In this paper, we explore a cooperative integrated sensing and communication (ISAC) framework that utilizes orthogonal frequency division multiplexing (OFDM) waveforms. Under the control of a central processing unit (CPU), multiple access points (APs) collaboratively perform multistatic sensing while providing communication service in a cell-free multiple-input multiple-output (MIMO) system. Achieving high sensing accuracy requires the collection of global sensing information at the CPU, which can lead to significant fronthaul signaling overhead due to the feedback of the sensing signals from each AP. To tackle this issue, we propose a collaborative processing scheme in which the APs locally compress and quantize the received sensing signals before forwarding them to the CPU. The CPU then aggregates the information from all APs to estimate the location and velocity of the targets. We develop a distributed vector-quantized variational autoencoder (D-VQVAE) to enable an end-to-end implementation of this scheme. D-VQVAE consists of distributed encoders at the APs to locally encode the received sensing signals, codebooks for quantizing the encoded results, and a decoder at the CPU for location and velocity estimation. It effectively reduces the amount of data transmitted from each AP to the CPU while maintaining a high sensing accuracy.We employ a collaborative learning-assisted scheme to train D-VQVAE in an end-to-end manner. Simulation results show that the proposed D-VQVAE network outperforms the baseline schemes in sensing accuracy and reduces fronthaul signaling overhead by 99% when compared with the centralized sensing approach.

Exploiting near-field spherical wavefronts can improve the performance of integrated sensing and communication (ISAC) systems. Existing studies on near-field ISAC mainly consider either fully-digital or static hybrid beamforming, which may not be able to efficiently exploit the distance-dependent degrees of freedom (DoFs) of near-field channels. In this paper, we propose a dynamic hybrid beamforming architecture that adaptively adjusts the number of active radio frequency (RF) chains and further utilize a reconfigurable intelligent surface (RIS) to enhance the performance in ISAC systems. We formulate an energy efficiency maximization problem which aims to jointly optimize the hybrid precoding matrices at the base station, phase-shifts at the RIS, and hybrid combining matrices at the user equipment, subject to the constraint on guaranteeing the minimum beam pattern gain toward sensing targets. To tackle this intractable problem, we propose an alternating optimization algorithm by leveraging fractional programming, matrix lifting, and semidefinite relaxation techniques. Simulation results demonstrate that our proposed algorithm outperforms three baseline schemes in both optimizing the number of active RF chains and maximizing the energy efficiency.

Semantic communication has shown outstanding performance in preserving the overall source information in wireless transmission. For semantically rich content such as images, human users are often interested in specific regions depending on their intent. Moreover, recent semantic coding models are mostly trained on specific datasets. However, real-world applications may involve images out of the distribution of training dataset, which makes generalization a crucial but largely unexplored problem. To incorporate user’s intent into semantic coding, in this paper, we propose a generalized user-oriented image semantic coding (UO-ISC) framework, where the user provides a text query indicating its intent. The transmitter extracts features from the source image which are relevant to the user’s query. The receiver reconstructs an image based on those features. To enhance the generalization ability, we integrate contrastive language image pre-training (CLIP) model, which is a pretrained large vision-language model (VLM), into our proposed UO-ISC framework. To evaluate the relevance between the reconstructed image and the user’s query, we introduce the user-intent relevance loss, which is computed by using a pretrained large VLM, large language-and-vision assistant (LLaVA) model. When performing zero-shot inference on unseen objects, simulation results show that the proposed UO-ISC framework outperforms the state-of-the-art query-aware image semantic coding in terms of the answer match rate.

In this paper, we present a cooperative integrated sensing and communication (ISAC) framework in cell-free multiple-input multiple-output (MIMO) systems, where multiple access points (APs), under the control of a central processing unit (CPU), collaboratively perform target sensing by using the reflected echo signals. Most of the existing works first estimate the sensing parameters (e.g., range, angle, relative velocity) observed by each AP and then use these estimated parameters for sensing tasks such as localization and velocity estimation. However, this approach may suffer from performance degradation due to errors in the estimated parameters. We propose a deep neural network (DNN)-based scheme to jointly process the echo signals received across the distributed APs and directly estimate the location and velocity of the targets. The proposed scheme bypasses the sensing parameter estimation stage and enhances the sensing performance. Simulation results show that our proposed scheme significantly reduces the localization and velocity estimation error when compared with a state-of-the-art approach.

Multi-task semantic communication (SC) can reduce the computational resources in wireless systems since retraining is not required when switching between tasks. However, existing approaches typically rely on task-specific embeddings to identify the intended task, necessitating retraining the entire model when given a new task. Consequently, this drives the need for a multitask SC system that can handle new tasks without additional training, known as zero-shot learning. Inspired by the superior zero-shot capabilities of large language models (LLMs), we leverage pre-trained instruction-tuned LLMs, referred to as fine-tuned language net (FLAN), to improve the generalization capability. We incorporate a mixture-of-experts (MoE) architecture in the FLAN model and propose MoE-FLAN-SC architecture for multi-task SC systems. Our proposed MoE-FLAN-SC architecture can further improve the performance of FLAN-T5 model without increasing the computational cost. Moreover, we design a multi-task feature extraction module (FEM) which can adaptively extract relevant features across various tasks given the provided features and signal-to-noise ratio (SNR). Simulation results show that our proposed MoE-FLAN-SC architecture outperforms three state-of-the-art models in terms of the average accuracy on four different unseen tasks.

The integration of cell-free multiple-input multiple-output (MIMO) technology within the open radio access network (O-RAN) architecture addresses the growing need for decentralized, scalable, and high-capacity networks that can support different applications and use cases. In this paper, we propose a distributed precoding framework to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) traffic in cell-free O-RANs, where each user is served by multiple open radio units (O-RUs). We consider short packet transmission in order to satisfy the latency requirements of URLLC traffic. We formulate a precoding optimization problem to maximize the aggregate throughput of eMBB users subject to the latency constraint of URLLC users. We propose a multi-agent deep reinforcement learning (DRL) algorithm to solve the formulated problem in a distributed manner. In particular, an actor-critic DRL agent is assigned to each O-RU. The actor determines the precoding matrices. The critic evaluates the actor's policy. The critics have global knowledge of all agents' policies, which stabilizes training and enables collaboration among agents. Simulation results show that the proposed algorithm provides an aggregate eMBB throughput improvement by up to 55.4 % when compared with three state-of-the-art baseline schemes.

Pinching antennas, implemented by applying small dielectric particles on a waveguide, have emerged as a promising flexible-antenna technology ideal for next-generation wireless communications systems. Unlike conventional flexible-antenna systems, pinching antennas offer the advantage of creating line-of-sight (LoS) links by enabling antennas to be activated on the waveguide at a position close to the users. This paper investigates a typical two-user non-orthogonal multiple access (NOMA) down-link scenario, where multiple pinching antennas are activated on a single dielectric waveguide to assist NOMA transmission. We formulate the problem of maximizing the data rate of one user subject to the quality-of-service (QoS) requirement of the other user by jointly optimizing the antenna positions and power allocation coefficients. The formulated problem is nonconvex and difficult to solve due to the impact of antenna positions on large-scale path loss and two types of phase shifts, namely in-waveguide phase shifts and free space propagation phase shifts. To this end, we propose an iterative algorithm based on block coordinate descent and successive convex approximation techniques. Moreover, we consider the special case with a single pinching antenna, which is a simplified version of the multi-antenna case. Although the formulated problem is still nonconvex, by using the inherent features of the formulated problem, we derive the global optimal solution in closed-form, which offers important insights on the performance of pinching-antenna systems. Simulation results demonstrate that the pinching-antenna system significantly outperforms conventional fixed-position antenna systems, and the proposed algorithm achieves performance comparable to the computationally intensive exhaustive search based approach.

Decentralized federated learning (DFL) enables clients to train a neural network model in a device-to-device (D2D) manner without central coordination. In practical systems, DFL faces challenges due to dynamic topology changes, timevarying channel conditions, and limited computational capability of the clients. These factors can affect the learning performance and efficiency of DFL. To address the aforementioned challenges, in this paper, we propose a graph neural network (GNN)–based algorithm to minimize the total delay and energy consumption on training and improve the learning performance of DFL in D2D wireless networks. In our proposed GNN, a multihead graph attention mechanism is used to capture different features of clients and wireless channels. We design a neighbor selection module which enables each client to select a subset of its neighbors for the participation of model aggregation. We develop a decoder that enables each client to determine its transmit power and computational resource. Experimental results show that our proposed algorithm achieves a lower total delay and energy consumption on training when compared with five baseline schemes. Furthermore, by properly selecting a subset of neighbors for each client, our proposed algorithm achieves similar testing accuracy to the full participation scheme.

360°videos require significant bandwidth to provide an immersive viewing experience. Wireless systems using terahertz (THz) frequency band can meet this high data rate demand. However, self-blockage is a challenge in such systems. To ensure reliable transmission, this paper explores THz-enabled 360° video streaming through multiple multi-antenna access points (APs). Guaranteeing users’ quality of experience (QoE) requires accurate viewport prediction to determine which video tiles to send, followed by asynchronous bitrate selection for those tiles and beamforming design at the APs. To address users’ privacy and data heterogeneity, we propose a content-based viewport prediction framework, wherein users’ head movement prediction models are trained using a personalized federated learning (PFL) algorithm. To address asynchronous decision-making for tile bitrates and dynamic THz link connections, we formulate the optimization of bitrate selection and beamforming as a macro-action decentralized partially observable Markov decision process (MacDec-POMDP) problem. To efficiently tackle this problem for multiple users, we develop two deep reinforcement learning (DRL) algorithms based on multi-agent actor-critic methods and propose a hierarchical learning framework to train the actor and critic networks. Experimental results show that our proposed approach provides a higher QoE when compared with three benchmark algorithms.

Wireless cellular traffic prediction is essential for efficient network management and monitoring, yet it is a challenging task due to the spatial and temporal characteristics of traffic. Recently, machine learning based traffic prediction algorithms have been proposed in the literature. However, these algorithms lack good generalization ability as they cannot adapt to frequent changes in traffic distribution typically encountered in wireless networks. In this paper, we propose a traffic prediction algorithm using large language models (LLMs). We first analyze the temporal characteristics of traffic and identify those timespans in the historical traffic information which are important for traffic prediction. We use a clustering algorithm to identify cells with similar traffic patterns. To predict the traffic in a cell, we incorporate the multi-timespan historical traffic information of the cell as well as those cells with similar traffic patterns into natural language sentences and provide them as input to the LLM. Using our proposed framework, we fine-tune three popular LLMs (BART, BigBird, and PEGASUS) on the traffic prediction task. Experimental results show that our proposed LLM framework outperforms a state-of-the-art graph neural network (GNN) baseline and achieves up to 12.32% improvement in terms of the mean absolute error (MAE). Moreover, the proposed LLM framework has excellent generalization ability under the zero-shot setting, reducing the MAE by up to 46.84% compared to the baseline. The ablation studies reveal that providing information from multiple timespans to the model reduces the MAE by up to 15.05% compared to only providing information from the most recent timespan.

Terahertz (THz) wireless systems aim to support content-rich applications with ultra-high data rate. Due to high molecular absorption, THz signals experience severe path loss. Adaptive sub-band bandwidth (ASB) allocation can mitigate absorption attenuation by allocating THz sub-bands with variable bandwidth to the users. However, in ASB allocation, since the bandwidth of sub-bands may not be known a priori, accurate channel estimation is challenging. To overcome this issue, in this paper, we propose a heterogeneous graph-transformer network (HGTN) to bypass the channel estimation phase. We formulate a sum-rate maximization problem with quality-of-service (QoS) constraints in a multiuser multiple-input multiple-output (MU-MIMO) THz system to optimize the precoding and ASB allocation. The proposed HGTN parameterizes the mapping from input features (e.g., location information, users' minimum data rate) to the optimized system parameters via unsupervised learning. The proposed HGTN can be applied to systems with different number of users once it is trained. Simulation results show that our proposed HGTN achieves a higher system sum-rate with faster convergence when compared with the unsupervised deep neural network learning algorithm.

360° videos demand substantial bandwidth to deliver an immersive viewing experience to users. In wireless networks, this high data rate demand can be accommodated by utilizing the terahertz (THz) frequency band. However, THz band communications are susceptible to self-blockage. To ensure reliable transmission, this paper studies the streaming of$360^{\circ}$videos over THz wireless systems using multiple multi-antenna access points (APs). Users' requests for video tiles give rise to an optimization problem that involves asynchronous bitrate selection for those tiles and beamforming design for the APs. We formulate this problem as a macro-action decentralized partially observable Markov decision process (MacDec-POMDP). To efficiently tackle this problem for multiple users, we propose an asynchronous deep reinforcement learning (DRL) algorithm using a multi-agent actor-critic method to determine the bitrate selection policy. The APs' beamforming is determined by solving an optimization problem using the weighted minimum mean square error (WMMSE) algorithm. Results show that our proposed approach provides a higher average quality of experience (QoE) for the users when compared with two benchmark algorithms.

Integrated sensing and communication (ISAC) is one of the usage scenarios for the sixth generation (6G) wireless networks. In this paper, we study cooperative ISAC in cell-free multiple-input multiple-output (MIMO) systems, where multiple MIMO access points (APs) collaboratively provide communication services and perform multistatic sensing. We formulate an optimization problem for the ISAC beamforming design, which maximizes the achievable sum-rate while guaranteeing the sensing signal-to-noise ratio (SNR) requirement and total power constraint. Learning-based techniques are regarded as a promising approach for addressing such a nonconvex optimization problem. By taking the topology of cell-free MIMO systems into consideration, we propose a heterogeneous graph neural network (GNN), namely SACGNN, for ISAC beamforming design. The proposed SACGNN framework models the cell-free MIMO system for cooperative ISAC as a heterogeneous graph and employs a transformer-based heterogeneous message passing scheme to capture the important information of sensing and communication channels and propagate the information through the graph network. Simulation results demonstrate the performance gain of the proposed SACGNN framework over a conventional null-space projection based scheme and a deep neural network (DNN)-based baseline scheme.

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.

Terahertz (THz) wireless systems aim to support content-rich applications with ultra-high data rate. Due to high molecular absorption, THz signals experience severe path loss over long distance. To alleviate distance limitation, reconfigurable intelligent surface (RIS) can improve the coverage range. Adaptive sub-band bandwidth (ASB) allocation can mitigate absorption attenuation by allocating THz sub-bands with variable bandwidth to the users. However, in ASB allocation, since the bandwidth of sub-bands may not be knowna priori, accurate channel estimation is challenging. To overcome this issue, in this paper, we propose a metapath-based heterogeneous graph-transformer network (MHGphormer) to bypass the channel estimation phase. We formulate a sum-rate maximization problem with quality-of-service (QoS) constraints in a RIS-aided multiuser multiple-input multiple-output (MU-MIMO) THz system to optimize the precoding, phase shifts, and ASB allocation. The proposed MHGphormer parameterizes the mapping from input (e.g., location information, users’ minimum data rate) to the optimized system parameters via unsupervised learning. The proposed MHGphormer has the permutation invariance/equivariance property. It can be applied to systems with different number of users. Simulation results show that our proposed MHGphormer achieves a higher system sum-rate when compared with the homogeneous graph neural network, unsupervised deep neural network, and alternating optimization baseline algorithms.

In cross-silo federated learning (FL), organizations cooperatively train a global model with their local datasets. However, some organizations may act as free riders such that they only contribute a small amount of resources but can obtain a high-accuracy global model. Meanwhile, some organizations can be business competitors, and they do not trust each other or any third-party entity. In this work, our goal is to design a framework that motivates efficient cooperation among organizations without the coordination of a central entity. To this end, we propose a blockchain-empowered incentive mechanism framework for cross-silo FL. Under this incentive mechanism framework, we develop a distributed algorithm that enables organizations to achieve social efficiency, individual rationality, and budget balance without private information of the organizations. Our proposed algorithm has a proven convergence guarantee and empirically achieves a higher convergence rate than a benchmark method. Moreover, we propose a transaction minimization algorithm to reduce the number of transactions made among organizations in the blockchain. This algorithm is proven to achieve a performance no worse than twice the minimum value. The experimental results in a testbed show that our proposed framework enables organizations to achieve social efficiency within a relatively short iterative process.

Wireless traffic prediction is indispensable for network planning and resource management. Due to different population distributions and user behavior, there exist strong spatial-temporal variations in wireless traffic across different regions. Most of the conventional traffic prediction approaches can only tackle a particular spatial-temporal pattern and cannot capture such variations in wireless traffic. This motivates us to develop an adaptive approach which can tackle spatial-temporal variations and predict wireless traffic in different regions. In this paper, we formulate an adaptive traffic prediction problem from a probabilistic inference perspective and develop a variational spatial-temporal Bayesian meta-learning (VST-BML) algorithm. We model the traffic prediction in different regions as different prediction tasks. The proposed VST-BML algorithm can learn the common spatial-temporal features shared by all prediction tasks, and adaptively infer the task-specific parameters to tackle spatial-temporal variations. We evaluate the performance of our proposed VST-BML algorithm using a real-world traffic dataset. Experimental results show that the proposed algorithm can quickly adapt to different prediction tasks by using only a small number of data samples and provide accurate traffic prediction in different regions. When compared with five baseline methods, the proposed algorithm can reduce the root- mean-square error (RMSE) and mean absolute error (MAE) by 53.0% and 48.4%, respectively.

Federated learning (FL) is a distributed learning framework where clients jointly train a global model without sharing their local datasets. In each communication round of FL, a subset of clients are scheduled to participate in training. Recent research has shown that diversity-based FL can improve the convergence performance of FL, especially when the client datasets are not independent and identically distributed (non-IID). In this paper, we show that by considering the channel state information and age of information (AoI) of each client, the convergence of FL can further be improved. We formulate a channel-aware joint AoI and diversity-based client scheduling problem as a constrained Markov decision process (CMDP). By using Lagrangian index and one-step lookahead approaches, we develop a two-stage online algorithm which is scalable and has a low computational complexity. For FL tasks with non-IID client datasets, our results show that the proposed algorithm can speed up the convergence of FL by up to 71%, through reducing the duration of uplink transmission, when compared with three state-of-the-art FL algorithms.

Decentralized federated learning (DFL) enables clients to train a neural network model in a device-to-device (D2D) manner without central coordination. In practical systems, DFL faces challenges due to the dynamic topology changes, time-varying channel conditions, and limited computational capability of devices. These factors can affect the performance of DFL. To address the aforementioned challenges, in this paper, we propose a graph neural network (GNN)-based approach to minimize the total delay on training and improve the learning performance of DFL in D2D wireless networks. In our proposed approach, a multi-head graph attention mechanism is used to capture different features of clients and channels. We design a neighbor selection module which enables each client to select a subset of its neighbors for the participation of model aggregation. We develop a decoder which enables each client to determine its transmit power and CPU frequency. Experimental results show that our proposed algorithm can achieve a lower total delay on training when compared with three baseline schemes. Furthermore, the proposed algorithm achieves similar performance on the testing accuracy when compared with the full participation scheme.

Virtual reality (VR) applications require wireless systems to provide a high transmission rate to support 360-degree video streaming to multiple users simultaneously. In this paper, we propose an intelligent reflecting surface (IRS)-aided rate-splitting (RS) VR streaming system. In the proposed system, RS exploits the shared interests of the users in VR streaming, and the IRS creates reflected channels to facilitate a high transmission rate. The IRS also mitigates the performance bottleneck caused by the requirement that all RS users have to be able to decode the common message. We formulate an optimization problem for maximization of the achievable bitrate of the streamed 360-degree video subject to the quality-of-service (QoS) constraints of the users. We propose a deep reinforcement learning (DRL)-based algorithm, in which we leverage imitation learning and the hidden convexity of the formulated problem to optimize the IRS phase shifts, RS parameters, beamforming vectors, and bitrate selection of the 360-degree video tiles. Simulations based on a real-world dataset show that the proposed IRS-aided RS VR streaming system outperforms two baseline schemes in terms of system sum-rate and average runtime.

Federated learning (FL) is a distributed learning framework where clients jointly train a global model without sharing their local datasets. In randomized client sampling, a subset of clients are uniformly chosen to participate in training in each communication round of FL. Recent research has shown that by jointly considering the age of information (AoI) and channel state information (CSI) of each client, the convergence of FL can be improved. In this paper, we formulate a joint AoI and CSI-based client scheduling problem as a constrained Markov decision process. We propose a low-complexity and scalable algorithm based on the Lagrangian index approach. Simulation results show that the proposed Lagrangian index-based approach achieves near-optimal performance. For FL tasks with the CIFAR-10 dataset, our results show that the proposed algorithm can speed up the convergence of FL by 40%, by reducing the duration of uplink transmission, when compared with two state-of-the-art FL algorithms.

Predictive analysis of wireless cellular traffic plays an important role in network resources provisioning. Accurate traffic prediction is a challenging task due to the dynamic spatial-temporal nature of wireless traffic. Most of the existing approaches do not consider spectral domain information for wireless traffic prediction. Some of the approaches cannot capture the spatial dependencies between neighbouring and distant cells. In this paper, we propose a dynamic Bernstein graph recurrent network for traffic prediction in wireless cellular networks. First, we design a spectral dynamic graph construction (SDGC) method to model the spatial dependencies between cells as a dependency graph in a data-driven fashion. A dynamic Bernstein polynomial filtering (DBPF) scheme based on the$K$-order Bernstein polynomial approximation is then developed to capture the spatial correlations and infer the cell-specific parameters. To predict the spatial-temporal traffic demands, we propose a dynamic Bernstein graph recurrent network (DBGRN), which integrates the proposed DBPF module with a gated recurrent unit (GRU) network. We evaluate the performance of our proposed model using a real-world dataset. Results show that our proposed model outperforms four state-of-the-art baseline schemes, and achieves up to 8% and 10% improvements in terms of the root mean squared error (RMSE) and mean absolute error (MAE), respectively.

Integrated sensing and communications (ISAC) has emerged as a promising technology for predictive beamforming design in vehicle-to-infrastructure (V2I) networks. Most of the existing works use a two-step approach for predictive beamforming design. The first step is to estimate the state parameters of a vehicle (e.g., angle of arrival (AoA), channel state information (CSI)) from the received sensing signal samples at the road side unit (RSU). The second step is to determine the beamforming vector based on the estimated parameters. However, estimation errors may be introduced in the first step which impacts the subsequent beamforming design and leads to degradation in the achievable rate. In this work, by using deep learning, we propose an ISAC-enabled end-to-end predictive beamforming (E2E-PB) approach to obtain the beamforming vector directly from the reflected signal samples. The proposed approach does not require an intermediate state parameters estimation step. We develop an attention-based long short-term memory (LSTM) network to capture the temporal correlation in the reflected signal samples and determine the beamformer. The network is trained in an unsupervised manner to maximize the achievable rate. We compare our proposed E2E-PB approach with two state-of-the-art schemes, namely, the extended Kalman filtering framework and a deep learning based two-step approach. The results show that our proposed E2E-PB approach obtains a higher achievable rate than the other two baseline schemes, and has close performance when compared with the optimal beamforming design with perfect CSI.

Viewport prediction is a key enabler for 360° video streaming over wireless networks. To improve the prediction accuracy, a common approach is to use a content-based viewport prediction model. Saliency detection based on traditional convolutional neural networks (CNNs) suffers from distortion due to equirectangular projection. Also, the viewers may have their own viewing behavior and are not willing to share their historical head movement with others. To address the aforementioned issues, in this paper, we first develop a saliency detection model using a spherical CNN (SPCNN). Then, we train the viewers’ head movement prediction model using personalized federated learning (PFL). Finally, we propose a content-based viewport prediction framework by integrating the video saliency map and the head orientation map of each viewer using fusion techniques. The experimental results show that our proposed framework provides higher average accuracy and precision when compared with three state-of-the-art algorithms from the literature.

The growing demand for virtual reality (VR) applications requires wireless systems to provide a high transmission rate to support 360-degree video streaming to multiple users simultaneously. In this paper, we propose an intelligent reflecting surface (IRS)-aided rate-splitting (RS) VR streaming system. In the proposed system, RS facilitates the exploitation of the shared interests of the users in VR streaming, and IRS creates additional propagation channels to support the transmission of high-resolution 360-degree videos. IRS also enhances the capability to mitigate the performance bottleneck caused by the requirement that all RS users have to be able to decode the common message. We formulate an optimization problem for maximization of the achievable bitrate of the 360-degree video subject to the quality-of-service (QoS) constraints of the users. We propose a deep deterministic policy gradient with imitation learning (Deep-GRAIL) algorithm, in which we leverage deep reinforcement learning (DRL) and the hidden convexity of the formulated problem to optimize the IRS phase shifts, RS parameters, beamforming vectors, and bitrate selection of the 360-degree video tiles. We also propose RavNet, which is a deep neural network customized for the policy learning in our Deep-GRAIL algorithm. Performance evaluation based on a real-world VR streaming dataset shows that the proposed IRS-aided RS VR streaming system outperforms several baseline schemes in terms of system sum-rate, achievable bitrate of the 360-degree videos, and online execution runtime. Our results also reveal the respective performance gains obtained from RS and IRS for improving the QoS in multiuser VR streaming systems.

Rate-splitting multiple access (RSMA) is a promising multiple access (MA) technique. It employs rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receiver. Most of the existing works on RSMA assume that all users use SIC to decode the common stream and the blocklength is infinite. The first assumption causes the data rate of the common stream to be limited by the user with the worst channel quality. The second assumption may lead to suboptimal performance in practical systems with finite blocklength. In this paper, we propose a flexible RSMA scheme, which allows the system to decide whether a user should use SIC to decode the common stream or not. We consider the effective throughput as the performance metric, which incorporates the data rate as well as the error performance of RSMA with finite blocklength. We first derive the effective throughput expression and then formulate an effective throughput maximization problem by jointly optimizing the beamforming vectors, transmission data rates, and RS-user selection. We develop an optimal algorithm as well as a low-complexity algorithm for beamforming design. We derive a semi-closed-form solution of the optimal data rates and propose an efficient algorithm for the RS-user selection. Numerical results demonstrate that the proposed algorithm obtains a higher effective throughput than space division multiple access (SDMA), non-orthogonal multiple access (NOMA), and two other RSMA schemes.

Predictive analysis on cellular traffic is important for the control and monitoring of wireless networks. Cellular traffic prediction is a challenging problem due to the non- stationarity and dynamic spatial-temporal correlation of the traffic. In this paper, we address the problem of accurate traffic prediction in a base station by proposing a deep neural network called RAConv. Its structure includes residual network, attention mechanism, and deep convolutional network. In the proposed architecture, a deep 3D residual convolutional network (ResConv3D) with three residual blocks are employed to learn the local spatial-temporal features. An attention-aided convolutional long short-term memory network (AConvLSTM) is then used to capture the long-term spatial-temporal dependencies. The use of the attention modules enable the network to focus on the most important spatial-temporal information. We evaluate the performance of the proposed RAConv network using a dataset provided by a Canadian wireless service provider. We consider the traffic prediction on two time scales (i.e., hourly and daily), which exhibit different spatial-temporal dependency patterns. Experimental results show that the proposed RAConv network can achieve accurate prediction under both time scales. Results also show that our proposed network provides a lower root-mean-square error (RMSE) than the conventional ConvLSTM baseline scheme.

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely theeffective throughput, which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes.

Many of the existing optimization-based transmit power control algorithms suffer from high computational complexity and require instantaneous global channel state information (CSI), both of which hinder their practical implementation. In this paper, we consider a wireless network with multiple transmitter-receiver pairs, where each transmitter only has access to its local CSI fed back from its intended receiver and does not require local CSI exchange with its neighboring transmitters. In such a network scenario, we propose a deep reinforcement learning based decentralized multi-agent power control (DEC-MAPC) algorithm for sum-rate maximization, where each transmitter acts as an intelligent agent. By leveraging the value decomposition technique, we establish a nonlinear mapping from the local reward of each agent to the global reward. Such a design allows each agent to independently control its transmit power based on its local CSI while enabling global collaboration among the agents. The proposed algorithm is scalable to large-scale networks as only local CSI is required, and is robust to the channel and interference variations via interacting with the environment. Simulation results show that the proposed DEC-MAPC algorithm with local CSI achieves comparable sum-rate performance with the centralized optimization algorithms with global CSI, while significantly reducing the computational complexity.

In mobile edge computing systems, an edge node may have a high load when a large number of mobile devices offload their tasks to it. Those offloaded tasks may experience large processing delay or even be dropped when their deadlines expire. Due to the uncertain load dynamics at the edge nodes, it is challenging for each device to determine its offloading decision (i.e., whether to offload or not, and which edge node it should offload its task to) in a decentralized manner. In this work, we consider non-divisible and delay-sensitive tasks as well as edge load dynamics, and formulate a task offloading problem to minimize the expected long-term cost. We propose a model-free deep reinforcement learning-based distributed algorithm, where each device can determine its offloading decision without knowing the task models and offloading decision of other devices. To improve the estimation of the long-term cost in the algorithm, we incorporate the long short-term memory (LSTM), dueling deep Q-network (DQN), and double-DQN techniques. Simulation results show that our proposed algorithm can better exploit the processing capacities of the edge nodes and significantly reduce the ratio of dropped tasks and average delay when compared with several existing algorithms.

360-degree video streaming provides users with immersive experience by letting users determine their field-of-views (FoVs) in real time. To efficiently utilize the limited bandwidth resources, recent works have proposed a viewport adaptive 360-degree video streaming model by exploiting the bitrate adaptation in spatial and temporal domains. In this paper, under this video streaming model, we propose an online bitrate selection algorithm to enhance the user’s quality of experience (QoE). This is achieved by characterizing the user’s personalized FoV and real-time downloading capacity in an online fashion. We address the unknown user-specific FoV by introducing the reference FoV and design an online bitrate selection algorithm to learn the difference between the user’s actual FoV and the reference FoV. We prove that as the number of video segments increases, the performance of the proposed online algorithm approaches the optimal performance asymptotically, with a bounded error. We perform trace-driven simulations with real-world datasets. Simulation results show that under the scenario where the available video bitrates are relatively high, our proposed algorithm can improve the user’s viewing quality level between$4.2\!-\!29.4$percent and reduce the average intra-segment quality switch by at least 12.4 percent when compared with several existing methods.

In this paper, we formulate a joint uplink scheduling, phase shift control, and beamforming optimization problem in intelligent reflecting surface (IRS)-aided systems. We consider maximizing the aggregate throughput and achieving the proportional fairness as objectives. We propose a deep reinforcement learning-based user scheduling, phase shift control, beamforming optimization (DUPB) algorithm to solve the joint problem. The proposed DUPB algorithm applies the neural combinatorial optimization (NCO) technique to solve the user scheduling subproblem, in which a stochastic user scheduling policy is learned by deep neural networks with attention mechanism. Curriculum learning with deep deterministic policy gradient (CL-DDPG) is used in the proposed DUPB algorithm to jointly optimize the phase shift control and beamforming vectors. The knowledge on the hidden convexity of the joint problem is exploited to facilitate the policy learning in CL-DDPG. Simulation results show that, with the maximum aggregate throughput as the objective, the proposed DUPB algorithm achieves an aggregate throughput that is higher than the alternating optimization (AO)-based algorithms. Moreover, the throughput fairness among the users is improved when proportional fairness is used as the objective. The proposed DUPB algorithm outperforms the AO-based algorithms in terms of runtime when the number of reflecting elements is large.

Network slicing is a promising technique for wireless service providers to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services in a shared radio access network (RAN) infrastructure. In this paper, we apply numerology, mini-slot based transmission, and punctured scheduling techniques to support eMBB and URLLC network slices. For efficient allocation of radio resources (e.g., physical resource blocks, transmit power) to the users, we formulate RAN slicing problem as a multi-timescale problem. To solve this problem and address the dynamics of the traffic, we propose a hierarchical deep learning framework. Specifically, in each long time slot, the service provider employs a deep reinforcement learning (DRL) algorithm to determine the slice configuration parameters. The eMBB and URLLC schedulers use their own attention-based deep neural network (DNN) algorithm to allocate radio resources to their corresponding users in each short and mini time slot, respectively. Simulation results show that the proposed framework can achieve a higher aggregate throughput and a higher service level agreement (SLA) satisfaction ratio compared to some other RAN slicing approaches, including the resource proportional placement algorithm, decomposition and relaxation based resource allocation algorithm, and distributed bandwidth optimization algorithm.

Intelligent reflecting surface (IRS) and cell-free multiple-input multiple-output (CF-MIMO) systems are two promising multi-antenna technologies for the fifth generation and beyond (B5G) wireless communication systems. In this paper, we formulate a joint phase shift control and beamforming opti-mization problem to maximize the aggregate throughput subject to the reliability constraint of the users in an IRS-aided CF-MIMO system. We propose an alternating optimization (AO)-based algorithm, in which the joint problem is decomposed into a phase shift control subproblem and a beamforming subproblem. For the phase shift control subproblem, we propose a complex gradient descent (CGD)-based algorithm, which tackles the unit-modulus constraint and guarantees the aggregate throughput to be monotonic increasing in each iteration. We then propose a difference of convex programming (DCP)-based algorithm for beamforming optimization. Simulation results show that the proposed AO-based algorithm achieves an aggregate throughput that is 53.8% and 25.1% higher than the cellular MIMO system with zero-forcing beamformer and the IRS-aided CF-MIMO system with random phase shift control, respectively. Moreover, the reliability requirements of the users are satisfied with the proposed AO-based algorithm. Our results also demonstrate that the proposed algorithm improves the minimum throughput of the users and reduces the standard deviation of the throughput distribution.

Software-defined networking (SDN) and network functions virtualization (NFV) enable service providers to accommodate diversified service requests in the fifth generation (5G) core networks. Given the time-varying traffic demand of the service requests, it is crucial for service providers to embed the service function chains (SFCs) of the service requests in the network to support load balancing, and to minimize the reconfiguration overhead due to virtual network functions (VNFs) migration while satisfying their quality of service (QoS) requirements. In this paper, we study a delay-aware VNF migration problem for embedding SFCs in a network with limited processing resource capacity for NFV-enabled nodes. We formulate it as a mixed-integer nonlinear optimization problem. We decompose this problem into two subproblems for stateful VNF mapping and allocation of processing resources, where the second subproblem is a convex optimization problem. To solve the first subproblem, we propose an algorithm based on deep neural network (DNN) with attention mechanism for learning the stochastic policy of a near-optimal VNF mapping. Simulation results show that our proposed algorithm provides a solution which is very close to the optimal solution obtained by solving a mixed-integer quadratically constrained programming problem.

Mixed-numerology has been proposed in the Third Generation Partnership Project (3GPP) standard for the fifth generation (5G) wireless networks, where flexible subcarrier spacing (SCS) can be applied to support uses cases with different quality-of-service (QoS) requirements. In this paper, we study the joint design of resource block allocation and beamforming with mixed-numerology for enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) use cases. We consider multiple multi-antenna base stations (BSs) cooperatively provide services to the users. By using beamforming, inter-user interference can be mitigated and a resource block can be utilized by more than one user. Short packet transmission is considered for URLLC users to satisfy their low-latency requirements. We formulate a mixed-integer nonlinear programming problem to maximize the aggregate throughput of eMBB users while guaranteeing the throughput, reliability, and latency requirements of URLLC users. We propose a low-complexity algorithm, which leverages fractional programming and successive convex approximation (SCA), to obtain the solutions. Simulation results show that our proposed algorithm can improve the aggregate eMBB throughput by 30% compared with the fixed-numerology based approach.

In cross-silo federated learning (FL), organizations cooperatively train a global model with their local data. The organizations, however, may be heterogeneous in terms of their valuation on the precision of the trained global model and their training cost. Meanwhile, the computational and communication resources of the organizations are non-excludable public goods. That is, even if an organization does not perform any local training, other organizations cannot prevent that organization from using the outcome of their resources (i.e., the trained global model). To address the organization heterogeneity and the public goods feature, in this paper, we formulate a social welfare maximization problem and propose an incentive mechanism for cross-silo FL. With the proposed mechanism, organizations can achieve not only social welfare maximization but also individual rationality and budget balance. Moreover, we propose a distributed algorithm that enables organizations to maximize the social welfare without knowing the valuation and cost of each other. Our simulations with MNIST dataset show that the proposed algorithm converges faster than a benchmark method. Furthermore, when organizations have higher valuation on precision, the proposed mechanism and algorithm are more beneficial in the sense that the organizations can achieve higher social welfare through participating in cross-silo FL.

Grant-free multiple access (GFMA) is a promising paradigm to efficiently support uplink access of Internet of Things (IoT) devices. In this paper, we propose a deep reinforcement learning (DRL)-based pilot sequence selection scheme for GFMA systems to mitigate potential pilot sequence collisions. We formulate a pilot sequence selection problem for aggregate throughput maximization in GFMA systems with specific throughput constraints as a Markov decision process (MDP). By exploiting multiagent DRL, we train deep neural networks (DNNs) to learn near-optimal pilot sequence selection policies from the transition history of the underlying MDP without requiring information exchange between the users. While the training process takes advantage of global information, we leverage the technique of factorization to ensure that the policies learned by the DNNs can be executed in a distributed manner. Simulation results show that the proposed scheme can achieve an average aggregate throughput that is within 85% of the optimum, and is 31%, 128%, and 162% higher than that of acknowledgement-based GFMA, dynamic access class barring, and random selection GFMA, respectively. Our results also demonstrate the capability of the proposed scheme to support IoT devices with specific throughput requirements.

Intelligent reflecting surface (IRS) is a promising paradigm for enhancing the spectrum efficiency of wireless communication systems. In this paper, we study the joint uplink scheduling and phase shift control in IRS-aided systems. We formulate the throughput maximization problem as a combinatorial optimization problem. We decompose the problem into two subproblems for user scheduling and phase shift control, respectively. We propose a neural combinatorial optimization (NCO)-based algorithm, in which a near-optimal stochastic policy for user scheduling is learned by deep neural networks (DNNs) with attention mechanism, while the phase shifts of the IRS are optimized using fractional programming. Unlike alternating optimization-based approaches which obtain a suboptimal solution by iteratively solving two subproblems, the proposed NCO-based algorithm is capable of obtaining a near-optimal solution while each subproblem is required to be solved only once. Simulation results show that the proposed NCO-based algorithm achieves an aggregate throughput which is within 98% of the exhaustive search algorithm, and outperforms both greedy scheduling and random scheduling algorithms.

Backscatter communication is a promising technology for energy-efficient communications. It enables the Internet of things (IoT) devices to send their data by backscattering and modulating the incident radio frequency (RF) signals. In this paper, we propose a scheme for improving the connection density of backscatter communication systems, i.e., increasing the number of backscattering-enabled IoT devices that meet a minimum threshold of the received signal-to-noise ratio (SNR) at the serving base station (BS). The aforementioned goal is achieved by allowing the user equipment (UE) devices to relay the backscattered signals from the IoT devices. A UE superimposes its own uplink data with the data from an associated IoT device using power-domain non-orthogonal multiple access (NOMA). Since the UEs are mobile and have higher transmit power, the IoT devices utilize the nearby UEs to relay their data. In addition, using UEs as relays helps the BS to support more backscattering-enabled IoT devices. We formulate the connection density maximization problem to pair the IoT devices with the available UE relays. The formulated problem is a mixed-integer linear programming (MILP) problem. Although the formulated problem can be solved optimally, it has an exponential complexity. Hence, we propose a suboptimal algorithm which decomposes the original problem into smaller subproblems that can be solved by low complexity algorithms. Simulation results show that the proposed scheme with UEs as relays can increase the connection density by up to 65% compared to deploying fixed relays.

Efficient allocation of resources (i.e., physical resource blocks (PRBs), transmit power) for remote radio heads (RRHs) in the fifth generation (5G) cloud radio access network (C-RAN) is crucial for the mobile network operators (MNOs) to support different use cases with diverse quality of service (QoS) requirements. In this paper, we study the resource allocation of enhanced mobile broadband (eMBB) and ultra-reliable lowlatency communications (URLLC) network slices in a 5G C-RAN. We formulate the resource allocation problem as a mixedinteger nonlinear program. We address the isolation between eMBB and URLLC network slices and the uncertainty in the traffic load by using the chance constraint. We consider short packet transmission to enable URLLC data transmission with low latency and high reliability. We propose an algorithm based on penalized successive convex approximation to determine a suboptimal solution of the formulated problem. The proposed algorithm has a polynomial time complexity. Simulation results show that the proposed algorithm on average achieves 30% higher throughput when compared with a baseline scheme that only optimizes the transmit power of users.

The recent development in Internet of Things necessitates caching of dynamic contents, where new versions of contents become available around-the-clock and thus timely update is required to ensure their relevance. The age of information (AoI) is a performance metric that evaluates the freshness of contents. Existing works on AoI-optimization of cache content update algorithms focus on minimizing the long-term average AoI of all cached contents. Sometimes user requests that need to be served in the future are known in advance and can be stored in user request queues. In this paper, we propose dynamic cache content update scheduling algorithms that exploit the user request queues. We consider a special use case where the trained neural networks (NNs) from deep learning models are being cached in a heterogeneous network. A queue-aware cache content update scheduling algorithm based on Markov decision process (MDP) is developed to minimize the average AoI of the NNs delivered to the users plus the cost related to content updating. By using deep reinforcement learning (DRL), we propose a low complexity suboptimal scheduling algorithm. Simulation results show that, under the same update frequency, our proposed algorithms outperform the periodic cache content update scheme and reduce the average AoI by up to 35%.

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies that enable low power communication for the Internet of Things (IoT) devices. In this paper, we consider a multicarrier NOMA (MC-NOMA) backscatter communication system. The objective is to maximize the aggregate data rate of the system by jointly optimizing the reflection coefficients and subcarrier allocation. The formulated problem is nonconvex and exhibits hidden monotonicity structure. To obtain the optimal solution, we propose an algorithm based on discrete monotonic optimization. The proposed algorithm can be considered as a performance benchmark. We also transform the nonconvex problem to another problem by using difference of convex functions and successive convex approximation and propose an algorithm to obtain a suboptimal solution in polynomial time. Simulation results show that the suboptimal scheme achieves an aggregate data rate close to the proposed optimal scheme. Results also show that our proposed schemes provide a higher aggregate data rate than the orthogonal multiple access (OMA) scheme.

In this paper, we consider downlink non-orthogonal multiple access (NOMA) transmission in millimeter wave (mmWave) networks with spatially random users. To facilitate NOMA transmission in mmWave networks, we propose a novel joint angle and distance based user pairing strategy. In particular, the user located nearest to the base station (BS) is paired with another user that is located within a distance threshold from the BS and has the minimum relative spatial angle difference. In consideration of the directional beamforming and the randomness of user locations, the BS opportunistically chooses to enable NOMA or orthogonal multiple access (OMA) based on the instantaneous spatial angle difference between the paired users. The proposed scheme fully exploits the antenna array gain for the paired NOMA users. By using tools from stochastic geometry, we derive the coverage probability of the proposed scheme. Simulations validate the theoretical analysis. Results reveal that the proposed scheme outperforms the angle-based NOMA, distance-based NOMA, and OMA schemes, confirming the importance of exploiting both the angle and distance information for user pairing in mmWave networks. Results also show that there exists an optimal value of the distance threshold that maximizes the coverage probability.

The recent development in mobile edge computing necessitates caching of dynamic contents, where new versions of contents become available around-the-clock, thus timely update is required to ensure their relevance. The age of information (AoI) is a performance metric that evaluates the freshness of contents. Existing works on AoI-optimization of cache content update algorithms focus on minimizing the long-term average AoI of all cached contents. Sometimes, user requests that need to be served in the future are known in advance and can be stored in user request queues. In this paper, we propose dynamic cache content update scheduling algorithms that exploit the user request queues. We consider a use case, where the trained neural networks (NNs) from deep learning models are being cached in a heterogeneous network (HetNet), as a motivating example. A queue-aware cache content update scheduling algorithm based on constrained Markov decision process (CMDP) is developed to minimize the average AoI of the dynamic contents delivered to the users. By using enforced decomposition technique and deep reinforcement learning, we propose two low-complexity suboptimal scheduling algorithms. Simulation results show that our proposed algorithms outperform the periodic cache content update scheme and reduce the average AoI by up to 30%.

Grant-free non-orthogonal multiple access (GF- NOMA) is a promising paradigm for reducing the access delay and improving the spectrum efficiency. As the signals of multiple users are superimposed in GF-NOMA systems, each user is required to select a user-specific pilot sequence to distinguish its own signal from the signals of other users. Packet collisions in the uplink occur when multiple users select the same pilot sequence. In this paper, we first formulate a pilot sequence selection problem for aggregate throughput maximization in GF-NOMA systems. We then design a deep reinforcement learning (DRL)- based distributed algorithm for each user to select its pilot sequence via learning from the past pilot sequence selections. The proposed algorithm does not rely on information exchange between the users and does not require centralized scheduling by the base station. Packet-level simulations show that, for the considered system parameters, the proposed DRL distributed algorithm can achieve an average aggregate throughput which is within 90% of the optimal value, and has a better performance than both acknowledgement-based and random selection GF-NOMA schemes.

Most of the existing caching schemes assume that the pushing of popular contents from the macro base station (MBS) to content servers (CSs) is performed during off-peak hours when the network traffic is low. However, since popular files, such as breaking news, may also be generated during peak hours, performing CS content update during peak hours is necessary. In this paper, we propose an optimal cache content update scheduling algorithm for heterogeneous networks (HetNets). The decision-making module is located in the MBS. The action set includes performing CS content update, letting the CSs simultaneously serve user requests, and using the MBS to directly serve user requests. The MBS aims to maximize the total throughput of the system within the duration of the peak hour under the uncertainty of the arrival of new user requests and the addition of new files. We formulate the peak hour CS cache content update scheduling problem as a Markov decision process and propose an optimal cache content update scheduling algorithm based on dynamic programming. We perform simulations and compare our proposed optimal scheduling algorithm with the periodic update and greedy scheduling heuristics. Simulation results show that our proposed algorithm outperforms those two heuristics under different scenarios.

The Paradigm of Internet of Things (IoT) is increasingly integrated with real-world applications. There are many critical IoT applications in the energy sector. Worldwide energy systems and infrastructure are experiencing tremendous transformation. There has been a drastic surge in the global energy consumption, which has tripled in the past 50 years. As a result, new measures have been introduced to improve the responsiveness and robustness of the energy systems, along with the global trends of deregulation and decarbonization.

Narrowband Internet of Things (NB-IoT) provides energy-efficient communications with extended coverage for the low data rate IoT devices. In this paper, we propose a power-domain non-orthogonal multiple access (NOMA) scheme for the NB-IoT systems to enhance the connection density by allowing multiple IoT devices to simultaneously access one subcarrier. We consider both single-tone and multi-tone transmission modes of the NB-IoT systems, where each device can access a single subcarrier or a bond of contiguous subcarriers, respectively. We formulate joint subcarrier and power allocation problems for both transmission modes to maximize the connection density while taking the quality of service requirements and the transmit power constraints of IoT devices into account. We solve the single-tone nonconvex mixed integer programming problem by transforming it into a mixed integer linear programming problem to obtain the optimal solution. The multi-tone problem is solved by using the difference of convex programming approach to obtain a close-to-optimal solution. We also propose low-complexity heuristic algorithms to solve both problems in a suboptimal manner. The simulations results show that our proposed scheme increases the connection density of NB-IoT systems by 87% in the single-tone mode and by 24% in the multi-tone mode compared to orthogonal multiple access.

Massive machine-type communications (mMTC) is a major use case in the fifth generation (5G) wireless networks. mMTC aims at supporting a large number of Internet of Things (IoT) connections within a coverage area. The current random access procedure in the Long Term Evolution (LTE) networks may not be able to handle a large number of simultaneous connection requests due to the limited number of random access preambles. Hence, it is essential to modify the random access procedure to support mMTC. In this paper, we propose a new preamble sequence design in which two Zadoff-Chu preamble sequences are aggregated together. This design enables us to have a larger set of random access preambles consisting of all combinations of pairing two Zadoff-Chu preamble sequences. Moreover, we consider a subset of all combinations that satisfy a certain maximum peak-to-average-power-ratio (PAPR) threshold criterion to reduce the energy consumption of the IoT devices. The proposed design requires only minor changes in the conventional transmitter and receiver design for generating and decoding the aggregated preamble sequences, respectively. Results show that the proposed design reduces the probability of preamble collision to less than 10-4, which is lower than LTE. Furthermore, it outperforms other collision avoidance techniques such as access class barring (ACB) in terms of a lower average total service time. The modified receiver detects the aggregated preambles successfully and avoids detecting false preambles. Both the probabilities of misdetection and false alarm are less than 10-3when the signal-to-noise ratio (SNR) is larger than -7 dB.

In this paper, we propose a novel joint caching and non-orthogonal multiple access (NOMA) scheme to facilitate advanced downlink transmission for next generation cellular networks. In addition to reaping the conventional advantages of caching and NOMA transmission, the proposed cache-aided NOMA scheme also exploits cached data for interference cancellation which is not possible with separate caching and NOMA transmission designs. Furthermore, as caching can help to reduce the residual interference power, several decoding orders are feasible at the receivers, and these decoding orders can be flexibly selected for performance optimization. We characterize the achievable rate region of cache-aided NOMA and investigate its benefits for minimizing the time required to complete video file delivery. Our simulation results reveal that, compared to several baseline schemes, the proposed cache-aided NOMA scheme significantly expands the achievable rate region for downlink transmission, which translates into substantially reduced file delivery times.

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth generation (5G) wireless networks. In this paper, we develop a general performance analysis framework for mmWave-NOMA networks with spatially random users taking into account link blockage and directional beamforming. To facilitate NOMA transmission in mmWave networks, we propose an angle-based user pairing strategy. Specifically, the base station first randomly selects one user and then pairs it with the line-of-sight user that has the minimum relative angle difference. NOMA is enabled when the beamwidth of the main lobe created by directional beamforming is not smaller than the angle difference between the paired NOMA users. Tools from stochastic geometry are utilized to derive the coverage probability and the sum rate of the proposed NOMA scheme, where beam misalignment at both the base station and the users is taken into account. Simulations validate the performance analysis and show that the proposed NOMA scheme achieves a larger coverage probability and a higher sum rate than conventional NOMA with distance-based user pairing and orthogonal multiple access.

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. In this paper, we study the allocation of fog computing resources to the IoT users in a hierarchical computing paradigm including fog and remote cloud computing services. We formulate a computation offloading game to model the competition between IoT users and allocate the limited processing power of fog nodes efficiently. Each user aims to maximize its own quality of experience (QoE), which reflects its satisfaction of using computing services in terms of the reduction in computation energy and delay. Utilizing a potential game approach, we prove the existence of a pure Nash equilibrium (NE) and provide an upper bound for the price of anarchy. Since the time complexity to reach the equilibrium increases exponentially in the number of users, we further propose a near-optimal resource allocation mechanism and prove that in a system with N IoT users, it achieves an ε-NE in O(N/ε time. Through numerical studies, we evaluate the users' QoE as well as the equilibrium efficiency. Our results reveal that by utilizing the proposed mechanism, more users benefit from computing services in comparison to an existing offloading mechanism. We further show that our proposed mechanism significantly reduces the computation delay and enables low-latency fog computing services for delay-sensitive IoT applications.

The Internet of Things (IoT) extends network connectivity and computing capability to physical devices. However, data from IoT devices may increase the risk of privacy violations. In this paper, we consider smart meters as a prominent early instance of the IoT, and we investigate their privacy protection solutions at customer premises. In particular, we design a load hiding approach that obscures household consumption with the help of energy storage units. For this purpose, we leverage the opportunistic use of existing household energy storage units to render load hiding less costly. We propose combining the use of electric vehicles (EVs) and heating, ventilating, and air conditioning (HVAC) systems to reduce or eliminate the reliance on local rechargeable batteries for load hiding. To this end, we formulate a Markov decision process to account for the stochastic nature of customer demand and use a Q-learning algorithm to adapt the control policies for the energy storage units. We also provide an idealized benchmark system by formulating a deterministic optimization problem and deriving its equivalent convex form. We evaluate the performance of our approach for different combinations of storage units and with different benchmark methods. Our results show that the opportunistic joint use of EV and HVAC units can reduce the need of dedicated large-capacity or fast-charging-cycle batteries for load hiding.

Device-to-device (D2D) communication, which can offload data from base stations by direct transmission between mobile devices, is a promising technology for fifth generation (5G) wireless networks. However, the limited battery capacity of mobile devices is a barrier to fully exploiting the benefits of D2D communication. Meanwhile, high data rate D2D communication is required to support the increasing traffic demand of emerging applications. In this paper, we study relay-assisted D2D communication in millimeter wave (mmWave)-based 5G networks to address these issues. Multiple D2D user pairs are assisted by full-duplex relays that are equipped with directional antennas. To design an efficient relay selection and power allocation scheme, we formulate a multi-objective combinatorial optimization problem, which balances the trade-off between total transmit power and system throughput. The problem is transformed into a weighted bipartite matching problem. We then propose a centralized relay selection and power allocation algorithm and prove that it can achieve a Pareto optimal solution in polynomial time. We further propose a distributed algorithm based on stable matching. Simulation results show that our proposed algorithms substantially reduce the total transmit power and improve the system throughput compared with two existing algorithms in the literature.

In this paper, we propose a novel wireless caching scheme to enhance the physical layer security of video streaming in cellular networks with limited backhaul capacity. By proactively sharing video data across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative joint transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. Assuming imperfect channel state information (CSI) at the transmitters, we formulate a two-stage non-convex mixed-integer robust optimization problem for minimizing the total transmit power while providing the quality of service and guaranteeing communication secrecy during video delivery, where the caching and the cooperative transmission policy are optimized in an offline video caching stage and an online video delivery stage, respectively. Although the formulated optimization problem turns out to be NP-hard, low-complexity polynomial-time algorithms, whose solutions are globally optimal under certain conditions, are proposed for cache training and video delivery control. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design. Simulation results confirm that the proposed caching scheme achieves simultaneously a low secrecy outage probability and a high power efficiency. Furthermore, due to the proposed robust optimization, the performance loss caused by imperfect CSI knowledge can be significantly reduced when the cache capacity becomes large.

This paper studies secure video streaming in cache-enabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted. Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding of video files to enable secure cooperative multiple-input multiple-output transmission and, at the same time, exploit the cache memory of both the trusted and untrusted BSs for improving the system performance. Considering imperfect channel state information at the transmitters, we formulate a two-timescale non-convex mixed-integer robust optimization problem to minimize the total transmit power required for guaranteeing the quality of service and secrecy during video streaming. We develop an iterative algorithm based on a modified generalized Benders decomposition to solve the problem optimally, where the caching and the cooperative transmission policies are determined via offline (long-timescale) and online (short-timescale) optimization, respectively. Furthermore, inspired by the optimal algorithm, a low-complexity suboptimal algorithm based on a greedy heuristic is proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to several baseline schemes.

Non-orthogonal multiple access (NOMA) is a promising spectrally-efficient multiple access technique for the fifth generation (5G) wireless networks. In this paper, we propose a dynamic decode-and-forward (DDF) based cooperative NOMA scheme for downlink transmission with spatially random users. In DDF-based cooperative NOMA, the base station transmits the superposition of the signals intended for the paired NOMA users. The user closer to the base station forwards the signal intended for the far user as soon as it can successfully decode its own signal and the signal intended for the far user. We consider two user pairing strategies, namely random and distance-based user pairing, which require one-bit feedback and the users' distance information, respectively. For each user pairing strategy, we derive the outage probability of the proposed NOMA scheme by using tools from stochastic geometry. Furthermore, based on the obtained outage probability, we derive the diversity order and the sum rate of the paired NOMA users. Simulation results validate the analytical results and demonstrate that the proposed DDF-based cooperative NOMA scheme achieves a lower outage probability and a higher sum rate than orthogonal multiple access, conventional NOMA, and cooperative NOMA.

In this paper, we consider downlink non-orthogonal multiple access (NOMA) transmission with dynamic traffic arrival for spatially random users of different priorities. By exploiting limited channel state information, we propose an opportunistic NOMA scheme to enable NOMA for high- and low-priority users when high-priority users experience good channel conditions. Opportunistic NOMA improves the transmission opportunities of low-priority users while reducing the adverse effect of NOMA on high-priority users. Moreover, we propose a cooperative NOMA scheme with full-duplex relaying, where low-priority users act as full-duplex relays to assist the high-priority users. The high-priority user constructively combines the signal and its delayed version transmitted by the base station and a selected relay, respectively. The adopted relay selection scheme takes into account the users' spatial distribution, queue status, and channel conditions. By using tools from queueing theory and stochastic geometry, we derive the stable throughput regions of both proposed schemes. Furthermore, we derive the conditions under which the proposed NOMA schemes achieve larger stable throughput regions than orthogonal multiple access (OMA). At the expense of a higher implementation complexity and with appropriate parameter setting, cooperative NOMA with full-duplex relaying achieves a larger stable throughput region than opportunistic NOMA, which in turn outperforms OMA.

Non-orthogonal multiple access (NOMA) and millimeter wave (mm-wave) are two key enabling technologies for fifth generation (5G) wireless networks. In this paper, we develop a general performance analysis framework for downlink NOMA transmission in mm-wave networks with spatially random users taking into account link blockages and directional beamforming. To facilitate NOMA transmission in mm-wave networks, we propose an angle-based user pairing strategy, where the base station first randomly selects one user and then pairs it with the line-of-sight user that has the minimum relative angle difference. The proposed strategy increases the probability that both NOMA users are covered by the main lobe created by directional beamforming. To account for the randomness of link blockages and user locations, we consider dynamic user ordering among the paired NOMA users. Tools from stochastic geometry are utilized to derive the coverage probability, outage sum rate, and ergodic sum rate of the proposed NOMA scheme, where beam misalignment at both the base station and users is taken into account. Simulations validate the performance analysis and show that the proposed NOMA scheme achieves a larger coverage probability and higher outage and ergodic sum rates than conventional NOMA with distance-based user pairing and orthogonal multiple access.

This paper studies secure video streaming in cache-enabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted. Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding (SVC) of video files to enable secure cooperative multiple-input multiple-output (MIMO) transmission and exploit the cache memory of all BSs for improving system performance. The caching and delivery design is formulated as a non-convex mixed-integer optimization problem to minimize the total BS transmit power required for secure video streaming. We develop an algorithm based on the modified generalized Benders decomposition (GBD) to solve the problem optimally. Inspired by the optimal algorithm, a low-complexity suboptimal algorithm is also proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to three baseline schemes.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique, which exploits the power domain to enhance the spectral efficiency of the fifth generation (5G) wireless networks. In this paper, we propose a dynamic decode-and-forward (DDF) based cooperative NOMA scheme for downlink transmission to enhance the reception reliability of spatially random users. In DDF-based cooperative NOMA, the user closer to the base station decodes the superimposed mixture of the users' signals received from the base station based on partial reception, and then forwards the signal intended for the far user. To avoid the need for instantaneous channel state information at the base station, we consider random user pairing, where the users are randomly paired for NOMA transmission. Tools from point process theory are utilized to derive the outage probability of the proposed DDF-based cooperative NOMA scheme. Simulation results validate the performance analysis and demonstrate the performance gains of the proposed DDF-based cooperative NOMA scheme over conventional NOMA and cooperative NOMA.

Narrowband Internet of Things (NB-IoT) is a recently standardized technology to support machine-type communications (MTC) in Long Term Evolution-Advanced (LTE-A) Pro networks. NB-IoT can enable energy-efficient communication with extended coverage on a narrow bandwidth of 180 kHz for low-cost MTC devices (MTCDs). The main challenge of supporting MTC in LTE-A Pro networks is to provide connectivity to a massive number of MTCDs. To overcome this challenge, in this paper, we propose a power-domain uplink non-orthogonal multiple access (NOMA) scheme for NB-IoT systems. By allowing multiple MTCDs to share the same sub-carrier, NOMA can provide connectivity to more MTCDs than orthogonal multiple access (OMA). We formulate a joint sub-carrier and transmission power allocation problem to maximize the number of MTCDs satisfying the quality of service (QoS) and transmission power requirements. We decompose the problem into two sub-problems and propose algorithms to solve them. Simulation results show that our proposed NOMA scheme can significantly increase the number of successfully connected MTCDs in NB-IoT systems compared to OMA.

Non-orthogonal multiple access (NOMA) has recently been proposed as a key enabling technology for the fifth generation (5G) wireless networks. Different from the existing works which focus on the performance analysis of NOMA with backlogged traffic, in this paper, we analyze the stable throughput region of downlink NOMA transmission with dynamic traffic arrival for users with different priorities. By utilizing limited instantaneous channel state information (CSI) at the base station, we propose an opportunistic NOMA scheme to enhance the network performance. Considering both NOMA and dynamic traffic arrival leads to interacting queues, which complicate the performance analysis. By using tools from stochastic geometry and queueing theory, we decouple the interacting queues and characterize the stable throughput region of the proposed opportunistic NOMA scheme in terms of the threshold to trigger NOMA and transmission power allocation coefficients. Numerical results show that, compared to the orthogonal multiple access scheme, the proposed opportunistic NOMA scheme can significantly enhance the stable throughput region when the design parameters are appropriately selected.

Mobile data offloading can help the mobile network operator (MNO) cope with the explosive growth of cellular traffic, by delivering mobile traffic through third-party access points. However, the access point owners (APOs) would need proper incentives to participate in data offloading. In this paper, we consider a data offloading market that includes both price-taking and price-setting APOs. We formulate the interactions among the MNO and these two types of APOs as a three-stage Stackelberg game, and study the MNO's profit maximization problem. Due to a non-convex strategy space, it is in general a non-convex game. Nevertheless, we transform the strategy space into a convex set and prove that a unique subgame perfect equilibrium exists. We further propose iterative algorithms for the MNO and price-setting APOs to obtain the equilibrium. Employing the proposed algorithms, the APOs do not need to obtain full information about the MNO and other APOs. Through numerical studies, we show that the MNO's profit can increase up to three times comparing with the no-offloading case. Furthermore, our proposed incentive mechanism outperforms an existing algorithm by 18 percent in terms of the MNO's profit. Results further show that price competition among price-setting APOs drives the equilibrium market prices down.

Mobile devices in close proximity can be connected in a device-to-device (D2D) manner to transfer digital objects (e.g., videos) to each other. By using D2D data offloading, mobile users can reduce the cost for data service from wireless cellular networks. However, due to users' mobility, the opportunity for a user to obtain his interested objects via D2D communication is transient. In this paper, we first propose an expected available duration (EAD) metric to evaluate the opportunity that an object can be downloaded by a user via D2D data offloading. The EAD metric takes into account the pairwise connectivity of users, social influence between users, diffusion of digital objects, and the time that users would like to wait for D2D data offloading. We then propose a distributed algorithm for a mobile device to determine the EAD of each object. Given a set of available objects in the neighborhood, a mobile device will first download the object that has the smallest EAD. We validate our model via trace-driven simulations. Results show that our proposed algorithm can effectively find the object that should be first downloaded. Comparing with existing schemes, our work can help users download more data via D2D data offloading.

The evolving mobile cloud computing (MCC) paradigm enables mobile users to offload their computing tasks to cloud servers. In this paper, we study the following problems in MCC systems: 1) which tasks should be offloaded to cloud servers? 2) and what is the optimal price of cloud services? We jointly address these issues by formulating two levels of optimization problems. On the mobile users side, we formulate a utility maximization problem that takes the energy consumption, delay, and price of cloud services into account and obtain the optimal scheduling for both delay-sensitive and delay-tolerant applications. On the cloud service provider (CSP) side, we determine the optimal pricing strategy by formulating a profit maximization problem, which is non-convex in general. We further propose an algorithm using convexification and primal-dual methods to mitigate the non-convexity. Through numerical studies, we investigate the mobile users' behavior and the CSP's pricing strategy. Our results reveal that the proposed scheduler effectively balances the tradeoff between the energy consumption and delay in comparison with different schedulers proposed in the literature. Furthermore, we show that with the proposed pricing algorithm, the CSP can improve its profit by up to 25% compared with static and dynamic pricing strategies.

In a mobile crowdsensing system, the platform utilizes ubiquitous smartphones to perform sensing tasks. For a successful mobile crowdsensing application, the consideration of the heterogeneity of quality of sensing from different users as well as a proper incentive mechanism to motivate users to contribute to the system are essential. In this paper, we introduce the quality of sensing into incentive mechanism design. Under a budget constraint, the platform aims to maximize the valuation of the performed tasks, which depends on the quality of sensing of the users. We propose ABSee, an auction-based budget feasible mechanism, which consists of a winner selection rule and a payment determination rule. ABSee is designed by adopting a greedy approach. We obtain the approximation ratio of ABSee, which significantly improves the approximation ratio of the existing budget feasible mechanisms in many cases. We further show that the approximation ratio approaches 2e/e-1 when a large number of smartphone users participate in the system. ABSee also satisfies the properties of computational efficiency, truthfulness, individual rationality, and budget feasibility. Extensive simulation results show that ABSee provides a higher valuation to the platform when compared with existing budget feasible mechanisms in the literature.

In this paper, we study the robust beamforming design in cloud radio access networks, where remote radio heads (RRHs) are connected to a cloud server that performs signal processing and resource allocation in a centralized manner. Different from traditional approaches adopting a concave increasing function to model the utility of a user, we model the utility by a sigmoidal function of the signal-to-interference-plus-noise ratio (SINR) to capture the diminishing utility returns for very small and very large SINRs in real-time applications (e.g., video streaming). Our objective is to maximize the aggregate utility of the users while considering the imperfection of channel state information (CSI), limited backhaul capacity, and minimum quality of service requirements. Because of the sigmoidal utility function and some of the constraints, the formulated problem is non-convex. To efficiently solve the problem, we introduce a maximum interference constraint, transform the CSI uncertainty constraints into linear matrix inequalities, employ convex relaxation to handle the backhaul capacity constraints, and exploit the sum-of-ratios form of the objective function. This leads to an efficient resource allocation algorithm, which outperforms several baseline schemes, and closely approaches a performance upper bound for large CSI uncertainty or large number of RRHs.

Full duplex relaying, which allows relays to transmit and receive signals simultaneously, can improve the spectrum efficiency and extend the range of device-to-device (D2D) communications. Due to the limited battery of mobile devices, it is essential to design a power-efficient relay selection scheme which can reduce the power consumption of devices and extend their lifetime. In this paper, we consider multiple D2D user pairs utilize full duplex relays to communicate using directional antennas. We formulate the power-efficient relay selection problem as a combinatorial optimization problem to minimize the power consumption of the mobile devices. Using a matching approach, we transform the problem into a one-to-one weighted bipartite matching problem. We then propose a power-efficient relay selection algorithm for relay-assisted D2D networks called PRS-D2D based on the Hungarian method to obtain the optimal solution in polynomial time. Simulation results show that our proposed algorithm improves the total power consumption of mobile devices by up to 32% comparing to an existing relay selection scheme in the literature.

Network slicing enables mobile virtual network operators (MVNOs) to lease network resources from a mobile network operator (MNO). The cloud radio access network (CRAN) architecture reduces the capital and operational expenditures for the MNO and also facilitates MVNOs running virtual machines on the cloud server. In this paper, we propose a beamforming scheme that coordinates multiple remote radio heads (RRHs) in C-RAN to improve the quality of experience (QoE) of users by maximizing their aggregate weighted quality of service (QoS). We model the QoS of each mobile user by a sigmoidal function and formulate the beamforming design as a non-convex optimization problem. By introducing an interference threshold, we first develop an iterative algorithm to determine a suboptimal solution of the original problem. Based on simulation results, we then show that a suitable interference threshold can be obtained in an off-line manner such that the suboptimal solution is a close-to-optimal solution of the original non-convex problem. Simulation results also show that the proposed scheme can significantly improve the aggregate weighted QoS of the mobile users compared to the traditional design where the weighted system sum rate is maximized.

In this special issue, we cover some recent results in the following four emerging areas: 5G cellular systems, big data systems, bio/nano/molecular networks, and smart grids. In the past several years, there are various technologies emerging, which are either directly or indirectly related to communication. Some of them are over the evolution of traditional communication systems, while others are over new systems such as smart grids, molecular networks, and big data systems.

Millimeter wave (mm-wave) communication is a promising technology for short-range communications and high-speed services. It provides potential solutions for multimedia content delivery in home networks. However, approaches for resource allocation in traditional wireless networks may not be efficient for multimedia data transmissions in mm-wave networks. This is due to the very wide bandwidth and highly directional transmissions, which bring challenges as well as opportunities to the resource allocation of mm-wave transmissions. In this paper, we first characterize different usage scenarios of multimedia content delivery by introducing a set of utility functions. We then formulate a joint power and channel allocation problem based on a network utility maximization framework, which captures the spatial and frequency reuse of mm-wave communications. The formulated problem is a non-convex mixed integer programming (MIP) problem. We reformulate the problem into a convex MIP problem and propose a resource allocation algorithm based on outer approximation (OA) method. We further develop an efficient heuristic algorithm, which has a lower complexity than the OA based algorithm. Simulation results present the tradeoffs between the OA based and heuristic algorithms for different scenarios and show that our proposed algorithms substantially outperform recently proposed schemes in the literature.

Cloud radio access network (C-RAN) as a promising and cost-efficient cellular architecture has been proposed to meet the increasing demand of wireless data traffic. The main concept of C-RAN is to decouple the baseband unit (BBU) and the remote radio head (RRH), and place the BBUs in a data center for centralized control and processing. In this paper, we study the resource sharing problem in a fronthaul constrained C-RAN, where multiple service providers lease radio resources from a network operator to serve their subscribers. To provide isolation among different service providers, we introduce a threshold-based policy to control the interference among RRHs, and define a new metric to provide minimum resource guarantee for service providers. By leveraging a mobility prediction method, the user locations are predicted for traffic demand estimation and interference control. We propose a multi-timescale resource sharing mechanism, which consists of a global resource allocation process and multiple local resource allocation processes that are performed at different time scales. Simulation results show that the proposed mechanism achieves efficient resource sharing and isolation among service providers.

This paper investigates the cross-layer optimization of fast video delivery and caching for minimization of the overall video delivery time in a two-hop relaying network. The half-duplex relay nodes are equipped with both a cache and a buffer which facilitate joint scheduling of fetching and delivery to exploit the channel diversity for improving the overall delivery performance. The fast delivery control is formulated as a two-stage functional non-convex optimization problem. By exploiting the underlying convex and quasi-convex structures, the problem can be solved exactly and efficiently by the developed algorithm. Simulation results show that significant caching and buffering gains can be achieved with the proposed framework, which translates into a reduction of the overall video delivery time. Besides, a trade-off between caching and buffering gains is unveiled.

In this paper, we propose to combine the emerging software defined networking (SDN) paradigm with the existing residential broadband infrastructure to enable home users to have dynamic control over their traffic flows. The SDN centralized control technology enables household devices to have virtualized services with quality of service (QoS) guarantee. SDN-enabled open application programming interfaces (APIs) allow Internet service providers (ISPs) to perform bandwidth slicing in home networks and implement time-dependent hybrid pricing. Given the requests from household devices for virtualized and non-virtualized services, we formulate a Stackelberg game to characterize the pricing strategy of ISP as well as bandwidth allocation strategy in home networks. In the Stackelberg game, the leader is the ISP and the followers are the home networks. We determine the optimal strategies which provide maximal payoff for the ISP. Numerical results show that our proposed SDN-enabled home network technology with the hybrid pricing scheme provides a better performance than a usage-based pricing scheme tailored for best-effort home networks.

In mobile crowdsourcing systems, smartphones can collectively monitor the surrounding environment and share data with the platform of the system. The platform manages the system and encourages smartphone users to contribute to the crowdsourcing system. To enable such sensing system, incentive mechanisms are necessary to motivate users to share the sensing capabilities of their smartphones. In this paper, we propose ProMoT, which is a Profit Maximizing Truthful auction mechanism for mobile crowdsourcing systems. In the proposed auction mechanism, the platform acts as an auctioneer. The smartphone users act as the sellers and submit their bids to the platform. The platform selects a subset of smartphone users and assigns the tasks to them. ProMoT aims to maximize the profit of the platform while providing satisfying rewards to the smartphone users. ProMoT consists of a winner determination algorithm, which is an approximate but close-to-optimal algorithm based on a greedy mechanism, and a payment scheme, which determines the payment to users. Both are computationally efficient with polynomial time complexity. We prove that ProMoT motivates smartphone users to rationally participate and truthfully reveals their bids. Simulation results show that ProMoT increases the profit of the platform in comparison with an existing scheme.

In machine to machine (M2M) communication systems based on the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), the machine type communication (MTC) devices compete in a random access channel (RACH) to access the network. An MTC device randomly chooses a preamble from a pool of preambles and transmits it during the RACH. The evolved node B (eNodeB) acknowledges the successful reception of a preamble if that preamble is transmitted by only one device. To reduce the burstiness of the connection requests in heavy traffic situations, access class barring (ACB) is proposed in the 3GPP standard. Using ACB, an MTC device postpones its request in a RACH with a probability p. In this paper, we propose a new adaptive ACB scheme for congestion control of bursty M2M traffic. The optimal value of the ACB depends on the total number of MTC devices competing in a RACH. To estimate this number, we derive a joint conditional probability distribution function (PDF) for the number of preambles selected by zero or one MTC device, conditioned on the number of MTC devices that passed the ACB check. We design a maximum likelihood estimator using this PDF. We use this estimation to dynamically adjust the ACB factor. To further improve our estimation, we use Kalman filtering based on the dynamics of the system. Numerical results show that the total service time for the proposed method is very close to the optimal case where the information of the number of MTC devices is given.

Downloading remote files (e.g., pictures, videos) from online social networks via smart user equipments (UEs) (e.g., smartphones, tablets) is becoming popular. Friends who are nearby may want to download the same files shared by their mutual acquaintance. People can obtain these files in a device-to-device (D2D) manner via opportunistic connections to reduce their payment for data service. This is referred to as D2D data offloading. However, D2D communications on unlicensed spectrum using Bluetooth or WiFi-Direct may not maintain high data rate when many D2D pairs nearby need to communicate simultaneously. Since D2D connections are transient, it is important to improve spatial reuse of communication resources and increase the data rate of opportunistic D2D communications. In this paper, we propose a scheme to reuse the downlink licensed spectrum of cellular networks for D2D data offloading. Our proposed scheme includes determining the availability of digital files on neighbouring devices, estimating the channel gains, and performing channel allocation and power control for D2D pairs. Simulation results show that our proposed scheme does not affect the existing cellular UEs and it can also offload more data traffic when compared with WiFi-Direct on an unlicensed spectrum.

Femtocell networks have the potential to satisfy the increasing demand of mobile data usage. The recently proposed concept of cognitive femtocell network provides an effective way to further improve the spectrum spatial and frequency reuse. In this paper, we study the subchannel allocation problem for orthogonal frequency division multiple access (OFDMA)-based hybrid overlay/underlay cognitive femtocell networks. While most of the previous related studies did not fully exploit the potential of spatial and frequency reuse of the network, we propose a hybrid overlay and underlay spectrum access mechanism to further improve the performance of cognitive femtocell networks. We formulate the subchannel allocation problem as a coalition formation game among femtocell users under the hybrid access scheme, and analyze the stability of the coalition structure. We propose an efficient algorithm based on the solution concept of recursive core, and achieve a stable and efficient allocation. Simulation results show that the proposed algorithm achieves an improvement in aggregate network throughput up to 72% comparing to the overlay only scheme, 35% comparing to the underlay only scheme, and 18% comparing to a recently proposed coalition formation algorithm in the literature.

The current wireless cellular networks can be used to provide machine-to-machine (M2M) communication services. However, the Long Term Evolution (LTE) networks, which are designed for human users, may not be able to handle a large number of bursty random access requests from machine-type communication (MTC) devices. In this paper, we propose a scheme that uses both access class barring (ACB) and timing advance information to prevent random access overload in M2M systems. We formulate an optimization problem to determine the optimal ACB parameter, which maximizes the expected number of MTC devices successfully served in each random access slot. Hence, the number of random access slots required to serve all MTC devices can be minimized. To reduce the computational complexity and improve the practicability of the proposed scheme, we propose a closed-form approximate solution to the optimization problem and present an algorithm to estimate the number of active MTC devices requiring access in each random access slot. The correctness of the analytical model and the accuracy of the estimation algorithm are validated via simulations. Results show that both numerical and approximate solutions provide the same performance. Our proposed scheme can reduce nearly half of the random access slots required to serve all MTC devices compared to the existing schemes, which use timing advance information only, ACB only, or cooperative ACB.

Millimeter wave (mm-wave) communication has been considered as a promising technology for providing short range, high speed data service in wireless networks. In this paper, we apply the mm-wave technology for multimedia content distribution among different wireless devices in smart home networks. We study the resource allocation problem and propose a new multi-channel medium access control (MAC) protocol considering mm-wave channelization and various types of multimedia services. We define a set of utility functions for battery-constrained devices considering different types of services in smart home networks. We formulate a joint power and channel allocation problem to maximize the aggregate network utility, which is a non-convex mixed integer programming problem. We transform the problem into a series of convex mixed integer programming problems and develop an efficient algorithm to find the solution. Simulation results show that the proposed MAC protocol has superior performance compared to the existing single-carrier MAC protocol in IEEE 802.15.3c standard.

In this paper, we present a novel cooperative resource management mechanism in mobile cloud computing environment. This mechanism is based on cooperation between mobile devices using their short range radio technology such as WiFi with the goal of maximizing the revenue of the cellular service provider. Users with poor cellular link quality connect with nearby devices through their WiFi interface. The service provider provides incentives to mobile devices to motivate them to contribute in such cooperative scheme. We first formulate the resource management problem as a mixed integer linear programming model. The optimal solution has an NP-hard complexity. To tackle the complexity of the problem, we then propose iCoMe, which is an Incentivized Cooperative MobilE resource management mechanism. The resource management problem in iCoMe is solved distributively by the service provider and mobile devices. We prove that iCoMe has a polynomial time computational complexity. Simulation results confirm the close to optimal performance of iCoMe. Results also show that our proposed mechanism considerably increases the revenue of the service provider compared to non-cooperative schemes.

In this paper, we study uplink channel selection in a system where a macro base station (MBS) and a number of cognitive femto base stations (FBSs) share the same spectrum to serve their intended users with quality of service (QoS) requirements. In this system, the MBS may experience significant aggregate interference when multiple FBSs select the same channel to serve their FUs. An FBS also experiences strong interference from nearby femtocells if the same channel is utilized by adjacent FBSs. We investigate how to coordinate the channel selection at FBSs to reduce the interference experienced at the MBS and FBSs. We propose a cluster-based coordination mechanism where the operator groups the set of FBSs into clusters, and FBSs in the same cluster can utilize a set of channels simultaneously without violating the QoS requirements. To find the desired clusters, we employ a graph-theoretic approach and propose an efficient FBS clustering scheme. Simulation results show that the proposed coordination mechanism achieves a better performance compared to the channel selection scheme without coordination.

A common assumption in the existing literature on energy consumption scheduling in smart grid is that users are aware in advance of their daily energy consumption needs. Therefore, most existing studies along this line of research have been inherently deterministic, e.g., see [1]-[3]. However, this assumption may not hold in practice. In particular, the energy consumption scheduling (ECS) devices may face load uncertainty. If a user is equipped with a behind-the-meter renewable generator, then the optimal operation of ECS devices becomes even more challenging due to combined load and supply uncertainties. Therefore, in this paper, we formulate a stochastic optimization problem to operate an ECS device in a residential unit that is equipped with a behind-the-meter renewable generator and a local battery bank. In our problem formulation, we consider different sets of must-run and controllable appliances. Our design only requires knowledge of some estimates of the users' future demand. To reduce computational complexity, we approximate the expected load in the upcoming time slots by adopting the certainty equivalent approximation technique. Simulation results show that the proposed energy consumption scheduling algorithm can tackle the uncertainties in load and supply and it can benefit both users and the utility companies.

The existing wireless cellular networks can provide machine-to-machine (M2M) service to machine-type communication (MTC) devices deployed in large coverage areas. However, the current Long Term Evolution (LTE) cellular networks designed for human users may not be able to handle a large number of bursty random access requests from MTC devices. In this paper, we propose to jointly use access class barring (ACB) and timing advance (TA) command to reduce the random access overload. In our proposed scheme, the expected number of MTC devices served in one random access slot is determined by the coverage of the base station, total number of devices to be served, the number of preambles, and ACB parameter. By choosing the optimal ACB parameter, we can maximize the number of MTC devices being served in each random access slot. The total number of random access slots required by an LTE base station to serve all MTC devices can be minimized. Simulation results show that in typical LTE cellular networks, our proposed scheme can reduce at least a half of the total slots required by the base station to serve all MTC devices.

Cognitive radio networks (CRNs) enable spectrum channels to be used by secondary users (SUs) without interfering with the transmission of primary users (PUs). Cooperation among SUs in CRNs not only improves sensing performance but also increases spectrum efficiency. In this work, we study a cooperation strategy in multi-channel CRNs, which allows an energy-constrained SU to selectively participate in cooperative sensing. We consider CRNs where SUs can make distributed decisions on cooperative sensing based on traffic demand. We formulate this problem as a non-transferable utility (NTU) coalition formation game problem, where each SU in a coalition has a coalition value that takes into account traffic demand and energy efficiency. We also propose a sequential coalition formation (SCF) algorithm to find the coalition structure. Simulation results show that our proposed algorithm achieves higher throughput and energy efficiency with a lower computational complexity compared to previously proposed coalition formation algorithm in [1].

Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated.

Recent results have shown that selfish users do not have an incentive to participate in intersession network coding in a static noncooperative game setting. Because of this, the worst-case network efficiency (i.e., the price-of-anarchy) can be as low as 20%. In this paper, we show that if the same game is played repeatedly, then the price-of-anarchy can be improved to 36%. We design a grim-trigger strategy that encourages users to cooperate and participate in the intersession network coding. A key challenge is to determine a common cooperative coding rate that the users should mutually agree on. We resolve the conflict of interest among the users through a bargaining process and obtain tight upper bounds for the price-of-anarchy that are valid for any possible bargaining scheme. Moreover, we propose a simple and efficient min-max bargaining solution that can achieve these upper bounds, as confirmed through simulation studies. The coexistence of multiple selfish network coding sessions as well as the coexistence of selfish network coding and routing sessions are also investigated. Our results represent a first step toward designing practical intersession network coding schemes that achieve reasonable performance for selfish users.

Multiple-input multiple-output (MIMO) wireless communication systems can achieve higher throughput through interference alignment. For a small number of users, determining the maximum possible degrees of freedom as well as the feasibility of interference alignment in MIMO systems is well studied. However, the issues of scheduling in systems employing interference alignment and serving a large number of users have received little attention so far. In this paper, we study the problem of joint scheduling, interference alignment, and packet admission control in MIMO wireless systems with the goal of maximizing system throughput subject to stability constraints. We formulate a stochastic network optimization problem and propose a scheduling and interference alignment (SIA) algorithm. In each time slot, SIA schedules some users among many competing ones to transmit data, and determines encoding and decoding matrices for the selected users. Packet admission control is performed in each time slot. In addition, we propose a heuristic semi-distributed algorithm (SDSIA), which has a lower computational complexity than the SIA algorithm. Via simulation, we evaluate the performance of SIA and SDSIA for different algorithm parameters and different numbers of users. We also compare the performance of SDSIA with other approaches which do not simultaneously exploit interference alignment and scheduling and find that the combination of these two techniques increases the achievable data rate dramatically.

Recently, the multimedia content delivery has replaced the traditional voice communication as the major source of traffic in wireless networks. The deployment of femtocells is promising in satisfying the requirements of these multimedia applications if the interference among the femtocell access points (FAPs) is well-managed. In this paper, we study the interference management problem of the FAPs in a cooperative multimedia femtocell network. We consider the network setting where the players (i.e., the FAPs) can coordinate their transmissions to reduce the level of interference within a coalition. We first formulate the interference management problem as a coalition formation game in partition form with negative externalities, where the payoff of a player depends on actions of other players in the same coalition and in different coalitions. Based on the solution concept of recursive core in coalitional games, we propose an efficient coalition formation algorithm, RECORD, to achieve a final stable coalition structure. Simulation results show that the RECORD algorithm results in a substantially higher flow throughput and aggregate utility than some previously proposed scheduling algorithms.

In this paper, we propose a novel real-time energy consumption scheduling algorithm that takes into account load uncertainty to minimize the energy payment for each user. We formulate the problem of load scheduling as an optimization problem. To reduce the computational complexity, we devise an approximate dynamic programming approach to schedule the operation of appliances. In our problem formulation, we consider different sets of appliances including must-run and controllable. Unlike most of the existing demand side management algorithms that assume perfect knowledge of users' energy needs, our design only requires knowledge of some estimates of the future demand. Simulation results confirm that the proposed energy scheduling algorithm can benefit both the users by reducing their energy expenses and the utility companies by improving the peak-to-average ratio in load demand.

The theory of compressive sensing has shown that with a small number of samples from random projections of a sparse signal, one can recover the original signal under certain conditions. In this paper, we use compressive sensing to design a random access protocol for requesting uplink data channels. A wireless node transmits a pseudo-random sequence to an access point (AP) when it requires an uplink channel. The AP receives multiple sequences in a random access shared channel. Due to different propagation delays, the received signals from different wireless nodes are not synchronized at the receiver. Assume that the number of sequence transmissions is substantially less than the number of wireless nodes in the system. Under such circumstances, we design an asynchronous compressive sensing based decoder to recover the original signals in a random access setting. The key difference between our proposed decoder and those presented in the literature is that we do not require any synchronization before sequence transmission which makes our approach practical. Simulation results show the throughput improvement of our proposed scheme compared to two other random access protocols.

We analyze inter-session network coding in a wired network using game theory. We assume that users are selfish and act as strategic players to maximize their own utility, which leads to a resource allocation game among users. In particular, we study a butterfly network, where a bottleneck link is shared by network coding and routing flows. We assume that network coding is performed using pairwise XOR operations. We prove the existence of Nash equilibrium for a wide range of utility functions. We also show that the number of Nash equilibria can be large (even infinite) for certain choices of parameters. This is in sharp contrast to a similar game setting with traditional packet forwarding, where the Nash equilibrium is always unique. We characterize the worst-case efficiency bound, i.e., the Price-of-Anarchy (PoA), compared to an optimal and cooperative network design. We show that by using a discriminatory pricing scheme which charges encoded and forwarded packets differently, we can improve the PoA in comparison with the case where a single pricing scheme is used. However, even when a discriminatory pricing scheme is used, the PoA is still worse than for the case when network coding is not applied. This implies that, although inter-session network coding can improve performance compared to routing, it is much more sensitive to users' strategic behavior.

In this paper, we study the problem of random access with interference pricing in wireless ad hoc networks using non-cooperative game theory. While most of the previous works in random access games are based on the protocol model, we analyze the game under the more accurate signal-to-interference-plus-noise-ratio (SINR) model. First, under the setting with fixed interference linear pricing, we characterize the existence of the Nash equilibrium (NE) in the random access game. In particular, when the utility functions of all the players satisfy a risk aversion condition, we show that the game is a S-modular game and characterize the convergence of the strategy profile to the NE. Then, under the setting with adaptive interference linear pricing, we propose an iterative algorithm that aims to solve the network utility maximization (NUM) problem. Convergence of the solution to a Karush-Kuhn-Tucker (KKT) point of the NUM problem is studied. It can be shown that the solution obtained under the protocol model may result in starvation for some users due to the inaccurate interference pricing. Simulation results show that our proposed algorithm based on the SINR model achieves a higher average utility than the algorithm based on the protocol model and a carrier sense multiple access (CSMA) scheme implemented in a slotted time system.

In this paper, we study downlink scheduling with transmission strategy selection in multi-cell multiple-input multiple-output (MIMO) systems. Depending on the level of inter-cell interference experienced by a user, the scheduler can choose between two MIMO transmission strategies, namely, spatial multiplexing and interference alignment. We formulate an optimization problem which aims to jointly select a user and the corresponding transmission strategy for each base station in order to maximize the overall system utility while stabilizing all transmission queues. We first develop a centralized dynamic scheduling scheme with transmission strategy selection by using a stochastic network optimization approach. To reduce the communication overhead, we then propose a distributed scheduling algorithm which only requires limited message exchange between the base stations. We also consider the impact of imperfect channel state information on the scheduling schemes and propose an efficient rate adjustment method to improve the performance for this case. Simulation results show that the performance of the proposed distributed scheduling scheme is close to that of the centralized scheduling scheme, and both schemes achieve a better performance than schemes employing a single transmission strategy.

In this paper, we propose TCP Vegas with online network coding (TCP VON), which incorporates online network coding into TCP. It is shown that the use of online network coding in transport layer can improve the throughput and reliability of the end-to-end communication. Compared to generation based network coding, in online network coding, packets can be decoded consecutively instead of generation by generation. Thus, online network coding incurs a low decoding delay. In TCP VON, the sender transmits redundant coded packets when it detects packet losses from acknowledgement. Otherwise, it transmits innovative coded packets. We establish a Markov chain to analytically model the average decoding delay of TCP VON. We also conduct ns-2 simulations to validate the proposed analytical model. Finally, we compare the delay and throughput performance of TCP VON and automatic repeat request (ARQ) network coding based TCP (TCP ARQNC). Simulation results show that TCP VON outperforms TCP ARQNC in terms of the average decoding delay and network throughput.

In cognitive radio (CR) systems, harmful interference from the secondary system degrades the data rate of the primary system. However, this interference may be beneficial to the primary system in terms of the secrecy rate, when unauthorized users eavesdrop on the primary link. This paper explores multiple-input single-output (MISO) CR systems where the secondary system secures the primary communication in return for permission to use the spectrum. In this context, the optimal transmission strategy has to be found which provides the best tradeoff between the useful and harmful effects of interference on the secrecy rate of the primary system. Considering the cases of perfect and imperfect channel state information of the eavesdroppers we formulate optimization problems for maximizing the primary secrecy rate under secondary data rate requirements. The resulting non-convex optimization problems are solved through a sequence of convex semidefinite programs. The simulation results reveal that the proposed schemes improve the secrecy level of the primary system while meeting the data rate requirements of the secondary system.

With the use of energy harvesting technologies, the lifetime of a wireless sensor network (WSN) can be prolonged significantly. Unlike a traditional WSN powered by non-rechargeable batteries, the energy management policy of an energy harvesting WSN needs to take into account the energy replenishment process. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor node aims to maximize the total throughput in a finite horizon subject to time-varying energy harvesting rate, energy availability in the battery, and channel fading. We formulate the energy allocation problem as a sequential decision problem and propose an optimal energy allocation (OEA) algorithm using dynamic programming. We conduct simulations to compare the performance between our proposed OEA algorithm and the channel-aware energy allocation (CAEA) algorithm from [1]. Simulation results show that the OEA algorithm achieves a higher throughput than the CAEA algorithm under different settings.

In this paper, we study the use of intra-session random linear network coding (RLNC) in wireless sensor networks. In RLNC, intermediate nodes buffer the packets received from upstream nodes. Using intra-session RLNC, intermediate nodes transmit coded packets by performing coding on the packets of various flows. The main challenge in using intra-session RLNC is to determine how many coded packets each node requires to transmit for each flow such that the sink can decode the packets of all the flows. We analytically find the time at which a node can stop transmission of packets for a particular flow without interrupting the decoding process at the sink. Using this analysis, we design a link-by-link feedback mechanism to acknowledge the packets of a particular flow. An intermediate node generates feedback packets for its upstream nodes. The upstream node stops transmission of a flow when it receives an acknowledgment for that flow. Simulation results show that the link-by-link feedback mechanism with intra-session RLNC achieves lower power consumption compared to RLNC without intra-session coding and also the CCACK algorithm [1].

Random linear network coding (RLNC) can improve the reliability of end-to-end communication in the presence of erasure channels. In RLNC with intra-session coding, an intermediate node creates coded packets by combining the packets of various sources destined for a destination. Each transmitted packet contains information of multiple sources. In this paper, we study the decoding delay of RLNC with intra-session coding for a line network with S sources and a destination. Given the generation size K, we show that the expected decoding delay is upper-bounded by √(η2K log(S)) + Kη1+ η, where η, η1, η2are constants. The analytical results are validated via simulations. We also compare the results with the case where intra-session coding is not used.

In this paper, we study random access in a drive-thru scenario, where roadside access points (APs) are installed on a highway to provide temporary Internet access for vehicles. We consider vehicle-to-roadside (V2R) communications for a vehicle that aims to upload a file when it is within the APs' coverage ranges, where both the channel contention level and transmission data rate vary over time. The vehicle will pay a fixed amount each time it tries to access the APs, and will incur a penalty if it cannot finish the file uploading when leaving the APs. First, we consider the problem of finding the optimal transmission policy with a single AP and random vehicular traffic arrivals. We formulate it as a finite-horizon sequential decision problem, solve it using dynamic programming (DP), and design a general dynamic optimal random access (DORA) algorithm. We derive the conditions under which the optimal transmission policy has a threshold structure, and propose a monotone DORA algorithm with a lower computational complexity for this special case. Next, we consider the problem of finding the optimal transmission policy with multiple APs and deterministic vehicular traffic arrivals thanks to perfect traffic estimation. We again obtain the optimal transmission policy using DP and propose a joint DORA algorithm. Simulation results based on a realistic vehicular traffic model show that our proposed algorithms achieve the minimal total cost and the highest upload ratio as compared with some other heuristic schemes. In particular, we show that the joint DORA scheme achieves an upload ratio 130% and 207% better than the heuristic schemes at low and high traffic densities, respectively.

Cooperative spectrum sensing is an effective technique to improve the sensing performance and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we consider a CRN with multiple primary users (PUs) and multiple secondary users (SUs). We first propose a cooperative spectrum sensing and access (CSSA) scheme for all the SUs, where the SUs cooperatively sense the licensed channels of the PUs in the sensing subframe. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission subframe. We then formulate this multi-channel spectrum sensing and channel access problem as a hedonic coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The value function of each coalition and the utility function of each SU take into account both the sensing accuracy and the energy consumption. We propose an algorithm for decision node selection in a coalition. Moreover, we propose an algorithm based on the switch rule to allow the SUs to make decisions on whether to join or leave a coalition. We prove analytically that the set with all the SUs converges to a final network partition, which is both Nash-stable and individually stable. Besides, the proposed algorithms are adaptive to changes in network conditions. Simulation results show that our proposed CSSA scheme achieves a better performance than the closest PU (CPU) scheme and the noncooperative spectrum sensing and access (NSSA) scheme in terms of the average utility of the SUs.

Fair allocation of resources is an important consideration in the design of wireless networks. In this paper, we consider the setting of multihop wireless networks with multiple routing paths and develop an online flow control and scheduling algorithm for packet admission and link activation that achieves high aggregate throughput while providing different data flows with a fair share of network capacity. For fairness provisioning, we seek to maximize the minimum throughput provided to flows in the network. To cope with different degrees of data reliability among the different links in the network, we use different channel code rates as appropriate. While we expect performance improvement using channel coding and multipath routing, the main contribution of our work is a joint treatment of network stability, multipath routing and link-level reliability in meeting the overarching goal of maxmin fairness. We develop a decentralized, and hence practical, scheduling policy that addresses various concerns and demonstrate, via simulations, that it is competitive with respect to an optimal centralized rate allocator. We also evaluate the fairness provisioning under the proposed algorithm and show that channel coding improves the performance of the network significantly. Finally, we show through simulations that the proposed algorithm outperforms a class of existing approaches on fairness provisioning, which are developed based on utility maximization.

Spectrum sensing is one of the key technologies to realize spectrum reuse and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we study energy-efficient cooperative multi-channel spectrum sensing in CRNs. We first propose a cooperative spectrum sensing and accessing (CSSA) scheme for all the secondary users (SUs). The SUs cooperatively sense the licensed channels of the primary users (PUs) in the sensing slot. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission slot. We then formulate this multi- channel spectrum sensing problem as a coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The utility function of each coalition takes into account both the sensing accuracy and energy efficiency. We propose distributed algorithms to find the optimal partition that maximizes the aggregate utility of all the coalitions in the system. We prove analytically that the proposed algorithms terminate at a stable partition that achieves the optimal aggregate utility. Simulation results show that the proposed algorithms result in the self-organization of the SUs that achieves a higher aggregate utility after each iteration. Also, the convergence and optimality of the proposed algorithms are proved by simulation results.

Multiple-input multiple-output (MIMO) processing is a promising technique to achieve high speed data service in next generation cellular networks. However, the performance of multi-cell MIMO networks is limited by the cell-edge users who experience significant inter-cell interference. In this paper, we propose to use interference alignment to mitigate interference experienced by these cell-edge users, and spatial multiplexing for users with less interference. To achieve the optimal performance in a two- cell MIMO network, we formulate a joint scheduling problem to select a pair of users and the corresponding transmission strategies for both cells. We design an efficient dynamic scheduling scheme based on a stochastic network optimization framework. Numerical results show that the proposed scheme achieves a superior performance compared to scheduling schemes with a single transmission strategy.

In a drive-thru scenario where vehicles drive by a roadside access point (AP) to obtain temporary Internet access, it is important to design efficient resource allocation schemes to fully utilize the limited communication opportunities. In this paper, we study the random access problem in drive thru communications in a dynamic environment, where both the channel contention level and channel capacity vary over time. We assume that a vehicle has a file to upload when it is within the coverage range of the AP. The vehicle will pay a fixed amount each time it tries to access the AP, and will incur a penalty if it cannot finish the file uploading when leaving the AP. We first formulate the optimal transmission problem as a finite-horizon sequential decision problem. Then we solve the problem using dynamic programming, and design a dynamic optimal random access algorithm. Simulation results based on a realistic vehicular traffic model show that our algorithm achieves the minimal total cost, the highest probability of completing file upload, and the highest upload ratio as compared with two other heuristic schemes.

Wireless links are often unreliable and prone to transmission error due to varying channel conditions. These can degrade the performance in wireless networks, particularly for applications with tight quality-of-service requirements. A common remedy is to use channel coding where the transmitter node adds redundant bits to the transmitted packets in order to reduce the error probability at the receiver. However, this per-link solution can compromise the link data rate, leading to undesired end-to-end performance. In this paper, we show that this latter shortcoming can be mitigated if the end-to-end transmission rates and channel code rates are selected properly over multiple routing paths. We formulate the joint channel coding and end-to-end data rate allocation problem in multipath wireless networks as a network throughput maximization problem, which is non-convex. We tackle the non-convexity by using function approximation and iterative techniques from signomial programming. Simulation results confirm that by using channel coding jointly with multi-path routing, the end-to-end network performance can be improved significantly.

In radio frequency identification (RFID) systems, location information is of great importance to provide location-aware services combined with identification. Conventional RFID systems can only provide coarse localization information. In this paper, we propose a novel approach named MDS-RFID to locate active RFID tags based on multidimensional scaling (MDS), an efficient data analysis technique. The approach has the advantage of fully utilizing the distance information in the network simultaneously, and thus can achieve better localization results than previous multilateration-based methods. The MDS-RFID algorithm first infers the tag-to-reader distances from the received signal strength (RSS). To obtain the distance matrix, the inter-tag distances are estimated using a triangular method. Then, classical MDS algorithms can be applied to determine the estimated locations of the tags. An optional refinement step can be added to further improve the accuracy using maximum likelihood estimation at the expense of additional computation costs. Simulation results show that the MDS-RFID algorithm can achieve a significant gain in accuracy over the previous localization schemes based on multilateration using only a few readers.

In cognitive radio networks, the secondary users (SUs) can be selected as the cooperative relays to assist the transmission of the primary user (PU). In order to increase the utility, the PU needs to consider whether it is beneficial to use cooperative transmission and which SU should be chosen as the cooperative relay. In addition, if the PU selects a secondary relay, it needs to allocate time resources for cooperative transmission. Then, the SUs need to determine their strategies of random access when the licensed spectrum of the PU is available. In this paper, we first establish a model for cooperative cognitive radio networks with one PU and multiple SUs. We then propose a cooperative transmission and random access (CTRA) scheme. Based on the sequential structure of the decision-making, we study the cooperative cognitive radio network and determine the equilibrium strategies for both the PU and the SUs using the Stackelberg game. Simulation results show that both the PU and the SUs obtain higher utilities when compared with the noncooperative transmission and random access (NTRA) scheme.

Radio frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique serial number. For some RFID applications and in the ALOHA-based anticollision algorithms, the number of tags in the system needs to be estimated. In Trans. Autom. Sci. Eng., vol 6, no. 1, pp. 9-15, Jan. 2009, Chen, a probabilistic method for tag estimation in ALOHA-based RFID systems was proposed, based on the maximum a posteriori probability. Although this approach is novel and useful, it has a mathematical error in modeling the problem. In this short paper, we address this problem and provide the correct probabilistic model for the ALOHA-based RFID systems. Some consequences of correcting the error in Trans. Autom. Sci. Eng., vol 6, no. 1, pp. 9-15, Jan. 2009, Chen, are discussed and the model is validated via simulation. Using the correct model, the performance of the ALOHA-based anticollision algorithm can be improved.

In this paper, we propose a novel optimization-based pre-equalization filter (PEF) design framework for direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining. The key feature in our design is that we explicitly take into account the spectral mask constraints that are usually imposed in practice by the telecommunications regulation and standardization bodies. This avoids the need for an inefficient power back-off, which is necessary for the existing pre-Rake-based transmitter structures designed solely based on average transmit power constraints. We consider two PEF design structures. In the first structure, the PEF is placed before the up-sampling unit at the transmitter. In the second structure, the PEF is placed after the up-sampling unit. We show that these two structures are significantly different in terms of their capabilities in reducing the residual inter-symbol interference at the receiver and also in following the spectral mask at the transmitter. This introduces a trade-off such that one design structure outperforms the other one, depending on the system parameters. In this regard, we show that if the spreading factor is large enough, the second design structure has a superior performance. Simulation results confirm that both of the proposed PEF structures lead to significant performance gains over PEF designs without explicit spectral mask considerations. In addition, our PEF designs have the capability of adhering to spectral masks with arbitrary shapes.

Radio-frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique serial number. Using RFID tags can simplify many applications and provide many benefits. Meanwhile, the privacy of the customers should be taken into account. A potential threat for the privacy of a user is that of anonymous readers obtaining information about the tags in the system. The use of a blocker tag has been proposed as a solution to avoid unwanted tag interrogations. A blocker tag can simulate all or a portion of tag IDs in the system. This prevents the malicious readers from identifying the tags and obtaining information from the system. Although this solution is simple to implement and has a low cost, it may add another threat to the RFID system if used as a malicious tool to attack the system. A malicious blocker tag can deteriorate the performance of an RFID system by simulating fake tag IDs. In this paper, we study the use of blocker tags for malicious attacks that can prevent nearby legitimate readers from correctly receiving the reply messages from the tags. The blocker attack is a medium access control (MAC)-layer denial of service (DoS) threat and we propose a lower-layer solution for this attack. We mathematically model the blocker attack for RFID systems which operate based on the binary tree walking or ALOHA singulation techniques. Using the developed analytical framework, we propose a probabilistic blocker tag detection (P-BTD) algorithm to detect the presence of an attacker in the RFID system. The P-BTD algorithm can detect the existence of a blocker tag using the information extracted from the interrogations performed by the reader. Simulation results show that our proposed algorithm has a better performance than the threshold-based detection algorithm in terms of the number of required interrogations.

The aggregate capacity of wireless ad hoc networks can be increased substantially if each node is equipped with multiple network interface cards (NICs) and each NIC operates on a distinct frequency channel. Most of the recently proposed channel assignment algorithms are based on combinatorial techniques. Combinatorial channel assignment schemes may sometimes result in computationally complicated algorithms as well as inefficient utilization of the available frequency spectrum. In this paper, we analytically model channel and interface assignment problems as tractable continuous optimization problems within the framework of network utility maximization (NUM). In particular, the link data rate models for both single-channel reception and multichannel reception scenarios are derived. The assignment of both nonoverlapped and partially overlapped channels is also considered. We then propose two distributed multi-interface multichannel random access (DMMRA) algorithms for single-channel reception and multichannel reception scenarios. The DMMRA algorithms are fast, distributed, and easy to implement. Each algorithm solves the formulated NUM problem for each scenario. DMMRA requires each node to only iteratively solve a local, myopic, and convex optimization problem. Convergence and optimality properties of our algorithms are studied analytically. Simulation results show that our proposed algorithms significantly outperform utility-optimal combinatorial channel assignment algorithms in terms of both achieved network utility and throughput.

In this paper, we study the problem of multi-channel medium access control (MAC) in cognitive radio (CR) networks. While most of the previously proposed MAC protocols for CR networks are heuristic and are based on the simplistic protocol model, we design a distributed MAC protocol using the more accurate signal-to-interference-plus-noise-ratio (SINR) model. First, we assume that the secondary users are cooperative and formulate the problem of assigning transmission and listening probabilities for random access as a non-convex network utility maximization problem. We propose a three-phase algorithm that converges to a near-optimal solution after solving a number of convex optimization problems distributively. Simulation results show that our proposed algorithm based on the SINR model achieves a higher aggregate throughput than other schemes which are based on the protocol model. Then, we consider the case that the secondary users are rational. We use coalitional game theory to study the incentive issues of user cooperation in a given channel for the SINR model. In particular, we use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoff among the users. We show that the Shapley value lies in the core when all the users are one-hop neighbours of each other. We illustrate the Shapley value and the core with a numerical example.

Radio frequency identification (RFID) is an emerging technology for automatic object identification. An RFID system consists of a set of readers and several objects, with each object equipped with a small chip, called a tag. In this paper, we consider the anonymous cardinality estimation problem in an RFID system consisting of several readers. To achieve complete system coverage and increase the accuracy of measurement, multiple readers with overlapping interrogation zones are deployed. We study the problem under two different circumstances. First, we assume that the readers cannot perform interrogations synchronously. This models the case when the readers are not equipped with accurate clocks or synchronization imposes a high overhead. Under such condition, we propose an asynchronous exclusive estimator to estimate the number of tags that are exclusively located in the zone of a selected reader. By using this estimator, we propose an asynchronous multiple-reader cardinality estimation (A-MRCE) algorithm. In the second scenario, we assume that readers can perform interrogations synchronously. We propose a synchronous exclusive estimator and a synchronous multiple-reader cardinality estimation (S-MRCE) algorithm to estimate the total number of tags. For the exclusive estimators, we show that they are asymptotically unbiased and we derive upper bounds on the variance of error. We validate our analytical model via simulations. Results show that although the A-MRCE algorithm enjoys the asynchronous operation of the readers, it performs worse than the S-MRCE algorithm in terms of estimation error. Compared to the enhanced zero-based (EZB) and lottery frame (LoF) algorithms, the variance of the estimation error for both A-MRCE and S-MRCE algorithms increases linearly with the number of readers, while it increases exponentially for EZB and LoF algorithms.

Abstract Mobility management, which includes location management and handoff management, is essential in cellular wireless networks to provide service to mobile users. Location management enables call delivery to mobile users, while handoff management maintains the connectivity of ongoing calls while users move between cells. In next generation networks, mobile users will avail themselves with terminals capable of accessing wireless networks employing multiple technologies, thus making the task of mobility management more challenging. This paper reviews recent developments in location management, and surveys methods for handoff management between heterogeneous systems. Methods for inter‐system handoffs in packet‐switched inter‐networks are discussed according to the protocol layer in which the handoffs take place, i.e., network layer, transport layer, and application layer. Open problems for mobility management in future wireless networks are also presented. Copyright © 2010 John Wiley & Sons, Ltd.

For transmission control protocol (TCP), CUBIC is a TCP-friendly high-speed variant, in which the window size is a cubic function of time since the last loss event. TCP CUBIC is implemented in Linux operating systems and performs well in wired networks with large bandwidth-delay product. Most of the evaluations of TCP CUBIC are conducted via simulations or experiments. Analytical models for TCP CUBIC are few. In this paper, we propose a Markovian model to determine the steady state throughput of TCP CUBIC in wireless environment. The proposed model considers both congestion loss and random packet loss due to fading. We derive the stationary distribution of the Markov chain and obtain the average throughput based on the stationary distribution. Simulations are carried out to validate the analytical model. Results show that the simulated stationary distribution and the average throughput are both very close to our analytical results. Furthermore, we analyze the throughput performance of TCP CUBIC. Results show that random packet loss reduces the normalized average throughput more for end-to-end flow with large bandwidth-delay product. We propose an improvement to increase the throughput performance of TCP CUBIC by moderately increasing the window growth factor and the multiplicative decrease factor.

Due to fading and interference, data transmission via wireless links may sometimes be prone to error. For some applications in wireless sensor networks, it is of interest to monitor the link status and infer the packet loss rate. It has been shown that randomized network coding can improve the reliability of wireless sensor networks with lossy links. With network coding, the loss rate of a chosen path in a wireless sensor network is the maximum link loss rate among all the links in that path. This behavior changes the link identification problem and imposes challenges on the link loss inference. In this paper, we study the passive loss tomography problem in coded packet wireless sensor networks. We show that by inspecting the content of the coded packets at the sink (i.e., destination), one can estimate the path loss rates not only from the source nodes but also from various intermediate nodes to the sink. By utilizing such information at the sink, we determine the set of links whose loss rates can be identified. We propose a passive loss inference with random linear network coding (PLI-RLC) algorithm to estimate the link loss rates. Results show that in coded packet wireless sensor networks, our proposed algorithm can identify the status of a higher number of links compared to a Bayesian inference algorithm.

In wireless sensor networks, location-aware applications require an accurate and robust sensor localization algorithm. Among them, most of the multihop-based localization algorithms approximate the shortest path distances to the Euclidean distances. This approximation is valid only if the sensors are uniformly and densely deployed in a convex area where the shortest paths are close to straight lines. However, in a real- world setting, the convexity assumption may not always be valid. Non-convex deployment areas, such as C-shaped or S-shaped topologies, can corrupt the localization results severely due to erroneous distance estimations distorted by the non-convex topology. In this paper, we formulate the localization problem in irregular areas as a constrained least-penalty problem. We then propose a two-phase algorithm to eliminate the impact of irregularities. In the first phase, the estimated position is confined in the intersection area of the communication range constraints. In the second phase, the distorted measurements are eliminated by using a robust position estimator. Simulation results show that the two-phase algorithm outperforms some of the existing multihop localization algorithms in terms of a lower average localization error in both C-shaped and S-shaped topologies. The effects of anchor density, range error and communication range on localization performances are studied as well.

Loss tomography aims to infer link loss rates using end-to-end measurements. We investigate active loss tomography on mesh topologies. When network coding is applied, based on the content of the received probe packet, a receiver should distinguish which paths have successfully transmitted a probe and which paths have not. We establish a lower bound on probe size which is necessary for obtaining such end-to-end observations. Furthermore, we propose a linear algebraic (LA) approach to developing consistent estimators of link loss rates. Our approach exploits the inherent correlation between the losses on links and the losses on different sets of paths, so that the estimators converge to the actual loss rates as the number of probes increases. We also prove that the identiflability of a link is a necessary and sufficient condition for the consistent estimation of its loss rate. Simulation results show that the LA approach achieves better estimation accuracy than the belief propagation (BP) algorithm, after sending reasonably sufficient probes.

In this paper, we propose a novel optimization-based pre-equalization filter (PEF) design for direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining. The key feature in our design is that we explicitly take into account the spectral mask constraints that are usually imposed by the telecommunication regulation bodies. This avoids the need for an inefficient power back-off, which is necessary for transmit structures designed solely based on average transmit power constraints. Simulation results confirm that the proposed PEF design leads to significant performance gains over UWB PEF structures without explicit spectral mask considerations.

Radio frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique serial number. In this paper, we study the use of blocker tags by malicious attackers which can cause the nearby readers not being able to successfully receive the reply messages from the RFID tags. We mathematically model the blocker tag attack problem using information extracted from the interrogations performed by the reader. Using this analytical framework, we propose a probabilistic blocker tag detection (PBTD) algorithm to detect the presence of an attacker in the system. The probability of false alarm for the P-BTD algorithm is determined via simulation. Simulation results show that our proposed algorithm has a better performance than the threshold-based detection algorithm in terms of using a shorter time (i.e., fewer interrogations) to detect the presence of blocker tags.

Most of the previous work on network coding has assumed that the users are not selfish and always follow the designed coding schemes. However, recent results have shown that selfish users do not have the incentive to participate in inter-session network coding in a static non-cooperative game setting. As a result, the worst-case network efficiency (i.e., the price-of-anarchy) can be as low as 22%. In this paper, we show that if the same game is played repeatedly, then the price-of-anarchy can be significantly improved to 48%. We propose a grim-trigger strategy that encourages users to cooperate and participate in the inter-session network coding. A key challenge is to determine a common cooperative coding rate that the users should mutually agree on. We propose to resolve the conflict of interest among the users through a bargaining process. We derive a tight upper bound for the price-of-anarchy which is valid for any bargaining scheme. Moreover, we propose a simple and efficient min-max bargaining solution that can achieve this upper bound. Our results represent one of the first steps towards designing practical inter-session network coding schemes that can achieve reasonable performance for selfish users.

Random access protocols, such as Aloha, are commonly modeled in wireless ad-hoc networks by using the protocol model. However, it is well-known that the protocol model is not accurate and particularly it cannot account for aggregate interference from multiple interference sources. In this paper, we use the more accurate physical model, which is based on the signal-to-interference-plus-noise-ratio (SINR), to study optimization-based design in wireless random access systems, where the optimization variables are the transmission probabilities of the users. We focus on throughput maximization, fair resource allocation, and network utility maximization, and show that they entail non-convex optimization problems if the physical model is adopted. We propose two schemes to solve these problems. The first design is centralized and leads to the global optimal solution using a sum-of-squares technique. However, due to its complexity, this approach is only applicable to small-scale networks. The second design is distributed and leads to a close-to-optimal solution using the coordinate ascent method. This approach is applicable to medium-size and large-scale networks. Based on various simulations, we show that it is highly preferable to use the physical model for optimization-based random access design. In this regard, even a sub-optimal design based on the physical model can achieve a significantly better performance than an optimal design based on the inaccurate protocol model.

In this paper, we consider the problem of random access in wireless local area networks (WLANs) with each station generating either elastic or inelastic traffic. Elastic traffic is usually non-real-time, while inelastic traffic is usually coming from real-time applications. We formulate a network utility maximization (NUM) problem, where the optimization variables are the persistent probabilities of the stations and the utilities are either concave or sigmoidal functions. Sigmoidal utility functions can better represent inelastic traffic sources compared to concave utility functions commonly used in the existing random access literature. However, they lead to non-convex NUM problems which are not easy to solve in general. By applying the dual decomposition method, we propose a subgradient algorithm to solve the formulated NUM problem. We also develop closed-form solutions for the dual subproblems involving sigmoidal functions that have to be solved in each iteration of the proposed algorithm. Furthermore, we obtain a sufficient condition on the link capacities which guarantees achieving the global optimal solution when our proposed algorithm is being used. If this condition is not satisfied, then we can still guarantee that the optimal value of the objective function is within some lower and upper bounds. We perform various simulations to validate our analytical models when the available link capacities meet or do not meet the sufficient optimality condition.

Radio frequency identification (RFID) is an emerging wireless communication technology which allows objects to be identified automatically. An RFID system consists of a set of readers and several objects, equipped with small and inexpensive computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. High probability of collision impairs the benefit of multiple reader deployment and results in misreading. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not achieve optimal system performance. In this paper, we systematically design two optimization-based distributed channel selection and randomized interrogation algorithms for dense RFID systems. For this purpose, we develop elaborate models for the reader-to-tag and reader-to-reader collision problems. The first algorithm is fully distributed and is guaranteed to find a local optimum of a max-min fair resource allocation problem for RFID systems. The second algorithm is semi-distributed and achieves the global optimal system performance. Max-min fair optimality balances the performance and the processing load among readers. Simulation results show that our algorithms have significantly better performance than the previous heuristic algorithms.

In this paper, we study the problem of supporting multicast traffic in wireless sensor networks with network coding. On one hand, coding operations can reduce power consumption and consequently improve the network lifetime. On the other hand, performing network coding requires the use of the limited resources of the sensor nodes such as memory and energy. We study the tradeoff between maximizing the network lifetime and minimizing the number of network coding operations. We introduce the coding flow variables which enable us to determine the rate at which different operations (e.g., forwarding, replication, and coding) are performed in each sensor node. Using the coding flow variables, we formulate the maximum-lifetime minimum-resource (MLMR) coding subgraph problem as a linear programming problem. The objective in MLMR problem is to jointly maximize the network lifetime and minimize the rate of performing network coding. We propose an MLMR algorithm in order to obtain the optimal coding subgraph. We investigate the lifetime-resource tradeoff assuming that the cost of performing network coding varies for intermediate nodes. Simulation results show that the network lifetime can considerably be improved when the cost of performing network coding is relatively low compared to the case that this cost is high for intermediate nodes in the network. Moreover, results show that the network lifetime can substantially be increased using MLMR algorithm compared with the classical multicast with Steiner tree and another algorithm which uses network coding without considering the broadcast nature of wireless links.

A radio frequency identification (RFID) system consists of a set of readers and several objects, equipped with small computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not fully utilize the achievable system performance. In this paper, we develop an optimization-based distributed randomized multi-channel interrogation algorithm, called FDFA, for large-scale RFID systems. For this purpose, we develop elaborate models for reader-to-tag and reader-to-reader collision problems. FDFA algorithm is guaranteed to find a local optimum of a max-min fair resource allocation problem to balance the processing load among readers. Simulation results show that FDFA has a significantly better performance than the existing heuristic algorithms in terms of the number of successful interrogations. It also better utilizes the frequency spectrum.

Network coding has recently received increasing attention to improve performance and increase capacity in both wired and wireless communication networks. In this paper, we focus on inter-session network coding, where multiple unicast sessions jointly participate in network coding. Wireless links are often unreliable because of varying channel conditions. We consider multi-hop unicast sessions over unreliable links and propose a distributed end-to-end transmission rate adjustment mechanism to maximize the aggregate network utility by taking into account the wireless link reliability information. This includes an elaborate modeling of end-to-end reliability. Simulation results show that by taking into account the reliability information, we can increase the network throughput by up to 100% for some network topologies. We can also increase the aggregate network utility significantly for various choices of utility functions.

In this paper, we study the anonymous cardinality estimation problem in radio frequency identification (RFID) systems. To preserve privacy and anonymity, each tag only transmits a portion of its ID to the reader when it is being queried. To achieve complete system coverage and increase the accuracy of measurement, multiple readers with overlapping interrogation zones are deployed. The cardinality estimation problem is to estimate the total number of tags (or the tag population) in an RFID system. We first propose an exclusive estimator to estimate the number of tags that are exclusively located in the interrogation zone of a selected reader. We then present a multiple-reader tag estimation (MRTE) algorithm that can accurately estimate the tag population using the measurement from different readers and the exclusive estimator. The accuracy of our proposed algorithm and the approximation are validated via simulations. We compare our proposed MRTE algorithm with the enhanced zero-based (EZB) and maximum a posteriori tag estimation (MPTE) algorithms. Although the mean of the estimation error for all three algorithms approaches zero under certain circumstances, the variance of the estimation error for MRTE algorithm increases linearly with the number of readers while it increases exponentially for EZB and MPTE algorithms.

In wireless local area networks (WLANs), quality of service (QoS) can be provided by mapping applications with different requirements (e.g., delay and throughput) into one of the available access categories (ACs), as is done in the IEEE 802.11e standard. With the increasing programmability of network adapters, a malicious user can strategically declare a higher AC for its application to gain an unfair share of resources. This can drastically degrade the network performance and avoid adequate service distinction among different ACs. In this paper, we use the technique of mechanism design in game theory to tackle this problem in WLANs with random access. We propose to use the Vickrey-Clarke-Groves (VCG) mechanism in order to motivate each station to inform the access point (AP) truthfully, about the required AC of its application. The AP will then inform each station about its persistent probability and the price it needs to pay for the offered service. The result of the allocation of the persistent probabilities can be used for admission control. Simulation results show that the use of mechanism design can lead to a higher aggregate utility and prevents malicious users from gaining an unfair share of the network bandwidth.

A common assumption in the network coding literature is that the users are cooperative and will not pursue their own interests. However, this assumption can be violated in practice. In this paper, we analyze inter-session network coding in a wired network, assuming that the users are selfish and act as strategic players to maximize their own utility. We prove the existence of Nash equilibria for a wide range of utility functions. The number of Nash equilibria can be large (even infinite) under certain conditions, which is in sharp contrast to a similar game setting with traditional packet forwarding. We then characterize the worst-case efficiency bounds, i.e., the price-of-anarchy (PoA), compared to an optimal and cooperative network design. We show that by using a novel discriminatory pricing scheme that charges encoded and forwarded packets differently, we can improve PoA in comparison with the case where a single pricing scheme is being used. However, PoA is still worse than the case when network coding is not applied. This implies that inter-session network coding is more sensitive to strategic behavior. For example, for the case where only two network coding flows share a single bottleneck link, the efficiency at certain Nash equilibria can be as low as 48%. These results generalize the well-known result of guaranteed 67% efficiency bounds shown by Johari and Tsitsiklis for traditional packet forwarding networks.

Advances in mobile networks and positioning technologies have made location information a valuable asset in vehicular ad-hoc networks (VANETs). However, the availability of such information must be weighted against the potential for abuse. In this paper, we investigate the problem of alleviating unauthorized tracking of target vehicles by adversaries in VANETs. We propose a vehicle density-based location privacy (DLP) scheme which can provide location privacy by utilizing the neighboring vehicle density as a threshold to change the pseudonyms. We derive the delay distribution and the average total delay of a vehicle within a density zone. Given the delay information, an adversary may still be available to track the target vehicle by a selection rule. We investigate the effectiveness of DLP based on extensive simulation study. Simulation results show that the probability of successful location tracking of a target vehicle by an adversary is inversely proportional to both the traffic arrival rate and the variance of vehicles' speed. Our proposed DLP scheme also has a better performance than both Mix-Zone scheme and AMOEBA with random silent period.

Wireless mesh networks can provide scalable highspeed Internet access at a low cost. Fair channel access among different nodes in the wireless mesh network, however, is an important consideration that needs technological solutions before mesh networks can be widely deployed. Lack of fairness significantly decreases the throughput of nodes that are more than one hop away from mesh gateways. We propose an analytical model and use simulation studies to establish the existence of starvation in mesh networks even when we can ameliorate problems due to exposed terminals. Motivated by the inability of standard medium access control (MAC) protocols to limit starvation, we propose a modification to the MAC protocol to alleviate flow starvation. Our proposed algorithm improves the channel usage of short-term flows with nodes that are multiple hops from the gateway by a factor of 7 in some cases with a penalty of 20% reduction in total throughput across all nodes. Our proposed algorithm also has a better performance than two other schemes in terms of a higher fairness index.

In this paper, we propose two distributed contention-based medium access control (MAC) algorithms for solving a network utility maximization (NUM) problem in wireless ad hoc networks. Most of the previous NUM-based random access algorithms have one or more of the following performance bottlenecks: (1) extensive signaling among the nodes to achieve semi-distributed implementations, (2) synchronous updates of contention probabilities, (3) small update stepsizes to ensure convergence but with typically slow speed, and (4) supporting a limited range of utility functions under which the NUM is shown to be convex. Our proposed algorithms overcome the bottlenecks in all four aspects. First, only limited amount of message passing among nodes is required. Second, fully asynchronous updates of contention probabilities are allowed. Furthermore, our algorithms are robust to arbitrary large message passing delay and message loss. Third, we do not utilize any stepsize during updates, thus our algorithms can achieve faster convergence. Finally, our proposed algorithms have provable convergence, optimality, and robustness properties under a wider range of utility functions, even if the NUM problem is non-convex. Simulation results show the optimality and fast convergence of our algorithms, performance improvements compared with the subgradient-based MAC, and better efficiency-fairness tradeoff compared with the IEEE 802.11 distributed coordination function.

Random access has been studied for decades as a simple and practical wireless medium access control (MAC). Some of the recently developed distributed scheduling algorithms for throughput or utility maximization also take the form of random access, although extensive message passing among the nodes is required. In this paper, we would like to answer this question: is it possible to design a MAC algorithm that can achieve the optimal network utility without message passing? We provide the first positive answer to this question through a simple Aloha-type random access protocol. We prove the convergence of our algorithm for certain sufficient conditions on the system parameters, e.g., with a large enough user population. If each wireless node is capable of decoding the source MAC address of the transmitter from the interferring signal, then our algorithm indeed converges to the global optimal solution of the NUM problem. If such decoding is inaccurate, then the algorithm still converges, although optimality may not be always guaranteed. Proof of these surprisingly strong performance properties of our simple random access algorithm leverages the idea from distributed learning: each node can learn as much about the contention environment through the history of collision as through instantaneous but explicit message passing.

In a multi-hop wireless access network, where each node is an independent self-interested commercial entity, pricing is helpful not only to encourage collaboration but also to utilize the network resources efficiently. In this paper, we propose a market-based model with two-fold pricing (TFP) for wireless access networks. In our model, the relay-pricing is used to encourage nodes to forward packets for other nodes. Each node receives a payment for the relay service that it provides. We also consider interference-pricing to leverage optimal resource allocation. Together, the relay and interference prices incorporate both cooperative and competitive interactions among the nodes. We prove that TFP guarantees positive profit for each individual wireless node for a wide range of pricing functions. The profit increases as the node forwards more packets. Thus, the cooperative nodes are well rewarded. We then determine the relay and interference pricing functions such that the network social welfare and the aggregate network utility are maximized. Simulation results show that, compared to two recently proposed single-fold pricing models, where only the relay or only the interference prices are considered, our proposed TFP scheme significantly increases the total network profit as well as the aggregate network throughput. TFP also leads to more fair revenue sharing among the wireless relay nodes.

The aggregate capacity of wireless ad-hoc networks can be substantially increased if each wireless node is equipped with multiple network interface cards (NICs) and each NIC operates over a distinct orthogonal frequency channel. Most of the recently proposed channel assignment algorithms are based on formulating combinatorial channel assignment problems. The key is to assign exactly one frequency channel to each NIC. However, combinatorial channel assignment models may result in computationally complicated algorithms as well as inefficient utilization of the available frequency spectrum. In this paper, we revisit channel assignment problem by formulating a novel continuous multi-interface multi-channel random access model. This includes elaborate modeling of the link data rates for various multi-interface multi-channel networking scenarios. We then propose a fast, fully distributed and easy to implement multi- interface multi-channel random access algorithm. Simulation results show that our proposed algorithm significantly outperforms combinatorial channel assignment algorithms in terms of achieved network utility and aggregate network throughput.

In a multi-hop wireless access network where each node is an independent self-interested commercial entity, pricing is helpful not only to encourage collaboration but also to utilize the network resources efficiently. In this paper, we propose a market-based model with two-fold pricing (TFP) for wireless access networks. In our model, the relay-pricing is used to encourage nodes to forward each other's packets. That is, each node is paid off for the relay service it provides. We also consider interference- pricing to leverage optimal resource allocation. We prove that TFP guarantees positive profit for each individual wireless node for a wide range of pricing functions. The profit increases as the node forwards more packets. Thus, the cooperative nodes are well rewarded. We then determine the relay and the interference pricing functions such that the network social welfare and the aggregate network utility are maximized. Simulation results show that, compared to a recently proposed single-fold pricing (SFP) model where only the relay prices are considered, our proposed TFP scheme significantly increases the total network profit and the network throughput. TFP also leads to more fair revenue sharing and profit distribution among the wireless relay nodes.

It has been shown that network coding can lead to significant improvement in network capacity and reduction in power consumption for multicast traffic in wireless networks. In this paper, we study the problem of supporting multicast in wireless sensor networks. The objective is to jointly maximize the network lifetime and minimize the number of packets undergoing network coding. We formulate the problem of establishing coding subgraph in the network as a linear programming problem, which is suitable for distributed implementation. We propose a new set of information flow variables, which enables us to determine the rate of performing network coding. Simulation results show that the network lifetime achieved by our proposed scheme is 20% higher than the maximum lifetime Steiner tree algorithm.

In this paper, we examine one of the security issues in vehicular ad-hoc network (VANETs): position-spoofing attack. We propose a novel Secure Location Verification (SLV) scheme to detect and prevent position-spoofing attack. SLV uses distance bounding, plausibility checks, and ellipse-based location estimation to verify the claimed location of a vehicle. Simulation results show that our proposed SLV scheme has a better performance than both autonomous position verification (APV) and greedy forwarding algorithms.

The European Computer Manufacturers Association (ECMA) International recently defined the ECMA-368 standard, which specifies the physical and media access control (MAC) layers for Ultra Wideband (UWB) based wireless personal area networks (WPANs). The MAC protocol in ECMA-368 has a superframe structure. Each superframe is divided into three different time periods. One of them is the prioritized contention access (PCA) period which supports contention-based access between different traffic classes. In this paper, we propose an analytical model to evaluate the performance of PCA in ECMA- 368 MAC protocol. We assume that packets follow the Markovian Arrival Process (MAP) and various service times can be modeled by different phase type distributions (PHs). We apply the Matrix Geometric Method (MGM) technique and model the system as a MAP/PH/1 queueing system. We derive the probability mass function for the number of the packets in the queue, and the cumulative distribution function for the packets' waiting time. The correctness of our proposed analytical model is validated via OPNET simulations.

Wireless local area networks (WLANs) have become a ubiquitous high-speed data access technology. The recent 802.11n proposal further increases the transmission rate by using the multiple-input-multiple-output (MIMO) technique. Multiple antennas can be used to achieve a performance gain by either increasing the transmission reliability through spatial diversity or increasing the transmission rate through spatial multiplexing. This new characteristic at the wireless physical layer requires the corresponding adaptation at the medium access control (MAC) layer to reach the best performance gain. This paper proposes a cross-layer optimization framework for jointly optimizing the MIMO configuration at the physical layer and the persistent probabilities for different classes of multimedia traffic at the MAC layer with slotted Aloha. A distributed algorithm NUM-D based on dual decomposition is proposed. Numerical results are compared with a simplified algorithm NUM-S to show the effectiveness of the proposed methods.

Wireless sensor networks (WSNs) have recently received increasing attention from research and development communities. In a WSN, the field information (e.g., temperature, humidity, airflow) is acquired via several battery-equipped wireless devices and is relayed towards a sink node. As the size of the WSNs increases, it becomes inefficient to gather all information in one sink. To tackle this problem, the number of sinks can be increased. The data information flow towards each of the sinks is called a commodity. In this paper, we formulate a lexicographically optimal commodity lifetime (LOCL) routing problem. A stepwise algorithm is proposed to obtain the optimal routing solution which can lead to lexicographical fairness among commodity lifetimes. Simulation results show that our proposed algorithm increases the normalized commodity lifetime compared to MLMS [1] and LMM [2] routing algorithms.

The 4th generation (4G) wireless communication systems aim to provide users with the convenience of seamless roaming among heterogeneous wireless access networks. To achieve this goal, the support of vertical handoff in mobility management is crucial. This paper focuses on the vertical handoff decision algorithm, which determines under what criteria vertical handoff should be performed. The vertical handoff decision problem is formulated as a constrained Markov decision process (CMDP). The objective is to maximize the expected total reward of a connection subject to the expected total access cost constraint. In our model, a benefit function is used to assess the quality of the connection, and a penalty function is used to model signaling and call dropping. The user's velocity and location information are considered when making the handoff decisions. The value iteration and Q-learning algorithms are used to determine the optimal policy. Numerical results show that our proposed vertical handoff decision algorithm outperforms another scheme which does not consider the user's velocity.

Wireless ad-hoc networks can experience significant performance degradation under fading channels. Diversity through space has been shown to be an effective way of combating wireless fading with the multiple-input-multiple-output (MIMO) technique by transmitting correlated information through multip

The recent ECMA-368 standard specifies the use of ultra wideband (UWB) technology for high rate communications in wireless personal area networks (WPANs). This paper proposes an analytical model for the performance analysis of the medium access control (MAC) protocol standardized in ECMA-368. The MAC protocol uses a superframe structure. Each superframe has a beacon period, a distributed reservation protocol (DRP) period, and a prioritized contention access (PCA) period. By using the Markovian arrival process (MAP) and phase type distribution (PH), we model this MAC layer as a MAP/PH/1 queueing system, and focus our study on the performance of the DRP period in this paper. The probability mass function of the number of DRP packets in the system, as well the cumulative distribution of the DRP packet's waiting time are derived and compared with the simulation results in OPNET.

The IEEE 802.11-based wireless local area networks (WLANs) are widely used for high-speed wireless data access. With the recent 802.11e quality-of-service (QoS) extension, realtime applications such as voice over IP and video streaming are finding their way to be running over WLANs. The recent 802.11n proposal aims to provide higher throughput support for bandwidth-intensive multimedia applications. It uses the multiple-input-multiple-output (MIMO) technology at the physical layer to increase the transmission rate. MIMO introduces several new features at the physical layer such as the spatial diversity and spatial multiplexing gains. These new characteristics at the wireless physical layer require corresponding adaptation at higher layers to achieve a better performance. This paper proposes a joint adaptation of the MIMO physical layer and the 802.11e MAC layer through the formulation of a network utility maximization problem. The MIMO configuration at the physical layer and the contention window sizes for different access categories' traffic at the MAC layer are jointly optimized. Simulations are carried out in the ns-2 simulator to show the effectiveness of the proposed method.

Wireless wide area networks (WWANs) and wireless local area networks (WLANs) have complementary characteristics which make them suitable to jointly offer an ubiquitous wireless solution. In cellular/WLAN interworking, the quality of service (QoS) requirements for different services (e.g., voice and real-time video) can be guaranteed by using connection admission control. In this paper, we propose the use of virtual partitioning (VP) [S. Borst and D. Mitra] resource sharing scheme to facilitate admission control in a multi-service integrated cellular/WLAN system. VP pre-allocates a nominal capacity for each service based on the expected traffic and the required blocking probabilities. We first determine the policy functions corresponding to VP for new and handoff connection requests. Then, three different nominal capacities for VP are compared with the cutoff priority policy. Numerical results show that lower blocking and dropping probabilities can be achieved by VP in a wide range of conditions.

In a wireless mesh network (WMN) with a number of stationary wireless routers, the aggregate capacity can be increased when each router is equipped with multiple network interface cards (NICs) and each NIC is assigned to a distinct orthogonal frequency channel. In this paper, given the logical topology of the network, we mathematically formulate a crosslayer fair bandwidth sharing problem as a non-linear mixedinteger network utility maximization problem. An optimal joint design, based on exact binary linearization techniques, is proposed which leads to a global maximum. A near-optimal joint design, based on approximate dual decomposition techniques, is also proposed which is practical for deployment. Performance is assessed through several numerical examples in terms of network utility, aggregate network throughput, and fairness index. Results show that our proposed designs can lead to multi-channelWMNs which are more efficient and fair compared to their singlechannel counterparts. The performance gain on both efficiency and fairness increase as the number of available NICs per router or the number of available frequency channels increases.

The emerging high-rate wireless personal area network (WPAN) technology is capable of supporting high-speed and high-quality real-time multimedia applications. In particular, MPEG-4 video streams are deemed to be a widespread traffic type. However, in the current IEEE 802.15.3 standard for media access control (MAC) of high-rate WPANs, the implementation details of some key issues such as scheduling and quality of service (QoS) provisioning have not been addressed. In this paper, we first propose a mathematical model for the optimal scheduling scheme for MPEG-4 flows in high-rate WPANs. We also propose an RL scheduler based on the reinforcement learning (RL) technique. Simulation results show that our proposed RL scheduler achieves nearly optimal performance and performs better than F-SRPT (Mangharam et al., 2004), EDD+SRPT (Torok et al., 2005), and PAP (Kim and Cho, 2005) scheduling algorithms in terms of a lower decoding failure rate.

The maximum lifetime routing problem in wireless sensor networks has received increasing attention in recent years. One way is to formulate it as a linear programming problem by maximizing the time at which the first node runs out of energy subject to the flow conservation constraints. The solutions in this problem correspond to the rates allocated to each link. In this paper, we first show that, under certain conditions, the solutions of this problem are not unique for some network topologies. Given the feasible solutions set, one can further define a secondary optimization problem by minimizing the end-to-end packet transfer delay or power consumption. Rather than solving two sequential optimization problems, in this paper, we propose the use of a regularization method which can jointly maximize the network lifetime and minimize another objective (e.g., packet delay). We describe the fully distributed implementation and provide performance comparisons with other algorithms.

The complementary characteristics of cellular systems and wireless local area networks (WLANs) make them attractive candidates to jointly offer a seamless wireless solution. In an integrated cellular/WLAN system, the quality of service (QoS) requirements for different services (e.g., voice and real-time video) require adequate admission control policies to limit the number of connections in each network. In this paper, we analytically develop a comprehensive model to facilitate the optimal evaluation of different admission control policies in a multi-service integrated cellular/WLAN system. Given the model, we formulate two optimization problems to adjust admission control parameters. The first problem is to maximize the network revenue while the second problem is to minimize the required network resources subject to QoS constraints. We evaluate each problem when different combinations of cutoff priority and fractional guard channel admission control policies are being used. We show that a combination of two cutoff priority policies achieves the best solution for both problems.

Video traffic, especially MPEG-4 streams, is envisioned to be a dominant traffic type in the IEEE 802.15.3 high-rate wireless personal area networks (WPANs). The unique hierarchical structure of MPEG-4 streams calls for special measures at the MAC layer, in order to improve the quality of service (QoS). In this paper, we propose a frame-decodability aware (FDA) technique to make the scheduling algorithms aware of the hierarchical structure and decoding dependencies in MPEG-4 streams. Simulation results show that the FDA technique can significantly reduce the decoding failure rate of various scheduling algorithms, under MPEG-4 traffic.

The aggregate capacity of wireless mesh networks can be increased by the use of multiple frequency channels and multiple network interface cards in each router. Recent results have shown that the performance can further be increased when both non-overlapped and partially overlapped channels are being used. In this paper, we propose a linear model for a joint channel assignment, interface assignment, and scheduling design. We propose the channel overlapping matrix and mutual interference matrices to model the non-overlapped and partially overlapped channels. Since the model is formulated as a linear mixed-integer program with a few integer variables, the computation complexity is low and it is feasible for implementation. Simulation results show that the aggregate network capacity increases by 90% when all partially overlapped channels within the 802.11b frequency band are being used.

In a wireless mesh network (WMN) with a number of stationary wireless routers, the aggregate capacity can be increased when each router is equipped with multiple network interface cards (NICs) and each NIC within a router is assigned to a distinct orthogonal frequency channel. In this paper, given the logical topology of the network, we formulate the joint channel allocation, interface assignment, and media access control (MAC) problem as a cross-layer non-linear mixed-integer network utility maximization problem. An optimal joint design, based on exact binary linearization techniques, is proposed which leads to a global maximum. A near-optimal joint design, based on approximate dual decomposition techniques, is also proposed which is of more interest in terms of practical deployment. Performance evaluation is given through a number of numerical examples in terms of network utility maximization and aggregate network throughput.

The future fourth generation wireless heterogeneous networks aim to integrate various wireless access technologies and to support the IMS (IP multimedia subsystem) sessions. In this paper, we propose the Loosely Coupled Satellite-Cellular-WiMax-WLAN (LCSCW2) interworking architecture. The LCSCW2 architecture uses the loosely coupling approach and integrates the satellite networks, 3G wireless networks, WiMax, and WLANs. It can support IMS sessions and provide global coverage. The LCSCW2 architecture facilitates independent deployment and traffic engineering of various access networks. We also propose an analytical model to determine the associate cost for the signaling and data traffic for inter-system communication in the LCSCW2 architecture. The cost analysis includes the transmission, processing, and queueing costs at various entities. Numerical results are presented for different arrival rates and session lengths.

In this paper, we consider a mobile wireless sensor network where both sensor nodes and the seeds are moving. We propose and analyze two variations of the Monte Carlo localization (MCL) algorithms, namely: dual MCL and mixture MCL, for mobile sensor networks. We conduct simulation experiments to evaluate the performance of these two algorithms by varying the number of seeds, number of nodes, number of samples, velocity of nodes, and radio pattern degree of irregularity. Results show that both dual MCL and mixture MCL are more accurate than the original MCL algorithm. In terms of the trade off between the computational time and estimated location accuracy, the mixture MCL has a better performance than both dual MCL and the original MCL algorithms.

One of the major design issues in heterogeneous wireless networks is the support of vertical handoff. Vertical handoff occurs when a mobile terminal switches from one network to another (e.g., from WLAN to CDMA 1timesRTT). The objective of this paper is to determine the conditions under which vertical handoff should be performed. The problem is formulated as a Markov decision process. A link reward function and a signaling cost function are introduced to capture the tradeoff between the network resources utilized by the connection and the signaling and processing load incurred on the network. A stationary deterministic policy is obtained when the connection termination time is geometrically distributed. Numerical results show good performance of our proposed scheme over two other vertical handoff decision algorithms, namely: SAW (simple additive weighting) and GRA (grey relational analysis).

A multi-channel wireless mesh network (MC-WMN) consists of a number of stationary wireless routers, where each router is equipped with multiple network interface cards (NICs). Each NIC operates on a distinct frequency channel. Two neighboring routers establish a logical link if each one has an NIC operating on a common channel. Given the physical topology of the routers and other constraints, four important issues should be addressed in MC-WMNs: logical topology formation, interface assignment, channel allocation, and routing. Logical topology determines the set of logical links. Interface assignment decides how the logical links should be assigned to the NICs in each wireless router. Channel allocation selects the operating channel for each logical link. Finally, routing determines through which logical links the packets should be forwarded. In this paper, we mathematically formulate the logical topology design, interface assignment, channel allocation, and routing as a joint linear optimization problem. Our proposed MC-WMN architecture is calledTiMesh. Extensive ns-2 simulation experiments are conducted to evaluate the performance ofTiMeshand compare it with two other MC-WMN architecturesHyacinth[1] andCLICA[2]. Simulation results show thatTiMeshachieves higher aggregated network throughput and lower end-to-end delay thanHyacinthandCLICAfor both TCP and UDP traffic. It also provides better fairness among different flows.

The IEEE 802.11a/b/g have been widely accepted as thedefactostandards for wireless local area networks (WLANs). The recent IEEE 802.11n proposals aim at providing a physical layer transmission rate of up to 600 Mbps. However, to fully utilize this high data rate, the current IEEE 802.11 medium access control (MAC) needs to be enhanced. In this paper, we investigate the performance improvement of the MAC protocol by using the two frame aggregation techniques, namely A-MPDU (MAC Protocol Data Unit Aggregation) and A-MSDU (MAC Service Data Unit Aggregation). We first propose an analytical model to study the performance under uni-directional and bi-directional data transfer. Our proposed model incorporates packet loss either from collisions or channel errors. Comparison with simulation results show that the model is accurate in predicting the network throughput. We also propose an optimal frame size adaptation algorithm with A-MSDU under error-prone channels. Simulation results show that the network throughput performance is significant improved when compared with both randomized and fixed frame aggregation algorithms.

A multi-channel wireless mesh network (MC- WMN) consists of a number of stationary wireless routers, where each router is equipped with multiple network interface cards (NICs). Each interface operates on a distinct frequency channel. Two neighboring routers establish a logical link if each one has an interface operating on a common channel. Given the physical topology of the routers and other constraints, the logical topology formation algorithm determines the set of logical links. In general, since the number of NICs is limited, some logical links need to share an NIC in a router. The interface assignment algorithm determines the interface that a logical link should be attached to. In this paper, we formulate the logical topology design and interface assignment as a joint optimization problem to obtain an MC-WMN architecture, called TiMesh. We conducted extensive ns-2 simulation experiments to evaluate our algorithm and compared it with another MC-WMN architecture called Hyacinth. Simulation results show that our proposed scheme achieves a higher aggregated network goodput and lower end-to-end delay for both TCP and UDP traffic.

The aggregate capacity of wireless mesh networks can be increased by the use of multiple channels. Stationary wireless routers are equipped with multiple network interface cards (NICs). Each NIC is assigned with a distinct frequency channel. In this paper, we formulate the Joint Optimal Channel Assignment and Congestion Control (JOCAC) as a decentralized utility maximization problem with constraints that arise from the interference of the neighboring transmissions. Unlike other previous work, the JOCAC algorithm is able to assign not only the non-overlapping (orthogonal) channels, but also the partially-overlapping channels within the IEEE 802.11 frequency bands. Using 802.11b with 3 non-overlapping channels, simulation results show that our algorithm provides a higher aggregated goodput than the recently proposed load-aware algorithm by 20%. The goodput is further increased by 40% when all the 11 partially-overlapping channels are being used.

Many applications in wireless sensor networks require sensor nodes to obtain their absolute or relative geographical positions. Although various localization algorithms have been proposed recently, most of them require nodes be equipped with range-determining hardware to obtain distance information. In this paper, we propose a concentric anchor-beacons (CAB) localization algorithm for wireless sensor networks. CAB is a range-free approach and uses a small number of anchor nodes. Each anchor emits beacons at different power levels. From the information received by each beacon heard, nodes determine which annular ring they are located within each anchor. Each node uses the approximated center of intersection of the rings as its position estimate. Simulation results show that the estimation error reduces by half when anchors transmit beacons at two different power levels instead of at a single level. CAB also gives a lower estimation error than other range-free localization schemes (e.g., Centroid, APIT) when the anchor-to-node range ratio is less than four.

Recently, wireless local area network (WLAN) hotspots have been deployed in many areas (e.g., cafes, airports, university campuses). The new IEEE 802.11e standard further provides quality of service (QoS) provisioning by grouping the applications (or traffic) into four different access categories. The coverage area of WLANs can be extended by allowing the neighboring mobile devices to relay data to the access points. This concept is known as multi-hop WLANs. Due to the limited network capacity and the contention-based channel access mechanism, admission control is required to regulate the number of simultaneous flows to maintain QoS. The multi-hop extension of WLANs present further challenges for admission control design due to the location-dependent contention in the network. In this paper, we propose an admission control algorithm for multi-hop 802.11e WLANs. The admission control algorithm first constructs the network's contention graph to break down the network contention situation into areas comprised of maximal cliques. Then, the admission decision is made by analyzing the available capacity of each maximal clique with 802.11e saturation throughput analysis. Simulation results show that our proposed algorithm is effective in providing QoS guarantee to the existing voice and video flows while maintaining a good performance for best effort traffic.

The energy consumption is a key design criterion for the routing protocols in wireless sensor networks. Some of the conventional single path routing schemes may not be optimal to maximize the network lifetime and connectivity. In this paper, we propose a distributed, scalable and localized multipath search protocol to discover multiple node-disjoint paths between the sink and source nodes. We also propose a load balancing algorithm to distribute the traffic over the multiple paths discovered. We compare our proposed scheme with the directed diffusion, directed transmission, and the energy-aware routing protocols. Simulation results show that our proposed scheme has a higher node energy efficiency, lower average delay and control overhead than those protocols.

The next generation wireless networks will support the vertical handoff mechanism in which users can maintain the connections when they switch from one network to another (e.g., from IEEE 802.11b to CDMA 1timesRTT network, and vice versa). Although various vertical handoff decision algorithms have been proposed in the literature recently, there is a lack of performance comparisons between different schemes. In this paper, we compare the performance between four vertical handoff decision algorithms, namely, MEW (multiplicative exponent weighting), SAW (simple additive weighting), TOPSIS (technique for order preference by similarity to ideal solution), and GRA (grey relational analysis). All four algorithms allow different attributes (e.g., bandwidth, delay, packet loss rate, cost) to be included for vertical handoff decision. Results show that MEW, SAW, and TOPSIS provide similar performance to all four traffic classes. GRA provides a slightly higher bandwidth and lower delay for interactive and background traffic classes

There are various applications in wireless sensor networks which require knowing the relative or actual position of the sensor nodes. Recently, there have been different localization algorithms proposed in the literature. The algorithms based on classical Multidimensional Scaling (MDS) only require 3 or 4 anchor nodes and can provide higher accuracy than some other schemes. In this paper, we propose and analyze another type of MDS (calledordinalMDS) for localization in wireless sensor networks. Ordinal MDS differs from classical MDS by that it only requires a monotonicity constraint between the shortest path distance and the Euclidean distance for each pair of nodes. We conduct simulation studies under square and C-shaped topologies with different connectivity levels and number of anchors. Results show that ordinal MDS provides a lower position estimation error than classical MDS.

The IEEE 802.11-based wireless LANs have been widely deployed for local area high-speed data access. The IEEE 802.11e amendment aims at providing QoS provisioning to support real-time multimedia traffic in WLANs. The enhanced distributed channel access (EDCA) is a QoS extension of the distributed coordination function (DCF) in IEEE 802.11a/b/g. In this paper, we propose an analytical model to evaluate the saturation throughput of the IEEE 802.11e EDCA. Our analytical model is based on the use of mean value analysis. We carry out extensive simulation study to validate the accuracy of the proposed model. Our scheme models accurately the effects of the change of contention window size and AIFS (arbitration inter-frame space). Our analytical model is applicable to real-time system tuning and on-line admission control algorithms which require a low computation complexity

In wireless sensor networks (WSNs), when a stimulus or event is detected within a particular region, data reports from the neighboring sensor nodes (sources) are sent to the sink or destination. Data from these sources are usually aggregated along their way to the sink. The data aggregation via in-network processing reduces communication cost and improves energy efficiency. In this paper, we propose an overlay structure in which the sources within the event region form a tree to facilitate data aggregation. We call this tree a lifetime-preserving tree (LPT). LPT aims to prolong the lifetime of the sources which are transmitting data reports periodically. In LPT, nodes which have higher residual energy are chosen as the aggregating parents. LPT also includes a self-healing feature by which the tree will be re-constructed again whenever a node is no longer functional or a broken link is detected. By choosing the directed diffusion as the underlying routing platform, simulation results show that in a WSN with 250 sensor nodes, the lifetime of sources can be extended significantly when data are aggregated using the LPT algorithm

In this paper, we propose an adaptive scheduling algorithm (ASA) for Bluetooth scatternets. ASA is adaptive in that the bandwidth allocated on each link or session is dynamically adjusted based on the estimated traffic. ASA integrates both intra-piconet and inter-piconet scheduling to improve the aggregate throughput and delay. ASA prevents the bridge node conflict and satisfies the max-min fairness criterion. We compare our proposed ASA with two other scheduling algorithms via simulations. Results show that ASA provides good performance in terms of fairness, aggregate throughput, and average delay.

Quality-of-service (QoS) provisioning and location management (LM) in cellular networks are solved separately in previous work. For realistic network environments, we have proposed a framework of combining QoS provisioning and LM by using all available user mobility information. In this paper, we present performance evaluation to show that this framework can yield more efficient solutions for both. We propose a novel path-based LM scheme in this combined framework and evaluate the performance gain of the new scheme over the original path-based LM scheme by simulations. Further, we propose a new connection admission control (CAC) scheme derived from this combined framework for QoS provisioning and present results showing performance enhancements over CAC schemes proposed previously.

The scarcity and large fluctuations of link bandwidth in wireless networks have motivated the development of adaptive multimedia services in mobile communication networks, where it is possible to increase or decrease the bandwidth of individual ongoing flows. This paper studies the issues of quality of service (QoS) provisioning in such systems. In particular, call admission control and bandwidth adaptation are formulated as a constrained Markov decision problem. The rapid growth in the number of states and the difficulty in estimating state transition probabilities in practical systems make it very difficult to employ classical methods to find the optimal policy. We present a novel approach that uses a form of discounted reward reinforcement learning known as Q-learning to solve QoS provisioning for wireless adaptive multimedia. Q-learning does not require the explicit state transition model to solve the Markov decision problem, therefore more general and realistic assumptions can be applied to the underlying system model for this approach than in the previous schemes. Moreover, the proposed scheme can efficiently handle the large state space and action set of the wireless adaptive multimedia QoS provisioning problem. Handoff dropping probability and average allocated bandwidth are considered as QoS constraints in our model and can be guaranteed simultaneously. Simulation results demonstrate the effectiveness of the proposed scheme in adaptive multimedia mobile communication networks.

A Bluetooth scatternet can be formed by interconnecting two or more piconets together. To reduce the traffic load of master and bridge nodes, a two-phase scatternet formation (TPSF) algorithm was proposed (Kawamoto, Y. et al., Proc. IEEE WCNC '03, 2003). A control scatternet is created for the transmission of control packets. For each source and destination pair, an on-demand scatternet is created for the transmission of data packets. The original TPSF does not consider the support of node mobility. We propose TPSF+, which is an extension of the on-demand scatternet formation in the original TPSF. In TPSF+, route information is discovered when a communication session is required between the two nodes. Simulation results show that TPSF+ has a higher successful path connection ratio when compare with the original TPSF. The proposed TPSF+ also has a higher aggregate throughput and smaller end-to-end delay when compared with BTCP (Salonidis, T. et al., Proc. IEEE INFOCOM'01, 2001) and Bluenet (Wang, Z. et al., Proc. 35th Hawaii Int. Conf. on System Sciences - HICSS-35, 2002).

Third generation cellular wireless networks are designed to support adaptive multimedia by controlling individual ongoing flows to increase or decrease their bandwidth in response to changes in traffic load There is growing interest in quality of service (QoS) provisioning under this adaptive multimedia framework, in which a bandwidth adaptation algorithm needs to be used in conjunction with the call admission control algorithm. This paper presents a novel method for QoS provisioning via the use of the average reward reinforcement learning, which can maximize the network revenue subject to several predetermined QoS constraints. By considering handoff dropping probability, average allocated bandwidth and intraclass fairness simultaneously, our algorithm formulation guarantees that these QoS parameters are kept within predetermined constraints. Unlike other model-based algorithms, our scheme does not require explicit state transition probabilities and therefore the assumptions behind the underlying system model are more realistic than those in previous schemes. Moreover, by considering the status of neighboring cells, the proposed scheme can dynamically adapt to changes in traffic condition. Simulation results demonstrate the effectiveness of the proposed approach in adaptive multimedia cellular networks.

In this paper, we consider a mobile ad hoc wireless access network in which mobile nodes can access the Internet via one or more stationary gateway nodes. Mobile nodes outside the transmission range of the gateway can continue to communicate with the gateway via their neighboring nodes over multihop paths. On-demand routing schemes are appealing because of their low routing overhead in bandwidth restricted mobile ad hoc networks, however, their routing control overhead increases exponentially with node density in a given geographic area. To control the overhead of on-demand routing without sacrificing performance, we present a novel extension of the ad hoc on-demand distance vector (AODV) routing protocol, called LB-AODV, which incorporates the concept of load-balancing (LB). Simulation results show that as traffic increases, our proposed LB-AODV routing protocol has a significantly higher packet delivery fraction, a lower end-to-end delay and a reduced routing overhead when compared with both AODV and gossip-based routing protocols.

In this paper, we consider a mobile ad-hoc wireless access network in which mobile nodes can access the Internet via a stationary gateway node or access point. Mobile nodes that are outside the transmission range of the gateway can continue to communicate with the gateway via a multi-hop connection with their neighboring nodes. The ad-hoc on-demand distance vector (AODV) routing protocol is extended by incorporating the concept of load-balancing (LB). We call this the LB-AODV routing protocol. Simulation results show that in a congested network environment, our proposed LB-AODV has a higher packet delivery fraction, a lower end-to-end delay and control overhead when compared with both AODV and gossip-based routing protocols.

Bluetooth is a promising short-range radio technology for wireless personal area networks. There is an interest to expand the coverage of such networks by interconnecting them to form scatternets. Most of the scatternet formation algorithms recently proposed in the literature do not support dynamic topology changes, and the master of bridge nodes in the resulting scatternet may become the traffic bottleneck and reduce network throughput. In this paper, we propose a two-phase scatternet formation (TPSF) protocol to support dynamic topology changes while maintaining a high aggregate throughput. In the first phase, a control scatternet is constructed to support topology changes and route determination. The second phase creates a separate on-demand scatternet whenever a node wants to initiate data communications with another node. The on-demand scatternet is torn down when the data transmissions are finished. Since all the time slots in an on-demand scatternet are dedicated to a single communication session, a high aggregate throughput is achieved at the expense of a slightly higher connection setup delay.

Multi-protocol label switching (MPLS) has begun to deploy in the Internet backbone to support service differentiation and traffic engineering. In recent years, there has also been an interest to extend the MPLS capability to the wireless access networks. In this paper, we provide an overview of the MPLS-based micro-mobility management including label switching path setup, packet forwarding, handoff processing, and paging. In order to prevent packet loss during handoff, we propose a medium access protocol (MAC) layer assisted packet recovery scheme. A MAC buffer in the old base station caches the packets dropped by MAC layer and forwards these packets to the new base station. Simulation results show that our proposed scheme can eliminate the packet loss due to handoff and improve the TCP throughput dramatically when compared with IP micro-mobility protocols including cellular IP, HAWAII, and hierarchical mobile IP.

Quality of service (QoS) provisioning and location management (LM) in cellular networks are solved separately in previous work. For realistic network environments, we have proposed a framework of combining QoS provisioning and LM by using all available user mobility information. We present performance evaluations to show that this framework can yield more efficient solutions for both. We propose a novel path-based LM scheme in this combined framework and evaluate the performance gain of the new scheme over the original path-based LM scheme by simulations. Further, we propose a new connection admission control (CAC) scheme derived from this combined framework for QoS provisioning and present results showing performance enhancements over CAC schemes proposed previously.

We propose a stochastic model to compute the optimal update boundary for the distance-based location update algorithm. The proposed model captures some of the real characteristics in the wireless cellular environment. The model can adapt to arbitrary cell topologies in which the number of neighboring base stations at different locations may vary. The cell residence time can follow general distributions which captures the fact that the mobile user may spend more time at certain locations than others. The model also incorporates the concept of a trip in which the mobile user may follow a particular path to a destination. For implementation, the decision of location update can be made by a simple table lookup. Numerical results indicate that the proposed model provides a more accurate update boundary in a real environment than that derived from a hexagonal cell configuration with random walk movement pattern. The proposed model allows the network to maintain a better balance between the processing incurred due to location update and the radio bandwidth utilized for paging between call arrivals.

In this paper, we propose a stochastic model to compute the optimal update boundary for the distance-based location update algorithm. The proposed model is flexible and captures some of the real characteristics in the wireless cellular environment. The model can adapt to arbitrary cell topologies in which the number of neighboring base stations at different locations may vary. The cell residence time can follow general distributions which captures the fact that the mobile user may spend more time at certain locations than others. The model also incorporates the concept of a trip in which the mobile user may follow a particular path to a destination. For implementation, the decision of location update can be made by a simple table lookup. Numerical results indicate that the proposed model provides a more accurate update boundary in real environment than that derived from a hexagonal cell configuration with a random walk movement pattern. The proposed model allows the network to maintain a better balance between the processing incurred due to location update and the radio bandwidth utilized for paging between call arrivals.

One of the major design issues in wireless ATM networks is the support of inter-switch handoffs. An inter-switch handoff occurs when a mobile terminal moves to a new base station connecting to a different switch. Apart from resource allocation at the new base station, inter-switch handoff also requires connection rerouting. With the aim of minimizing the handoff delay while using the network resources efficiently, the two-phase handoff protocol uses path extension for each inter-switch handoff, followed by path optimization if necessary. The objective of this paper is to determine when and how often path optimization should be performed. The problem is formulated as a semi-Markov decision process. Link cost and signaling cost functions are introduced to capture the tradeoff between the network resources utilized by a connection and the signaling and processing load incurred on the network. The time between inter-switch handoffs follows a general distribution. A stationary optimal policy is obtained when the call termination time is exponentially distributed. Numerical results show significant improvement over four other heuristics.

Path optimization is required for those connection rerouting schemes where the path after an inter-switch handoff is not optimal. In this paper, we evaluate the performance of three path optimization schemes (namely: exponential, periodic, and Bernoulli) for the two-phase inter-switch handoff protocol. Using analytical modeling and simulation, we determine the optimal operating point and the minimum expected cost for each scheme. We also study the effect of the relative change of the expected cost when the estimates of the average call duration or the mean time between inter-switch handoff are inaccurate. Results indicate that the Bernoulli scheme provides the lowest expected cost per call, while the periodic scheme is relatively insensitive to the changes of the average call duration.

One of the major design issues in wireless Am is the support of inter-switch handoffs.An inter-switch handoff occurs when a mobile terminal moves to a new base station connecting to a different switch.Recently a two-phase handoff protocol has been proposed to address this issue.It employs path extension to process the handoff request immediately, followed by possible path optimization to reduce the path cost.To implement the two-phase handoff protocol efficiently, we need to determine when to trigger path optimization.~s paper presents three path optimization schemes, namely: mponential, periodic, and Bernoulli, for the twm phase handoff protocol.A discrete time analytical model is proposed to enable evaluations and comparisons of the expected connection coss between these schemes.A major result is the closed form expression of the expected cost and its minimization at the optimal operating point for each scheme, derived from the andyticd model.Numerical resul~indicate that the performance of all three schemes are comparable, with the Bernoulli scheme outperforming the other two schemes.-F~-.