusters in sleep cells associate to the selected inter-CH (Cluster Head) in neighbor cells according to location information and residual energy, namely forming a merger cluster. Extensive simulation results confirm that the proposed algorithm reduces efficiently energy consumption and achieves low delay.

Device-to-device (D2D) communication brings significant benefits to improve resource utilization and users' throughput as an underlay to cellular networks. This paper first proposes an efficient admission policy based D2D clustering scheme to increase the system rate. By analyzing the interplay between the D2D clusters and the arrival user who intends to join a D2D cluster, we present two attraction functions describing the mutual suitability by considering social interaction, energy balance, and location as well. Further, we formulate the probability of the arrival user joining a certain D2D cluster based on Chinese Restaurant Process (CRP) and utilize a matching function to assign an optimal D2D cluster for each arrival user. On the other hand, we also illustrate how the cluster head can be selected in a D2D cluster. Finally, numerical results demonstrate that our clustering scheme efficiently leads to a good performance on the system rate and the stability of D2D clusters.

Carrier Aggregation (CA) is one of the promising techniques to support the wider bandwidth and fulfil the higher data rate requirement in LTE-Advanced system. Combined with Cognitive Radio (CR), the packet scheduler is more intelligent to assign the radio resources dynamically. To the best of our knowledge, Carrier Scheduling (CS) scheme in the cognitive LTE-A system with CA has not thoroughly been studied yet till now, even the CS scheme based on multi-service. In this paper, we design an efficient multi-user multi-service carrier scheduling scheme in such scenario, named as QoS-based Separated Random User Scheduling (QSRUS), which considers multi-service QoS requirements and fairness among different services simultaneously. From the simulation results, the proposed scheme can achieve remarkable performance improvements in terms of the QoS performance, the mean switching frequency and the fairness when compared with the traditional scheme.

In this paper, we focus on an energy efficient resource allocation in the multi-secondary user (SU) cognitive radio networks with network coding based cooperative transmission (NcCT). We set up a framework for multi-SU resource allocation game with Nash bargaining solution (NBS) under the cognitive radio scenario (CR-MSU-NBS game) where the sum of pairwise NBS function with pairing strategy is exploited as the network optimization objective and context conditions as constraints. Thereby, the network performance is improved by pairwise SUs' win-win cooperation on both system throughput and fairness. Since the CR-MSU-NBS game is NP-hard, we resolve it by a heuristic method. After testifying the global network NBS optimization pairing method, we propose the energy efficient suboptimal resource allocation scheme for multi-SUs. The simulation results show that the proposed scheme achieves a good tradeoff between fairness and efficiency and outperforms the familiar distance-pairing schemes; meanwhile, it achieves a 33.4% energy efficiency improvement but only 7.8% fairness loss averagely to the high computation complexity optimal method with ergodic search.

Routing is one of the most challenging problems in the opportunistic network owing to the occasion-connected mobile wireless environment. To overcome this weakness, many routing protocols have been put forward to solve it by exploiting the nodes' mobility history. Meanwhile, to parallel the current trend of the social network, some of them design the solution by utilizing the social relationship characteristics from the real world. Nevertheless, few of these works could close the gap between the two totally different meanings and improve the efficiency of the whole network based on both. In this paper, we propose social relationship enhanced predicable routing (SREP) in the opportunistic network. The whole algorithm depends on this truth- the nodes in the opportunistic network only visits some defined place because of its necessary relationship with other people, thus we could adapt the semi-deterministic Markov process to model the behavior of the node. And we also introduce Page Rank algorithm to quantify social degree of node. The simulation shows that SREP is an effective routing protocol in a specific scenario based on the human motion.

In this paper, focusing on the spectrum sharing and competition, we propose a double auction based spectrum trading (DAST) scheme which resolves the spectrum access between the primary network (PN) and secondary networks (SNs) subtly. Two different utility functions for primary users (PUs) and secondary users (SUs) are designed basing on a supply-and-demand relationship between them. Also, we adopt expectation and learning process in the module formulation, which takes consideration of the variance of channels, transmission forecasting, afore trading histories and etc. Numerical results with four bidding strategies are presented to reinforce the effectiveness of the two proposed utility evaluation based decision modules in supply falling short of demand cases. Meanwhile, the proposed DAST maintains comparable frequency efficiency with traditional centralized cognitive radio (CR) access approaches.

In this paper, we investigate the transmission control protocol (TCP) throughput in cooperative relaying networks and take an cross-layer design approach when selecting a relay to optimize the TCP throughput. A first-order finite-sate Markov channel (FSMC) is used to model the wireless time varying channels, and the TCP throughput is estimated as a function of physical layer signal-to-noise ratio (SNR) and link-layer frame size and retransmission times. Since relay selection is crucial in improving the TCP performance, we proposed a stochastic decision making approach to select the optimal relay for every TCP packet according to the states of each relay. We formulated the cross-layer TCP throughput optimization problem as a restless bandit system and obtained the statistically optimal relay selection policy, which has an indexability property and can be easily implemented in real system. We compare the proposed scheme through simulations under different parameters of physical layer and link-layer, simulation results show that the TCP throughput can be improved significantly by the optimal relay selection scheme.

A novel adaptive scheduling with coordination for the performance improvement of delay-sensitive applications over heterogeneous wireless networks is proposed. It can bring such contributes i) adaptation capabilities at different layers of the protocol stack, and ii) the coordinated scheduling initiation procedure. It uses information from the physical layer and data link layer to determine the appropriate transmission power level and media encoding rate for a connection, or initialize coordinated scheduling. The main contribution of this paper is the integration of the coordinated scheduling initiation into cross-layer mechanism adopting adaptive modulation and coding (AMC) and automatic repeat request (ARQ) to improve the overall system performance. Extensive simulation results show that the proposed design achieves significantly improved performance in terms of packet loss rate, average delay, and throughput, as well as an increased system capacity.

This paper presents a novel Q-learning based auction (QL-BA) algorithm for dynamic spectrum access in a one primary user multiple secondary users (OPMS) scenario. In the auction market, the secondary user provides a bidding price dynamically and intelligently using a Q-learning based bidding strategy to compete for current access opportunity; meanwhile primary user decides to whom to release the unused spectrum according to the maximal bidding principle. To obtain the limited and time-varying spectrum opportunities, each bidder presents a preference utility through Q-learning, considering the current packet transmission and future expectation. Simulation results show that the proposed QL-BA can significantly improve secondary users' bidding strategies and, hence, the performance in terms of packet loss, bidding efficiency and transmission rate is improved progressively.

This paper proposes a distributed optimal relay selection scheme in wireless multi-hop cooperative networks where the wireless channels are modeled as first-order finite-state Markov channels (FSMCs) and adaptive modulation and coding (AMC) is applied. The FSMC model is used to approximate the time variations of the average received signal-to-noise ratio (SNR). The state of a relay consists of the channel states of both source-to-relay and relay-to-destination links. In this scheme, a stochastic decision making approach is taken to select the optimal relay according to the states of all available relays with the quality of service (QoS) optimization goals of mitigating error propagation and increasing spectral efficiency. Simulation results show that the proposed scheme outperforms the existing scheme.

An adaptive resource allocation scheme for QoS oriented OFDMA system, which schedules two different utility functions for the Real-time/Non Real-time traffic simultaneously, is proposed in this paper. Instead of partial consideration of uniform kinds of QoS, we introduce an updating ratio factor to schedule users of heterogeneous traffic. Due to the complex optimization objective, the general convex optimal methods are no longer feasible. We are motivated to study a heuristic natural genetic approach to solve this problem. Due to the weak convergence of Genetic Algorithm (GA), we improve it by a well-selected initial population. Numerical results are presented to illustrate that our scheme not only tackles the diverse QoS requirement but also alleviates the unfairness between real-time and non-real-time services under various traffic loads.

With the development of mobile network and computer technology, MIPv6 is brought to the internet. Taking care of the security concerns about network connection, we bring AAA system into the mobile network. In order to be permitted in the integrated architecture of MIPv6 and AAA systems, the users have to get network access permission and AAA response from AAAH. This paper presents an optimization method to enhance handover performance. Above all, we build up a hierarchical AAA architecture and temporarily store AAA credentials at the AAASL. So that mobile user does not have the need to send request to AAAH. Then we encapsulate BU or HoT1 into authentication/authorization request information and save time needed for the BU to travel from MN to HA. Also an improved efficient security association is considered to solve the network access problem. Finally, Experiments indicate that compared with current MIPv6, this optimization method could shorten handover time, especially when the distance between MN and AAAS is long.

Adaptive resource allocation to exploit multiuser diversity and spacial diversity in OFDMA relay-aided cooperative cellular networks is studied. Assuming that the BS has all the channel state information (CSI), an optimization problem for subcarrier assignment, relay selection and power allocation that maximizes downlink capacity of the system is formulated. Since the optimal solution is complex to obtain, a suboptimal solution dividing the problem into two sub-problems is proposed. Assuming equal power allocation in BS and each relay, the first sub-problem for joint subcarrier assignment and relay selection is a combinatorial optimization problem, for which an iterative algorithm derived from necessary optimality condition is proposed. Given the results from the first sub- problem, the second sub-problem for power allocation becomes a convex optimization problem and can be solved by an iterative method which optimizes the power of BS and each relay separately. Numerical results showed that two iterative algorithms can converge in reasonable steps and achieve significant improvement on system capacity due to two diversity schemes and power fully exploited.