Large Language Models (LLMs), despite their impressive capabilities across domains, have been shown to be vulnerable to backdoor attacks.Prior backdoor strategies predominantly operate at the token level, where an injected trigger causes the model to generate a specific target word, choice, or class (depending on the task).Recent advances, however, exploit the long-form reasoning tendencies of modern LLMs to conduct reasoning-level backdoors: once triggered, the victim model inserts one or more malicious reasoning steps into its chainof-thought (CoT).These attacks are substantially harder to detect, as the backdoored answer remains plausible and consistent with the poisoned reasoning trajectory.Yet, defenses tailored to this type of backdoor remain largely unexplored.To bridge this gap, we propose Critical-CoT, a novel defense mechanism that conducts a two-stage fine-tuning (FT) process on LLMs to develop critical thinking behaviors, enabling them to automatically identify potential backdoors and refuse to generate malicious reasoning steps.Extensive experiments across multiple LLMs and datasets demonstrate that Critical-CoT provides strong robustness against both in-context learning-based and FT-based backdoor attacks.Notably,

Diffusion models have emerged as state-of-the-art generative frameworks, excelling in producing high-quality multi-modal samples. However, recent studies have revealed their vulnerability to backdoor attacks, where backdoored models generate specific, undesirable outputs called backdoor target (e.g., harmful images) when a pre-defined trigger is embedded to their inputs. In this paper, we propose PureDiffusion, a dual-purpose framework that simultaneously serves two contrasting roles: backdoor defense and backdoor attack amplification. For defense, we introduce two novel loss functions to invert backdoor triggers embedded in diffusion models. The first leverages trigger-induced distribution shifts across multiple timesteps of the diffusion process, while the second exploits the denoising consistency effect when a backdoor is activated. Once an accurate trigger inversion is achieved, we develop a backdoor detection method that analyzes both the inverted trigger and the generated backdoor targets to identify backdoor attacks. In terms of attack amplification with the role of an attacker, we describe how our trigger inversion algorithm can be used to reinforce the original trigger embedded in the backdoored diffusion model. This significantly boosts attack performance while reducing the required backdoor training time. Experimental results demonstrate that PureDiffusion achieves near-perfect detection accuracy, outperforming existing defenses by a large margin, particularly against complex trigger patterns. Additionally, in attacking scenarios, our attack amplification approach elevates the attack success rate (ASR) of existing backdoor attacks to nearly 100% while reducing training time by up to 20×.

This study investigates the application of non-orthogonal multiple access (NOMA) to enable massive connectivity for a stacked intelligent metasurface (SIM) system that performs signal processing in the electromagnetic wave domain. To realize the full potential of NOMA assisted SIM systems, a radio resource allocation problem is accordingly formulated to jointly optimize the key decision variables, including the decoding order of users, the transmit power at the base station, as well as the phase shift at the SIM. By adhereing to the users’ quality-of-service (QoS) requirements, as well as the power budget of the base station, the problem is aimed at maximizing the admission rate of the system. Due to the problem’s inherent non-convexity and complexity, we recast it as a Markov decision process and employ a quantile regression deep Q-network (QRDQN) agent to optimize the decision variables. Recognizing the mobility of users and the dynamic reconfiguration of the system, we further enhance the QR-DQN model’s adaptability and generalization capabilities by incorporating a meta-learning strategy. Simulation results demonstrate that integrating NOMA with SIM systems yields a significant increase of 39% in the average number of served users compared to the conventional orthogonal multiple access based approach. The proposed resource allocation mechanism additionally improves deep deterministic policy gradient (DDPG) in literature by 27% in the number of served users.

Unmanned aerial vehicles (UAVs) can be equipped with relatively strong servers so they can collaboratively perform inference for pre-trained Deep Neural Networks (DNNs), enabling complex recognition tasks based on onboard sensing data such as image and video. Such the collaborative inference is critical for applications where ground communications and computing infrastructure is not available, not secure or costefficient such as those for military, disaster recovery and rescue. Collaborative DNN inference in the UAV wireless network, is, however, challenging because one must decide how the computation load related to different layers of the DNN is distributed among UAVs and how to efficiently allocate both radio and computing resources to facilitate the underlying offloading process. To this end, we formulate the joint DNN layer assignment, radio and computing resource allocation problem as an optimization problem which aims to minimize the total inference latency. To solve this difficult mixed integer and non-linear problem, we employ an alternating optimization technique and develop an efficient algorithm, named LARA. Numerical studies show that LARA performs very well in different studied scenarios and achieves up to 80 % improvement in terms of inference latency compared to other baselines which perform DNN layer assignment and resource allocation in a heuristic manner. Furthermore, LARA achieves up to 43.17 % improvement compared to OULD [13] framework.

Diffusion models (DMs) are state-of-the-art generative models that learn to model complex data distributions based on iterative noise addition and denoising. Thanks to their superior capacity in generative tasks, DMs have been investigated for various applications in the communication field such as network optimization, channel estimation, semantic communication, and cybersecurity. However, recent studies have shown their vulnerability regarding backdoor attacks, in which backdoored DMs consistently generate a designated harmful result called backdoor target when the models' input contains a backdoor trigger. Although various backdoor techniques have been investigated to attack DMs, defense methods against these threats are still limited and underexplored. In this paper, we introduce PureDiffusion, a novel backdoor defense framework that can efficiently detect backdoor attacks by inverting backdoor triggers embedded in DMs. Our extensive experiments on various trigger-target pairs show that PureDiffusion outperforms existing defense methods with a large gap in terms of fidelity (i.e., how much the inverted trigger resembles the original trigger) and backdoor success rate (i.e., the rate that the inverted trigger leads to the corresponding backdoor target). Notably, in certain cases, backdoor triggers inverted by PureDiffusion even achieve higher attack success rate than the original triggers.

The increasing sophistication of cyber threats demands robust and adaptive Intrusion Detection Systems (IDS) capable of generalizing across diverse network environments. However, traditional AI-driven IDS models suffer from performance degradation when deployed in unseen domains due to domain shift discrepancies in data distributions caused by varying network configurations, attack patterns, or data collection methods. While unsupervised domain adaptation has recently been applied to address domain shift, its use in IDS remains limited and often lacks adaptation to the unique challenges of network data. To bridge this gap, we propose an Enhanced Adversarial Domain Adaptation (E-ADDA) Framework for IDS, designed to align feature representations between source and target domains, enhancing model generalizability. Our framework is rigorously evaluated on three publicly available IDS datasets, demonstrating significant improvements in key metrics such as accuracy, F1 score, and loss compared to existing domain adaptation methods. The results highlight the viability of adversarial domain adaptation in improving IDS resilience against zero-day attacks and evolving threats, offering a promising direction for real-world cybersecurity applications.

This paper studies the wireless scheduling design to coordinate the transmissions of (local) model parameters of federated learning (FL) for a swarm of unmanned aerial vehicles (UAVs). The overall goal of the proposed design is to realize the FL training and aggregation processes with a central aggregator exploiting the sensory data collected by the UAVs but it considers the multi-hop wireless network formed by the UAVs. Such transmissions of model parameters over the UAV-based wireless network potentially cause large transmission delays and overhead. Our proposed framework smartly aggregates local model parameters trained by the UAVs while efficiently transmitting the underlying parameters to the central aggregator in each FL global round. We theoretically show that the proposed scheme achieves minimal delay and communication overhead. Extensive numerical experiments demonstrate the superiority of the proposed scheme compared to other baselines.

Metaverse allows a 3D virtual mapping of the physical world to the digital world in which users interact with each other via digital avatars with a wide range of virtual activities. To realize this, the metaverse will inevitably employ numerous machine learning (ML) systems to enable the virtual-physical mapping process and offer intelligent virtual services to metaverse users (MUs). However, metaverse service providers (MSPs), who need ML models for their services (e.g., virtual events and healthcare services), may not have the expertise or resources required to build these underlying ML models. In addition, although ML models can be offered by a crowd of experienced ML workers (MLWs), the MLWs might not be able to collect the desired data for training their ML models due to privacy issues and the large-scale, distributed nature of the metaverse. In this paper, we propose MetaCrowd, a blockchain-based ML crowdsourcing framework that aims to overcome the mentioned issues and make ML accessible to a wide range of MUs and MSPs. Unlike traditional crowdsourcing systems which rely on central authorities, MetaCrowd is decentralized and automatic thanks to blockchain and smart contracts, thereby mitigating the single point of failure and trust issues. Experimental results illustrate the efficiency of MetaCrowd in both performance and cost. In addition, a decentralized application is also implemented and published widely to show its feasibility in practice.

Machine learning (ML)-based malware detection plays a crucial role in cyber-security by enabling the identification of potential malware threats without relying solely on predefined signatures or rules. Conventional ML approaches require a feature engineering step to analyze and convert collected data (e.g., captured network traffic and malware programs) into a format suitable for model training and prediction. However, this particular step typically requires a considerable depth of domain-specific expertise and also adds additional complexity to the learning process. To mitigate this limitation, we propose to perform malware detection directly based on the byte-level representation of malware data. We employ a byte embedding layer to convert byte sequences into higher-dimension representations. Then, we employ the multi-head attention technique to capture their correlation before forwarding the output to a fully connected deep neural network for malware detection. Extensive experiments on multiple datasets with diverse file formats demonstrated the superior performance of our proposed method. Additionally, we performed an ablation study on the role of the byte-embedding layer to show that our approach does not depend on a high embedding dimension for strong predictive performance, which helps reduce training complexity.

Recent generative diffusion models are attracting enormous attention with various breakthroughs in text-to-image, text-guided image-to-image, and text-to-3D generation. In this paper, we propose MobileGen3D, a bridge between text-driven real-world-to-3D generation and real-time on-device rendering. Given several real-world images of a person/object and a text prompt, MobileGen3D can provide a 3D model of the given content which has been customized according to the text prompt and can be rendered on mobile devices in real-time. No additional 3D training data is required in our method. Based on neural light fields (NeLF), MobileGen3D speeds up the inference process dramatically compared to other 3D synthesis methods that rely on neural radiance fields (NeRF). As a result, we demonstrate that our method can generate high-resolution 3D contents with realistic edits and low disk storage requirement of just 6.48 MB. These 3D contents can be rendered directly by mobile devices and augmented/virtual reality devices with a high rendering speed of 61.2 FPS on our experimented iPhone 14. Our implementation is available with detailed guidelines at this page: https://github.com/tuanvu171/MobileGen3D

In this article, we study the computation offloading problem in hybrid edge-cloud based space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, user scheduling, edge-cloud computation, radio resource allocation, and admission control is performed. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by ground users (GUs) via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve four sub-problems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and multi-UAV trajectory control until convergence. Moreover, feasibility verification and admission control strategies are proposed to handle overloaded network scenarios. Furthermore, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex computation resource and bandwidth allocation and UAV trajectory control sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines.

Intrusion detection is a crucial task to ensure the security of the Internet of Things (IoT) wireless networks. While different Machine Learning (ML) methods have been leveraged to detect network intrusion, they often require data from multiple devices to be collected and stored on a central server to train underlying ML models, raising privacy concerns. Federated Learning (FL) can preserve data privacy where local devices can iteratively update the model parameters trained by using their own local datasets and send them to a server for model ag-gregation. Moreover, most existing ML-based intrusion detection designs are based on supervised ML using labeled data, which may not be available or time-consuming to build. Therefore, semi-supervised learning methods that effectively utilize both labeled and unlabeled data would be very desirable and necessary. This paper proposes a semi-supervised FL method for intrusion detection in IoT networks. Specifically, our proposed framework leverages the attention-based architecture called TabNet to selec-tively focus on important features of network flow and we propose a crucial data preparation procedure before training the ML model using the FL approach. We conduct extensive numerical studies to demonstrate the effectiveness of our approach and compare its performance to other baselines. We also present empirical evidence to support the interpretability of our method. We also show that the proposed data pre-processing procedure indeed greatly enhances the intrusion detection performance.1

The emerging metaverse is envisioned as a virtual mapping of the real world, thus it would inevitably employ numerous Machine Learning (ML) frameworks to analyze and process massive data for the virtual-physical synchronization process. As a distributed ML paradigm, Federated Learning (FL) can naturally take advantage of numerous IoT, wearable devices, and edge, cloud servers under the metaverse infrastructure to train ML models with privacy guarantee. However, the large-scale and decentralized nature of the metaverse can pose significant challenges to traditional FL schemes, where there is a centralized server aggregating the local models received from local devices. It is not only vulnerable to Single Point of Failure (SPoF), but also lacks incentive mechanisms encouraging metaverse users to contribute their resources and data. In this paper, we propose BFLMeta, a blockchain-based FL scheme for the metaverse in which the aggregation process is performed in a decentralized manner, while the framework can estimate the non-IID degree of data to flexibly adjust blockchain committee size, thereby mitigating the impact of malicious aggregators. Security analysis shows that BFLMeta can resist SPoF, poisoning attack, privacy leakage, and sybil attack. Besides, our evaluation on computation, communication, and performance illustrates the efficiency of BFLMeta. Notably, BFLMeta can converge even with more than 50% poisoning nodes.

In the current age of digital world with the emergence of metaverse, digital assets are increasingly recognized and become more and more valuable. Unlike real-world assets, managing digital contents is more challenging since their associated information might be leaked widely on the Internet, making them worthless. Traditional digital asset management (DAM) systems based on third-party authorities and centralized databases have various weaknesses, threatening the benefits of stakeholders. In this paper, we propose a blockchain-based DAM framework utilizing smart contract, InterPlanetary File System (IPFS), and multi-layer encryption mechanisms for access control of digital assets in the metaverse. Our proposed design eliminates the intervention of intermediaries and offers a wide range of advanced security features such as resistance against data leakage and data alteration without trust assumptions among participants. Besides, key features of blockchain are leveraged to provide the system with immutability, traceability and transparency of information. To prove the feasibility of our design, we build a Decentralized Application (DApp) operating as a marketplace for digital assets using the proposed DAM framework. Experimental results indicate that the framework is more cost-effective than existing platforms, while advanced security features are integrated and automation is maximized.

Many practical Internet of Things (IoT) applications require deploying end nodes (ENs) in hard-to-access places where replacing batteries is difficult or impossible. As a result, the ENs demand high-energy efficiency. Long-range wide area network (LoRaWAN) is an IoT protocol that aims to achieve low-energy consumption. However, the energy consumption in LoRaWAN is related to transmission power, which can be set mainly based on path loss and shadow fading modeling and link budget analysis. Hence, appropriately setting this transmission power parameter saves energy and guarantees reliable communication links. Traditional path loss and shadow fading modeling and transmission power setting do not consider the variations caused by different environmental effects. In this work, we show via real-life data analysis that path loss and shadow fading depend on environmental variables. We propose machine learning models to calculate the empirical path loss and shadow fading, which is used to set the transmission power to save ENs’ energy. Our models include the effects of distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and signal-to-noise ratio. Specifically, the models are based on multiple linear regression, support vector regression, random forests, and artificial neural networks, exhibiting a root mean square error (RMSE) up to 1.566 dB and$R^{2}$up to 0.94. For energy saving, the developed models serve to set the transmission power and spreading factor based on the adaptive data rate (ADR) algorithm principles, which reduces the link margin saving energy up to 43% compared with the traditional ADR protocol.

In this paper, we study the computation offloading problem in space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicles (UAVs) trajectory control, user scheduling, computation and resource allocation is performed. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by ground users (GUs) via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve five sub-problems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource, bandwidth allocation, and multi-UAV trajectory control until convergence. In addition, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex bandwidth allocation and UAV trajectory control sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under different network settings.

In this work, we study the joint optimization of multiple unmanned aerial vehicles (UAVs)’ trajectories, power allocation, user-UAV association, and user pairing for UAV-assisted wireless networks employing the nonorthogonal multiple access (NOMA) for uplink communications. The design aims to minimize the total energy consumption of ground users while guaranteeing to successfully transmit their required amount of data to the UAV-mounted base stations. The underlying problem is a mixed-integer nonlinear program (MINLP), which is difficult to solve optimally. To tackle this problem, we derive the optimal power allocation as a function of other variables, which is used to transform the optimization problem into an equivalent form. We then propose an iterative algorithm to solve the resulting optimization problem by using the block coordinate descent (BCD) method where three subproblems are solved in each iteration and this process is repeated until convergence. Specifically, given the UAVs’ trajectories and data rates, we solve the NOMA user pairing, and user-UAV association subproblem optimally by exploiting its special structure. Then, we describe how to optimize the users’ data rates and tackle the UAV trajectory optimization in the second and third subproblems, respectively, by using the successive convex approximation (SCA) method. Numerical results show that our proposed algorithm can provide efficient active-inactive schedules (by setting user’s transmit powers to zero), and lower energy consumption compared to an existing baseline, and an OMA-based resource allocation and UAV-trajectory optimization strategy.

In this article, we study the trajectory control, subchannel assignment, and user association design for unmanned aerial vehicles (UAVs)-based wireless networks. We propose a method to optimize the max-min average rate subject to data demand constraints of ground users (GUs) where spectrum reuse and co-channel interference management are considered. The mathematical model is a mixed-integer nonlinear optimization problem which we solve by using the alternating optimization approach where we iteratively optimize the user association, subchannel assignment, and UAV trajectory control until convergence. For the subchannel assignment subproblem, we propose an iterative subchannel assignment (ISA) algorithm to obtain an efficient solution. Moreover, the successive convex approximation (SCA) is used to convexify and solve the nonconvex UAV trajectory control subproblem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design considering different UAV flight periods and number of subchannels and GUs as compared with a simple heuristic.

With the help of inter-vehicle communication (IVC), autonomous vehicles (AVs) can drive cooperatively, thus significantly improve road safety, traffic efficiency, and environmental sustainability. While substantial research has been conducted to investigate the efficiency of AVs in transportation systems, few attempts have been carried out to explore the lane-changing operations of AVs in multi-lane freeways, especially the dynamics of AVs at lane-drop bottlenecks. To this end, this paper aims to develop a cooperative space distribution method (CSDM) not only to increase the lane-drop bottleneck’s throughput but also to equally distribute the AVs in the dropped lane to other lanes by efficiently coordinating the dynamics of AVs upstream of the bottleneck in a multi-lane freeway. More specifically, we propose a novel framework where the freeway (region of interest) is divided into three segments: i) platoon segment, ii) acceleration segment, and iii) merging segment. In the first segment, AVs travel together in the platoon to guarantee their safety; they will then speed up to attain the maximum velocity and reach the determined position in the second segment. Finally, these AVs change lanes in the last segment and pass through the bottleneck with maximum velocity and minimum gap (i.e., gap distance). Simulation results are tested to demonstrate the performance of the proposed method, where we show that the proposed framework can significantly decrease the average travel time of all vehicles.

In this paper, we study the trajectory control and sub-channel assignment for unmanned aerial vehicle (UAV) based wireless networks with wireless backhauls. This design aims to maximize the min rate achieved by ground users (GUs) subject to their data transmission demands. To tackle the underlying mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively optimize the sub-channel assignment and UAV trajectory control until convergence. Toward this end, we propose a heuristic algorithm to obtain a feasible solution for the sub-channel assignment sub-problem. In addition, the successive convex approximation (SCA) method is used to convexify and solve the non-convex UAV trajectory control sub-problem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design for different network settings.

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high overhead of raw data communications. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without directly exposing their underlying data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main issues in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions and the lessons learned along with the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this paper, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms, which efficiently converge to an ME, are introduced. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques.

Intelligent offloading of computation-intensive tasks to a mobile cloud server provides an effective mean to expand the usability of wireless devices and prolong their battery life, especially for low-cost internet-of-things (IoT) devices. However, realization of this technology in multiple-input multiple-output (MIMO) systems requires sophisticated design of joint computation offloading and other network functions such as channel state information (CSI) estimation, beamforming, and resource allocation. In this paper, we study the computation task offloading and resource allocation optimization in MIMO based mobile edge computing systems considering perfect/imperfect-CSI estimation. Our design aims to minimize the maximum weighted energy consumption subject to practical constraints on available computing and radio resources and allowable latency. The optimal and low-complexity algorithms are proposed to solve the underlying mixed integer non-linear problems (MINLP). For the perfect-CSI, we employ bisection search to find the optimal solution. The low-complexity algorithms are developed by decomposing the original optimization problem into the offloading optimization (OP) and power allocation (PA) subproblems and solve them iteratively. Moreover, the difference of convex functions (DC) method is employed to deal with non-convex structure of (PA) subproblems in the imperfect-CSI scenario. Numerical results confirm the advantages of proposed designs over conventional local computation strategies in energy saving and fairness.

In this paper, we study the trajectory control and sub-channel assignment for unmanned aerial vehicles (UAVs) based wireless networks with wireless backhaul links. This design aims to optimize the max-min rate subject to data transmission demands of ground users (GUs). The underlying problem is a mixed integer nonlinear optimization problem because of the complicated relationship between the UAV-GU channel gains and the UAV's location in each time slot of the flight period. To tackle this problem, we employ the alternating optimization approach where we iteratively optimize the sub-channel assignment and UAV trajectory control until convergence. Moreover, the difference of convex functions (DC) optimization method and the arithmetic and geometric means (AM-GM) inequality are employed to convexify and solve the non-convex UAV trajectory sub-problem. Via extensive numerical studies, we illustrate the effective UAV's trajectory considering capacity-limited access and backhaul links and the non-negligible rate gain of the proposed design compared to a baseline employing the circular UAV trajectory around the center of service area and a heuristic algorithm for sub-channel assignment.

Network function Virtualization (NFV) and Mobile Edge Computing (MEC) are promising 5G technologies to support resource-demanding mobile applications. In NFV, one must process the service function chain (SFC) in which a set of network functions must be executed in a specific order. Moreover, the MEC technology enables computation offloading of service requests from mobile users to remote servers to potentially reduce energy consumption and processing delay for the mobile application. This paper considers the optimization of the computation offloading, resource allocation, and SFC placement in the multi-site MEC system. Our design objective is to minimize the weighted normalized energy consumption and computing cost subject to the maximum tolerable delay constraint. To solve the underlying mixed integer and non-linear optimization problem, we employ the decomposition approach where we iteratively optimize the computation offloading, SFC placement and computing resource allocation to obtain an efficient solution. Numerical results show the impacts of different parameters on the system performance and the superior performance of the proposed algorithm compared to benchmarking algorithms.

In this paper, we study the flight scheduling and trajectory control for UAV-based wireless networks. Particularly, we are interested in optimizing the flight time, trajectory, and power allocation for the UAVs serving the set of ground users. Our design allows UAVs to be dispatched at different time with different flight durations and trajectories to balance between communication and flying energy consumption considering UAVs' limited energy. To gain insights into the problem, we first study the single-UAV setting where we show mathematically that there exists a unique optimal flying duration to maximize the system throughput. Then, we investigate the double-UAV network scenario and we present an algorithm to optimize the trajectories, power allocation, user association for the UAVs. Via numerical studies, we show that the network throughput is maximized when the flying duration for each UAV is equal to its corresponding optimal flying duration in case of the single-UAV network.

Under the connected vehicle environment, autonomous vehicles (AVs) could bring numerous advantages including: improving the traffic flow, enhancing safety and alleviating air pollution. However, optimally operating AVs at signalized multi-lane intersections is a challenging problem due to the complex interaction of vehicles between lanes. It is thus a desire to manage and control the dynamics of AVs at signalized multi-lane intersections. To this end, this paper puts forward a bi-level control framework to optimize the intersection throughput. In our proposed method, the upper level (i.e. the intersection controller) is used to optimize the lane usages of each approach and the AVs' positions. In contrast, the lower level (i.e. the vehicle controllers) receives information from the upper level to control the AVs to get the maximum speed. More specifically, in the upper level, we apply a novel Space Distribution Method (SDM) for the AVs to maximize the throughput (i.e. a number of AVs) of the (multi-lane) intersection where signal timings are predefined. The SDM is divided into three steps: i) platoon formulation; ii) lane-mode optimization; and iii) AVs' position distribution. To maximize the throughput, the intersection controller receives information about the states of the AVs (e.g. the trajectories), then optimizes the lane usages for each approach, the desired speed, and the gap of the AVs as well as the AV's position along the approach. After that, each AV which is allowed to cross the intersection will determine its own trajectory and travel with the scheduled time without crash. Numerical simulations are set up to show that the throughput increases significantly, even more than twice of the throughput obtained from other methods in some circumstances.

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC.

In this paper, we study the problem of unmanned aerial vehicles (UAVs) placement and bandwidth allocation for wireless networks with wireless backhaul links. The general model with different possible configurations of line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions of wireless links between UAVs and ground users (GUs) is explicitly considered based on which we derive the average rates for wireless access links. The underlying problem is a difficult non-convex optimization problem due to the strong co-channel interference and the complicated relationship between the LoS/NLoS probabilities and UAVs' locations. To solve this challenging problem, we employ the alternative optimization approach where we iteratively optimize the bandwidth allocation and UAV placement until convergence. Moreover, we employ the difference of convex functions (DC) optimization and quadratic transformation approaches to convexify and tackle the non-convex UAV placement sub-problem. Via numerical studies, we show that the proposed scheme achieves a significant throughput gain compared to a baseline in which UAVs are deployed at the centers of hotspot areas.

The next generation 5G and beyond network, which necessitates advanced physical layer design utilizing distributed data and computational resources intelligently with improved context awareness, is expected to support multi-service traffics fundamentally different from the traditional ones. For this network, orthogonal frequency division multiplexing (OFDM) is believed to be one of the promising candidate waveforms where its performance depends on the accuracy of estimated CSI coefficients obtained via the discrete Fourier transform (DFT) method. This method first estimates the channel impulse response (CIR) followed by taking the DFT of the CIR coefficients. In practice, however, such an estimator suffers from performance degradation when the number of dominant CIR taps (i.e., taps with non-negligible amplitudes) is very small compared to the total size of CIR taps. This paper addresses this limitation by first examining the dominant CIR tap identification problem, and then using only dominant CIR taps in DFT based CSI estimation. In this regard, we propose a novel approach to formulate this problem as a signal to noise ratio (SNR) maximization convex problem where its global optimal solution can be obtained with a simple integer based bisection search. The formulated problem depends on the SNR of each CIR tap which is estimated from the received samples of the previous OFDM data blocks using a new and computationally manageable adaptive bootstrapping technique. We carry out extensive simulations to validate the analytical expressions and examine the effects of different parameters including channel stationarity duration and number of reference sub-carriers which are not used during data transmission (i.e., null sub-carriers). Numerical simulations corroborate the relevance of identifying dominant CIR taps for CSI estimation. In a typical long-term evolution (LTE) channel environment, the proposed approach can achieve up to 60% improvement in spectrum efficiency.

In this paper, we investigate the integration of the Non-Orthogonal Multiple Access (NOMA) technology into the Unmanned Aerial Vehicle (UAV)-based wireless system. In particular, we study the joint NOMA power allocation, user pairing, and UAV deployment (placement) for this wireless system. To gain insight into the optimal structure of this problem, we derive the optimal power allocation and UAV placement to maximize the sum-rate of the two-user (one NOMA pair) network. We then address the general setting with multiple NOMA pairs where users must be paired into two-user groups using NOMA. For this setting, we optimize the user pairing, power allocation, and UAV placement to maximize the minimum sum rate for individual user pairs. Solving this optimization problem optimally requires exhaustive search over all possible pairing scenarios, which has very high complexity. To overcome this challenge, we propose a heuristic pairing algorithm based on the minimum sum-of-squared-distance criteria whose pairing result is then applied to perform optimal power allocation and UAV placement. Through numerical studies, we show the significance of UAV placement optimization and the fact that the proposed heuristic user pairing scheme achieves close-to-optimal performance.

Network slicing is an emerging technology that enables network operators to efficiently monetize their infrastructure investment while meeting the ever-increasing network traffic demand. In this paper, we study a network slicing scenario where wireless access and wireless backhaul infrastructures are owned and managed by a wireless access service provider (WASP) and backhaul service providers (BHSPs), respectively. These service providers (SPs) are interested in providing an end-to-end network service to user equipments (UEs) while making profits through network resource trading. To study the market-based interactions among the WASP, BHSPs and UEs, we propose a Stackelberg game framework for access and backhaul resource pricing and allocation. The Stackelberg game equilibrium is determined by transforming the underlying bilevel programming (BLP) problem into a single-level program (SLP). Then, we apply the big-M and multi-parametric disaggregated techniques (MDT) to respectively address the non-convexity caused by the complementary slackness constraints and bilinear product terms. Numerical results are presented to confirm the efficacy of our proposed framework in terms of market stability and profitability for all the players.

In this paper, we jointly optimize computation offloading and resource allocation to minimize the weighted sum of energy consumption of all mobile users in a backhaul limited cooperative MEC system with multiple fog servers. Considering the partial offloading strategy and TDMA transmission at each base station, the underlying optimization problem with constraints on maximum task latency and limited computation resource at mobile users and fog servers is non-convex. We propose to convexify the problem exploiting the relationship among some optimization variables from which an optimal algorithm is proposed to solve the resulting problem. We then present numerical results to demonstrate the significant gains of our proposed design compared to conventional designs without exploiting cooperation among fog servers and a greedy algorithm.

This paper proposes a novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. With the proposed channel estimation and beamforming approach, we determine the number of cells$N_{c}$that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has a maximum of$L$uncorrelated multipath taps (or correlated multipath taps satisfying modest criteria which is valid in most practical scenarios), it is shown that$N_{c}=L$cells can estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination. The proposed approach can also be applied for general correlated multipath taps, and achieves good performance for this scenario as well. In a typical long term evolution (LTE) channel environment having delay spread$T_{d}=4.69\,\,\mu \text{s}$and channel bandwidth$B=5$MHz, we have found that$L=36$cells can use this band. In practice,$T_{d}$is constant for a particular environment and carrier frequency, and hence$L$increases as the bandwidth increases. All the analytical expressions have been validated, and the superiority of the proposed design over the existing ones is demonstrated using extensive numerical simulations both for correlated and uncorrelated channels. The proposed channel estimation and beamforming design is linear and simple to implement.

More network operators have recently provided content delivery network (CDN) services, where traffic engineering techniques, such as the base station (BS) association, are jointly employed with content delivery. This deployment attempts to reduce the network operating cost and enhance the quality of service (QoS) of end users. Toward this end, we study the BS association and file caching problem considering the spatial diversity of the file popularity and the realistic time-scale separation between the file caching and the BS association decisions in this paper. Our design aims to minimize the file delivery latency and operating power consumption in the cellular networks, where the tradeoff between these conflicting objectives is controlled by a single parameter. The short time-scale BS association problem is solved by using the convex optimization technique for a given file caching solution. However, the long time-scale file caching problem considering the varying BS association decisions taken at the short time-scale is difficult to tackle. To solve this file caching problem, we prove and leverage the submodularity property of the underlying objective function to develop a greedy content caching algorithm that guarantees a constant approximation ratio of the optimal objective value. Via simulations using real-world datasets, we show that the proposed algorithms outperform file caching and BS association algorithms that do not consider the spatial diversity of the file popularity in terms of the power consumption and delay performance in the geographically heterogeneous file popularity scenario.

In wireless network virtualization, guaranteeing service contracts with different mobile virtual network operators (MVNOs) and optimizing energy efficiency are crucial for the success of the virtualization scheme deployed by an infrastructure provider (InP). In this paper, a novel design framework is proposed for resource allocation in an OFDMAvirtualized wireless network (VWN). Treating the virtual resources for a VWN as commodities, the InP wants to maximize its revenue by leasing the infrastructure and resources to the MVNOs while meeting certain contract agreements. Moreover, MVNOs want to serve their users at the best performance and pay the minimum cost to the InP. A Lyapunov based online algorithm is proposed to solve the InP's long-term optimization problem. The shortterm optimization problem of the InP is considered as a combinatorial nonconvex problem. A multiple time-scale framework is proposed to solve the optimization problem of the InP, which decomposes the pricing decision, base station assignment, and resource allocation into different time-scale algorithms to achieve the design objectives. First, a distributed matching based algorithm is proposed to solve the base station assignment problem. Second, we propose a successive convex approximation approach to solve the joint subchannel assignment and energy efficiency problem. Finally, we propose a branch and bound based algorithm to optimally solve the price decision problem. Simulation results show the trade-off between energy efficiency, InP's revenue, and the isolation provisioning.

Fog computing is transforming the network edge into an intelligent platform by bringing storage, computing, control, and networking functions closer to end users, things, and sensors. How to allocate multiple resource types (e.g., CPU, memory, bandwidth) of capacity-limited heterogeneous fog nodes to competing services with diverse requirements and preferences in a fair and efficient manner is a challenging task. To this end, we propose a novel market-based resource allocation framework in which the services act as buyers and fog resources act as divisible goods in the market. The proposed framework aims to compute a market equilibrium (ME) solution at which every service obtains its favorite resource bundle under the budget constraint, while the system achieves high resource utilization. This paper extends the general equilibrium literature by considering a practical case of satiated utility functions. In addition, we introduce the notions of non-wastefulness and frugality for equilibrium selection and rigorously demonstrate that all the non-wasteful and frugal ME are the optimal solutions to a convex program. Furthermore, the proposed equilibrium is shown to possess salient fairness properties, including envy-freeness, sharing-incentive, and proportionality. Another major contribution of this paper is to develop a privacy-preserving distributed algorithm, which is of independent interest, for computing an ME while allowing market participants to obfuscate their private information. Finally, extensive performance evaluation is conducted to verify our theoretical analyses.

This paper proposes a novel approach for designing channel estimation, beamforming and scheduling jointly for wideband massive multiple input multiple output (MIMO) systems. With the proposed approach, we first quantify the maximum number of user equipments (UEs) that can send pilots which may or may not be orthogonal. Specifically, when the channel has a maximum of ℒ multipath taps, and we allocate $\tilde{M}$ sub-carriers for the channel state information (CSI) estimation, a maximum of $\tilde{M}$ UEs' CSI can be estimated (ℒ times compared to the conventional CSI estimation approach) in a massive MIMO regime. Then, we propose to schedule a subset of these UEs using greedy based scheduling to transmit their data on each sub-carrier with the proposed joint beamforming and scheduling design. We employ the well known maximum ratio combiner (MRC) beamforming approach for the uplink channel data transmission. All the analytical expressions are validated via numerical results, and the superiority of the proposed design over the conventional orthogonal frequency division multiplexing (OFDM) transmission approach is demonstrated using extensive numerical simulations in the long term evolution (LTE) channel environment. The proposed channel estimation and beamforming design is linear and simple to implement.

Intelligent offloading of computation-intensive tasks to a mobile edge computing server provides an effective mean to expand the usability of mobile devices and prolong their battery life. However, realization of this technology in today's multiple input multiple output (MIMO) wireless systems requires the sophisticated design of joint computation offloading and other communications functions such as channel state information (CSI) estimation and resource allocation. In this paper, we study the optimization of computation task offloading and resource allocation in MIMO wireless systems considering perfect and imperfect CSI estimation. Our design aims to minimize the maximum weighted energy consumption (Min-max W.C.E) subject to practical constraints on computing and radio resources and service latency. The optimal and sub-optimal algorithms are proposed to solve the underlying mixed integer non-linear problem (MINLP). In particular, the bisection search and difference of convex (DC) optimization methods are employed to determine the global and sub-optimal solutions for the perfect and imperfect CSI scenarios, respectively. Numerical results confirm the advantages of the proposed design over the conventional local computation strategy in handling computationally heavy tasks.

In this paper, we study the joint prioritized link scheduling and resource allocation for OFDMA-based wireless networks, which serve two classes of wireless links, namely, non-prioritized (low-priority) and prioritized (high-priority) links. Our design aims to maximize the number of scheduled non-prioritized links and their sum rate, while guaranteeing the minimum required rates of all active prioritized and non-prioritized links. We present the problem formulation as a single-stage optimization problem, which simultaneously maximizes the number of scheduled non-prioritized links and their sum rate. We propose a monotonic-based optimal approaching (MBOA) algorithm to solve this problem by employing the monotonic global optimization technique and an efficient rounding procedure. We prove that the MBOA algorithm can schedule the maximum number of non-prioritized links with slight and controllable degradation in the minimum required rates of non-prioritized links. For low-complexity design, we propose an iterative convex approximation algorithm, which sequentially performs power allocation and link removal in each iteration. We then describe how the proposed algorithms can be implemented in the standardized LTE-based cellular system. Finally, we conduct numerical studies for device-to-device communications underlaid cellular networks under perfect or imperfect channel state information (CSI). Numerical results demonstrate that the proposed algorithms can be applied to the imperfect CSI scenario with slight degradation in the network performance. Moreover, in the perfect CSI scenario, the proposed algorithms significantly outperform the conventional algorithms both in the number of scheduled non-prioritized links and their sum rate.

Most existing content caching designs require accurate estimation of content popularity, which can be challenging in the dynamic mobile network environment. Moreover, emerging hierarchical network architecture enables us to enhance the content caching performance by opportunistically exploiting both cloud-centric and edge-centric caching. In this paper, we propose a hybrid content caching design that does not require the knowledge of content popularity. Specifically, our design optimizes the content caching locations, which can be original content servers, central cloud units (CUs) and base stations (BSs) where the design objective is to support as high average requested content data rates as possible subject to the finite service latency. We fulfill this design by employing the Lyapunov optimization approach to tackle an NP-hard caching control problem with the tight coupling between CU caching and BS caching control decisions. Toward this end, we propose algorithms in three specific caching scenarios by exploiting the submodularity property of the sum-weight objective function and the hierarchical caching structure. Moreover, we prove the proposed algorithms can achieve finite content service delay for all arrival rates within the constant fraction of capacity region using Lyapunov optimization technique. Furthermore, we propose practical and heuristic CU/BS caching algorithms to address a general caching scenario by inheriting the design rationale of the aforementioned performance-guaranteed algorithms. Trace-driven simulation demonstrates that our proposed hybrid CU/BS caching algorithms outperform the general popularity based caching algorithm and the independent caching algorithm in terms of average end-to-end service latency and backhaul/fronthaul load reduction ratios.

In this paper, we develop a content caching and flow level BS-user association framework in a network environment with the spatial variation of content popularity. Because the studied content caching and BS-user association functions are tightly intertwined with each other, and their decision time scales can be very different in practice, our design considers the time-scale separation of these network functions to tackle and develop the BS-user association and content caching policies. Specifically, we propose an optimal BS-user association algorithm, namely OptUA, operating in the short time scale for a given content caching solution, and a greedy content caching algorithm, namely GCC, operating in the long time scale. The GCC algorithm exploits the submodularity characteristics of the objective function which ensures that the GCC algorithm achieves a constant fraction of the optimal performance for most feasible caching sets. Via extensive numerical studies in heterogeneous cellular networks, we demonstrate that proposed OptUA and GCC algorithms outperform other algorithms which do not consider spatial variations of content popularity in terms of average end-to-end delay per content request and average system load per content at each BS.

In this paper, we study the joint computation offloading and resource allocation problem in the two-tier wireless heterogeneous network (HetNet). Our design aims to optimize the computation offloading to the cloud jointly with the subchannel allocation to minimize the maximum (min-max) weighted energy consumption subject to practical constraints on bandwidth, computing resource and allowable latency for the multi-user multitask computation system. To tackle this non-convex mixed integer non-linear problem (MINLP), we employ the bisection search method to solve it where we propose a novel approach to transform and verify the feasibility of the underlying problem in each iteration. In addition, we propose a low-complexity algorithm, which can decrease the number of binary optimization variables and enable more scalable computation offloading optimization in the practical wireless HetNets. Numerical studies confirm that the proposed design achieves the energy saving gains about 55% in comparison with the local computation scheme under the strict required latency of 0.1s.

Content caching is an important fifth-generation (5G) technique, which aims to improve the user quality of service and relieve backhaul congestion, by placing popular contents at the network edge. Meanwhile, wireless network virtualization (WNV) provides a novel paradigm shift in 5G system design which enables to better utilize network resources, reduces the operation cost (OPEX), and allows rapid deployment of new services. Efficient deployment of the content caching technology in the virtualized wireless network environment, however, requires a suitable radio resource allocation framework to realize the great benefits of these technologies. In this paper, we study the joint resource allocation and content caching problem which aims to efficiently utilize the radio and content storage resources. This design problem targets at minimizing the maximum content request rejection rate experienced by users of different mobile virtual network operators (MVNO) in different cells, which results in a mixed-integer non-linear program (MINLP).We solve this NP-hard problem by developing a bisection-search based algorithm that iteratively optimizes the resource allocation and content caching solution. Extensive numerical results confirm the efficacy of our proposed framework which significantly reduce the maximum rejection rate compared to other benchmark algorithms.

In this paper, we study an overlay RF-powered cognitive radio network with ambient backscatter communications. In the network, when the channel is occupied, the secondary transmitter (ST) can perform either energy harvesting or data transmission using ambient backscattering technique to a gateway. We consider the case that the gateway charges the ST a certain price if the ST transmits information. This leads to questions of how to determine the best price for the gateway and how to find the optimal backscatter time. To address this problem, we propose a Stackelberg game in which the gateway is the leader adapting the price to maximize its profit in the first stage. Meanwhile, the ST chooses its backscatter time to maximize its utility in the second stage. To analyze the game, we apply the backward induction technique. We show that the game always has a unique subgame perfect Nash equilibrium. Additionally, our results provide insights on the impact of the competition on the players' profit and utility.

In this paper, we consider the content caching design without requiring historical content access information or content popularity profiles in a hierarchical cellular network architecture. Our design aims to dynamically select caching locations for different contents where caching locations can be content servers, cloud units (CUs), and base stations (BSs). Our design objective is to support as high content request rates as possible while maintaining the finite service time. To tackle this design problem, we employ the Lyapunov optimization method where the caching algorithm is developed by minimizing the Lyapunov drift of a quadratic Lyapunov function of virtual queue backlogs. This solution approach requires to solve a max-weight problem, which is an NP-hard and difficult problem to solve due to the coupling between CU caching and BS caching decisions. By exploiting the submodularity of the objective function, we propose a hybrid caching algorithm which achieves the constant approximation ratio to the optimal performance. Trace-driven simulation results demonstrate that the proposed joint CU/BS caching algorithm achieves almost the same performance with the exhaustive search and outperforms the independent caching algorithm and heuristic joint caching algorithms in terms of average end-to-end latency and backhaul load reduction ratio.

In this paper, we study the joint computation offloading and resource allocation problem exploiting computing resources from both mobile edge cloud and mobile peers. Our design aims to optimize the computation load assignments to local processors in the mobile users, mobile peers and the edge cloud jointly with the resource allocation to achieve the minimum weighted energy consumption subject to practical constraints on the bandwidth and computing resources and allowable latency. To tackle this non-convex optimization problem, we employ the successive convex approximation (SCA) method where we transform the underlying problem and iteratively solve a sequence of approximated convex problems. Moreover, the geometric programming (GP) method is applied to find the optimal solution of the approximated problem. The proposed SCA-based approach employs the arithmetic-geometric mean (AGM) approximation and the proposed algorithm is proved to converge to a local optimal solution. Finally, numerical studies confirm that the proposed scheme achieves energy saving gains about 60% and 10% in comparison with the local computation strategy and cloud offloading strategy under the strict required latency of 0.25s, respectively.

We propose a wireless virtualization framework based on the Stackelberg game model for resource allocation in downlink orthogonal frequency division multiple access (OFDMA) wireless networks. Specifically, the wireless virtualization model enables an infrastructure provider (InP) to effectively lease radio resources to multiple mobile virtual network operators (MVNOs) through adaptively setting resource prices whereas the MVNOs selfishly optimize the resource allocation to maximize their utilities. The Stackelberg game approach is employed to solve the underlying hierarchical problems where the InP acts as a leader while the MVNOs play the roles of the followers. In particular, we derive the Stackelberg equilibrium (SE), at which no player has incentive to deviate unilaterally. Extensive numerical results are presented to confirm the efficacy of our proposed framework in balancing the achievable utilities of the InP and MVNOs compared to other traditional pricing schemes.

In this paper, we study dynamic bandwidth slicing and resource allocation problems to support a mixture of IoT (Internet of Things) and video streaming services. By employing Lyapunov optimization method with time-scale separation approach, we develop algorithms for long time-scale bandwidth slicing, and short time-scale IoT device scheduling, power allocation (for IoT service) and quality decision (for video streaming service). We show through simulations that proposed dynamic bandwidth slicing and resource allocation algorithms outperform the static bandwidth slicing and resource allocation policies in terms of average total cost and average total delay.

In this work, we consider adjacent channel interference (ACI) cancellation in the dual satellite communication system which supports simultaneous uplink and downlink communications between an airplane and two satellites over adjacent frequency bands. The transmitter of the uplink communication toward one satellite creates strong ACI to the receiver on the downlink from the second satellite to the airplane. Most ACI cancellation techniques in the literature consider the scenario where both interfering and interfered signals have identical bandwidth (and same symbol rate) where our design is motivated by the uplink/downlink communications between an airplane and Iridium/Inmarsat satellites which happen over frequency bands of different sizes (hence, different symbol rates) for which the conventional ACI cancellation techniques are not applicable. Toward this end, we present the signal and system models for simultaneous communications on adjacent bands with different bandwidths and propose a constant-norm Least Square (CN-LS) based interference cancellation strategy. Our design also addresses the nonlinearity of the High Power Amplifier (HPA) where the first and third order interference terms are identified and canceled out by using the Volterra filter. We demonstrate through numerical studies that our design is robust against the frequency spacing and significantly outperforms the conventional detection scheme without ACI cancellation.

One of the important use-cases of 5G network is the vehicle to infrastructure (V2I) communication which requires accurate understanding about its dynamic propagation environment. As 5G base stations (BSs) tend to have multiple antennas, they will likely employ beamforming to steer their radiation pattern to the desired vehicle equipment (VE). Furthermore, since most wireless standards employ an OFDM system, each VE may use one or more sub-carriers. To this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without the training signal and channel impulse response (CIR) model. In this regard, first we utilize the well known adaptive recursive least squares (RLS) technique for predicting the next block CIR from the past and current block received signals (a block may have one or more OFDM symbols). Then, we jointly design the beamforming and VE scheduling for each sub- carrier to maximize the uplink channel average sum rate by utilizing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution is guaranteed. And, the VE scheduling design is formulated as an integer programming problem which is solved by employing a greedy search. The superiority of the proposed channel prediction and scheduling algorithms over those of the existing ones is demonstrated via numerical simulations.

To realize content caching at base stations (BSs), a caching system fetches contents to the appropriate base stations in advance then it uses the fetched contents to serve end users in the serving phase. This paper studies a caching problem for heterogeneous small-cell networks with QoS-aware and adaptive BS association where end users can be associated with either small-cell or macro-cell BSs. Toward this end, we derive the cache miss ratio for general caching strategy based on which we formulate a caching problem which aims at minimizing the cache miss ratio. To solve this problem, we propose two algorithms, namely Sparse Network Caching (SNC) and Two-Stage Caching (TSC) algorithms. We prove that the SNC algorithm can obtain the optimal caching solution as the request rate to each BS is much smaller than its serving capability. Numerical results demonstrate that the SNC algorithm performs well in the sparse network scenario while the TSC algorithm operates efficiently in all studied scenarios. Moreover, the proposed algorithms significantly outperform the random caching (RDC) and most popular caching (MPC) algorithms.

Satellites operating on same frequency bands with overlapping coverage can suffer from co-channel interference from the others. Hence, resource allocation and interference management for the multi-satellite system are very important to maintain the reliable communications and effective utilization of the radio resources. Such design typically requires the channel state information (CSI) of both desirable and interfering communication links; however, the large round trip delay in satellite communication renders the estimation of instantaneous CSI a difficult task. In this paper, we study the resource allocation for the uplink communications of two satellites using the cognitive radio concept where the two satellites are treated as the primary and secondary satellites. Many of conventional resource allocation problems for sum rate maximization deal with power management. Our design which does not require the instantaneous CSI knowledge aims to maximize the average sum rate of the secondary satellite by optimize both the secondary users' (SU) powers and angles toward the secondary satellite. To tackle the underlying non-convex resource allocation problem, we propose a block coordinate descent based iterative algorithm where in each iteration, the power allocation is solved optimally by using the dual based algorithm and the angles of SUs are determined to achieve the maximum average sum rate while maintaining the average interference constraints. We then conduct numerical studies and show significant performance improvement of the proposed algorithm compared to other conventional algorithms.

We develop a general resource allocation framework to solve the bandwidth provisioning problem for wireless virtualization deployment in OFDMA based wireless networks. Our design aims to maximize the profit achieved by an infrastructure provider (InP) through leasing radio resources to mobile virtual network operators (MVNOs) while minimizing the amount of allocated spectrum for efficient spectrum provisioning. Toward this end, we describe how to formulate such a resource allocation problem and then propose a dual-based algorithm to attain the asymptotically optimal solution.We also present a low-complexity resource allocation algorithm, which is suitable for online operation. Numerical results confirm the excellent performance of the proposed asymptotically optimal and low-complexity algorithms in achieving high profit for the InP as well as efficient resource provisioning.

In this paper, we study the resource allocation problem for a full-duplex (FD) multiuser wireless system consisting of one FD base-station (BS) and multiple FD mobile nodes. Our main focus is to jointly optimize the power allocation (PA) and subcarrier assignment (SA) for both uplink (UL) and downlink (DL) transmissions of all users to maximize the system sum-rate. Our design captures the self-interference of FD transceivers and allows the utilization of each subcarrier for multiple concurrent UP and DL transmissions. Since the joint optimization problem is a nonconvex mixed integer program, which is difficult to tackle, we propose to employ the bipartite matching method to address the SA. Toward this end, a fast greedy allocation algorithm is developed to perform initial assignment of UL/DL links to each subcarrier that offers the best sum rate. Then from the obtained SA solution, we adopt the successive convex approximation approach to solve the PA problem whose results are used to calculate the SA weights for re-optimizing the SA by using the bipartite matching method. We then present the numerical results to demonstrate the improvement of our proposed algorithm in comparison with the greedy FD and half-duplex (HD) resource allocation algorithms.

This paper studies the data scheduling and admission control problem for a backscatter sensor network (BSN). In the network, instead of initiating their own transmissions, the sensors can send their data to the gateway just by switching their antenna impedance and reflecting the received RF signals. As such, we can reduce remarkably the complexity, the power consumption, and the implementation cost of sensor nodes. Different sensors may have different functions, and data collected from each sensor may also have a different status, e.g., urgent or normal, and thus we need to take these factors into account. Therefore, in this paper, we first introduce a system model together with a mechanism in order to address the data collection and scheduling problem in the BSN. We then propose an optimization solution using the Markov decision process framework and a reinforcement learning algorithm based on the linear function approximation method, with the aim of finding the optimal data collection policy for the gateway. Through simulation results, we not only show the efficiency of the proposed solution compared with other baseline policies, but also present the analysis for data admission control policy under different classes of sensors as well as different types of data.

This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN) data transmission and detection approach for a single input multiple output system. It is assumed that the signal having a bandwidth B is transmitted through a wireless channel with L multipath components. Under this assumption, this paper provides a novel and simple OFTN transmission and symbol-by-symbol detection approach that exploits the multiplexing gain obtained by the multipath characteristic of wideband wireless channels. It is shown that the proposed design can achieve a higher transmission rate than the existing one [i.e., orthogonal frequency division multiplexing (OFDM)]. Furthermore, the achievable rate gap between the proposed approach and that of the OFDM increases as the number of receiver antennas increases for a fixed value of L. This implies that the performance gain of the proposed approach can be very significant for a large-scale multi-antenna wireless system. The superiority of the proposed approach is shown theoretically and confirmed via numerical simulations. Specifically, we have found upper-bound average rates of 15 and 28 bps/Hz with the OFDM and proposed approaches, respectively, in a Rayleigh fading channel with 32 receive antennas and signal-to-noise ratio of 15.3 dB. The extension of the proposed approach for different system setups and associated research problems is also discussed.

This paper examines a coordinated multi-point transmission/reception system where multiple base-stations (BSs) employ coordinated beamforming to serve multiple mobile-stations (MSs). Under the dynamic point selection mode, each MS can be assigned to only one BS at any time. This paper then presents a solution framework to optimize the BS associations and coordinated beamformers for all MSs. With target signal-to-interference-plus-noise ratios at the MSs, the design objective is to minimize either the weighted sum transmit power or the per-BS transmit power margin. Since the original optimization problems contain binary variables indicating the BS associations, finding their optimal solutions is a challenging task. To circumvent this difficulty, we first relax the original problems into new optimization problems by expanding their constraint sets. Based on the nonconvex quadratic constrained quadratic programming framework, we show that these relaxed problems can be solved optimally. Interestingly, with the first design objective, the obtained solution from the relaxed problem is also optimal to the original problem. With the second design objective, a suboptimal solution to the original problem is then proposed, based on the obtained solution from the relaxed problem. Simulation results show that the resulting jointly optimal BS association and beamforming design significantly outperforms fixed BS association schemes.

This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN) data transmission and detection approach for a single input multiple output (SIMO) system. It is assumed that the signal having a bandwidth is transmitted through a wireless channel having multipath components. Under this assumption, the current paper provides novel OFTN transmission and symbol-by-symbol detection approach that exploits the multiplexing gain obtained by the inherent characteristics of multipath components of wideband channels. In doing so, the proposed design achieves a higher transmission rate than the existing orthogonal frequency division multiplexing (OFDM) approach. It is also shown that the capacity gap between the proposed approach and that of OFDM increases as the number of receiver antennas increases for fixed . The superiority of the proposed approach has been shown theoretically and confirmed via numerical simulations.

To ensure reliable communications in randomly varying and error-prone channels, wireless systems use adaptive modulation and coding (AMC) as well as hybrid ARQ (HARQ). In order to elucidate their compatibility and interaction, we compare the throughput provided by AMC, HARQ, and their combination (AMC-HARQ) under two operational conditions: in slow- and fast block-fading channels. Considering both incremental redundancy HARQ and repetition redundancy HARQ, we optimize the rate-decision regions for AMC/HARQ and compare them in terms of attainable throughput. Under a fairly general model of the channel variation and the decoding functions, we conclude that: 1) adding HARQ on top of AMC may be counterproductive in the high average signal-to-noise ratio regime for fast fading channels and 2) HARQ is useful for slow fading channels, but it provides moderate throughput gains. We provide explanations for these results which allow us to propose paths to improve AMC-HARQ systems.

In this paper, we study the joint prioritized link scheduling and resource allocation for the OFDMA-based wireless network which serves two classes of user links, namely non-prioritized (low-priority) and prioritized (high-priority) links. Our design objectives are to maximize the number of non-prioritized links to be scheduled and to maximize the weighted sum rate of all scheduled links while guaranteeing the minimum rate requirements of all prioritized links. To solve this problem, we first transform the original problem into a singlestage optimization problem which is a Mixed Integer Nonlinear Program (MINLP). Then, we propose an iterative algorithm to solve the transformed problem where we sequentially perform modified power allocation and link removals. We prove the convergence and characterize important properties of the proposed algorithm. Numerical results show that the proposed algorithm significantly outperforms the greedy uniform power allocation and the rounding-based admission algorithm in term of the average number of scheduled non-prioritized links and the weighted sum rate.

Full-duplex (FD) has emerged as a promising solution for increasing the data rate of wireless communication systems. With FD, a wireless terminal can transmit and receive concurrently at the same frequency band. This paper focuses on the resource allocation in a FD multiuser system. With a FD-enabled base-station (BS) and multiple half-duplex (HD) mobile stations (MS), we are interested in jointly optimizing the uplink and downlink channel assignment for each MS and maximizing the system sum-rate. Since the joint optimization problem is a difficult nonconvex problem, we then propose an iterative algorithm to obtain at least a locally optimal solution. In the proposed algorithm, the system sum-rate is maximized via an equivalent problem of minimizing the weighted sum mean-squared error, whereas the channel assignment is updated by a gradient method. Simulation results show that the FD mode has the potential to substantially enhance a multiuser system's data-rate, compared to the HD mode.

Millimeter-wave (mmWave) communication has emerged as one of the most promising technologies to deal with the increasing demand in data transmissions over wireless networks. However, due to the propagation characteristic at the mmWave band, much higher pathloss is observed compared to the commonly-used microwave band. Thus, antenna arrays become a necessary ingredient in mmWave systems because of their needed beamforming gains. Beamforming for multiple users, also known as multiuser precoding, can be utilized to further improve the spectral efficiency of mmWave systems. Unfortunately, fully digital precoding with large antenna arrays is difficult to implement due to the hardware cost and power constraint in mmWave systems. Recent works in literature have advocated the structure of hybrid analog/digital precoding for mmWave systems, in which only minor performance degradation is observed. In this work, we study hybrid precoding for multiuser mmWave systems. After reviewing recent works in literature on hybrid precoding designs, we then develop a new hybrid minimum mean-squared error (MMSE) precoder. The proposed precoder can be easily obtained by an orthogonal matching pursuit-based algorithm. Simulation results show significant performance advantages of the proposed precoder over known designs in various system settings.

In this paper, we propose a multi-channel full-duplex Medium Access Control (MAC) protocol for cognitive radio networks (MFDC-MAC). Our design exploits the fact that full-duplex (FD) secondary users (SUs) can perform spectrum sensing and access simultaneously, and we employ the randomized dynamic channel selection for load balancing among channels and the standard backoff mechanism for contention resolution on each available channel. Then, we develop a mathematical model to analyze the throughput performance of the proposed MFDC-MAC protocol. Furthermore, we study the protocol configuration optimization to maximize the network throughput where we show that this optimization can be performed in two steps, namely optimization of access and transmission parameters on each channel and optimization of channel selection probabilities of the users. Such optimization aims at achieving efficient self-interference management for FD transceivers, sensing overhead control, and load balancing among the channels. Numerical results demonstrate the impacts of different protocol parameters and the importance of parameter optimization on the throughput performance as well as the significant performance gain of the proposed design compared to traditional design.

This paper considers the joint fronthaul resource and rate allocation for the OFDMA uplink cloud radio access networks (C-RANs). This amounts to determine users' transmission rates and quantization bit allocation for I/Q baseband signals, which must be transferred from remote radio heads (RRHs) to the cloud over the capacity-limited fronthaul network. Our design aims at maximizing the system sum rate through optimal allocation of fronthaul capacity and cloud computation resources. Toward this end, we propose a novel two-stage approach to solve the underlying non-linear integer problem. In the first stage, we relax the integer variables to attain a relaxed problem, which is solved by employing a pricing-based method. Interestingly, we show that the pricing-based problem is convex with respect to each optimization variable, which can be, therefore, solved efficiently. In addition, we develop a novel mechanism to iteratively update the pricing parameter which is proved to converge. In the second stage, we propose two different rounding strategies, which are applied to the obtained continuous solution of the relaxed problem to achieve a feasible solution for the original problem. Finally, we present numerical results to demonstrate the significant sum-rate gains of our proposed design with respect to a standard greedy algorithm.

Among several potential applications of Full- Duplex (FD) technology, FD Cognitive Radio (CR) communication is one important area where FD can provide several advantages and possibilities such as concurrent sensing and transmission, improved sensing efficiency and the secondary throughput. However, the main challenge is to mitigate the harmful effects of the residual Self-Interference (SI) which depends on the SI mitigation capability of the employed technique. One way to mitigate this effect is to control the transmit power of the CR node, however, this power control over the entire frame duration results in a power- throughput tradeoff. In this context, we propose a novel Two-Phase Concurrent Sensing and Transmission (2P-CST) framework in which a CR performs concurrent sensing and transmission for a certain fraction of the frame duration by employing a power control mechanism and for the remaining fraction of the frame duration, the CR only transmits with the full power. The proposed framework allows the flexibility to optimize the sensing time and the transmit power in order to maximize the achievable throughput of the FD-CR system. Our results demonstrate that the proposed 2P-CST FD transmission strategy provides better performance in terms of the achievable throughput than the conventional Periodic Sensing and Transmission (PST) and CST techniques.

This paper focuses on the resource allocation in a full-duplex (FD) multiuser single cell system consisting of one FD base-station (BS) and multiple FD mobile nodes. In particular, we are interested in jointly optimizing the power allocation (PA) and subcarrier assignment (SA) for uplink (UL) and downlink (DL) transmission of all users to maximize the system sum-rate. First, the joint optimization problem is formulated as nonconvex mixed integer program, a difficult nonconvex problem. We then propose an iterative algorithm to solve this problem. In the proposed algorithm, the PA is obtained by employing the SCALE algorithm, whereas the SA is updated by a gradient method. Finally, we present numerical results to demonstrate the significant gains of our proposed design compared to that due to two fast greedy algorithms.

Resource allocation and interference management for a multibeam multiple input and single output (MISO) satellite communication system are very important to maintain reliable communications and efficient utilization of radio resources. In this paper, we consider two different resource allocation problems which aim at maximizing the system sum rate and the sum utility of all users, respectively. By choosing the utility function to be a logarithmic function of the user rate, we can achieve the so-called proportional fairness for the users. We propose two different resource allocation algorithms to solve the considered problems, namely the max sum rate algorithm (MSRA) and max utility algorithm (MUA), respectively. Both algorithms are based on decomposed beamforming and user scheduling designs where the beamforming design is based on the advanced weighted minimum mean squared error (WMMSE) approach and scheduling schemes are proposed to solve the two resource allocation problems. Through numerical studies, we show that the proposed MSRA achieves the rate performance very close to that attained by the exhaustive search algorithm and the MSRA significantly outperforms the MUA in terms of system sum rate. However, the MUA leads to better fairness than the MSRA and the fairness indices achieved by both algorithms tend to be closer to each other when the number of users per beam becomes larger.

Modern wireless systems deal with the adverse and unpredictable channel conditions using two main transmission schemes considered as complimentary: adaptive modulation and coding (AMC) and hybrid ARQ (HARQ). In this work we use the effective capacity as the performance measure to evaluate different design options. We thus show how to calculate this performance measure under independent, identically distributed (i.i.d.) block-fading channel model considering AMC, HARQ and more importantly, a combination of thereof. Numerical results indicate that AMC alone outperforms the other schemes in the region of high average SNR; we provide an explanation for this observation and discuss possible paths for improvement.

This paper considers the joint processing design for the cloud radio access network (C-RAN) with limited cloud computation capacity. This amounts to determine the set of remote radio heads (RRHs) serving each user and the corresponding precoding vectors whose corresponding computation effort (CE) is a non-linear function of the number of antennas pooled from all serving RRHs and the modulation bits. Toward this end, we propose a novel three-step approach to solve the underlying mixed non-linear integer program. First, we transform this problem into a group association problem (GAP) with additional association constraints where each user must be associated with exactly one particular group of RRHs. Second, we study the relaxed power minimization problem (PMP) where the group association integer variables are relaxed and the computational constraint functions are approximated by weighted linear functions of transmission powers. We prove that this relaxed PMP can be solved optimally and the obtained optimal solution satisfies all association constraints of the original GAP problem. Third, we develop an iterative procedure to update the weight parameters of the approximated computational constraint functions to drive the achieved solution to an efficient and feasible solution of the original problem. Finally, we present numerical results to demonstrate the significant gains of our proposed design compared to that due to a fast greedy algorithm.

We consider a three-node buffer-aided relaying network with statistical quality-of-service (QoS) constraint in terms of maximum acceptable end-to-end queue-length bound outage probability. In particular, we study the adaptive link selection relaying problem that aims to maximize the constant supportable arrival rate μ to the source (i.e., the effective capacity). Fixed and adaptive source and relay power allocation are investigated. By employing asymptotic delay analysis, we first convert the QoS constraint into minimum QoS exponent constraints at the source and relay queues. We then derive the link selection and power allocation solutions as functions of the instantaneous link conditions and QoS exponents using Lagrangian approach. Solutions for various special cases of link conditions and QoS constraints are presented. Moreover, we compare the effective capacities of the proposed relaying schemes and other existing schemes under different link conditions and QoS constraints. Illustrative results indicate that the proposed schemes offer substantial performance gains, and power adaption outperforms fixed power allocation at low signal-to-noise power ratio (SNR) region or under loose QoS constraints.

This paper considers hybrid beamforming (HB) for downlink multiuser massive multiple-input multiple-output (MIMO) systems with frequency selective channels. The proposed HB design employs sets of digitally controlled phase (fixed phase) paired phase shifters (PSs) and switches. For this system, first we determine the required number of radio frequency (RF) chains and PSs such that the proposed HB achieves the same performance as that of the digital beamforming (DB) which utilizes N (number of transmitter antennas) RF chains. We show that the performance of the DB can be achieved with our HB just by utilizing rtRF chains and 2rt(N-rt+ 1) PSs, where rt≤ N is the rank of the combined digital precoder matrices of all subcarriers. Second, we provide a simple and novel approach to reduce the number of PSs with only a negligible performance degradation. Numerical results reveal that only 20-40 PSs per RF chain are sufficient for practically relevant parameter settings. Finally, for the scenario where the deployed number of RF chains (Na) is less than rt, we propose a simple user scheduling algorithm to select the best set of users in each subcarrier. Simulation results validate theoretical expressions, and demonstrate the superiority of the proposed HB design over the existing HB designs in both flat fading and frequency selective channels.

In this paper, we study the non-orthogonal dynamic spectrum sharing for device-to-device (D2D) communications in the D2D underlaid cellular network. Our design aims to maximize the weighted system sum-rate under the constraints that: 1) each cellular or active D2D link is assigned one subband and 2) the required minimum rates for cellular and active D2D links are guaranteed. To solve this problem, we first characterize the optimal power allocation solution for a given subband assignment. Based on this result, we formulate the subband assignment problem by using the graph-based approach, in which each link corresponds to a vertex and each subband assignment is represented by a hyper-edge. We then propose an iterative rounding algorithm and an optimal branch-and-bound (BnB) algorithm to solve the resulting graph-based problem. We prove that the iterative rounding algorithm achieves at least 1/2 of the optimal weighted sum-rate. Extensive numerical studies illustrate that the proposed iterative rounding algorithm significantly outperforms the conventional spectrum sharing algorithms and attains almost the same system sum-rate as the optimal BnB algorithm.

Abstract In this paper, we consider the joint design of data compression and 802.15.4‐based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a data concentrator. Our design exploits the correlation of the reported data in both time and space to efficiently design the data compression using the compressed sensing technique and the MAC protocol so that the reported data can be recovered reliably within minimum reporting time. Specifically, we perform the following design tasks: (i) we employ the two‐dimensional (2D) compressed sensing technique to compress the reported data in the distributed manner; (ii) we propose to adapt the 802.15.4 MAC protocol frame structure to enable efficient data transmission and reliable data reconstruction; and (iii) we develop an analytical model based on which we can obtain efficient MAC parameter configuration to minimize the reporting delay. Finally, numerical results are presented to demonstrate the effectiveness of our proposed framework compared with existing solutions. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. Using our channel estimation and beamforming approach, we determine the number of cells Nc that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has L multipath taps, it is shown that Nc≤ L cells can reliably estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination. For example, in a long term evolution (LTE) channel environment having delay spread Td= 4.69μ second and channel bandwidth B = 2.5MHz, we have found that L = 18 cells can use this band. In practice, Tdis constant for a particular environment and carrier frequency, and hence L increases as the bandwidth increases. The proposed channel estimation and beamforming design is linear, simple to implement and significantly outperforms the existing designs, and is validated by extensive simulations.

In this paper, we study the energy-efficient resource allocation for device-to-device (D2D) communication underlying cellular networks. Specifically, we aim to maximize the minimum weighted energy-efficiency (EE) of D2D links while guaranteeing the minimum data rates of the cellular links. This design, therefore, guarantees fairness for D2D links and quality-of-service (QoS) for cellular links. Toward this end, we first characterize the optimal power allocation for cellular links based on which the original resource allocation problem can be transformed into the joint sub-channel and power allocation problem for D2D links. We then propose a dual decomposition based algorithm to solve the resource allocation problem in the dual domain. Theoretical analysis demonstrates that the proposed algorithm achieves strong performance guarantee. Numerical studies show that the proposed algorithm achieves nearly optimal performance, and it performs much better than the spectrum-efficient algorithm.

This work considers the relay selection and resource allocation problem (i.e., link scheduling, and rate allocation) for multi-source, multi-relay dual-hop wireless networks. The relays employ buffers to store the received data from the sources for future transmissions. End-to-end (E2E) delay of each traffic flow originated from a source or a relay is constrained in terms of maximum allowable delay-outage probability. To solve this problem, we first study the resource allocation problem to maximize the constant supportable arrival rate of a non-prioritized source under minimum rate requirements of the prioritized sources and relays for a given relay selection solution. Then, the optimal relay selection can be determined to support the largest rate of the non-prioritized source among all possible relay selection solutions. We derive the resource allocation solutions using asymptotic delay analysis and convex optimization techniques. We also develop an online allocation algorithm which does not require the knowledge of the fading statistics by using stochastic approximation theory. Numerical results are presented to demonstrate the usefulness of the proposed resource allocation design for relay selection under different delay and rate constraint regimes.

In this paper, we consider the Medium Access Control (MAC) protocol design for full-duplex cognitive radio networks (FDCRNs). Our design exploits the fact that full-duplex (FD) secondary users (SUs) can perform spectrum sensing and access simultaneously, which enable them to detect the primary users' (PUs) activity during transmission. The developed FD MAC protocol employs the standard backoff mechanism as in the 802.11 MAC protocol. However, we propose to adopt the frame fragmentation during the data transmission phase for timely detection of active PUs where each data packet is divided into multiple fragments and the active SU makes sensing detection at the end of each data fragment. Then, we develop a mathematical model to analyze the throughput performance of the proposed FD MAC protocol. Furthermore, we propose an algorithm to configure the MAC protocol so that efficient self-interference management and sensing overhead control can be achieved. Finally, numerical results are presented to evaluate the performance of our design and demonstrate the throughput enhancement compared to the existing half-duplex (HD) cognitive MAC protocol.

This paper proposes a new user scheduling and sub-carrier allocation algorithm for multiuser downlink massive multiple input multiple output (MIMO) orthogonal frequency division multiple access (OFDMA) systems with hybrid analogdigital beamforming (HB). We assume that the transmitter having N antennas is serving Kidecentralized single antenna receivers by sub-carrier i. The scheduling algorithm leverages the solutions of the digital beamforming (DB) result and is designed to maximize the total sum rate of all sub-carriers under per carrier power constraint. For this system and problem setup, the proposed algorithm is explained as follows: First, we express the HB matrix of sub-carrier i as a product of ABi, where the high dimensional matrix A ∈ CN×Nais common to all subcarriers whereas, Bi∈ CNa×Kiis a low dimensional matrix which is designed for sub-carrier i, and Na is the number of RF chains satisfying N ≥ Na ≥ Ki. Second, we compute A as the first Na eigenvectors of the left singular value decomposition of the combined DB precoder matrices of sub-carriers having the highest sum rate. Finally, for fixed A, we compute Bi and its corresponding users such that the total sum rate of all sub-carriers is maximized. The performance of the proposed scheduling is studied analytically. Furthermore, the superiority of the proposed algorithm over that of the existing one is quantified analytically and demonstrated by computer simulations.

This paper studies resource allocation for the device-to-device (D2D) underlying cellular system where we aim to maximize two different energy-efficiency metrics of D2D links while guaranteeing the minimum data rates for cellular links. Specifically, we formulate two resource allocation problems that optimize two different objective functions corresponding to System Energy-Efficiency (SEE) and Total Individual Energy-Efficiency (TIEE). To solve these problems, we propose elegant algorithms, which decompose the considered problems into power control and cellular-D2D matching sub-problems. We prove that our proposed iterative algorithms for the SEE and TIEE problems converge to their optimal solutions. Numerical results show that the energy-efficiency achieved by the proposed SEE and TIEE algorithms is much higher than that of the optimal spectrum-efficiency (SE) algorithm with slight degradation in the system sum rate.

This paper presents a solution framework to jointly optimize the base station association strategy and linear beamforming design for downlink transmission in a multicell system. Assuming each mobile station can only be assigned to one base station, our design objective is to minimize the sum transmit power across the base stations with a set of target signal-to-interference-plus-noise ratios at the mobile stations. Since the original optimization problem involves binary variables for base station associations, finding its optimal solution is a challenging task. To circumvent this difficulty, the original problem is relaxed into a new optimization problem by expanding its constraint set. Interestingly, it is shown that the relaxed problem can be solved optimally and its solution is also optimal to the original problem. We then propose two solution approaches to tackle the relaxed problem: one via its Lagrangian dual problem and the other via its dual uplink problem. Simulation results show that the resulting jointly optimal base station association and beamforming design can significantly outperform fixed base station association schemes.

In two-tier LTE heterogeneous networks (HetNets), picocells can be offered radio resource in order to mitigate interference to picocell users in downlink transmission from high-power macrocell base station (MBS). This becomes important in order to maintain efficient operation of the network and generate benefit tradeoff between macrocell and picocells. In this paper, we propose a game based approach for joint resource partitioning and data offloading scheme to determine the amount of radio resource a MBS should offer to picocells and to determine how much traffic each picocell access point (AP) should admit from MBS. In our proposal, a two-stage Stackelberg game theory is applied to optimize the strategies of both MBS and APs in order to maximize both of their utilities and this scheme is implemented using the notion of Almost Blank Subframes (ABS) proposed in the LTE standard.

This paper deals with the energy-efficient resource allocation for device-to-device (D2D) communication underlaid cellular networks. Specifically, our design objective is to maximize the weighted energy-efficiency (EE) of D2D links while guaranteeing the minimum data rate for each cellular link. To solve this problem, we first characterize the optimal power allocation solution for the cellular links so that the original resource allocation problem can be transformed into the joint subchannel and power allocation problem for D2D links. We then propose a relaxation-based algorithm to solve the transformed problem. We prove that the proposed algorithm converges to the optimal solution of the original problem if no D2D link utilizes the maximum transmit power. Extensive numerical results demonstrate that the proposed algorithm achieves the optimal performance and it outperforms existing algorithms.

Spectrum Sensing (SS) is one of the fundamental mechanisms required by a Cognitive Radio (CR). Among several SS techniques, cyclostationary feature detection is considered as an important technique due to its robustness against noise variance uncertainty and its capability to distinguish among different systems on the basis of their cyclostationary features. However, one of the main limitations of this detector in practical scenarios is its performance degradation in the presence of cyclic frequency mismatch, which mainly arises due to the lack of knowledge about the transmitter clock/oscillator errors at the detector. In this context, this paper proposes a novel solution to address the cyclic frequency mismatch problem utilizing the Slepian basis expansion instead of the widely used Fourier basis expansion. It is shown that the proposed approach captures the deviation in the cyclic frequency caused by the aforementioned imperfections and hence provides a significant improvement in the sensing performance in the presence of cyclic frequency mismatch.

This paper provides experimental results of the edge detection and spectrum sensing algorithms for wideband cognitive radio networks which are recently proposed in [1] using software defined radio (SDR) platform. The considered algorithms employ ratio based test statistics for detecting the edges of all sub-bands and generalized energy detection (GED) for examining the status of each sub-band. In particular, we validate the theoretical detection and false alarm probabilities of the edge detection and GED algorithms of [1] experimentally for a number of practically relevant parameters such as sensing time and bandwidth. We also compare the performances of these algorithms with and without calibrating the Cognitive Radio Device (CRD). Through extensive experiments, we have found that the theoretical performances claimed in [1] can be achieved reliably just by performing appropriate calibration at the CRD. Moreover, we also verify that the considered detection algorithms are robust against noise variance uncertainty, carrier frequency and timing offsets.

The development of native wireless network virtualization implies introducing a new set of base station schedulers that considers the efficient allocation of wireless resources to different Service Providers (SPs) based on flexible Service Level Agreements (SLAs). In this paper we develop an efficient and fast centralized heuristic to allocate the radio resource blocks in multi-cell LTE networks. The scheme maximizes the network-wide sum rate while keeping track of the SLA of each SP expressed as a minimum bandwidth allocation in each cell. We also propose an iterative solution procedure for the non-convex power allocation problem based on DC programming. We find that the results of the heuristic are quite close to those of the iterative method. We also show that there is a significant rate reduction due to the service contracts. Finally, we find that even if the heuristic cannot meet all the requirements in one particular scheduling period, it does provide the required rate over a large number of periods.

This paper is concerned with linear precoding designs in a multiuser multiple-input single-output system. With the design objective of maximizing the system sum-rate, we take into consideration multiple linear power constraints at the base-station, including sum, per-antenna, and interference power constraints. We then propose two mean-squared error (MSE)-based precoders, namely minimum MSE (MMSE) and iterative minimization of weighted MSE (IWMMSE) precoders. Both proposed precoding designs are obtained by specialized iterative algorithms. To enforce the multiple power constraints, a certain set of auxiliary variables are introduced and updated iteratively at each algorithm. The proposed precoders are then given in closed-form at each iteration. Convergence of both proposed algorithms is then proved and verified by numerical simulations. Simulation results also show significant enhancements in sum-rate performance by the proposed precoding designs over zero-forcing precoding.

In this paper, we study the joint pilot assignment and resource allocation for sum-rate (SR) (or throughput) and system energy efficiency (SEE) maximization in the multi-user and multi-cell (MU-MC) massive MIMO network. We consider the pilot contamination effect in the considered problems, which is known to fundamentally limit the network performance, and we jointly optimize the number of activated antennas together with power allocation and pilot assignment. We transform the problems into a subtractive optimization form based on which we develop efficient algorithms to solve both SR and SEE maximization problems. Specifically, we decompose each considered problem into two subproblems, namely optimization of power and number of antennas in the first sub-problem and pilot assignment in the second one, and iteratively solve both sub-problems until convergence. To solve the first sub-problem, we employ a successive convex approximation (SCA) technique to attain a solvable convex problem. In addition, we propose a novel iterative low-complexity algorithm based on the Hungarian method to solve the pilot assignment sub-problem. Numerical studies are conducted to illustrate the convergence of the proposed algorithms, impacts of different parameters on the total SR and SEE, and significant performance gains of the proposed solution compared to the case with conventional pilot assignment.

In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a data concentrator. Our design exploits the correlation of the reported data in both time and space to perform efficient data compression using the compressed sensing (CS) technique and efficiently engineer the MAC protocol so that the reported data can be recovered reliably within minimum reporting time. Specifically, we perform the following design tasks: i) we employ the two-dimensional (2D) CS technique to compress the reported data in the distributed manner; ii) we propose to adapt the 802.15.4 MAC protocol frame structure to enable efficient data transmission and reliable data reconstruction; and iii) we develop an analytical model based on which we can obtain the optimal parameter configuration to minimize the reporting delay. Finally, numerical results are presented to demonstrate the effectiveness of our design.

This paper considers a sparse precoding design for sum-rate maximization in a cloud radio access network (Cloud-RAN). Constrained by the fronthaul link capacity and transmit power limit at each remote radio head (RRH), the sparse design amounts to determine the precoders at the RRHs as well as the set of serving RRHs for each mobile user. In this work, we first formulate the fronthaul link constraints as non-convex and discontinuous constraints with sparsity terms. These sparsity terms are then iteratively approximated into linear forms by means of reweighted ℓ1-norm with conjugate functions. Finally, to determine the beamforming vectors, the non-convex sum-rate maximization problem with linear constraints is transformed into an equivalent problem of iterative weighted mean-squared error minimization. Convergence of the proposed iterative algorithm is then proved and verified by the presented numerical results. In addition, numerical results demonstrate the superior performance by the proposed algorithm over a previously proposed one in literature.

In the dynamic spectrum access (DSA), pricing is an efficient approach providing economic incentives for operators, whereas load balancing yields congestion-avoidance incentives for secondary users (SUs). Despite complexities of 1) the couplings among pricing, load balancing, and SUs' spectrum access decision, and 2) the heterogeneity of primary users' traffic and SUs classes/types, we tackle the joint load balancing and pricing problem to maximize operators' revenue in two cognitive radio markets: monopoly and duopoly. For the monopoly market, we first show there exists a unique SUs' equilibrium arrival rate to the monopolist's channels. We then show that the joint problem can be solved efficiently by exploiting its convex structure. For the duopoly market, we first characterize a unique SUs' equilibrium arrival rate to two operators employing different DSA approaches. When two operators are noncooperative, we show that there exists a unique Nash equilibrium for each operator's revenue. When they are cooperative, we show that the social revenue optimization can achieve a unique optimal solution. Using the Nash bargaining framework, we also present a sharing contract that determines the optimal fraction of the social revenue for each operator. In both markets, we propose two algorithms that can find the largest SU class supportable by the operators.

This paper develops hybrid analog-digital channel estimation and beamforming techniques for multiuser massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF) chains. For these systems, first, we design novel minimum-mean-squared error (MMSE) hybrid analog-digital channel estimator by considering both cases with perfect and imperfect channel covariance matrix knowledge. Then, we utilize the estimated channels to enable beamforming for data transmission. When the channel covariance matrices of all user equipments (UEs) are known perfectly, we show that there is a tradeoff between the training duration and throughput. Specifically, we exploit the fact that the optimal training duration that maximizes the throughput depends on the covariance matrices of all UEs, number of RF chains, and channel coherence time (Tc). We also show that the training time optimization problem can be formulated as a concave maximization problem where its global optimal solution can be obtained efficiently using existing tools. The analytical expressions are validated by performing extensive Monte Carlo simulations.

This paper considers wide-band spectrum sensing and optimization for cognitive radio (CR) networks with noise variance uncertainty. It is assumed that the considered wide-band contains one or more white sub-bands. Under this assumption, we consider throughput maximization of the CR network while appropriately protecting the primary network. We address this problem as follows. First, we propose novel ratio based test statistics for detecting the edges of each sub-band. Second, we employ simple energy comparison approach to choose one reference white sub-band. Third, we propose novel generalized energy detector (GED) for examining each of the remaining sub-bands by exploiting the noise information of the reference white sub-band. Finally, we optimize the sensing time (To) to maximize the CR network throughput using the detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to noise ratio (SNR) wall and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED) is quantified analytically. We show that the optimal To depends on the noise variance information. In particular, with 10TV bands, SNR = -20 dB and 2s frame duration, we found that the optimal Tois 28.5 ms (50.6 ms) with perfect (imperfect) noise variance scenario.

We present a distributed power control algorithm to address the uplink interference management problem in cognitive radio networks where the underlaying secondary users (SUs) share the same licensed spectrum with the primary users (PUs) in multi-cell environments. Since the PUs have a higher priority of channel access compared to the SUs, minimal number of SUs should be gradually removed, subject to the constraint that all primary users are supported with their target signal-to-interference-plus-noise ratios (SINRs), which is assumed feasible. In our proposed algorithm, each primary user rigidly tracks its target-SINR by employing the conventional target-SINR tracking power control algorithm (TPC). Each transmitting SU employs the TPC as long as the total received power at the primary receiver is below a given threshold; otherwise, it decreases its transmit power in proportion to the ratio between the given threshold and the total received power at the primary receiver, which is referred to as the total received-power-temperature. We show that our proposed distributed power-update function has at least one fixed-point. We also show that our proposed algorithm not only improves the number of supported SUs but also guarantees that all primary users are supported with their (feasible) target-SINRs. Finally, we also propose an enhanced power control algorithm that achieves zero-outage for PUs and a better outage ratio for SUs. To this end, we provide a robust power control method that considers the uncertainties in channel gains.

This paper designs a novel hybrid (a mixture of analog and digital) beamforming and examines the relation between the hybrid and digital beamformings for downlink multiuser massive multiple input multiple output (MIMO) systems. We assume that perfect channel state information is available only at the transmitter and we consider the total sum rate maximization problem. For this problem, the hybrid beamforming is designed indirectly by considering a weighed sum mean square error (WSMSE) minimization problem incorporating the solution of digital beamforming which is obtained from the block diagonalization technique. The resulting WSMSE problem is solved by applying the theory of compressed sensing. The relation between the hybrid and digital beamformings is studied numerically by varying different parameters, such as the number of radio frequency (RF) chains, analog to digital converters (ADCs) and multiplexed symbols. Computer simulations reveal that for the given number of RF chains and ADCs, the performance gap between digital and hybrid beamformings can be decreased by decreasing the number of multiplexed symbols. Moreover, for the given number of multiplexed symbols, increasing the number of RF chains and ADCs will increase the total sum rate of the hybrid beamforming which is expected.

This paper deals with the dynamic spectrum sharing between underlaying device-to-device (D2D) and cellular links in a multi-carrier cellular network to maximize the weighted network sum-rate while guaranteeing the minimum individual cellular link data rates and proportional fairness among D2D links. In particular, we formulate an NP-hard non-orthogonal resource allocation problem, and develop an iterative algorithm that alternates between the sub-carrier assignment and power allocation in each iteration until convergence. It is shown that the sub-carrier assignment problem corresponds to an integer linear program while the power allocation is reformulated into a difference-between-two-concave-functions (DC) problem. We establish the important properties of the developed algorithms and prove their convergence behavior. Illustrative results indicate that the proposed non-orthogonal resource allocation algorithm significantly outperforms the orthogonal spectrum sharing counterpart.

This work studies the problem of joint multiuser downlink beamforming and admission control in multiple-input multiple-output (MIMO) heterogeneous networks. Considered is a network where a newly deployed femtocell base-station (FBS) has the coverage overlapped with that of an existing macrocell base-station (MBS). Our design objective is to serve as many femto-users (FUEs) as possible at their quality-of-service (QoS) requirements while maintaining the QoS requirements at the macro-users (MUEs). In the first part of this work, we consider the joint downlink beamforming and admission control problem as a joint optimization problem, which can be solved in a centralized manner with full coordination between the MBS and the FBS. In the second part, we propose a distributed algorithm in performing joint downlink beamforming and admission control at the femtocell with only limited MBS-FBS coordination. Specifically, after acquiring certain design parameters from the MBS, the FBS unilaterally determines its beamforming and admission control strategy while coordinating its induced interference to the macrocell. We then prove that the distributed algorithm will converge to a fixed-point where the QoS at the MUEs and admitted FUEs is guaranteed. Simulation results show that the distributed algorithm performs as well as the centralized one in terms of number of FUEs served with only a small penalty on the power usage at the MBS and the FBS.

In dynamic spectrum access (DSA) control, the prevalent approach to provide economics incentives for operators is pricing, whereas load balancing gives congestion-avoidance incentives to secondary users (SUs). Despite complexities of i) the couplings between pricing, load balancing and SUs' spectrum access decision, and ii) the heterogeneity of primary users' traffic and SUs types, we propose to solve the joint load balancing and pricing problem to maximize operator' revenue in a monopoly market. In this market, we first show there exists a unique SUs' equilibrium arrival rate to the monopolist's channels, and then we show that the joint problem can be solved efficiently by exploiting its convex structure. We next propose a low-complexity algorithm that enable the operator to maximize its revenue.

In this paper, we consider the joint coordinated beamforming and admission control design for cloud radio access networks (Cloud-RANs). Specifically, the set of multi-antenna remote radio heads (RRHs) serving each single-antenna user and the corresponding beamforming vectors are optimized to minimize the total transmission power subject to constraints on the capacity of fronthaul links, maximum powers of RRHs, and the minimum signal to interference plus noise ratios (SINRs) of users. Since the minimum SINR requirements of all users may not be guaranteed, some users may need to be removed so that all constraints can be satisfied. This NP-hard beamforming and admission control problem can be typically solved via a greedy algorithm. We instead propose a novel convex relaxation approach to formulate the underlying problem to a single-stage semi-definite program (SDP) based on which we develop an iterative algorithm to solve it. We then present numerical results to demonstrate the significant gains of the proposed algorithm compared to the greedy counterpart. Also, the impacts of the target SINR and cluster size on the number of supported users and total transmission power are also studied.

This paper considers an admission control problem for a mobile cloud computing hotspot with a cloudlet. We first formulate the admission control problem as a Markov decision process (MDP). The objective is to maximize the average reward in terms of revenue for cloudlet service providers. However, the MDP could suffer from the complexity problem (i.e., curse of dimensionality). Therefore, we apply the simulation-based algorithm to obtain the optimal policy for the MDP. The algorithm can estimate the performance measure to update the policy gradient in an online fashion. The performance evaluation of the proposed algorithm uses parameters setting profiled from real mobile applications. The extensive simulation results clearly show the convergence and the efficiency of the proposed algorithm.

The successful virtualization of wireless access networks is strongly affected by the way in which radio resources are managed. The Infrastructure Provider (InP) is required to deploy efficient and flexible scheduling techniques to dynamically allocate the resources for the users associated with different Service Providers (SPs). Service contracts with different SPs and fairness among their users are crucial to the success of the virtualization scheme deployed by the InP. In this paper we develop an efficient resource allocation scheme to allocate the radio resource blocks in LTE networks. The scheme keeps track of the service contracts with the SPs and also the fairness requirements between cell-center users and cell-edge users. Also the scheme allows the flexible definition of fairness requirements for different SPs. The performance of the proposed schemes is evaluated and the results show that the proposed low-complexity scheme is very efficient in terms of computation time and its performance in terms of sum rate is close to the results due to the relaxed solution and coordinate search algorithm.

Zigbee and wireless local area networks (WLAN), based on IEEE 802.15.4 and IEEE 802.11 standards, respectively, operate in overlapping unlicensed frequency bands. Therefore, one network can create harmful interference for the other if they are located in the same geographical area. The coexistence performance of the two networks has been analyzed mostly via computer simulation in the literature. In this paper, we develop a mathematical model to evaluate the throughput performance of coexisting 802.15.4 Zigbee network and 802.11 WLAN operating on the same channel. Our proposed analytical model is based on the analysis of a Markov chain for one pair of typical WLAN-Zigbee nodes, which capture detailed operations and interactions of the MAC protocols in the two networks. Moreover, we propose to employ the developed model for channel allocation that achieves fair throughput sharing among Zigbee nodes. Numerical results confirm the excellent accuracy of the proposed model and its usefulness for coexistence performance evaluation and design of the heterogeneous network.

In this paper, we consider the uplink subchannel and power allocation problem for device-to-device (D2D) and cellular links in the Orthogonal Frequency Division Multiple Access (OFDMA)-based D2D cellular network. This resource allocation problem aims to maximize the weighted sum throughput of D2D links while guaranteeing the minimum rate of each cellular link. The proposed formulation allows non-orthogonal spectrum sharing between the cellular and the D2D links to enhance the total D2D throughput. We develop an iterative algorithm that decouples the bandwidth and power allocation in two different steps and improves the objective function over iterations. For the power allocation sub-problem, we exploit the DC (difference between concave functions) structure of the objective function in the underlying problem and transform it into the convex optimization problem. We establish the convergence of the proposed algorithm. Numerical results confirm that our resource allocations algorithm outperforms other orthogonal spectrum sharing schemes.

We propose a QoS-aware admission control scheme (ACS) considering slow and fast calls for the integrated WLAN and OFDMA-based cellular network where only slow calls are allowed to connect with WLAN to maintain low handover overhead. The proposed ACS allows efficient traffic offloading from the macrocell to WLAN and considers QoS requirements for users in terms of minimum rates in all regions. The fractional frequency reuse (FFR) technique is assumed to be employed for interference mitigation in the cellular network. We also propose a novel bandwidth (BW) borrow-return strategy in the proposed ACS to improve the system performance. We then develop an analytical model to derive the blocking probabilities for calls in different areas. Numerical results demonstrate the performance enhancement of the ACS with the BW borrow-return strategy and the usefulness of the proposed analytical model in determining the size of WLAN offloading region.

In this paper, we consider the sensing-based dynamic spectrum access problem for femtocells in the two-tier network. We propose to exploit the information of the nearest macrocell base stations (MBSs) activity (i.e., on or off activity on the target frequency band) so that each femtocell base station (FBS) can make its access decision differently depending on the activity of the nearest MBS and the detected interference plus noise energy level. We then develop an analytical model to evaluate the performance of the proposed sensing-based access scheme in terms of success probability and total throughput by employing the stochastic geometry technique. We also provide an efficient and low-complexity algorithm to determine the sensing time and access thresholds for throughput maximization. Numerical studies are then conducted to validate the analysis and demonstrate that our proposed spectrum access with the information of nearest MBS activity achieves better total throughput performance than other scheme with no sensing or sensing without nearest MBS activity information.

In this paper, we propose a cognitive-based opportunistic spectrum sharing strategy for performance enhancement of a femtocell networks. This cognitive-based opportunistic spectrum access strategy aims to achieve better spectrum utilization with quality of service (QoS) protection for macrocell user equipments (MUEs) since the macrocell tier can be over-allocated spectrum resources. We analyze the performance of the two-tier network where the success probability with respect to the received signal to interference ratio (SIR) at each user and the total network throughput are derived under both closed and open access policies. In addition, we describe how to optimally choose the SIR threshold Q to maximize the total network throughput subject to QoS constraints for macrocell and femtocell users in terms of success probability. Via numerical studies, we show that our proposed opportunistic spectrum access scheme can achieve significant throughput gain compared to the conventional spectrum partitioning strategy.

We investigate the cooperative transmission design for the cloud radio access network (C-RAN) considering fronthaul capacity and cloud processing constraints. Specifically, we consider the joint transmission scheme where the baseband signals and precoding vectors are processed and calculated by the cloud, which are delivered over the fronthaul links to the remote radio heads (RRHs) to form the RF signals for being transmitted to the users. We formulate the joint optimization problem for precoding design and allocation of RRHs, fronthaul capacity, and BBU processing resources to minimize the total transmission power subject to QoS constraints of the users. We present both optimal exhaustive search algorithm and two low-complexity algorithms to solve the resource allocation problem where the first one can achieve the Pareto optimality and the second one can determine an efficient solution with pretty low complexity. Numerical results confirm the excellent performance of the proposed low-complexity algorithms.

Pricing is an effective approach for spectrum access control in cognitive radio (CR) networks. In this paper, we study the pricing effect on the equilibrium behaviors of selfish secondary users' (SUs') data packets which are served by a CR base station (BS). From the SUs' point of view, a spectrum access decision on whether to join the queue of the BS or not is characterized through an individual optimal strategy that is joining the queue with a joining probability. This strategy also requires each SU to know the average queueing delay, which is a non-trivial problem. Toward this end, we provide queueing delay analysis by using the M/G/1 queue with breakdown. From the BS's point of view, we consider a duopoly market based on the two paradigms: the opportunistic dynamic spectrum access (O-DSA) and the mixed O-DSA & dedicated dynamic spectrum access (D-DSA). In the first paradigm, two co-located opportunistic-spectrum BSs utilize freely spectrum-holes to serve SUs. Then, we show the advantages of the cooperative scenario due to the unique solution that can be obtained in a distributed manner by using the dual decomposition algorithms. For the second paradigm, there are one opportunistic-spectrum BS and one dedicated-spectrum BS. We study a price competition between two BSs as a Stackelberg game. The cooperative behavior between two BSs is modeled as a bargaining game. In both paradigms, bargain revenues of the cooperation are always higher than those due to competition in both cases. Extensive numerical analysis is used to validate our derivation.

We consider the mobility- and QoS-aware admission control problem for OFDMA femtocell networks. To mitigate strong cross-tier interference in the downlink communication, we assume each macrocell is partitioned into cell center and cell edge zones where femtocells in the edge zone share the same bandwidth with the macrocell while femtocells in the center zone use different bandwidth from that allocated for the macrocell. We propose an admission control algorithm that efficiently associates low-speed and high-speed users with femto and macro BSs (FBS and MBS) to avoid large handoff overhead. In addition, calls from low-speed users that fail to connect with their nearby FBSs are allowed to overflow to the macrocell tier. Then, we develop an analytical model for performance evaluation of the proposed admission control scheme. Finally, numerical results are presented to demonstrate the impacts of different parameters (e.g., bandwidth requirements) and access design (i.e., closed versus hybrid access) on the user blocking probabilities.

In this paper, we consider the probabilistic channel contention resolution problem for net revenue maximization in multi-hop wireless ad-hoc networks (MHAHNs) under collision-rate-constrained opportunistic spectrum access (OSA) approach. Specifically, we focus on the interference-dependent contention model, in which secondary users (SUs) must coordinate to each other to simultaneously balance between interference and collision, leading a more efficient MAC protocol than the location-dependent one proposed in the literature. By introducing some auxiliary variables and noisy channel estimations, we can then develop a novel heuristic cross-layer cognitive MAC protocol (HCC-MAC) in OSA-based MHAHNs to solve the formulated MAC optimization problem which is shown non-convex and inseparable. More importantly, our proposed protocol can achieve near-optimal throughput in a distributed manner without control overhead. Finally, the numerical results show that HCC-MAC can outperform the existing MAC protocols under OSA paradigm.

In this paper, we investigate the joint cooperative spectrum sensing and access design problem for multi-channel cognitive radio networks. A general heterogeneous setting is considered where the probabilities that different channels are available, SNRs of the signals received at secondary users (SUs) due to transmissions from primary users (PUs) for different users and channels can be different. We assume a cooperative sensing strategy with a general a-out-of-b aggregation rule and design a synchronized MAC protocol so that SUs can exploit available channels. We analyze the sensing performance and the throughput achieved by the joint sensing and access design. Based on this analysis, we develop algorithms to find optimal parameters for the sensing and access protocols and to determine channel assignment for SUs to maximize the system throughput. Finally, numerical results are presented to verify the effectiveness of our design and demonstrate the relative performance of our proposed algorithms and the optimal ones.

We consider the joint subchannel allocation and power control problem for OFDMA femtocell networks in this paper. Specifically, we are interested in the fair resource sharing solution for users in each femtocell that maximizes the sum min rate of all femtocells subject to protection constraints for the prioritized macro users. Toward this end, we describe the mathematical formulation for the problem and present an optimal exhaustive search algorithm. Given the exponential complexity of the optimal exhaustive search algorithm, we then develop a distributed and low-complexity algorithm to solve the resource allocation problem. We prove that the proposed algorithm converges. Finally, numerical results are presented to demonstrate the desirable performance of the proposed algorithms.

We propose a novel scheme for delay-optimal scheduling in multi-user multi-relay cellular wireless networks. The cell area is divided into several sectors, each serviced by an individual relay station (RS). In order to have simultaneous transmissions by the users in neighbouring sectors, we assume that users of each individual sector use separate set of orthogonal channels to communicate with the RS and the base station (BS). Moreover, a separate orthogonal channel is shared among relays for transmission to the BS. For uplink communication, users are allowed to choose between two modes of transmission, namely, direct transmission mode and relayed transmission mode through a simple transmission mode selection algorithm. Users are allocated fractions of the time-slot for the first phase of transmission (from the users to the BS and the RSs) in a time-division multiple access (TDMA) fashion. For the second phase of transmission (from the RSs to the BS), each RS is allocated a fraction of the time-slot. We model the problem of end-to-end (e2e) delay-optimal scheduling as an infinite-horizon average reward Markov decision process (MDP) for users and relays in two separate stages. An online learning approach is then employed to solve the problem in a distributed manner for both users and relays in each phase of transmission. The proposed online stochastic learning solution converges to the optimal solution almost surely (with probability 1) under some realistic conditions. Simulation results show that the proposed approach outperforms the conventional scheduling schemes.

Abstract—We consider the joint resource allocation and admis-sion control problem for Orthogonal Frequency-Division Multi-ple Access (OFDMA)-based femtocell networks. We assume that Macrocell User Equipments (MUEs) can establish connections with Femtocell Base Stations (FBSs) to mitigate the excessive cross-tier interference and achieve better throughput. A cross-layer design model is considered where multiband opportunistic scheduling at the Medium Access Control (MAC) layer and admission control at the network layer working at different time-scales are assumed. We assume that both MUEs and Femtocell User Equipments (FUEs) have minimum average rate constraints, which depend on their geographical locations and their application requirements. In addition, blocking probability constraints are imposed on each FUE so that the connections from MUEs only result in controllable performance degradation for FUEs. We present an optimal design for the admission control problem by using the theory of Semi-Markov Decision Process (SMDP). Moreover, we devise a novel distributed femtocell power adaptation algorithm, which converges to the Nash equilibrium of a corresponding power adaptation game. This power adaptation algorithm reduces energy consumption for femtocells while still maintaining individual cell throughput by adapting the FBS power to the traffic load in the network. Finally, numerical results are presented to demonstrate the desirable operation of the optimal admission control solution, the significant performance gain of the proposed hybrid access strategy with respect to the closed access counterpart, and the great power saving gain achieved by the proposed power adaptation algorithm. Index Terms—Femtocell network, admission control, Markov decision process, blocking probability, channel assignment.

In this paper, we investigate the cross-layer design for congestion, contention, and power control in multi-hop cognitive radio ad-hoc networks (CRAHNs). In particular, we develop a unified optimization framework achieving flexible tradeoff between energy efficiency and network utility maximization where we design two novel cross-layer cognitive algorithms comprising efficient powered-controlled MAC protocols for CRAHNs based on the concepts of social welfare and net revenue in economics. The proposed framework can balance interference, collision, and congestion among cognitive users (CUs) including cognitive sources and cognitive links while utilizing stochastic spectrum holes vacated by licensed users (LUs). The former allows both cognitive sources and cognitive links to simultaneously adjust their transmission parameters (i.e., transmit power, persistence probability, and rate) following the law of diminishing returns whereas the latter forces cognitive links to control the persistence probability and transmit power in order to asymptotically balance the offered load regulated by cognitive sources. Our proposed protocols are then validated and their performance is compared with the existing MAC schemes in the literature via numerical studies.

In this paper, we investigate the QoS-aware BS switching and cell zooming (BS power control) problem for green wireless cellular networks. In particular, we develop a unified cross-layer model that captures interaction between the physical and network layers. By partitioning each cell into cell partitions we can explicitly model the inter-cell interference and location-dependent users' QoSs. This enables us to design an efficient BS switching mechanism that can maintain user QoS requirements while exploiting heterogeneous traffic distribution over space and time for energy saving. In addition, we develop a power control algorithm that can further improve the energy efficiency. Specifically, we propose a power control strategy that adjusts the cell zooming level for the chosen network configuration by using non-cooperative game theory. We prove that the proposed power control algorithm converges to the Nash equilibrium of the corresponding power control game. Importantly, the proposed BS switching and cell zooming design can be implemented distributively. Finally, we demonstrate the efficacy and the significant energy saving gains of the proposed algorithms via numerical studies.

In this paper, we consider the fair resource allocation problem for device-to-device (D2D) communications in Orthogonal Frequency Division Multiple Access (OFDMA)-based wireless cellular networks. In particular, we propose a two-phase solution approach where resource allocation for cellular downlink and uplink flows with max-min fairness is performed in the first phase and resource allocation for D2D flows with rate protection for cellular flows is conducted in the second phase. We present both optimal formulations and low-complexity algorithms to solve the corresponding problems in the two phases. We also analyze the complexity of both solutions. Finally, we present numerical results to demonstrate the efficacy of the proposed algorithms in exploiting the spatial spectrum opportunities for D2D communications.

We investigate the fair channel assignment and access design problem for cognitive radio ad hoc network in this paper. In particular, we consider a scenario where ad hoc network nodes have hardware constraints which allow them to access at most one channel at any time. We investigate a fair channel allocation problem where each node is allocated a subset of channels which are sensed and accessed periodically by their owners by using a MAC protocol. Toward this end, we analyze the complexity of the optimal brute-force search algorithm which finds the optimal solution for this NP-hard problem. We then develop low-complexity algorithms that can work efficiently with a MAC protocol algorithm, which resolves the access contention from neighboring secondary nodes. Also, we develop a throughput analytical model, which is used in the proposed channel allocation algorithm and for performance evaluation of its performance. Finally, we present extensive numerical results to demonstrate the efficacy of the proposed algorithms in achieving fair spectrum sharing among traffic flows in the network.

In this paper, we consider the admission control problem for hybrid access in OFDMA-based femtocell networks. We assume that Macrocell User Equipments (MUEs) can establish connections with Femtocell Base Stations (FBSs) to improve their QoSs. Both MUEs and Femtocell User Equipments (FUEs) have minimum rate requirements, which depend on their geographical locations and maybe their running applications. In addition, blocking probability constraints are imposed on each FUE so that connections from MUEs only result in controllable performance degradation for FUEs. We show how to formulate the admission control problem as a Semi-Markov Decision Process (SMDP) and present a Linear Programming (LP) based solution approach. Moreover, we develop a novel femtocell power adaptation algorithm, which can be implemented in a distributed manner jointly with the proposed admission control scheme. This power adaptation algorithm enables to achieve better cell throughput and more energy-efficient operation of the femtocell network considering the heterogeneity of traffic load in the network. Finally, numerical results are presented to illustrate the desirable performance of the optimal admission control solution and the significant throughput and power saving gains of the proposed cross-layer solution.

We consider fair delay-optimal user selection and power allocation for a relay-based cooperative wireless network. Each user (mobile station) has an uplink queue with heterogeneous packet arrivals and delay requirements. Our system model consists of a base station, a relay station, and multiple users working in a time-division multiplexing (TDM) fashion, where per-user queuing is employed at the relay station to make the analysis of such system tractable. We model the problem as an infinite-horizon average reward Markov decision problem (MDP) where the control actions are functions of the instantaneous channel state information (CSI) as well as the queue state information (QSI) at the mobile and relay stations. To address the challenge of centralized control and huge complexity of MDP problems, we introduce a distributive and low-complexity solution. A linear structure is employed which approximates the value function of the associated Bellman equation by the sum of per-node value functions. Our online stochastic value iteration solution converges to the optimal solution almost surely (with probability 1) under some realistic conditions. Simulation results show that the proposed approach outperforms the conventional delay-aware user selection and power allocation schemes.

This paper addresses the critical problem of interference management in two-tier networks, where the newly-deployed femtocell users (FUEs) operate in the licensed spectrum owned by the existing macrocell. A Pareto-optimal power-control algorithm is devised that jointly maximizes the utilities of both macrocell and femtocell networks while robustly guaranteeing the macrocell's quality-of-service (QoS) requirements. After effectively enforcing the minimum signal-to-interference-plus-noise ratios (SINRs) prescribed by the macrocell users (MUEs) with the use of a penalty function, the Pareto- optimal boundary of the strongly-coupling SINR feasible region is characterized. Based upon the specific network utility functions and also the target SINRs of the MUEs, a unique operating SINR point is determined, and transmit power adapted to achieve such a design objective. We prove that the developed algorithm converges to the global optimum, and more importantly, it can be distributively implemented at individual links. Effective mechanisms are also available to flexibly designate the access priority between macrocells and femtocells, as well as to fairly share the system resources among different users. The merits of the proposed approach are verified by numerical examples.

In this paper, we propose a universal power control (PC) algorithm that can provide QoS support in minimum signal-to-interference-plus-noise ratios (SINRs) for all users while exploiting differentiated channel conditions to enhance the network throughput. In particular, we design the PC algorithm by using non-cooperative game theory and establish sufficient conditions for its convergence. Then, we apply it to design a hybrid access scheme for two-tier macrocell-femtocell networks. Specifically, we devise a distributed load-award association algorithm for macro users, which enables flexible user association to BSs of either tier. In addition, we develop an efficient mechanism based on which users can steer the equilibrium in such a way that they achieve their desirable performance targets. Numerical results are then presented to validate the theoretical results and demonstrate the desirable performance of the proposed algorithms.

In this paper, we consider the channel allocation problem for throughput maximization in cognitive radio networks with hardware-constrained secondary users. Specifically, we assume that secondary users exploit spectrum holes on a set of channels where each secondary user can use at most one available channel for communication. We develop two channel assignment algorithms that can efficiently utilize spectrum opportunities on these channels. In the first algorithm, secondary users are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm, that can improve the throughput performance compared to the non-overlapping channel assignment algorithm. In addition, we design a distributed MAC protocol for access contention resolution and integrate the derived MAC protocol overhead into the second channel assignment algorithm. Finally, numerical results are presented to validate the theoretical results and illustrate the performance gain due to the overlapping channel assignment algorithm.

We consider a dynamic server allocation problem over parallel queues with randomly varying connectivity and server switchover delay between the queues. At each time slot, the server decides either to stay with the current queue or switch to another queue based on the current connectivity and the queue length information. Switchover delay occurs in many telecommunications applications and is a new modeling component of this problem that has not been previously addressed. We show that the simultaneous presence of randomly varying connectivity and switchover delay changes the system stability region and the structure of optimal policies. In the first part of this paper, we consider a system of two parallel queues, and develop a novel approach to explicitly characterize the stability region of the system using state-action frequencies which are stationary solutions to a Markov decision process formulation. We then develop a frame-based dynamic control (FBDC) policy, based on the state-action frequencies, and show that it is throughput optimal asymptotically in the frame length. The FBDC policy is applicable to a broad class of network control systems and provides a new framework for developing throughput-optimal network control policies using state-action frequencies. Furthermore, we develop simple myopic policies that provably achieve more than 90% of the stability region. In the second part of this paper, we extend our results to systems with an arbitrary finite number of queues. In particular, we show that the stability region characterization in terms of state-action frequencies and the throughput optimality of the FBDC policy follows for the general case. Furthermore, we characterize an outer bound on the stability region and an upper bound on sum throughput and show that a simple myopic policy can achieve this sum-throughput upper bound in the corresponding saturated system. Finally, simulation results show that the myopic policies may achieve the full stability region and are more delay efficient than the FBDC policy in most cases.

We develop a distributed throughput-optimal power allocation algorithm in wireless networks. The study of this problem has been limited due to the nonconvexity of the underlying optimization problems that prohibits an efficient solution even in a centralized setting. By generalizing the randomization framework originally proposed for input queued switches to SINR rate-based interference model, we characterize the throughput-optimality conditions that enable efficient and distributed implementation. Using gossiping algorithm, we develop a distributed power allocation algorithm that satisfies the optimality conditions, thereby achieving (nearly) 100 percent throughput. We illustrate the performance of our power allocation solution through numerical simulation.

This paper considers network control for wireless networks with finite buffers. We investigate the performance of joint flow control, routing, and scheduling algorithms that achieve high network utility and deterministically bounded backlogs inside the network. Our algorithms guarantee that buffers inside the network never overflow. We study the tradeoff between buffer size and network utility and show that under the one-hop interference model, if internal buffers have size$(N-1)/(2 \epsilon)$, then$\epsilon $-optimal network utility can be achieved, where$\epsilon $is a control parameter and$N$is the number of network nodes. The underlying scheduling/routing component of the considered control algorithms requires ingress queue length information (IQI) at all network nodes. However, we show that these algorithms can achieve the same utility performance with delayed ingress queue length information at the cost of a larger average backlog bound. We also show how to extend the results to other interference models and to wireless networks with time-varying link quality. Numerical results reveal that the considered algorithms achieve nearly optimal network utility with a significant reduction in queue backlog compared to existing algorithms in the literature.

This paper proposes two Pareto-optimal power control algorithms for a two-tier network, where newly-deployed femtocell user equipments (FUEs) operate in the licensed spectrum owned by an existing macrocell. Different from homogeneous network settings, the inevitable requirement of robustly protecting the quality-of-service (QoS) of all prioritized macrocell user equipments (MUEs) here lays a major obstacle that hinders the successful application of any available solutions. Directly targeting at this central issue, the first algorithm jointly maximizes the total utility of both user classes. Specifically, we adopt the log-barrier penalty method to effectively enforce the minimum signal-to-interference-plus-noise ratios (SINRs) imposed by the macrocell, paving the way for the adaptation of load-spillage solution framework. On the other hand, the second algorithm is applied to the scenario where only the sum utility of all FUEs needs to be maximized. At optimality, we show that the MUEs' prescribed SINR constraints are met with equality in this case. With the search space for Pareto-optimal SINRs substantially reduced, the second algorithm features scalability, low computational complexity, short converging time, and stable performance. We prove that the two developed algorithms converge to their respective global optima, and more importantly, they can be implemented in a distributive manner at individual links. Effective mechanisms are also available to flexibly designate the access priority to MUEs and FUEs, as well as to fairly share radio resources among users. Numerical results confirm the merits of the devised approaches.

This paper proposes distributed joint power and admission control algorithms for the management of interference in two-tier femtocell networks, where the newly-deployed femtocell users (FUEs) share the same frequency band with the existing macrocell users (MUEs) using code-division multiple access (CDMA). As the owner of the licensed radio spectrum, the MUEs possess strictly higher access priority over the FUEs; thus, their quality-of-service (QoS) performance, expressed in terms of the prescribed minimum signal-to-interference-plus-noise ratio (SINR), must be maintained at all times. For the lower-tier FUEs, we explicitly consider two different design objectives, namely, throughput-power tradeoff optimization and soft QoS provisioning. With an effective dynamic pricing scheme combined with admission control to indirectly manage the cross-tier interference, the proposed schemes lend themselves to distributed algorithms that mainly require local information to offer maximized net utility of individual users. The approach employed in this work is particularly attractive, especially in view of practical implementation under the limited backhaul network capacity available for femtocells. It is shown that the proposed algorithms robustly support all the prioritized MUEs with guaranteed QoS requirements whenever feasible, while allowing the FUEs to optimally exploit the remaining network capacity. The convergence of the developed solutions is rigorously analyzed, and extensive numerical results are presented to illustrate their potential advantages.

We study the robust transmission scheduling and power control problem for spectrum sharing between secondary and primary users in a spatial reuse time-division multiple access (STDMA) network. The objective is to find a robust minimum-length schedule for secondary users (in terms of time slots) subject to the interference constraints for primary users and the traffic demand of secondary users. We consider the fact that power allocation based on average (or estimated) link gains can be improper since actual link gains can be different from the average link gains. Therefore, transmission of the secondary links may fail and require more time slots. We also consider this demand uncertainty arising from channel gain uncertainty. We propose a column generation-based algorithm to solve the scheduling and power control problem for secondary users. The column generation method breaks the problem down to a restricted master problem and a pricing problem. However, the classical column generation method can have convergence problem due to primal degeneracy. We propose an improved column generation algorithm to stabilize and accelerate the column generation procedure by using the perturbation and exact penalty methods. Furthermore, we propose an efficient heuristic algorithm for the pricing problem based on a greedy algorithm. For the simulation scenario considered in this paper, the proposed stabilized column generation algorithm can obtain the optimal schedules with 18.85% reduction of the number of iterations and 0.29% reduction of the number of time slots. Also, the heuristic algorithm can achieve the optimality with 0.39% of cost penalty but 1.67×10-4times reduction of runtime.

We consider a dynamic server control problem for two parallel queues with randomly varying connectivity and server switchover delay between the queues. At each time slot the server decides either to stay with the current queue or switch to the other queue based on the current connectivity and the queue length information. The introduction of switchover time is a new modeling component of this problem, which makes the problem much more challenging. We develop a novel approach to characterize the stability region of the system by using state-action frequencies, which are stationary solutions to a Markov Decision Process (MDP) formulation of the corresponding saturated system. We characterize the stability region explicitly in terms of the connectivity parameters and develop a frame-based dynamic control (FBDC) policy that is shown to be throughput-optimal. In fact, the FBDC policy provides a new framework for developing throughput-optimal network control policies using state-action frequencies. Furthermore, we develop simple Myopic policies that achieve more than 96% of the stability region. Finally, simulation results show that the Myopic policies may achieve the full stability region and are more delay efficient than the FBDC policy in most cases.

In this paper, we consider a joint cooperative scheduling and power control problem in interference-limited wireless ad hoc networks. In particular, we investigate the scenario where multiple pairs of users wish to communicate with their corresponding partners and each communication requires that its Signal-to-Interference-plus-Noise Ratio (SINR) is greater than a predetermined value for QoS guarantees. We propose to employ a decode-and-forward cooperative protocol jointly with the distributed Foschini-Miljanic power control algorithm to maximize the number of scheduled users in the network. In addition, we develop efficient handshaking and interference-aware relay selection mechanisms to achieve good throughput-overhead tradeoff for the proposed distributed protocol. We show that the proposed cooperative scheduling and power control algorithm is guaranteed to achieve better throughput than a non-cooperative scheduling algorithm based on direct communications and illustrate the performance gain through numerical studies.

This paper aims to devise a power control solution for femtocell networks that can be implemented distributively and, more importantly, is optimal in Pareto sense. For this, a complete characterization of the Pareto-optimal boundary of the signal-to-interference-plus-noise ratio (SINR) feasible region is first derived. The complicated interdependency between the macrocell and femtocell networks, whose access priorities and design objectives are inherently distinct, is also revealed. Our result confirms that the Pareto-optimal SINRs of the femtocell network can only be achieved conditionally upon the guaranteed performance of macrocell users. Through a suitable parametrization, we show that all SINR points on the aforementioned boundary can be realized. A unique operating SINR point is chosen among those infinite possible solutions, based upon the specific utility of femtocell users as well as the minimum SINR requirements of the macrocell network. Distributed transmit power adaptation is then performed to attain such optimal design target. We prove that the developed algorithm converges to the global optimum, wherein the performance of femtocell users is optimized while macrocell users being robustly protected with their minimum required SINRs maintained at all times. The merits of our approach are illustrated by numerical results.

This paper considers a two-tier cellular network wherein femtocell users, who communicate with their home-owner-deployed base stations, share the same frequency band with macrocell users by code-division multiple access (CDMA) technology. Since macrocell users have strictly higher priority in accessing the available radio spectrum, their quality-of-service (QoS) performance, expressed in terms of the minimum required signal-to-interference-plus-noise ratio (SINR), should be maintained at all times. Femtocell users, on the other hand, are allowed to exploit residual network capacity for their own communications. In this work, we develop a joint power- and admission-control algorithm for interference management in such two-tier networks. Specifically, throughput-power tradeoff optimization is achieved for femtocell users while all macrocell users being supported with guaranteed QoS requirements whenever feasible. Importantly, the proposed algorithm makes power and admission control decisions in an autonomous and distributive manner with minimal coordination signaling, a desirable feature in two-tier networks where only limited exchange of signaling information can be afforded on backhaul links. Under certain practical conditions, the developed scheme is shown to converge to a stable solution. An effective technique is also proposed to improve the efficiency of such equilibrium in lightly-loaded networks. The performance of our proposed algorithm is demonstrated by numerical results.

This paper considers network control for wireless networks with finite buffers. We investigate the performance of joint flow control, routing, and scheduling algorithms which achieve high network utility and deterministically bounded backlogs inside the network. Our algorithms guarantee that buffers inside the network never overflow. We study the tradeoff between buffer size and network utility and show that if internal buffers have size (N - 1)/¿ then a high fraction of the maximum utility can be achieved, where ¿ captures the loss in utility and N is the number of network nodes. The underlying scheduling/routing component of the considered control algorithms requires ingress queue length information (IQI) at all network nodes. However, we show that these algorithms can achieve the same utility performance with delayed ingress queue length information. Numerical results reveal that the considered algorithms achieve nearly optimal network utility with a significant reduction in queue backlog compared to the existing algorithm in the literature. Finally, we discuss extension of the algorithms to wireless networks with time-varying links.

We investigate the performance of longest-queue-first (LQF) scheduling (i.e., greedy maximal scheduling) for wireless networks under the SINR interference model. This interference model takes network geometry and the cumulative interference effect into account, which, therefore, capture the wireless interference more precisely than binary interference models. By employing the ρ-local pooling technique, we show that LQF scheduling achieves zero throughput in the worst case. We then propose a novel technique to localize interference which enables us to decentralize the LQF scheduling while preventing it from having vanishing throughput in all network topologies. We characterize the maximum throughput region under interference localization and present a distributed LQF scheduling algorithm. Finally, we present numerical results to illustrate the usefulness and to validate the theory developed in the paper.

In this paper we consider resource allocation for an OFDMA-based cognitive radio point-to-multipoint network with fixed users. Specifically, we assume that secondary users are allowed to transmit on any subchannel provided that the interference that is created to any primary users is below a critical threshold. We focus on the downlink. We formulate the joint subchannel, power and rate allocation problem in the context of finite queue backlogs with a total power constraint at the base station. Thus, users with small backlogs are only allocated sufficient resources to support their backlogs while users with large backlogs share the remaining resources in a fair and efficient fashion. Specifically, we formulate the problem as a max-min problem that is queue-aware, i.e., on a frame basis. We maximize the smallest rate of any user whose backlog cannot be fully transmitted. While the problem is a large non-linear integer program, we propose an iterative method that can solve it exactly as a sequence of linear integer programs, which provides a benchmark against which to compare fast heuristics. We consider two classes of heuristics. The first is an adaptation of a class of multi-step heuristics that decouples the power and rate allocation problem from the subchannel allocation and is commonly found in the literature. To make this class of heuristics more efficient we propose an additional (final) step. The second is a novel approach, called selective greedy, that does not perform any decoupling. We find that while the multi-step heuristic does well in the non-cognitive setting, this is not always the case in the cognitive setting and the second heuristic shows significant improvement at reduced complexity compared to the multi-step approach. Finally, we also study the influence of system parameters such as number of primary users and critical interference threshold on secondary network performance and provide some valuable insights on the operation of such systems.

Optimal power allocation for multi-user amplify- and-forward wireless relay networks in which multiple source-destination pairs are assisted by a set of relays is investigated. Two relay power allocation strategies based on maximization of either i) the minimum rate among all users or ii) the weighted sum of rates are developed. A distributed implementation of the maximum weighted-sum-rate power allocation strategy is also studied. Numerical results demonstrate the efficiency of the proposed strategies and reveal their interesting throughput-fairness tradeoff in resource allocation.

We investigate downlink resource allocation for OFDM-based cognitive radio networks. It is assumed that secondary users are allowed to transmit on all subchannels as long as the interference they create for primary users remains below a critical threshold. We consider a practical setting where secondary users have finite queue backlogs and a total power constraint at the base station and we perform resource allocation either over one or multiple time slots. Specifically, secondary users with small queue backlogs are only allocated sufficient rates to support their traffic demands and the remaining radio resources are shared among highly backlogged users. Under this setting, we formulate the joint subchannel and power problem with max-min fairness for highly backlogged users. Then, we propose an iterative procedure to find an optimal resource allocation solution using an integer program solver. For online implementation, we develop several heuristics of increasing complexity and performance. Numerical results show that the proposed heuristics achieve very good performance compared to the optimal solutions and that taking queue backlogs into account does not make the heuristics much slower while making the system more responsive to users' need.

We consider throughput-optimal power allocation in multi-hop wireless networks. The study of this problem has been limited due to the non-convexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. We take a randomization approach to deal with this difficulty. To this end, we generalize the randomization framework originally proposed for input queued switches to an SINR rate-based interference model. Further, we develop distributed power allocation and comparison algorithms that satisfy these conditions, thereby achieving (nearly) 100% throughput. We illustrate the performance of our proposed power allocation solution through numerical investigation and present several extensions for the considered problem.

We report in this paper measurements from France Telecom commercial networks carrying traffic generated and received by ADSL and FTTH customers. By adopting a flow- based approach to traffic analysis, we show that both types of customers experience similar peer-to-peer services in that the bit rates that they see is rather low. In order to understand the origin of these similarities, we develop a mathematical model, which could be seen as an abstraction of a file sharing process between peers according to the principles of eDonkey. This model allows us to exhibit a phase transition phenomenon which is nested in the file sharing principle. We believe that this phenomenon explains why both types of customers see a congested peer-to- peer network.

We consider a resource allocation problem for spectrum sharing in cognitive radio networks. Specifically, we investigate the joint subchannel, rate and power allocation for secondary users which share, in a non-disruptive manner, some frequency bands with primary users using OFDM technology. We consider the resource allocation problem for downlink and take into account the maximum total power constraints of the base station and the power constraints determined by distributed spectrum sensing and scanning. We formulate a resource allocation problem as an optimization problem which achieves max-min rate sharing among users. We propose both integer program based optimal and suboptimal fast and low complexity approaches for the spectrum sharing problem. Numerical results are then presented for the proposed heuristics and compared with the optimal solution.

In a cognitive wireless network, the licensed users (i.e., primary users) can sell redundant spectrum to unlicensed users (i.e., secondary users) or secondary service providers. We consider a scenario where routers in the secondary users' network form a wireless infrastructure mesh network, which is overlaid on networks of several primary service providers, to relay the secondary users' traffic through multiple hops to the destination. For such a cognitive wireless mesh network, we investigate two levels of competitions. The first level of competition is among the primary users (or primary service providers) to choose the price for spectrum opportunities to maximize their revenues. The second level of competition is among the secondary users for spectrum usage to choose the source rate to maximize their utilities. Assuming that both primary and secondary users are selfish and they both wish to optimize their self-interest, we show how to use noncooperative games to formulate each of these competitions. Nash equilibrium is considered as the solution for both competitions. Performance evaluation of the proposed spectrum sharing and pricing framework for cognitive wireless mesh networks is carried out which shows several interesting aspects of the problem.

We consider the problem of quality of service (QoS) routing in multi-hop wireless networks where data are transmitted from a source node to a destination node via multiple hops. The routing component of a QoS-routing algorithm essentially involves the link and path metric calculation which depends on many factors such as the physical and link layer designs of the underlying wireless network, transmission errors due to channel fading and interference, etc. The task of link metric calculation basically requires us to solve a tandem queueing problem which is the focus of this paper. We present a unified tandem queue framework which is applicable for many different physical layer designs. We present both exact and approximated decomposition approaches. Using the queueing framework, we can derive different performance measures, namely, end-to-end loss rate, end-to-end average delay, and end-to-end delay distribution. The proposed decomposition approach is validated and some interesting insights into the system performance are highlighted. We then present how to use the decomposition queueing approach to calculate the link metric and incorporate this into the route discovery process of the QoS routing algorithm. The extension of the queueing and QoS routing framework to wireless networks with class-based queueing for QoS differentiation is also presented.

We investigate the dynamic spectrum sharing problem among primary and secondary users in a cognitive radio network. We consider the scenario where primary users exhibit on-off behavior and secondary users are able to dynamically measure/estimate sum interference from primary users at their receiving ends. For such a scenario, we solve the problem of fair spectrum sharing among secondary users subject to their QoS constraints (in terms of minimum SINR and transmission rate) and interference constraints for primary users. Since tracking channel gains instantaneously for dynamic spectrum allocation may be very difficult in practice, we consider the case where only mean channel gains averaged over short-term fading are available. Under such scenarios, we derive outage probabilities for secondary users and interference constraint violation probabilities for primary users. Based on the analysis, we develop a complete framework to perform joint admission control and rate/power allocation for secondary users such that both QoS and interference constraints are only violated within desired limits. Throughput performance of primary and secondary networks is investigated via extensive numerical analysis considering different levels of implementation complexity due to channel estimation.

This paper presents an analytical model for a general automatic repeat request (ARQ) cooperative diversity (ACD) scheme in cluster-based multi-hop wireless networks. For the considered ACD scheme, transmission in each hop is supported by a number of relays using a finite number of transmission rounds. While prior works in the literature mostly focused on simulation and/or information theoretic analysis, we instead develop a model to analyze end-to-end performance in terms of probability of end-to-end delivery failure, end-to-end delay distribution, and end-to-end throughput. The application of the proposed analytical model for a transmission scheme which employs jointly a truncated ARQ protocol and a maximal ratio combiner is illustrated. Numerical results validate the proposed analytical model and compare the ACD scheme with the Amplify-and-Forward (AF) and traditional truncated ARQ schemes in a linear network. The ACD scheme exploiting both time diversity (through retransmission) and spatial diversity is shown to have several desirable adaptive characteristics compared to other schemes.

We propose cross-layer optimization frameworks for multihop wireless networks using cooperative diversity. These frameworks provide solutions to fundamental relaying problems of determining who should be relays for whom and how to perform resource allocation for these relaying schemes jointly with routing and congestion control such that the system performance is optimized. We present a fully distributed algorithm where the joint routing, relay selection, and power allocation problem to minimize network power consumption is solved by using convex optimization. Via dual decomposition, the master optimization problem is decomposed into a routing subproblem in the network layer and a joint relay selection and power allocation subproblem in the physical layer, which can be solved efficiently in a distributed manner. We then extend the framework to incorporate congestion control and develop a framework for optimizing the sum rate utility and power tradeoff for wireless networks using cooperative diversity. The numerical results show the convergence of the proposed algorithms and significant improvement in terms of power consumption and source rates due to cooperative diversity.

A resource allocation framework is presented for spectrum underlay in cognitive wireless networks. We consider both interference constraints for primary users and quality of service (QoS) constraints for secondary users. Specifically, interference from secondary users to primary users is constrained to be below a tolerable limit. Also, signal to interference plus noise ratio (SINR) of each secondary user is maintained higher than a desired level for QoS insurance. We propose admission control algorithms to be used during high network load conditions which are performed jointly with power control so that QoS requirements of all admitted secondary users are satisfied while keeping the interference to primary users below the tolerable limit. If all secondary users can be supported at minimum rates, we allow them to increase their transmission rates and share the spectrum in a fair manner. We formulate the joint power/rate allocation with proportional and max-min fairness criteria as optimization problems. We show how to transform these optimization problems into a convex form so that their globally optimal solutions can be obtained. Numerical results show that the proposed admission control algorithms achieve performance very close to that of the optimal solution. Also, impacts of different system and QoS parameters on the network performance are investigated for the admission control, and rate/power allocation algorithms under different fairness criteria.

The authors develop distributed algorithms for joint end-to-end rate control and resource (e.g., subcarrier, power) allocation in orthogonal frequency division multiple access (OFDMA)-based wireless mesh networks. These algorithms allow spatial reuse where the same subcarrier can be used for simultaneous transmissions on different links as long as they weakly interfere with each other. The subcarrier allocation algorithm is based on routing information and a novel definition of interference sets and it aims at providing fair transmission rate among traffic flows in an end-to-end basis. The joint rate and power control is treated as a network utility maximization problem considering interference of simultaneous transmissions on the same subcarrier with node power constraint. The numerical results confirm the convergence of the joint rate and power control algorithm and show that fair end-to-end transmission is achieved. With the distributed radio resource management framework developed in this paper, a proper spatial reuse can be done to achieve good system throughput performance.

We present a tandem queueing model for performance analysis and engineering of multihop wireless networks. To solve the queueing model, a direct (or exact) method and a decomposition method are proposed. The proposed decomposition method reduces the computational complexity significantly which requires us to solve L single queues instead of a full tandem system of L queues. The tandem queue model captures a batch arrival process and multi-rate transmission achieved by adaptive modulation and coding. We obtain the queue length distribution and derive all end-to-end performance measures including loss probability, average delay. The proposed decomposition approach is validated and some interesting insights into the system performance and guidelines for system design are highlighted.

A complete queueing model for radio link layer performance analysis is developed assuming adaptive modulation and coding (AMC) at the physical layer and truncated automatic repeat request (ARQ)-based error control at the link layer. From the model, queue length distribution and average queueing delay can be calculated. The average queueing delay is then used to estimate transmission control protocol (TCP) throughput performance using a fixed-point approach. Using the model, we are able to choose signal-to-noise ratio thresholds of different transmission modes for AMC at the physical layer for different persistence levels of ARQ at the link layer so that TCP throughput is maximized. We observe that channel correlation negatively impacts the TCP throughput performance. Also, throughput enhancement of TCP NewReno over TCP Reno is observed to be non-negligible only if no ARQ-based error recovery is employed at the link layer.

We present a queueing model for performance analysis of go-back-N (GBN) and selective repeat (SR) automatic repeat request (ARQ) protocols in wireless networks using dynamic radio link adaptation with non-instantaneous feedback. Link adaptation technique allows multi-rate transmission which is assumed to be achieved through adaptive modulation and coding. The radio link level queueing models for these two ARQ protocols are formulated in discrete time where the exact queue length and the delay statistics are obtained by using matrix geometric methods under different feedback delay values, channel and system parameters. The link layer delay statistics are useful in many ways, for example, to perform packet level admission control under statistical delay constraints. We validate the analysis by simulation and discuss useful implications of the analytical model on system performance. For dynamic link adaptation, the mode switching thresholds for the received signal-to-noise ratio (SNR) can be chosen to obtain very good link level delay performance. This SNR partitioning is shown to achieve significant cross-layer design gain compared to the case where the mode switching thresholds are chosen to maximize the physical layer throughput.

A complete queueing model for radio link layer performance analysis is developed assuming adaptive modulation and coding (AMC) at the physical layer and truncated automatic repeat request (ARQ)-based error control at the link layer. From the analysis the queue length distribution and the average queueing delay can be calculated. The average queueing delay is then used to estimate TCP (Transmission Control Protocol) throughput performance using a fixed point approach. The analytical model enables us to choose signal-to-noise ratio (SNR) thresholds of the different transmission modes for AMC at the physical layer for different persistence levels of ARQ at the link layer so that the TCP throughput is maximized. We observe that channel correlation negatively impacts the TCP throughput performance. Also, throughput enhancement of TCP NewReno over TCP Reno is non-negligible only if no ARQ-based error recovery is employed at the link layer of the protocol stack.

The radio link-level delay statistics in a wireless network using adaptive modulation and coding (AMC), weighted round-robin (WRR) scheduling, and automatic repeat request-based error control is analyzed in this letter. WRR scheduling can be used for service differentiation similar to that achievable by using the generalized processor sharing scheduling discipline. The analytical framework presented in this letter captures physical and radio link-level aspects of a multirate multiuser wireless network (e.g., general fading model, AMC, scheduling, error control) in a unified way. It can be used for admission control and cross-layer design under statistical delay constraints. The analytical results are validated by simulations. Typical numerical results are presented, and their useful implications on the system performance are discussed.

This letter presents an analytical framework for radio link level performance evaluation in a wireless network using adaptive modulation and coding (AMC) and automatic repeat request (ARQ)-based error control. Both the cases of finite and infinite buffer sizes at the radio link layer are considered when the packet arrival process is modeled by a batch Markovian arrival process (BMAP), which can capture correlation in the arrival process. Using the model, radio link level performance measures such as average delay, buffer overflow probability, packet loss rate, and average spectral efficiency can be obtained, and the impacts of channel parameters on the performance measures can be determined. Using the queue length distributions for finite and infinite buffer cases, the buffer size can be designed such that the packet overflow probability remains below the desired level. Such a cross-layer analytical framework would be very useful for network designers

An analytical framework for radio link level performance evaluation under scheduling and automatic repeat request (ARQ)-based error control in a multi-rate wireless network is presented. The multi-rate transmission is assumed to be achieved through adaptive modulation and coding (AMC) in a correlated fading channel. The analytical framework, which is developed based on a vacation queueing model, can be applied to any scheduling scheme as long as the evolution of the joint service/vacation and channel processes can be determined. The exact statistics of queue length and delay are obtained and the radio link level throughput is calculated under both saturated and non-saturated buffer scenarios. As an example of using the general analytical model, we analyze the performance of max-rate (MR) scheduling scheme which exploits multiuser diversity and compare its performance with the round-robin (RR) scheduling scheme. Although the MR scheduling always results in higher throughput than the RR counterpart, we observe that the RR scheduling offers better delay performance than the MR scheme under light traffic load conditions. The usefulness of the presented analysis is highlighted by illustrating its applications for cross-layer design and packet-level admission control under delay constraints. After all, this analytical framework would be very useful for comprehensive analysis of radio link level scheduling schemes and hence for design and engineering of radio link control protocols

We analyze the delay statistics for the selective repeat ARQ (SR-ARQ) protocol in a multi-rate wireless network with non-instantaneous feedback. Multi-rate transmission is assumed to be achieved through adaptive modulation, where each transmission mode corresponds to one state of a finite state Markov channel (FSMC) model. The problem is formulated as a quasi-birth and death (QBD) process from which the exact delay statistics for the SR-ARQ protocol is obtained. Our model removes the weaknesses of using a two-state Markov channel such as its inaccuracy in predicting the delay performance for a wireless access, error control protocol and its inability to capture multi-rate transmission. We validate the analysis by simulations and present typical numerical results, which reveal the impacts of the channel and the system parameters on the performance of a multi-rate wireless network

We analyze the queueing performance of the go-back-N ARQ (GBN-ARQ) protocol in multi-rate wireless networks considering feedback delay. Multi-rate transmission is captured by a finite state Markov channel (FSMC) model for a slow Nakagami-m fading channel. The queueing problem is formulated as a G1/M/1 Markov chain where the exact queue length and delay statistics for the GBN-ARQ protocol can be obtained. We validate our analysis by simulations. The impacts of the system and channel parameters on the system performance are then investigated. The optimal partitioning of the signal to noise ratio (SNR) for different transmission modes are obtained so that the delay is minimized. The delay statistics obtained in this paper enables us to design wireless systems under statistical delay constraints and would be useful to predict the higher-layer protocol performance

We analyze the radio link level queueing performance for a multi-rate wireless network using adaptive modulation and coding (AMC), scheduling, and automatic repeat request (ARQ)-based error control. The analytical framework, which is developed based on a vacation queueing model, can be applied to any scheduling scheme as long as the evolution of the joint service/vacation and channel processes can be determined. The exact statistics of queue length and delay are obtained. As an example of using the general analytical model, we analyze the performance of a max-rate (MR) scheduling scheme which exploits multiuser diversity. Based on the queueing analysis, the impacts of channel and system parameters on the radio link level performance can be determined and hence cross-layer design and engineering can be performed. Also, efficient admission control schemes can be designed for delay-constrained applications.

We analyse the queuing performance of a radio link level round-robin scheduler for downlink data transmission in a multi-rate TDMA (time division multiple access) wireless network. One broadcast channel in the downlink is shared by multiple mobile users in a time multiplexing fashion and a round-robin scheduler serves each user in exactly one time slot. The finite state Markov channel (FSMC) is used to capture different states of a slow Rayleigh fading channel. Depending on the channel condition, the modulation level at the transmitter is adapted and, therefore, one or multiple packets can be transmitted in one time slot. Using the matrix geometric method (MGM), the system is modeled as a quasi-birth and death (QBD) process and then the queue length and delay distributions are derived. We present typical numerical results and discuss their useful implications on system design.

We analyze the forward link spectral efficiency (SE) of a spatial multiplexing cellular MIMO system using adaptive modulation and scheduling (opportunistic and proportional fair). With the channel state information (CSI) available only at the receiver side, the minimum mean square error (MMSE)-based ordered successive interference cancellation is employed for detection with either forward or reverse ordering. When the channel state information (CSI) is available at the transmitter, separate channels are obtained via singular value decomposition (SVD) of the channel matrix. The post processing SNR for each stream is fed back to the transmitter to adapt the modulation level corresponding to each stream. The multi-user diversity gain due to scheeduling is observed to be very significant especially without power control. The SE gain from the SVD scheme becomes negligible in a high SNR region which would not justify the complexity of having the CSI at the transmitter. The proportional fair scheduling is a good choice to compromise SE and fairness when the average channel conditions of users are different.

Mobile location estimation has attracted much interest over the past few years. The most challenging issues, which render to reach the required accuracy for the time-based location system, are multipath and non line-of-sight (NLOS) problems. This paper suggests the simple but robust techniques using biased Kalman filter to smooth and mitigate the NLOS effect for TOA measurements. The processed TOAs are then used for DTOA formulation and provided for location estimation. The further tracking stage is shown not to improve the accuracy much but to be necessary to smooth the mobile trajectory. The better accuracy for mobile location is suggested for future work by using the geographical information through searching the match between the path loss measured at multiple BSs and that estimated by ray-tracing techniques.