In next-generation (xG) wireless communication networks, developing generalizable learning models that inherently adapt to diverse conditions is crucial. This paper proposes a reinforcement learning (RL) framework for subchannel (SC) allocation in low Earth orbit satellite (LEOS)-assisted integrated access and backhauling (IAB) networks. We consider an integrated terrestrial-satellite network, where a LEOS provides backhaul services to cellular base stations (BSs) in remote areas while forwarding data from mobile user equipments (UEs) to the core network. The objective is to maximize the achievable rate for UEs while satisfying demand requirements and backhaul constraints. To ensure generalizable and efficient SC allocation across environments, we formulate the resource management problem as an invariant policy learning framework, which is decomposed into two subproblems: state representation learning and policy optimization. Our approach learns state representations that remain invariant across diverse environments. Additionally, the invariant policy, obtained from the hypergraph output layer, captures the fundamental causes of successful actions, enabling robust decision-making. By embedding problem constraints into both the model architecture and the training objective, the framework enhances the transparency of the invariant policy optimization process. Furthermore, we derive a data-dependent generalization bound that characterizes the policy’s performance in unseen environments. Simulation results demonstrate that the proposed policy consistently outperforms traditional methods across multiple environments.

Federated Learning (FL) enables distributed model training across edge devices in a privacy-friendly manner. However, its efficiency heavily depends on effective device selection and high-dimensional resource allocation in dynamic and heterogeneous wireless environments. Conventional methods demand a confluence of domain-specific expertise, extensive hyperparameter tuning, and/or heavy interaction cost. This paper proposes a Tool-aided Evolutionary Large Language Model (T-ELLM) framework to generate a qualified policy for device selection in a wireless FL environment. Unlike conventional optimization methods, T-ELLM leverages natural language-based scenario prompts to enhance generalization across varying network conditions. The framework decouples the joint optimization problem mathematically, enabling tractable learning of device selection policies while delegating resource allocation to convex optimization tools. To facilitate the evolutionary process, T-ELLM interacts with a sample-efficient, model-based virtual learning environment that captures the relationship between device selection and learning performance. This developed virtual environment reduces reliance on real-world interactions, thus minimizing communication overhead while refining the LLM-based decision-making policy through group relative policy optimization. Theoretical analysis proves that the discrepancy between virtual and real environments is bounded, ensuring the advantage function learned in the virtual environment maintains a provably small deviation from real-world conditions. Experimental results demonstrate that T-ELLM outperforms benchmark methods in energy efficiency and exhibits robust adaptability to environmental changes.

Coherent states have been increasingly considered in optical quantum communications (OQCs).With the inherent non-orthogonality of coherent states, non-orthogonal multiple-access (NOMA) naturally lends itself to the implementation of multi-user OQC. However, this remains unexplored in the literature. This paper proposes a novel successive interference cancellation (SIC)-based photon-number-resolving detection (PNRD)-Kennedy receiver for uplink NOMA-OQC systems, along with a new approach for power allocation of the coherent states emitted by users. The key idea is to rigorously derive the asymptotic sum-rate of the considered systems, taking into account the impact of atmospheric turbulence, background noise, and lossy photon channel. With the asymptotic sum-rate, we optimize the average number of photons (or powers) of the coherent states emitted by the users. Variable substitution and successive convex approximation (SCA) are employed to convexify and maximize the asymptotic sum-rate iteratively. A new coherent-state power allocation algorithm is developed for a small-to-medium number of users. We further develop its low-complexity variant using adaptive importance sampling, which is suitable for scenarios with a medium-to-large number of users. Simulations demonstrate that our algorithms significantly enhance the sum-rate of uplink NOMA-OQC systems using coherent states by over 20%, compared to their alternatives.

Photon counting (PhC) is an effective detection technology for underwater optical wireless communication (OWC) systems. The presence of signal-dependent Poisson shot noise and asynchronous multi-user interference (MUI) complicates the processing of received data signals, hindering the effective signal detection of PhC OWC systems. This paper proposes a novel iterative signal detection method in grant-free, multi-user, underwater PhC OWC systems with signal-dependent Poisson shot noise. We first introduce a new synchronization algorithm with a unique frame structure design. The algorithm performs active user identification and transmission delay estimation. Specifically, the estimation is performed first on a user group basis and then at the individual user level with reduced complexity and latency.We also develop a nonlinear iterative multi-user detection (MUD) algorithm that utilizes a detection window for each user to identify interfering symbols and estimate MUI on a slot-by-slot basis, followed by maximuma-posterioriprobability detection of user signals. Simulations demonstrate that our scheme achieves bit error rates comparable to scenarios with transmission delays known and signal detection perfectly synchronized.

Cascaded or stacked intelligent metasurfaces (SIMs) have emerged as a promising technology to overcome the physical limitations of single-layer reconfigurable intelligent surfaces (RISs) in wideband wireless communication. By intelligently manipulating electromagnetic waves, SIMs enhance signal propagation in complex environments and offer additional degrees of freedom for beamforming. This paper proposes a coupling-aware, wideband, circuit-based framework that captures frequency-dependent mutual coupling and wideband channel responses over multiple subbands. Based on this model, we formulate a joint active and passive beamforming design that optimizes the base-station precoder to enable carrier aggregation across frequency-selective subbands, together with metasurface phase shifts, to maximize spectral efficiency. Simulation results reveal the importance of accounting for coupling and wideband effects, and show that performance depends strongly on operating conditions. Single-layer RIS configurations can be favorable in narrowband and/or low-SNR regimes, whereas SIMs can significantly outperform under wideband multi-user conditions by mitigating coupling-induced distortion and maintaining a more consistent phase response across frequencies. The results provide physical insights into design trade-offs between structural simplicity and wideband adaptability, highlighting SIMs as a scalable solution for future-generation wideband multi-user MIMO systems. We further show that partially reconfigurable SIM architectures achieve near-optimal performance with reduced complexity.

Mutual Coupling (MC) emerges as an inherent feature in Reconfigurable Intelligent Surface (RIS) structures, particularly when they are fabricated with sub-wavelength inter-element spacing. Hence, their realistic modeling and efficient optimization need to accurately incorporate MC-induced effects. In addition, the design of electromagnetics-compliant transmit/receive radiation patterns constitutes another critical factor for efficient RIS operation. These radiation patterns together with MC naturally lead to the emergence of non-local RIS structures, whose operation can be effectively described via non-diagonal phase configuration matrices. In this paper, we present a physically-consistent joint optimization framework for the MC and the radiation patterns of non-local RIS structures for the case of RIS-assisted multi-user Multiple-Input Single-Output (MISO) communication systems. Both conventional reflective as well as transmissive RIS setups are considered. Assuming the availability of statistical properties of the wireless environment for the targeted RIS deployment, we particularly devise a novel offline optimization approach for the static scattering S-parameters of the RIS, which is followed by a dynamic, per-channel-realization optimization of the metasurface’s response-tunable elements and the transmitter’s active precoder. Our extensive simulation results, using both parametric and geometric channel models, showcase the validity of the proposed two-step optimization framework over benchmark schemes, indicating that improved performance can be achievable without the need for optimizing the MC and the radiation patterns of the RIS on the fly, which can be rather cumbersome.

Accurate electric load forecasting is of critical importance for modern power grids. It can help optimize energy management, reduce operational costs, and enhance grid stability. Existing load forecasting tools typically perform well when modelling long-term trends with substantive data upon which to build a model, but can perform poorly for short-term load forecasting or when data is sparse or incomplete. Diffusion models have recently emerged as powerful generative tools that excel in modelling complex distributions, making them a promising approach for electric load forecasting. In this paper, we build upon recent diffusion models for time series forecasting and explore the potential of combining diffusion models with prior models to improve performance. Additionally, we propose a metric-based meta-learning approach for fast data adaptation. Experimental results with this metric-based meta-learning approach on real-world load forecasting datasets outperform state-of-the-art baselines, showcasing the potential of diffusion-based refinement in practical forecasting applications.

Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs’ systems still get overwhelmed when they encounter a complex dynamic change in the environment resulting from an accident or a roadblock for maintenance. The advanced features of Sixth Generation (6G) technology are set to offer strong support to AVs, enabling real-time data exchange and management of complex driving maneuvers. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework to improve AVs’ decision-making in dynamic and complex Intelligent Transportation Systems (ITS) utilizing 6G-V2X communication. The primary objective is to enable AVs to avoid roadblocks efficiently by changing lanes while maintaining optimal traffic flow and maximizing the mean harmonic speed. To ensure realistic operations, key constraints such as minimum vehicle speed, roadblock count, and lane change frequency are integrated. We train and test the proposed MARL model with two traffic simulation scenarios using the SUMO and TraCI interface. Through extensive simulations, we demonstrate that the proposed model adapts to various traffic conditions and achieves efficient and robust traffic flow management. Specifically, the proposed approach results in a harmonic mean speed increase of up to 15% and a reduction in lane-change frequency by 10%. The trained model effectively navigates dynamic roadblocks, promoting improved traffic efficiency in AV operations with more than 70% efficiency over other benchmark solutions.

Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/ transmitter of a multi-task semantic communication system, which enriches the features for multiple tasks by embedding the intermediate outputs of encoding in the features, compared to the existing techniques. The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features. Another important aspect is that we refine the node representation using a graph attention mechanism to extract the correlations and a multi-layer perceptron network to associate the node representations with different tasks. Consequently, the intermediate features are weighted and embedded into the features transmitted for executing multiple tasks at the receiver. Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset and outperforms the established state-of-the-art by 3.97% on the NYU V2 3Task dataset, respectively, when the bandwidth ratio of the communication channel (i.e., compression level for transmission over the channel) is as constrained as$\frac{1}{12}$.

Decentralized federated learning (D-FL) allows clients to aggregate learning models locally, offering flexibility and scalability. Existing D-FL methods use gossip protocols, which are inefficient when not all nodes in the network are D-FL clients. This article puts forth a new D-FL strategy, termed route-and-aggregate (R&A) D-FL, where participating clients exchange models with their peers through established routes (as opposed to flooding) and adaptively normalize their aggregation coefficients to compensate for communication errors. The impact of routing and imperfect links on the convergence of R&A D-FL is analyzed, revealing that convergence is minimized when routes with the minimum end-to-end (E2E) packet error rates (PERs) are employed to deliver models. Our analysis is experimentally validated through three image classification tasks and two next-word prediction tasks, utilizing widely recognized datasets and models. R&A D-FL outperforms the flooding-based D-FL method in terms of training accuracy by 35% in our tested ten-client network, and shows strong synergy between D-FL and networking. In another test with ten D-FL clients, the training accuracy of R&A D-FL with communication errors approaches that of the ideal centralized federated learning (C-FL) without communication errors, as the number of routing nodes (i.e., nodes that do not participate in the training of D-FL) rises to 28.

Massive multiple-input multiple-output (mMIMO) antenna systems and inter-band carrier aggregation (CA)-enabled multi-band communication are two key technologies to achieve very high data rates in beyond fifth generation (B5G) wireless systems. We propose a joint optimization framework for such systems where the mMIMO antenna spacing selection, pre-coder optimization, optimum subcarrier selection and optimum power allocation are carried out simultaneously. We harness the bandwidth gain existing in a tightly coupled base station mMIMO antenna system to avoid sophisticated, non-practical antenna systems for multi-band operation. In particular, we analyze a multi-band communication system using a circuit-theoretic model to consider physical characteristics of a tightly coupled antenna array, and formulate a joint optimization problem to maximize the sum-rate. As part of the optimization, we also propose a novel block iterative water-filling-based subcarrier selection and power allocation optimization algorithm for the multi-band mMIMO system. A novel subcarrier windowing-based subcarrier selection scheme is also proposed which considers the physical constraints (hardware limitation) at the mobile user devices. We carry out the optimizations in two ways: (i) to optimize the antenna spacing selection in an offline manner, and (ii) to select antenna elements from a dense array dynamically. Via computer simulations, we illustrate superior bandwidth gains present in the tightly-coupled colinear and rectangular planar antenna arrays, compared to the loosely-coupled or tightly-coupled parallel arrays. We compare the optimum sum-rate performance of the proposed optimizationbased framework under various power allocation schemes and various user capability scenarios. Also, we show that the proposed optimization framework is superior to the existing joint optimization frameworks in terms of sum-rate performance and we verify the convergence of the proposed iterative optimization algorithms.

The rapid growth of Internet of Things (IoT) applications, such as smart cities and industrial automation, necessitates efficient massive machine-type communication (mMTC) solutions for sixth-generation (6G) networks. Traditional access protocols struggle to accommodate many devices with sporadic activity and low data volumes, leading to increased latency and collisions. This paper proposes a novel grant-free random access (RA) protocol that leverages Reconfigurable Intelligent Surfaces (RIS) to enhance connectivity and channel conditions in mMTC scenarios. The protocol consists of two stages: first, the base station transmits downlink (DL) pilots while the RIS sweeps its reflection configurations, enabling devices to identify optimal transmission opportunities. In the second stage, devices utilize these opportunities to transmit data, minimizing collisions and improving throughput. By employing a predefined codebook of reflection configurations, previously optimized to thoroughly scan the covered space in a few rounds of multiple narrow beams, the protocol reduces overhead and meets a maximum latency constraint of 20 ms under certain reliability constraints, demonstrating significant performance improvements over existing methods. This approach enhances network efficiency while supporting a vast number of devices and encourages the deployment of RIS technology in future wireless communication systems.

This paper presents a novel user-centric sensing framework, where a user equipment (UE) acts as the receiver of a multi-static radar sensing system and utilizes the communication signals emitted by base stations (BSs) and scattered by the targets for joint UE and target tracking. Specifically, we propose to locate the UE using the least squares (LS) estimator with a one-dimensional (1D) search and then develop a two-dimensional (2D) target identification approach using the estimated target location and motion of each path based on mean-shift clustering. After the motion parameters of the UE and targets are estimated, a joint UE and target tracking algorithm is designed based on the analysis of the localization and motion estimation errors. Extensive simulations corroborate the ability of our approach to estimate target parameters and cluster and identify targets. Specifically, the average estimation error of the target number is only 0.18. The speed and heading accuracy of the UE and targets is [0.121 m/s, 3.879°] and [0.199 m/s, 4.669°], respectively. In joint UE and target tracking, our scheme outperforms the benchmarks of the extended Kalman filter (EKF) and belief propagation (BP) by at least 33.16% and 10.29%, respectively, even though the EKF and BP require a-priori knowledge of the motion parameters and target identification.

This paper develops a novel framework for differentially private (DP) wireless federated learning (FL) with integrated sensing and communication (ISAC). In this framework, which is referred to as DP-ISAC-FL, wireless devices sense data and upload the trained local models using ISAC technique. The local training can take place concurrently with sensing at each device. We analyze the convergence upper bound of DP-ISAC-FL and rigorously capture the impact of device selection (for model training), time allocation between sensing/training and model uploading for the selected devices, and the allocations of channels, modulations, and transmit powers. We also develop an algorithm that enforces the convergence of DP-ISAC-FL by minimizing the convergence upper bound in an OFDMA system with discrete modulations. The beamforming for sensing, device selection, and the allocations of time, subchannels, modulations, and transmit powers are jointly optimized using successive convex approximation (SCA), adapting to the channels and computing capabilities of the devices. Experiments on multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) show that DP-ISAC-FL with optimal allocations can significantly improve the learning convergence and accuracy under different privacy levels, e.g., by 7% and 18%, compared with its benchmarks. This is attributed to 68% more sensing data that DP-ISAC-FL can admit for model training.

The joint problem of carrier aggregation (CA), load balancing, and backhauling (JCALB) is studied in the context of non-terrestrial networks (NTNs) based on low-earth orbit satellites (LEOS). While CA can potentially enhance the communication capacity by dynamic selection of component carriers (CCs) or frequency bands for the LEOS, load balancing adjusts the portion of each CC utilized by individual LEOS in order to optimize resource utilization. Aiming to minimize the usage of each CC by individual satellites while maximizing their achievable total rate, we formulate the JCALB problem as a mixed-integer stochastic optimization problem involving both discrete and continuous decision variables, which is NP-hard. To solve the problem suboptimally, we divide it into two sub-problems: backhauling and activating/deactivating CCs for the satellites, and load balancing over the CCs. For the first subproblem, we develop a generative AI-based decision-making (GADM) algorithm based on a diffusion model. We apply the GADM algorithm to actor-critic and multi-arm bandit frameworks in reinforcement learning, in order to develop diffusion-based actor-critic CA and backhauling (DA2CAB) and diffusion-based upper confidence bound (UCB) CA and backhauling (DU2CAB) methods for LEOS-based NTNs. Finally, given the activated CCs for the satellites, we develop an iterative and distributed load balancing algorithm within NTNs. The simulation results demonstrate that our derived diffusion-based algorithms enable LEOS to achieve a higher transmission capacity while allocating fewer CCs and subchannels (SCs) compared to algorithms based on the double deep Q-Network (DDQN) and the traditional UCB approach.

Machine learning (ML)-aided wireless communication studies are initiating the investigation of the domain generalization capabilities of deep neural networks (DNNs) when applied to communication problems. They do so by adopting the out-of-distribution (OOD) performance evaluation by comparing it to the in-distribution (ID) performance as usually done within the ML community. In this paper, we demonstrate that such blind adoption can yield a misleading OOD performance analysis of DNNs unless wireless communication metrics are involved in the OOD evaluation. By analyzing the OOD performance of an end-to-end (E2E) ML communication system over additive white Gaussian noise (AWGN) channels in terms of bit error rate (BER), we show that smaller (resp. larger) BER gaps between ID and OOD performance do not necessarily translate into a high (resp. low) reconstruction accuracy. Our results suggest that the comparison between ID and OOD performances is not enough to judge whether the OOD performance is acceptable or not. The ID and OOD performances of E2E communication systems should instead be carried out based on wireless metrics.

In order to facilitate massive connectivity and connecting the unconnected, aerial communications are becoming increasingly essential as a complement to terrestrial infrastructure. The integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and flexible deployment. This paper introduces a framework designed to optimize the cooperation between aerial and terrestrial networks, with the goal of maximizing the deployment cost efficiency (DCE) of the network (i.e., the ratio of the network's total data transmission rate to the combined deployment and energy costs). The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we formulate a problem focused on maximizing the DCE while adhering to power constraints and zero intra-cell pilot contamination. Subsequently, we propose a pilot-contamination aware user clustering, and a distributed coalition formation game for BSs and UAVs clustering in C-CF-M-MIMO-enabled IATN. Our numerical findings demonstrate the efficacy of the proposed algorithm when compared to conventional benchmark methods. Furthermore, the C-CF-M-MIMO-enabled IATN outperforms BSs-only and UAVs-only network equipped with typical cell-free configurations, such as (i) traditional CF-MIMO and (ii) user-centric CF-MIMO.

As fast-moving low Earth orbit (LEO) satellite communication systems gain increasing prominence, the significance of analytical performance models that account for mobility becomes more crucial than ever. Additionally, while considerable progress has been made in modeling the coverage performance of single-antenna LEO satellites, there is a noticeable gap when it comes to considering multi-antenna satellites. This paper presents a novel stochastic geometry framework to characterize the user coverage probability in a downlink LEO satellite network in the presence of multi-antenna satellites, handoffs (HOs), and the Shadowed-Rician fading model. We first determine the distribution of the desired and interfering channel power gains under zero-forcing beamforming. Then, we characterize the HO probability per unit time referred to as the HO rate under distance-based association. Next, we derive the handoff-aware coverage probability expression, and we validate our findings through numerical results obtained from Monte-Carlo simulations, offering insights into the effects of HO and multi-antenna processing on user coverage probability.

This paper explores the evolution of wireless communication networks from utilizing the sub-6 GHz spectrum and the millimeter wave frequency band to incorporating extremely high frequencies like optical and terahertz for 6G and beyond. While these higher frequencies offer broader bandwidths and extreme data rate capabilities, the transition from single-band and heterogeneous networks to multi-band networks (MBNs), where various frequency bands coexist introduces novel challenges in channel modeling, transceiver and antenna design, programmable simulation platforms, standardization, and resource allocation. This paper provides a tutorial overview from the communication design perspective of the various frequency bands, elaborating on the above issues. Then, we introduce and examine typical MBN architectures for future networks and provide a detailed overview of state-of-the-art resource allocation problems for existing MBNs that typically operate on two frequency bands. The considered resource allocation optimization problems and solution techniques are discussed comprehensively. We then identify key performance metrics and constraint sets that should be considered for resource allocation optimization in future MBNs and provide numerical results to depict how various system parameters and user behaviors can influence their performance. Finally, we present several potential research issues as future work for the design and performance optimization of MBNs.

Radio simultaneous localization and mapping (SLAM) is challenging due to multipath propagation. While line-of-sight (LoS) and first-order non-LoS (NLoS) paths, referred to as NLoS-1 paths, play a critical role in SLAM, no existing techniques can effectively separate them from high-order NLoS paths, i.e., NLoS-npaths (n≥ 2). This paper presents a new framework to accurately identify the LoS/NLoS-1 paths and conduct SLAM. The key idea is to define the virtual user equipment (UE) of a NLoS-npath as then-th order reflection of the UE. We discover that the centers of the circles encompassing the UE, a virtual UE associated with a LoS/NLoS-1 path, and each of some other virtual UEs are aligned in a line, if and only if those virtual UEs are all associated with NLoS-1 paths. Accordingly, we propose to identify the LoS/NLoS-1 paths using Hough transform-based line detection, and estimate the UE’s location and the environments with the identified LoS/NLoS-1 paths using maximum likelihood estimation and mean-shift clustering. We analytically confirm that the localization error asymptotically approaches the Cramér-Rao Lower Bound. Simulations show that our approach outperforms the state of the art in localization accuracy by up to 91.93%, even when the latter assumed all NLoS-1 paths are perfectly identifieda-priori.

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the ideal global models requiring perfect channels and aggregations. The bias reveals that excessive local aggregations can accumulate communication errors and degrade convergence. Another important aspect is that we analyze a convergence upper bound of D-FL based on the bias. By minimizing the bound, the optimal number of local aggregations is identified to balance a trade-off with accumulation of communication errors in the absence of knowledge of the channels. With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations. Experiments validate our convergence analysis and also identify the optimal number of local aggregations on two widely considered image classification tasks. It is seen that D-FL, with an optimal number of local aggregations, can outperform its potential alternatives by over 10% in training accuracy.

We leverage covert communication to enhance the security of a large-scale multi-tier Low Earth Orbit (LEO) satellite network against vigilant adversarial terrestrial Base Stations (BSs) aiming at detecting satellite transmissions. This approach involves deploying massive LEO satellites at different altitudes around Earth to form a multi-tier network serving as a backhaul for near-ground Unmanned Aerial Vehicles (UAVs) that provide network services to terrestrial mobile users. Meanwhile, terrestrial BSs attempt to detect satellite transmissions based on their own received signal powers. To evade detection, the LEO satellite network performs power control to obscure the satellite transmission within the co-channel interference among the LEO satellites. We formulate a two-stage Stackelberg game to model the conflict dynamics between the terrestrial BSs and the LEO satellite network. In this game, the terrestrial BSs act as non-cooperative followers at the lower stage aiming to minimize their detection errors. On the other hand, the LEO satellite network acts as the leader at the upper stage aiming to maximize its utility while ensuring communication covertness. In contrast to existing works that focus on a small set of network nodes, our study considers a large-scale multi-tier LEO satellite network and employs stochastic geometry to model the spatial distribution of network nodes. To achieve the Stackelberg equilibrium, we develop a bi-level algorithm based on Successive Convex Approximation (SCA) and golden-section search. Our numerical results provide practical insights, revealing a trade-off in leveraging co-channel interference (i.e., while it improves the communication covertness of satellite transmission, it simultaneously degrades the link reliability).

Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6 G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-of-the-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results, we show the effectiveness of our proposed resource allocation mechanism.

To enable massive connectivity and connecting the unconnected, aerial communications are becoming critical to complement with the terrestrial infrastructure. Integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and deployment flexibility. This paper presents a framework to optimize the deployment of aerial network and cooperation among aerial-terrestrial network such that the network deployment cost efficiency (i.e. the ratio of network sum-rate and deployment-plus-energy-cost) is maximized. The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we first formulate a Deployment Cost Efficiency (DCE) maximization problem subject to power budget, zero intra-cell pilot contamination, and UAV location constraints. We then propose a grid-based joint UAV density and location optimization, a pilot-contamination aware user clustering, and a distributed coalition game approach for clustering in C-CF-M-MIMO-enabled IATN. Complexity and convergence of the proposed algorithm are presented. Our numerical results show the efficacy of the proposed algorithm compared to conventional benchmarks. The proposed C-CF-M-MIMO-enabled IATN also outperforms terrestrial-only and aerial-only networks enabled with typical cell-free configurations, namely, (i) traditional CF-MIMO, and (ii) user-centric CF-MIMO.

We study the problem of end-to-end (E2E) network slicing, i.e., joint slicing of the radio access network (RAN) and core network (CN), for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC) services in future generation cellular (e.g., 5G-Advanced/6G) networks. The E2E resource slicing problem is defined as a mixed-integer non-linear programming problem to minimize the E2E energy consumption and the cost of utilized resources. To overcome the difficulty of solving this problem, we decompose it into two sub-problems, namely, RAN resource allocation (RRA) and CN resource allocation (CRA) problems. In both RRA and CRA problems, the existence of binary variables makes them intractable. To tackle this difficulty, we relax the binary variables by introducing penalty functions. Then, we make the RRA and CRA problems convex by employing the majorization-minimization approximation method. Via simulation results, we compare our proposed joint RAN and CN resource allocation algorithm (JRCRA) with the disjoint solution where RAN and CN resources are allocated to users separately. The joint allocation of resources in the RAN and CN has the advantage that the E2E tolerable latency of users can be flexibly divided between RAN and CN. In contrast, if resources in RAN and CN are allocated separately, a predefined part of the E2E tolerable latency should be considered as the tolerable latency in RAN and CN. The simulation results illustrate that our proposed JRCRA algorithm obtains a 34% improvement in energy consumption and a 24% improvement in cost compared to the disjoint one. Moreover, via simulation results, we illustrate that in comparison with existing algorithms, our proposed JRCRA obtains a higher performance. Besides, simulation results confirm that JRCRA reaches a close performance to the optimal solution.

Security is an issue in non-orthogonal multiple access (NOMA) and partial-NOMA because a user may decode the message of its paired-user with which it shares a resource element (RE). Three scenarios are studied where, of the paired-users, the eavesdropper is: 1) an actively malicious strong-user, 2) a passive strong-user, 3) an actively malicious weak-user. We define the event of secure-communication in each scenario and derive the corresponding secrecy probabilities for partial-NOMA and NOMA. Our results highlight that with careful selection of the RE’s overlap α, partial-NOMA can significantly outperform NOMA in terms of secrecy probability. Further, careless selection of α can cause partial-NOMA to perform worse than NOMA. We show the non-trivial impact of incorporating the impact of intercell interference on secrecy. Our results shed light on parameter-selection if knowledge of the eavesdropper type is available highlighting that security can be improved without traditional techniques such as jamming that increase power consumption and interference. While NOMA decoding uses successive-interference-cancellation (SIC), partial-NOMA decoding employs receive-filtering followed by flexible-SIC (FSIC). We show that not employing receive-filtering or using SIC instead of FSIC can have a drastic negative impact on secrecy, highlighting the role of the partial-NOMA decoding approach in enhancing secure-communication.

We aim to develop a system-level security solution for a large-scale device-to-device (D2D) network against adversaries based on covert communication. The D2D network underlays a downlink cellular network to reuse the cellular spectrum and is enabled for simultaneous wireless information and power transfer (SWIPT). In the D2D network, the D2D transmitters communicate with the D2D receivers, and the D2D receivers extract information and energy from their received radio-frequency (RF) signals. In the meantime, the adversaries aim to detect the D2D transmission. The D2D network applies power control and leverages the cellular signal to achieve covert communication (i.e., hide the presence of transmissions) so as to defend against the adversaries. We model the interaction between the D2D network and adversaries by using a two-stage Stackelberg game. Therein, the adversaries are the followers minimizing their detection errors at the lower stage and the D2D network is the leader maximizing its network utility constrained by the communication covertness and power outage at the upper stage. Both power splitting (PS)-based and time switch (TS)-based SWIPT schemes are explored. We characterize the spatial configuration of the large-scale D2D network, adversaries, and cellular network by stochastic geometry. We analyze the adversary’s detection error minimization problem and adopt the Rosenbrock method to solve it, where the obtained solution is the best response from the lower stage. Taking into account the best response from the lower stage, we develop a bi-level algorithm to solve the D2D network’s constrained network utility maximization problem and obtain the Stackelberg equilibrium. We present numerical results to reveal interesting insights. For example, the PS-based SWIPT scheme outperforms the TS-based SWIPT scheme in terms of both network performance (e.g., link reliability and power outage probability) and resistance to the adversary, i.e., steady network utility against increasing aggressiveness of the adversary.

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. We develop a bi-level optimization algorithm to search for the equilibrium of the proposed Stackelberg game based on the successive convex approximation (SCA) method and Rosenbrock method. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility.

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the spectral efficiency (SE) of the system. It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a very desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might become even worse due to the users’ proximity. Reconfigurable intelligent surfaces (RISs), which are capable of smartly controlling the wireless channel, can be leveraged to achieve intra-cell pilot reuse. In this paper, our main contribution is an RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we then optimize the RIS phase-shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements achieved by the proposed scheme (for both uplink and downlink transmissions) compared to other alternatives.

Channel estimation in reconfigurable intelligent surfaces (RIS)-aided systems is crucial for optimal configuration of the RIS and various downstream tasks such as user localization. In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links. In the literature, two approaches are proposed: (i) cascaded channel estimation where the two channels are collapsed into a single one and estimated using training signals at the BS, and (ii) separate channel estimation that estimates each channel separately either in a passive or semi-passive RIS setting. In this work, we study the separate channel estimation problem in a fully passive RIS-aided millimeter-wave (mmWave) single-user single-input multiple-output (SIMO) communication system. First, we adopt a variational-inference (VI) approach to jointly estimate the UE-RIS and RIS-BS instantaneous channel state information (I-CSI). In particular, auxiliary posterior distributions of the I-CSI are learned through the maximization of the evidence lower bound. However, estimating the I-CSI for both links in every coherence block results in a high signaling overhead to control the RIS in scenarios with highly mobile users. Thus, we extend our first approach to estimate the slow-varying statistical CSI of the UE-RIS link overcoming the highly variant I-CSI. Precisely, our second method estimates the I-CSI of RIS-BS channel and the UE-RIS channel covariance matrix (CCM) directly from the uplink training signals in a fully passive RIS-aided system. The simulation results demonstrate that using maximum a posteriori channel estimation using the auxiliary posteriors can provide a capacity that approaches the capacity with perfect CSI. Leveraging the UE-RIS CCM enhances spectral efficiency by minimizing the training overhead required to control the RIS, and exploiting its low-rank structure reduces training overhead compared to the maximum likelihood estimator.

Reconfigurable Intelligent Surface (RIS) is considered one of the most promising technologies for the next generation of wireless communication networks. Despite its great potential, RIS faces new challenges in integrating efficiently into wireless networks, including reflection optimization, channel estimation (CE), and optimization of deployment locations. This paper presents and analyzes a CE solution in RIS-assisted systems using compressive sensing techniques. The steering vector and the complex channel gains of the base station (BS)-RIS/user equipment (UE)-RIS links are estimated separately by using thematching pursuitwith phase rotation (MP-PR) by deploying a few active elements at the RIS panel. The performance and complexity of the proposed method are comprehensively analyzed in different scenarios and compared against those of several other related methods in the literature. Numerical results show the effectiveness of the proposed method, which achieves better (or at least very similar) performance compared to other relevant techniques but with a significant decrease in computational complexity.

Federated learning (FL) can suffer from communication bottlenecks when deployed in mobile networks, limiting participating clients and deterring FL convergence. In this context, the impact of practical air interfaces with discrete modulation schemes on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F2L) over an orthogonal frequency division multiple-access (OFDMA) air interface, termed as “OFDMA-F2L”, allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulation scheme, adapting to channel conditions and computing power. Specifically, we derive an upper bound on the optimality gap of OFDMA-F2L capturing the impact of these selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a “winner-takes-all” policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F2L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18% and 5%, compared to potential alternatives.

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and ensure a prescribed signal-to-clutter-plus-noise ratio for radar sensing. Considering the non-convexity of this problem arising from multiplicative coupling of the analog and digital beamforming, we convert the sum-rate maximization into an equivalent weighted mean-square error minimization and apply penalty dual decomposition to decouple the analog and digital beamforming. Specifically, a second-order cone program is first constructed to optimize the fully digital counterpart of the HAD beamforming. Then, the sparsity of the RS architecture is exploited to obtain a low-complexity solution for the HAD beamforming. The convergence and complexity analyses of our algorithm are carried out under the RS architecture. Simulations corroborate that, with the RS architecture, DFRC offers effective communication and sensing and improves energy efficiency by 83.4% and 114.2% with a moderate number of radio frequency chains and phase shifters, compared to the persistently- and fully-connected architectures, respectively.

We examine the problem of joint active and passive beamforming in a controllable multi-user reconfigurable intelligent surface (RIS)-assisted downlink and uplink wireless communication system, considering the mutual coupling among RIS elements. Due to the sub-wavelength structure, mutual coupling among RIS elements is unavoidable, and it inherently leads to multiple reflection effects that are ignored in conventional (approximative) RIS models. We formulate a joint non-convex problem under the MMSE criterion and use alternative optimization to convert the non-convex problem into two sub-problems for downlink and uplink transmissions separately. In both transmissions, one sub-problem involves optimizing the phase-shift matrix of RIS. In downlink, the other sub-problem is the optimization of active precoding for the base station (BS), while the equivalent sub-problem in uplink is the optimization of the linear receiver matrix. We optimize the phase shift matrix under a physically-consistent model using the gradient descent algorithm for both transmissions. We use the Lagrange multiplier method to optimize active precoding in the downlink and apply the First Order Necessary Condition (FONC) to optimize the linear receiver in the uplink. Simulation results are represented for both lossless and lossy RIS scenarios under perfect and imperfect channel state information. We discuss the impact of changing the number of RIS elements and the RIS element spacing on system performance. The results show that, with optimized phase shifts and active precoding, the inherent multiple reflection effect can improve the performance of RIS-aided wireless communications systems.

With the increasing use of data-intensive mobile applications and the number of mobile users, the demand for wireless data services has been increasing exponentially in recent years. In order to address this demand, a large number of new cellular base stations are being deployed around the world, leading to a significant increase in energy consumption and greenhouse gas emission. Consequently, energy consumption has emerged as a key concern in the fifth-generation (5G) network era and beyond. Reinforcement learning (RL), which aims to learn a control policy via interacting with the environment, has been shown to be effective in addressing network optimization problems. However, for reinforcement learning, especially deep reinforcement learning, a large number of interactions with the environment are required. This often limits its applicability in the real world. In this work, to better deal with dynamic traffic scenarios and improve real-world applicability, we propose a transfer deep reinforcement learning framework for energy optimization in cellular communication networks. Specifically, we first pre-train a set of RL-based energy-saving policies on source base stations and then transfer the most suitable policy to the given target base station in an unsupervised learning manner. Experimental results demonstrate that base station energy consumption can be reduced significantly using this approach.

The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critical to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.

We consider a controllable multi-user wireless communications system based on reconfigurable intelligent surface (RIS) and investigate the problem of optimizing active and passive beamforming jointly, with the presence of mutual coupling effects. Due to the sub-wavelength structure, mutual coupling between RIS elements is unavoidable. We investigate the effect of mutual coupling among RIS elements resulting in multiple reflection effects which are ignored in conventional (approximative) RIS models. We propose a novel method to optimize the RIS phase shifters considering the effect of multiple reflections using a more physically-consistent model (exact RIS model). Numerical results show that the inherent multiple reflection effects along with the optimized phase shifters and the active precoder can improve the performance of RIS-aided wireless communications systems.

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training. This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches. In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model. We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion. We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings. Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time.

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility.

We propose a variational inference-based channel estimation method in fully passive reconfigurable intelligent surface (RIS)-aided mmWave single-user single-input multiple-output (SIMO) communication systems. The main goal is to jointly estimate the user equipment (UE)-to-RIS (UE-RIS) and RIS-to-base station (RIS-BS) channels using uplink training signals in a passive RIS setup. Specifically, by using a variational inference framework, we approximate the posterior of the channels with convenient distributions given the received uplink training signals. The parameters of the approximated distributions are generated by deep neural networks trained using variational loss functions derived using a lower bound on the log-likelihood of the received signal. Then, the learned distributions, which are close to the true posterior distributions in terms of Kullback Leibler divergence, are leveraged to obtain the maximum a posteriori (MAP) estimation of the UE-RIS and RIS-BS channels. We evaluate the proposed channel estimation solution under two channel priors. The first channel prior models Rayleigh fading channels with Gaussian prior, whereas the second one represents sparse channels in the angular domain with Laplace prior. The simulation results demonstrate that MAP channel estimates using the approximated posteriors yield a capacity which is close to the one achieved with the true posteriors, thus demonstrating the effectiveness of the proposed method.

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.

Downlink transmission of non-orthogonal multiple access visible light communication systems empowered by an unmanned aerial vehicle (UAV) is considered for multiplexing enhanced mobile broadband (eMBB) and massive machine type communication (mMTC) services. Accordingly, a resource allocation problem of joint transmit power control and motion trajectory design of the DAVs is formulated, whose goal is to characterize a multi-objective trade-off as a weighted sum of the UAVs' power consumption and the perceived quality-of-experience (QoE) of eMBB users, while ensuring the eMBB and mMTC service-specific requirements. We leverage an alternative decomposition and tools from convex optimization and actorcritic multi-agent deep reinforcement learning to address this problem in an iterative fashion. We analytically derive the upper-and lower-bounds on the reward of the DAVs as the learning agents and demonstrate that the proposed resource allocation method outperforms the similar scheme in literature, by up to 17% average reduced power consumption, as well as 12% average perceived QoE gain.

Photon counting is an effective technique to detect low-power optical signals in underwater optical wireless communications (UOWC), but undergoes signal-dependent Poisson shot noises that lead to intractable data rate expressions and hinder effective power allocation of photon-counting systems. This paper presents a new approach to the optimal power allocation of a multiuser photon-counting UOWC system, where we first derive the asymptotic achievable rate as the background radiation is large under the signal-dependent Poisson shot noises. With the tractability of the asymptotic achievable rate, we formulate a new power allocation problem to maximize the weighted sum-rate of the multiuser photon-counting UOWC system. A new algorithm is developed to decompose the problem into subproblems with deterministic convexity or concavity and accordingly convexified and solved using successive convex approximation. We also propose to pre-select the subproblems, thereby reducing the complexity significantly with negligible loss of the weighted sum-rate. Simulations validate our asymptotic achievable rate, and show that the proposed algorithms can improve the weighted sum-rates of the UOWC systems by orders of magnitude, compared to the existing approaches.

Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size$11\times 11$, up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.

In multi-UAV networks, the downlink (DL) and uplink (UL) associations between a UAV and a user equipment (UE) is typically coupled, which restricts each UE to associate to the same UAV for both DL and UL. However, this mode may not be efficient since UAV networks can be heterogeneous (e.g., multi-tier UAV networks) and can experience high link uncertainty due to the mobility of UAVs. The introduction of full-duplex communication in a multi-UAV network further complicates the UE-UAV association. For this reason, the idea of DL-UL decoupling (DUDe) is introduced in this work, with which each UE is allowed to associate with separate UAVs for UL and DL transmissions. Besides, the UE-UAV association depends on the flight trajectory of the UAVs, which makes the DUDe design challenging. In this article, we study the joint decoupled UL-DL association and trajectory design problem for full-duplex multi-UAV networks. A joint optimization problem is formulated with the objective of maximizing the UEs’ sum-rate in both UL and DL. Since the problem is non-convex with sophisticated states and an individual UAV may not know the reward functions of other UAVs, a robust partially observable Markov decision process (POMDP) model is proposed to characterize the model uncertainty. A multi-agent deep reinforcement learning (MADRL) approach is proposed which enables each UAV to select its policy in a distributed manner. To train the actor-critic neural networks in the MADRL approach, an improved clip and count-based proximal policy optimization (PPO) algorithm is developed. In particular, a modified clip distribution is designed to deal with the hard restrictions between current and old policies, and an intrinsic reward is introduced to enhance the exploration capability. Simulation results illustrate the superiority of our proposed schemes when compared to the benchmarks. The codes are made publicly available in GitHub (https://github.com/isdai/MADRL-PPO).

We study the resource sharing problem in a drone-based wireless network by considering a distributed control setting under uncertainty (e.g., due to lack of full information). The drones cooperate in serving the users while pooling their spectrum and energy resources in the absence of prior knowledge about different system characteristics such as the amount of available power at the other drones. Compared to the state-of-the-art research in drone-based wireless networks, which is mainly based on the assumption of accurate global information availability at every drone, our setting is realistic and practical. We cast the efficient resource pooling problem as a Bayesian cooperative game in which the agents (drones) engage in a coalition formation process, where the goal is to maximize the overall transmission rate of the network. The drones update their beliefs by using a novel technique that combines the maximum likelihood estimation with Kullback-Leibler divergence. We propose a decision-making strategy for repeated coalition formation that converges to a stable coalition structure. We analyze the performance of the proposed approach by both theoretical analysis and simulations. We provide the comparison of our scheme with the baseline and the socially optimal solution obtained from the exhaustive search. Simulation results demonstrate the superior performance of the proposed method in terms of the sum-rate of the network, the individual rate of the drones, and convergence properties.

Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11×11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

We study the joint decoupled uplink (UL)-downlink (DL) association and trajectory design problem for full-duplex multi-UAV networks. A joint optimization problem is formulated aiming to maximize the sum-rate of user equipments (UEs) in both UL and DL. Since the formulated problem is non-convex and with sophisticated states, a multi-agent deep reinforcement learning (MADRL) approach is employed for enabling each agent (i.e., UAV) to select policy in a distributed manner. Moreover, in order to obtain the optimal policy, a clip-and-count based proximal policy optimization (PPO) algorithm is proposed to train actor-critic neural networks. In particular, a modified clip distribution is designed to deal with the hard restrictions between current and old policies, and an intrinsic reward is introduced to enhance the exploration capability. Simulation results demonstrate the significant performance improvement of our proposed schemes when compared to the benchmarks.

We derive an achievability bound in an uplink setting where N single-antenna devices, of which a random subset of Kausers are active in each transmission period, attempt to update a base-station (BS), equipped with M antennas, with their status packets. Motivated by emerging applications of massive connectivity we consider the asymptotic scenario where both the total number of users and the number of antennas at the BS grow large at a fixed ratio $\zeta = \frac{M}{N}$. Under maximal-ratio combining and perfect channel state information at the receiver, we find that the achievable rate approaches ${\log _2}\left( {1 + \frac{M}{{{K_a}}}} \right)$ in the large system limit. We explore the trade-offs between this achievable rate and the freshness of the status packets using the age of information (AoI) metric. In the limiting regime, we find that the penalty one pays for increasing the data rate is a rise in the minimum AoI obtainable. Finally, we compare recent massive unsourced random access (URA) schemes against the newly established bound.

Load balancing has become a key technique to handle the increasing traffic demand and improve the user experience. It evenly distributes the traffic across network resources by offloading users from overloaded base stations or channels to less crowded ones. Load balancing is a multi-objective optimization problem involving the automatic adjustment of several parameters to simultaneously maximize multiple network performance indicators. However, the existing methods mostly rely on single-objective approaches which lead to sub-optimal solutions. In this paper, we introduce the first multi-objective reinforcement learning (MORL) framework for load balancing. Specifically, we propose a solution based on meta-reinforcement learning (meta-RL) to learn a general policy capable of quickly adapting to new trade-offs between the objectives. We further enhance the generalization of our proposed solution using policy distillation techniques. To showcase the effectiveness of our framework, experiments are conducted based on real-world traffic scenarios. Our results show that our load balancing framework can (i) significantly outperform the existing rule-based and single-objective solutions, (ii) compute better Pareto front approximations compared to MORL baselines, and (iii) quickly adapt to new objective trade-offs.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.

In a cell-free wireless network, distributed access points (APs) jointly serve all user equipments (UEs) within their coverage area by using the same time/frequency resources. In this paper, we develop a novel downlink cell-free multiple-input multiple-output (MIMO) millimeter wave (mmWave) network architecture that enables all APs and UEs to dynamically self-partition into a set of independent cell-free subnetworks in a time-slot basis. For this, we propose several network partitioning algorithms based on deep reinforcement learning (DRL). Furthermore, to mitigate interference between different cell-free subnetworks, we develop a novel hybrid analog beamsteering-digital beamforming model that zero-forces interference among cell-free subnetworks and at the same time maximizes the instantaneous sum-rate of all UEs within each subnetwork. Specifically, the hybrid beamforming model is implemented by using a novel mixed DRL-convex optimization method in which analog beamsteering between APs and UEs is conducted based on DRL while digital beamforming is modeled and solved as a convex optimization problem. The DRL models for network clustering and hybrid beamsteering are combined into a single hierarchical DRL design that enables exchange of DRL agents’ experiences during both network training and operation. We also benchmark the performance of DRL models for clustering and beamsteering in terms of network performance, convergence rate, and computational complexity. Results show a significant rate enhancement due to the proposed hybrid beamforming scheme compared to its conventional all-digital counterpart. This performance enhancement becomes more significant as the number of network partitions increases. For DRL-based network clustering, the policy gradient (PG) algorithm offers the best possible performance in terms of stability and convergence rate while the state-action-reward-state-action (SARSA) algorithm suffers from significant variance, slower convergence, and slightly inferior performance than other algorithms. For DRL-based beamsteering, the soft actor-critic (SAC) algorithm with continuous action space shows the best performance. Also, online training of the agents with varying channel state information (CSI) is observed to increase the variance of the Q-values and decrease the convergence rate, with no significant effect on the average reward. The simulation codes are available at:https://github.com/yasser-aleryani/mmWaveCellFree.git

This paper introduces a novel approach of utilizing the reconfigurable intelligent surface (RIS) for joint data modulation and signal beamforming in a multi-user downlink cellular network by leveraging the idea of backscatter communication. We present a general framework in which the RIS, referred to as modulating intelligent surface (MIS) in this paper, is used to:$i$) beamform the signals for a set of users whose data modulation is already performed by the base station (BS), and at the same time,$ii$) embed the data of a different set of users by passively modulating the deliberately sent carrier signals from the BS to the RIS. To maximize each user’s spectral efficiency, a joint non-convex optimization problem is formulated under the sum minimum mean-square error (MMSE) criterion. Alternating optimization is used to divide the original joint problem into two tasks of:$i$) separately optimizing the MIS phase-shifts for passive beamforming along with data embedding for the BS- and MIS-served users, respectively, and$ii$) jointly optimizing the active precoder and the receive scaling factor for the BS- and MIS-served users, respectively. While the solution to the latter joint problem is found in closed-form using traditional optimization techniques, the optimal phase-shifts at the MIS are obtained by deriving the appropriate optimization-oriented vector approximate message passing (OOVAMP) algorithm. Moreover, the original joint problem is solved under both ideal and practical constraints on the MIS phase shifts, namely, the unimodular constraint and assuming each MIS element to be terminated by a variable reactive load. The proposed MIS-assisted scheme is compared against state-of-the-art RIS-assisted wireless communication schemes and simulation results reveal that it brings substantial improvements in terms of system throughput while supporting a much higher number of users.

Using stochastic geometry tools, we develop a comprehensive framework to analyze the downlink performance of various types of users (e.g., users served by direct base station (BS) transmissions and indirect intelligent reflecting surface (IRS)-assisted transmissions) in a cellular network with multiple BSs and IRSs. For the proposed users, we provide the approximate expressions for the performance in terms of coverage probability, ergodic capacity, and energy efficiency (EE). The proposed stochastic geometry framework can capture the impact of channel fading, locations of BSs and IRSs, arbitrary phase-shifts and interference experienced by a typical user supported by direct transmission and/or IRS-assisted transmission. For IRS-assisted transmissions, we first model approximate the desired signal power from the nearest IRS as a sum of scaled generalized gamma (GG) random variables whose parameters are functions of the IRS phase shifts. Then, we derive the Laplace Transform (LT) of the received signal power in a closed form. Also, we approximate the aggregate interference from multiple IRSs as the sum of normal random variables. Then, we derive the LT of the aggregate interference from all IRSs and BSs. The derived LT expressions are used to calculate coverage probability, ergodic capacity, and EE for users served by direct BS transmissions as well as users served by IRS-assisted transmissions. Finally, we derive the overall network coverage probability, ergodic capacity, and EE based on the fraction of direct and IRS-assisted users, which is defined as a function of the deployment density of IRSs, as well as blockage probability of direct transmission links. Numerical results validate the derived analytical expressions and extract useful insights related to the number of IRS elements, large-scale deployment of IRSs and BSs, and the impact of IRS interference on direct transmissions.

We investigate the age-limited capacity of the Gaussian many channel with total$N$users, out of which a random subset of$K_{a}$users are active in any transmission period, and a large-scale antenna array at the base station (BS). In an uplink scenario where the transmission power is fixed among the users, we consider the setting in which both the number of users,$N$, and the number of antennas at the BS,$M$, are allowed to grow large at a fixed ratio$\zeta = {M}/{N}$. Assuming perfect channel state information (CSI) at the receiver, we derive the achievability bound under maximal ratio combining. As the number of active users,$K_{a}$, increases, the achievable spectral efficiency is found to increase monotonically to a limit$\log _{2}\left ({1+\frac {M}{K_{a}}}\right)$. Further extensions of the analysis to the zero-forcing receiver as well as imperfect CSI are provided, demonstrating the channel estimation penalty in terms of the mean squared error in estimation. Using the age of information (AoI) metric, first coined by Kaul et al., as our measure of data timeliness or freshness, we investigate the trade-offs between the AoI and spectral efficiency in the context massive connectivity with large-scale receiving antenna arrays. As an extension of Liu and Yu, based on our large system analysis, we provide an accurate characterization of the asymptotic (finite system size) spectral efficiency as a function of the number of antennas and the number of users, the attempt probability, and the AoI. It is found that while the spectral efficiency can be made large, the penalty is an increase in the minimum AoI obtainable. The proposed achievability bound is further compared against recent massive MIMO-based massive unsourced random access (URA) schemes.

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) can be deployed to complement fixed charging stations. Currently, the strategy of MCSs is to move towards the EVs with insufficient energy (IEVs) only after being requested, which is not efficient. However, similar to online car-hailing services, more IEVs could be charged and the charging expenses could be reduced if idle MCSs can actively move towards the potential charging positions. In this paper, the problem of placements of idle MCSs in an IoEV is investigated in order to enhance the proportion of charged IEVs and reduce the charging expenses of IEVs. To this end, we propose a Federated Learning based Placement Decision Method of Idle MCSs (FL-PDMIM) to help the idle MCSs to predict the future charging positions, by exploiting the historical routes of MCSs which contain rich information regarding the charging demand of IEVs. In the proposed framework, the historical routes are trained locally by each MCS, and then the local model parameters and charging records are periodically uploaded to an edge server for a global parameter aggregation. Then, idle MCSs decide their placements according to the predicted charging positions (potential charging positions). The training time can be largely shortened, because the distributed learning on each MCS is executed in parallel. Extensive simulations and comparisons demonstrate the performance superiority of FL-PDMIM. Specifically, with the proposed federated learning-based predictions, the waiting time of IEVs to be served can be significantly shortened, and FL-PDMIM enhances the proportion of charged IEVs and reduces the charging expenses of IEVs effectively.

With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

User association is a crucial aspect which greatly affects the performance of wireless networks. In this work, we investigate the user association problem in full-duplex cellular networks, wherein base stations (BSs) are densely deployed with highly variable transmit powers and topologies (e.g., heterogeneous networks). To enhance the system performance, decoupled UL-DL (DUDe) association is considered, which enables each user equipment (UE) to associate with different BSs in uplink (UL) and downlink (DL), respectively. Considering the challenges raised by asymmetric information (e.g., channel gains and intercell interferences) between UEs and BSs, we propose a contract-theory based distributed user association approach. Specifically, the association process is modeled as a labor market, where the BSs act as employers and offer two-dimensional contracts to employees (i.e., UEs) for maximizing the utility of the BS. Theoretical proof for contract feasibility is presented by providing sufficient and necessary conditions. To reach the optimality, a contract-theoretic decoupled user association algorithm is developed, in which a BS broadcasts the drafted contracts, and each UE self-selects the optimal contract by considering her own demands. Numerical results are presented to demonstrate the performance of the proposed approach in terms of node utilities and social surplus. Impacts of system settings on the network performance are also investigated.

We study the joint user association and resource allocation problem in both uplink (UL) and downlink (DL) for full-duplex heterogeneous cellular networks (HCNs), wherein base stations (BSs) are densely deployed with reusable subchannels and highly variable transmit powers. To reap the benefits of BS densification, decoupled multiple-association (DMA) is considered, which enables each user equipment (UE) to associate with multiple BSs for UL and DL in a decoupled manner. Furthermore, in order to provide the best service, appropriate holistic subchannel and power allocation are jointly studied and an optimization problem is formulated. However, it is challenging to solve the joint problem due to its combinatorial nature. To this end, we formulate a novel distributed four-sided matching game in which the UEs, BSs, subchannels, and power levels are ranked based on designed preference metrics for optimal matching. To obtain the solution, a low-complexity algorithm is developed. The convergence of the algorithm to a stable matching is proved and the worst-case complexity is analyzed. Numerical results are presented to demonstrate the performance of the proposed scheme in terms of the UEs’ sum-rate in UL and DL, respectively. The superiority of DMA is also investigated by comparisons.

During the last few decades, wireless communication technologies and services have radically changed the way we live and interact at the personal, social, local and global levels. Such changes were mainly driven by the continuous emergence of innovative wireless communication services and products. These services and products represents a direct upshot of enduring research outcomes within the area. Nevertheless, the blessing of such innovation was accompanied by extremely high demands in forms of data traffic, per-user transmission rate, minimum transmission delay and in the number of wireless devices per unit area. Tackling these issues through cellular network densification was faced by many technical issues related to high interference levels, tedious user scheduling processes, and complicated network resource allocation algorithms. Trying to address these imperative technical issues in future wireless networks, this thesis develops several innovative enabling techniques for massive wireless multiple access. Specifically, we commence this work by introducing a new concept of partial spectrum overlapping among active users equipment (UEs). The proposed scheme represents a trade-off between fully orthogonal multiple access schemes (e.g. time division multiple access [TDMA], frequency division multiple access (FDMA) and orthogonal frequency division multiple access (OFDMA)) and that of non-orthogonal multiple access (NOMA). Second, we develop several innovative dynamic cell-free network architectures that support massive wireless connectivity through adaptive access points (APs)/base stations (BSs) coordination and/or cooperation. The proposed network models are then evaluated under different state-of-the-art enabling wireless techniques such as millimeter wave (mmWave) channel links and massive multiple-input multiple-output (mMIMO) systems. Furthermore, the performance of the proposed architectures is investigated through the derivation of several closed-form expressions of exact and/or asymptotic performance metrics (example, probability of outage, asymptotic outage, instantaneous rate and outage-capacity). Finally, for practical control and monitoring of the proposed access techniques and network models, we develop several low-complexity deep reinforcement learning (DRL)-based modeling frameworks that can efficiently learn the solution of several combinatorial optimization problems related to network partitioning (clustering) and uplink/downlink beamforming. This is achieved through innovative nested DRL designs that utilizes continuous and discrete deep neural networks (DNN) agents based on the nature of the problem. Several operating scenarios of the proposed techniques are evaluated through extensive Monte-Carlo simulations (Matlab and Python) with practical parameters and assumptions.

We put forward a new algorithmic solution to the massive unsourced random access (URA) problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays. This paper makes an observation that spatial signature is key to URA in massive connectivity setups. The proposed scheme relies on a slotted transmission framework but eliminates the need for concatenated coding that was introduced in the context of the coupled compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA rely on an inner/outer tree-based encoder/decoder to stitch the slot-wise recovered sequences. This paper takes a different path by harnessing the nature-provided correlations between the slot-wise reconstructed channels of each user in order to put together its decoded sequences. The required slot-wise channel estimates and decoded sequences are first obtained through the hybrid generalized approximate message passing (HyGAMP) algorithm which systematically accommodates the multiantenna-induced group sparsity. Then, a channel correlation-aware clustering framework based on the expectation-maximization (EM) concept is used together with the Hungarian algorithm to find the slot-wise optimal assignment matrices by enforcing two clustering constraints that are very specific to the problem at hand. Stitching is then accomplished by associating the decoded sequences to their respective users according to the ensuing assignment matrices. Exhaustive computer simulations reveal that the proposed scheme can bring performance improvements, at high spectral efficiencies, as compared to a state-of-the-art technique that investigates the use of large-scale antenna arrays in the context of massive URA.

We propose a novel antenna clustering-based method for simultaneous wireless information and power transfer (SWIPT) in a multiple-input multiple-output (MIMO) full-duplex (FD) system. For a point-to-point communication set up, the proposed method enables a wireless device with multiple antennas to simultaneously transmit information and harvest energy using the same time-frequency resources. And the energy transmitting device with multiple antennas simultaneously receives information from the energy harvesting (EH) device. This is achieved by clustering the antennas into two MIMO subsystems: one for information transmission (IT) and another for EH. Furthermore, the self-interference (SI) signal at the EH device caused by the FD mode of operation is harvested by the device. For implementation-friendly antenna clustering and MIMO precoding, we propose two methods: (i) a sub-optimal method based on relaxation of objective function in a combinatorial optimization problem, and (ii) a hybrid deep reinforcement learning (DRL)-based method. For the proposed DRL solution, we design a hybrid discrete/continuous action agent that jointly clusters the MIMO antennas between EH and IT, and at the same time, find the best values for MIMO precoding matrices at both devices. This is achieved by using two interacting agent learning subsystems, namely, deep double Q-learning (DDQN), for antenna clustering and deep deterministic policy gradient (DDPG), for MIMO precoding. The effect of imperfect CSI is also studied and investigated. Finally, we study the performances of the two implementation methods and compare them with the conventional time switching-based simultaneous wireless information and power transfer (SWIPT) technique. Our findings show that the proposed MIMO clustering-based SWIPT method gives a significant improvement in spectral efficiency compared to the time switching-based SWIPT method. In particular, the DRL-based method provides the highest spectral efficiency. Besides, the numerical results show that, for the considered system set up, the number of antennas in each device should exceed three to mitigate self-interference to an acceptable level.

This paper tackles the problem of joint active and passive beamforming optimization for an intelligent reflective surface (IRS)-assisted multi-user downlink multiple-input multiple-output (MIMO) communication system under both ideal and practical IRS phase shifts. We aim to maximize the spectral efficiency of the users by minimizing the sum mean square error (MSE) of the users’ received symbols. For this, a joint non-convex optimization problem is formulated under the sum minimum mean square error (MMSE) criterion. Alternating minimization is used to break the original joint optimization problem into the separate optimization of the active precoding matrix for the base station (BS) and the matrix of phase shifts for the IRS. While the MMSE active precoder is obtained in closed-form, the IRS phase shifts are optimized iteratively using a modified version (developed in this paper) of the vector approximate message passing (VAMP) algorithm. Moreover, the underlying joint optimization problem is solved under two different models for the IRS phase shifts, namely by assuming$i$) a unimodular (i.e., ideal) constraint on the reflection coefficients and$ii$) a more practical reflection elements termination by a variable reactive load (which inherently introduces the phase-dependent amplitude attenuation in the IRS phase shifts). Simulation results are presented to illustrate the performance of the proposed method under both perfect and imperfect channel state information (CSI) and to show the effect of the practical constraint on the system throughput. The results validate the superiority of the proposed method over the state-of-the-art techniques both in terms of throughput and computational complexity.

With growth in the number of smart devices and advancements in their hardware, in recent years, data-driven machine learning techniques have drawn significant attention. However, due to privacy and communication issues, it is not possible to collect this data at a centralized location. Federated learning is a machine learning setting where the centralized location trains a learning model over remote devices. Federated learning algorithms cannot be employed in the real world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. In this paper, we propose a federated learning algorithm that is suitable for cellular wireless networks. We prove its convergence, and provide a sub-optimal scheduling policy that improves the convergence rate. We also study the effect of local computation steps and communication steps on the convergence of the proposed algorithm. We prove, in practice, federated learning algorithms may solve a different problem than the one that they have been employed for if the unreliability of wireless channels is neglected. Finally, through numerous experiments on real and synthetic datasets, we demonstrate the convergence of our proposed algorithm.

This paper presents a theoretical framework to analyze the performance of an integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which the IRS provides an additional degree of freedom combined with the flexible deployment of full-duplex UAV to enhance communication between ground nodes. Our framework considers three different transmission modes: (i) UAV-only mode, (ii) IRS-only mode, and (iii) integrated UAV-IRS mode to achieve spectral and energy-efficient relaying. For the proposed modes, we provide exact and approximate expressions for the end-to-end outage probability, ergodic capacity, and energy efficiency (EE) in closed-form. We use the derived expressions to optimize key system parameters such as the UAV altitude and the number of elements on the IRS considering different modes. We formulate the problems in the form of fractional programming (e.g. single ratio, sum of multiple ratios or maximization-minimization of ratios) and devise optimal algorithms using quadratic transformations. Furthermore, we derive an analytic criterion to optimally select different transmission modes to maximize ergodic capacity and EE for a given number of IRS elements. Numerical results validate the derived expressions. The solutions obtained from the proposed optimization algorithms are compared with those obtained through exhaustive search. Insights are drawn related to the different communication modes, optimal number of IRS elements, and optimal UAV height.

We present a resource management framework for load-coupled downlink cellular OFDMA networks considering the load factor of an individual base station (BS) per resource block (RB), i.e., the number of adjacent sub-carriers (SCs), as the variable of interest in the resource management problem. The load factor of a BS per RB, which corresponds to the fraction of active SCs in the BS per RB, is an indicator of the level of resource consumption, and it affects the interference caused to that RB reused in other BSs, and thereby, results in a load-coupled OFDMA system. We first propose two distributed schemes to minimize: (i) the total load factor of the BSs (which would in turn increase the number of supportable users in the system), and (ii) the total downlink transmit power level of the BSs. Then, we derive the necessary and sufficient conditions for checking the feasibility of given target-rate requirements (also referred to as demand vector) for users. Accordingly, an iterative and distributed scheme is proposed to check the feasibility of a given demand vector. Next, for a priority-based load-coupled network, we propose a priority-based gradual removal algorithm to support the maximal number of low-priority users while satisfying the demands of the high-priority users. To evaluate the performance of our proposed schemes for resource management and admission control in load-coupled OFDMA networks, the theoretical investigations are complemented with Monte Carlo simulations.

We develop novel statistical models of the harvested energy from renewable energy sources considering harvest-store-consume (HSC) architecture. We consider three renewable energy harvesting scenarios, i.e., (i) harvesting from the solar power, (ii) harvesting from the wind power, and (iii) hybrid solar and wind power. In this context, we first derive the closed-form expressions for the density functions and moments of the harvested power solar and wind power. Then, we calculate the probability of energy outage at UAVs and signal-to-noise ratio (SNR) outage at ground cellular users. The energy outage occurs when the UAV is unable to support the flight consumption and transmission consumption from its battery power and the harvested power. Due to the intricate distribution of the hybrid solar and wind power, we derive novel closed-form expressions for the moment generating function (MGF) of the harvested solar power and wind power. Then, we apply Gil-Pelaez inversion to evaluate the energy outage at the UAV and SNR outage at the ground users. In addition, we formulate the SNR outage minimization problem and obtain closed-form solutions for the transmit power and flight time of the UAV. Furthermore, we demonstrate the application of moments in computing novel metrics such as the probability of charging the UAV battery within the flight time, average UAV battery charging time, probability of energy outage at UAVs, and the probability of eventual energy outage (i.e., the probability of energy outage in a finite duration of time) at UAVs. Numerical results validate the analytical expressions and reveal interesting insights related to the optimal flight time and transmit power of the UAV as a function of the harvested energy.

Impact of mobility will be increasingly important in future generation wireless services and the related challenges will need to be addressed. Sojourn time, the time duration that a mobile user stays within a cell, is a mobility-aware parameter that can significantly impact the performance of mobile users and it can also be exploited to improve resource allocation and mobility management methods in the network. In this paper, we derive the distribution and mean of the sojourn time in multi-tier cellular networks, where spatial distribution of base stations (BSs) in each tier follows an independent homogeneous Poisson point process (PPP). To obtain the sojourn time distribution in multi-tier cellular networks with maximum biased averaged received power association, as the first step, we derive the area of contact, based on which we then derive the linear contact distribution function and chord length distribution of each tier. We also study the relation between mean sojourn time and other mobility-related performance metrics. We show that the mean sojourn time is inversely proportional to the handoff rate, and the complementary cumulative distribution function (CCDF) of sojourn time is bounded from above by the complement of the handoff probability. Moreover, we study the impact of user velocity and network parameters on the sojourn time.

Edge computing allows an edge server to adaptively place virtual instances to serve different types of data. This article presents a new algorithm which jointly optimizes virtual service placement farsightedly and service data admission instantly to maximize the time-average service throughput of edge computing. The data admission is optimized, adapting to fast-changing data arrivals and wireless channels. The service placement is transformed into a two-dimensional knapsack problem by approximating future arrivals and channels with past observations, and solved over a slow timescale to allow services to be properly installed. Different from existing studies, our algorithm considers practical aspects of edge servers, such as finite memory size and bandwidth. We prove that the algorithm is asymptotically optimal and the optimality loss resulting from the approximation diminishes. Simulations show that our approach can improve the time-average throughput of existing alternatives by 16% for our considered simulation setup. The improvement becomes higher, as the memory size becomes increasingly tight. The number of services to be replaced is reduced without loss of throughput, after being placed farsightedly.

We analyze the end-to-end performance of an unmanned-aerial-vehicle (UAV)-assisted data ferrying network where the UAV serves as a data ferry between the source base station (BS) and multiple destination receivers. We evaluate the end-to-end reliability both with and without packet retransmission technique called automatic repeat request (ARQ), bit error probability (BEP), energy-efficiency, and transmission outage probability. We consider line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions in both the data loading and delivering links and model them with the Rician and Rayleigh fading channels, respectively. We derive tractable approximations for the derived SNR outage results and demonstrate their application in optimizing the distance that a UAV should travel in order to balance the energy-coverage trade-offs. We formulate two different optimization problems and convexify them to solve for the optimal ferrying distance, i.e. (i) outage-constrained energy minimization and (ii) energy-constrained SNR outage minimization. Closed-form optimal solutions are obtained for the second problem. In addition, we formulate a bi-objective optimization problem in order to minimize SNR outage and energy consumption with desired SNR outage probability constraints. The objective function is then reformulated using difference of convex functions (DC) and solved using a DC algorithm. Numerical results validate the derived expressions and show a comparison of the obtained solutions with the solutions obtained from exhaustive search. Insights related to the impact of LoS Rician and NLoS Rayleigh fading channels as well as the optimal ferrying distance are obtained considering a variety of objective functions.

With network slicing, physical networks are partitioned into multiple virtual networks tailored to serve different types of service with their specific requirements. In order to optimize the utilization of network resources for delay-critical applications, we propose a new multi-domain network virtualization framework based on a novel multipath multihop delay model. This framework encompasses a novel hierarchical orchestration mechanism for mapping network slices onto physical resources and a mechanism for dynamic slice resizing. The main idea is to locally redefine the delay requirements on each network domain depending on the conditions in the rest of the network. Delays larger than threshold (debt) are allowed in certain domains if there is a possibility to compensate such excessive delays in other segments of the network that can transmit the messages with less latency (credit). This tradeoff or delay threshold redefinition on different segments of the route is referred to as network latency equalization. For performance comparison, minimum cost routing with latency constraints is used as a baseline. We show that our approach enables significantly better utilization of the network resources measured in the number of slices with the same latency requirements that can be accommodated in the network.

Non-orthogonal multiple access (NOMA) allows users sharing a resource-block to efficiently reuse spectrum and improve cell sum rate$\mathcal {R}_{\mathrm{ tot}}$at the expense of increased interference. Orthogonal multiple access (OMA), on the other hand, guarantees higher coverage. We introduce partial-NOMA in a large two-user downlink network to provide both throughput and reliability. The associated partial overlap controls interference while still offering spectrum reuse. The nature of the partial overlap also allows us to employ receive-filtering to further suppress interference. For signal decoding in our partial-NOMA setup, we propose a new technique called flexible successive interference cancellation (FSIC) decoding. We plot the rate region abstraction and compare with OMA and NOMA. We formulate a problem to maximize$\mathcal {R}_{\mathrm{ tot}}$constrained to a minimum throughput requirement for each user and propose an algorithm to find a feasible resource allocation efficiently. Our results show that partial-NOMA allows greater flexibility in terms of performance. Partial-NOMA can also serve users that NOMA cannot. We also show that with appropriate parameter selection and resource allocation, partial-NOMA can outperform NOMA.

Mobile edge computing (MEC) enables mobile users to offload their computationally-intensive tasks to the servers located at the network's edge. One of the fundamental challenges of MEC is to develop methods to efficiently allocate the limited computational resources of the edge servers to the offloading users. To address this challenge, in this paper, we propose a reverse auction framework based on position auction consisting of pricing, bidding strategy optimization, and winner determination. The proposed solution allows the edge servers to maximize their utility through strategic participation. Moreover, it ensures users' satisfaction by taking the users' preferences into account. The solution has polynomial-time complexity and enjoys desirable economical characteristics including envy-free and individual rationality. In addition to the theoretical analysis, numerical results establish the sound performance of the proposed framework in terms of the system's resource utilization as well as the users' satisfaction level.

The problem of joint power and sub-channel allocation to maximize energy efficiency (EE) and spectral efficiency (SE) simultaneously in in-band full-duplex (IBFD) orthogonal frequency-division multiple access (OFDMA) network is addressed considering users' QoS in both uplink and downlink. The resulting optimization problem is a non-convex mixed integer non-linear program (MINLP) which is generally difficult to solve. In order to strike a balance between the EE and SE, we restate this problem as a multi-objective optimization problem (MOOP) which aims at maximizing system's throughput and minimizing system's power consumption, simultaneously. To this end, the ε-constraint method is adopted to transform the MOOP into single objective optimization problem (SOOP). The underlying problem is solved via an efficient solution based on the majorization minimization (MM) approach. Furthermore, in order to handle binary subchannel allocation variable constraints, a penalty function is introduced. Simulation results unveil interesting tradeoffs between EE and SE.

A novel generalized coordinated multi-point transmission (GCoMP)-enabled non-orthogonal multiple access (NOMA) scheme is proposed. In particular, distributed base stations (BSs) in a network coverage area cooperate on the downlink to serve a set of user equipments (UEs) using the same transmission frequency band. Furthermore, all UEs associated to a BS and using a particular frequency band forms a single NOMA cluster. The number of BSs serving a UE in a particular frequency band is referred to as theorder of clustering(or order of BS cooperation). To evaluate the proposed scheme, we derive a closed-form expression for the probability of outage for a UE with different orders of BS cooperation. To obtain important insights on the performance of the proposed system, approximate (asymptotic) expressions for the probability of outage and outage capacity are derived considering both perfect and imperfect channel state information (CSI) estimation. We observe that improved spectral efficiency with a large number of UEs per NOMA cluster can be achieved by increasing the clustering order (i.e., number of cooperating BSs per UE). Furthermore, an optimal transmission power allocation scheme that jointly allocates transmission power fractions from all cooperating BSs to all connected UEs is developed.

We develop an analytical framework to derive the meta distribution and moments of the conditional success probability (CSP), which is defined as success probability for a given realization of the transmitters, in large-scale co-channel uplink and downlink non-orthogonal multiple access (NOMA) networks with one NOMA cluster per cell. The moments of CSP translate to various network performance metrics such as the standard success or signal-to-interference ratio (SIR) coverage probability (which is the 1-st moment), the mean local delay (which is the −1st moment in a static network setting), and the meta distribution (which is the complementary cumulative distribution function of the success or SIR coverage probability and can be approximated by using the 1st and 2nd moments). For the uplink NOMA network, to make the framework tractable, we propose two point process models for the spatial locations of the inter-cell interferers by utilizing the base station (BS)/user pair correlation function. We validate the proposed models by comparing the second moment measure of each model with that of the actual point process for the inter-cluster (or inter-cell) interferers obtained via simulations. For downlink NOMA, we derive closed-form solutions for the moments of the CSP, success (or coverage) probability, mean local delay, and meta distribution for the users. As an application of the developed analytical framework, we use the closed-form expressions to optimize the power allocations for downlink NOMA users in order to maximize the success probability of a given NOMA user with and without latency constraints. Closed-form optimal solutions for the transmit powers are obtained for two-user NOMA scenario. We note that maximizing the success probability with latency constraints can significantly impact the optimal power solutions for low SIR thresholds and favor orthogonal multiple access.

We characterize the accuracy of analyzing the performance of a non-orthogonal multiple access (NOMA) system where users are ranked according to their distances instead of instantaneous channel gains, i.e., product of their distance-based path-loss and fading channel gains. Distance-based ranking of users is analytically tractable and can lead to important insights. However, it may not be appropriate in a multipath fading environment where a near user suffers from severe fading while a far user experiences weak fading. Since the ranking of users (and in turn interferers) in an NOMA system has a direct impact on coverage probability analysis, the impact of the traditional distance-based ranking, as opposed to instantaneous signal power-based ranking, needs to be understood. This will enable us to identify scenarios where distance-based ranking, which is easier to implement compared with instantaneous signal power-based ranking, is acceptable for the system performance analysis. To this end, in this paper, we derive the probability of the event when distance-based ranking yields the same results as instantaneous signal power-based ranking, which is referred to as theaccuracy probability. We characterize the probability of accuracy considering Nakagami-$m$fading channels and three different spatial distribution models of user locations in NOMA, namely, the Poisson point process (PPP), the Matern cluster process (MCP), and the Thomas cluster process (TCP). For all these models of users’ locations, we assume that the spatial locations of the base stations (BSs) follow a homogeneous PPP. We show that the accuracy probability decreases with the increasing number of users and increases with the path-loss exponent. In addition, through examples, we illustrate the impact of accuracy probability on uplink and downlink coverage probabilities. Closed-form expressions are presented for the Rayleigh fading environment. The effects of fading severity and users’ pairing on the accuracy probability are also investigated.

We develop and analyze a new user-centric networking model for ubiquitous spectrum sharing where every user can share and use the spectrum under uncertainty of their traffic models. In this concept, users when connected to the Internet (wired/wireless) can dynamically serve as access points for other users in their vicinity. For this reason, the concept is referred to as user-centric distributed spectrum sharing. Each user in spectrum sharing mode utilizes a part of its available spectrum for its own traffic and remaining part to share with users in spectrum demanding modes. The model is designed as an operator supervised double-Stackelberg game with network operators, access points, and users as main players. We study network reliability and latency of the system under uncertainty of users' traffic patterns. The numerical results show that the proposed model, depending on different settings, can significantly improve both profit and utility for network operators and users, respectively. Furthermore, network reliability is significantly improved depending on the network parameters for both users and operators.

This paper considers the problem of dynamic power allocation in the downlink of multi-cell networks, where each cell utilizes non-orthogonal multiple access (NOMA)-based resource allocation. Also, coordinated multi-point (CoMP) transmission is utilized among multiple cells to serve users experiencing severe inter-cell interference (ICI). Under this CoMP- NOMA framework, CoMP transmission is applied to a user experiencing less distinctive channel gain with multiple base stations (BSs)/cells (i.e., severe ICI-prone user) and non-CoMP transmission (i.e., transmission without any coordination among multiple BSs) is applied to a user experiencing dominating channel gain with only one BS/cell, while NOMA is utilized at each BS to schedule CoMP and non-CoMP users over the same transmission resources, i.e., time, spectrum and space. After discussing various CoMP- NOMA models for downlink power allocation in multi-cell networks, we focus on a joint transmission CoMP- NOMA (JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power allocation problem is formulated and the solution is derived for each CoMP- set consisting of multiple cooperating BSs (i.e., CoMP BSs). To avoid the huge computational complexity of the joint power optimization approach, we propose a distributed power optimization approach at each cooperating BS whose optimal solution is independent of the solution of other coordinating BSs. The distributed solution for the joint power optimization problem is validated and numerical performance evaluation is carried out for the proposed CoMP- NOMA models including JT-CoMP-NOMA and coordinated scheduling CoMP- NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in spectral and energy efficiency in comparison with conventional CoMP- orthogonal multiple access (CoMP-OMA) systems.

To improve the spectral efficiency in long-term evolution systems, the resource blocks (RBs) are shared among different cells/base stations (BSs) resulting in interference among the cells/BSs on each RB, although all the sub-carriers (SCs) in an RB may not be used in a cell. Defining the load of a given BS per RB as the fraction of the active SCs in that RB, in this paper, we present a generalized signal-to-interference-and-noise-ratio (SINR) model for downlink users on a given RB. This model considers both the transmit powers of the BSs and the loads of the cells over that RB. Under this load-coupled SINR model, to study the feasibility of a given rate demand vector for users, we formulate an optimization problem of minimizing the total load of the BSs on the RBs. Then, for two different scenarios of feasible and infeasible demand vectors, respectively, we study the load management problem (i.e., minimizing the total load of the BSs on the RBs) and admission control problem (i.e., finding the sub-set of users with maximum cardinality whose demands can be concurrently satisfied), respectively. Our theoretical investigations, which provide guidelines for designing radio resource management methods for load-coupled OFDMA networks, are complemented through Monte Carlo simulations.

We address the problem of distributed power control in a two-tier cellular network, where full-duplex small cells underlay a macro cell in a co-channel deployment scenario. We first formulate the distributed power control problem as a non-cooperative game and then extend it to a repeated game with imperfect public monitoring. The repeated game formulation prevents deceitful small cells from deviating from the social optimal solution for their own benefit. We establish the existence and uniqueness of the Nash equilibrium in the formulated non-cooperative game. We also characterize the set of public perfect equilibrium for the repeated game. A two-phase distributed algorithm is proposed to achieve and enforce a Pareto optimal transmit power profile. The solution obtained by this algorithm is also social optimal. Phase 1 of the algorithm is a fully distributed learning phase based on perturbed Markov chains, where each base station individually learns a Pareto optimal operating point. Phase 2 is composed of two rules: 1) a detection rule based on Page-Hinckley test to detect cheating and 2) a punishment rule to motivate cheating base stations to cooperate. Through theoretical analysis, we prove that the proposed distributed power control mechanism achieves a public perfect equilibrium point of the formulated repeated game. The power control algorithm is also cheat-proof and needs only a small amount of information exchange among network nodes. The effectiveness of the algorithm is shown through numerical analysis. Our proposed model, algorithm, and analysis are also valid for a half-duplex system as a special case.

The conflicting problems of growing mobile service demand and underutilization of dedicated spectrum has given rise to a paradigm where mobile network operators (MNOs) share their infrastructure among themselves in order to lower their operational costs, while at the same time increase the usage of their existing network resources. We model and analyze such an infrastructure sharing system considering a single buyer MNO and multiple seller MNOs. Assuming that the locations of the BSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability for a user served by the buyer MNO in an infrastructure sharing environment. We analyze the trade-off between increasing the transmit power of a base station (BS) and the intensity of BSs owned by the buyer MNO required to achieve a given quality-of-service (QoS) in terms of the SINR coverage probability. Also, for a seller MNO, we analyze the power consumption of the network per unit area (i.e., areal power consumption) which is shown to be a piecewise continuous function of BS intensity, composed of a linear and a convex function. Accordingly, the BS intensity of the seller MNO can be optimized to minimize the areal power consumption while achieving a minimum QoS for the buyer MNO. We then use these results to formulate a single-buyer multiple-seller BS infrastructure market. The buyer MNO is concerned with finding which seller MNO to purchase from and what fraction of BSs to purchase. On the sellers' side, the problem of pricing and determining the fraction of infrastructure to be sold is formulated as a Cournot oligopoly market. We prove that the iterative update of each seller's best response always converges to the Nash Equilibrium.

Signal-to-noise-plus-interference ratio (SINR) outage probability is among one of the key performance metrics of a wireless network. In this paper, we propose a semi-analytical method based on the saddle point approximation (SPA) technique to calculate the SINR outage of a wireless system whose SINR can be modeled in the form (Σi=1MXi/(1 + Σi=1NYi)) where Xi denotes the useful signal power and Yidenotes the power of the interference signal. Both M and N can also be random variables. The proposed approach is based on the saddle point approximation to cumulative distribution function as given by Wood-Booth-Butler formula. The approach is applicable whenever the cumulant generating function of the received signal and interference exists, and it allows us to tackle distributions with large skewness and kurtosis with higher accuracy. In this paper, we exploit a four parameter normal-inverse Gaussian (NIG) distribution as a base distribution. Given that the skewness and kurtosis satisfy a specific condition, NIG-based SPA works reliably. When this condition is violated, we recommend SPA based on normal or symmetric NIG distribution, both special cases of NIG distribution, at the expense of reduced accuracy. For the purpose of demonstration, we apply SPA for the SINR outage evaluation of a typical user experiencing a downlink coordinated multi-point transmission from the base stations that are modeled by homogeneous Poisson point process. Numerical results are presented to illustrate the accuracy of the proposed set of approximations.

We jointly optimize resource allocation with the dynamic activation of energy harvesting base stations in a two-tier orthogonal frequency-division multiple access-based heterogeneous network. We consider both energy harvesting constraints and interference constraints along with time-variation in channel condition, user activity, and energy arrival. We optimize the trade-off between throughput performance of the small cell (or hotspot) users and the associated power cost by maximizing the net reward, where positive reward is associated with achievable throughput of the hotspot users and negative reward with the corresponding non-renewable power consumption. Quality-of-service requirements of hotspot users as well as macrocell users are considered in the optimization problem. Assuming the availability of non-causal information, we propose offline resource allocation algorithm using discrete binary particle swarm optimization and dual decomposition technique. Assuming the availability of statistical information of future values, we propose dynamic programming-based online algorithm. Finally, we propose simple and greedy online algorithm assuming lack of any kind of future information. Numerical results demonstrate the performances of the proposed offline, dynamic programming-based online, and greedy online algorithms and highlight the scenarios, where the performance of the proposed algorithms is significantly better than the baseline schemes.

We propose a supervised machine learning (ML) approach based on Gaussian process (GP) regression to position users in a distributed massive multiple-input multiple-output (DM-MIMO) system from their uplink received signal strength (RSS). The proposed approach serves as a proof-of-concept that we can localize users by training an ML model with noise-free RSS and using the trained model to estimate the test user locations from their noisy RSS. We consider two GP methods for localization, namely, the conventional GP (CGP) and the numerical approximation GP (NaGP). We find that the CGP provides unrealistically small 2σ error-bars on the location estimates. Therefore, we derive the true predictive distribution and employ NaGP to obtain realistic 2σ error-bars on the location estimates. Next, we derive a Bayesian Cramer-Rao lower bound (BCRLB) on the root-mean-squared-error (RMSE) performance of the two GP methods. Numerical studies reveal that: 1) the NaGP indeed provides realistic 2σ error-bars on the estimated locations; 2) both the CGP and NaGP achieve RMSEs that are close to the BCRLBs; 3) the presence of correlated shadowing improves the RMSE performance; and 4) extrapolation to the zero input noise scenario can significantly improve the RMSE achieved by the NaGP.

This paper provides a stochastic geometry framework to perform the coverage and rate analysis of a typical user in co-existing visible light communication (VLC) and radio frequency (RF) networks. The framework can be customized to capture the performance of a typical user in various network configurations such as 1) RF-only, in which only small base-stations (SBSs) are available to provide the coverage to a user; 2) VLC-only, in which only optical BSs (OBSs) are available to provide the coverage to a user; 3) opportunistic RF/VLC, where a user selects the network with maximum received signal power; and 4) hybrid RF/VLC, where a user can simultaneously utilize the available resources from both RF and VLC networks. The developed model for VLC network precisely captures the impact of the field-of-view (FOV) of the photo-detector receiver on the number of optical interferers, distribution of the aggregate interference, association probability, the coverage probability, and average rate of a typical user. A closed-form approximation is presented for special cases and for asymptotic scenarios, such as when the intensity of SBSs becomes very low or the intensity of OBSs becomes very high. The closed-form solutions for network design parameters (such as intensity of OBSs and SBSs, transmit power, and/or FOV) enable network operators to distribute the users among RF and VLC networks according to their choice. Moreover, we also optimize the network parameters in order to prioritize the association of users to VLC network. Finally, simulations are carried out to verify the derived solutions. It is shown that the performance of VLC network depends significantly on the receiver's FOV/intensity of SBSs/OBSs and careful selection of such parameters is crucial to harness the benefits of VLC networks. Important trade-offs between height and intensity of OBSs are highlighted to optimize the performance of VLC networks.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for 5G wireless technology. In this paper, we first discuss the fundamentals of uplink and downlink NOMA transmissions in a cellular system and outline their key distinctions in terms of implementation complexity, detection and decoding at the SIC receiver(s), and the intra-cell and inter-cell interferences. Later, for both downlink and uplink NOMA, for each individual user in a two-user NOMA cluster, we theoretically derive the NOMA dominant condition, which refers to the condition under which the spectral efficiency gains of NOMA are guaranteed compared to conventional orthogonal multiple access (OMA). The conditions, which are distinct for uplink and downlink as well as for each individual user, provide direct insights into selecting appropriate users in two-user NOMA clusters. Numerical results show the significance of the derived conditions for user selection in uplink/downlink NOMA clusters and provide a comparison to the random user selection. Finally, a brief summary of the recent research investigations is provided which is followed by a discussion on the research challenges and future research directions.

We address the user association problem in multi-tier in-band full-duplex (FD) networks. Specifically, we consider the case of decoupled user association (DUA), in which users (UEs) are not necessarily served by the same base station (BS) for uplink (UL) and downlink (DL) transmissions. Instead, UEs can simultaneously associate to different BSs based on two independent weighted path-loss user association criteria for UL and DL. We use stochastic geometry to develop a comprehensive modeling framework for the proposed system model, where BSs and UEs are spatially distributed according to independent point processes. We derive closed-form expressions for the mean rate utility in FD, half-duplex (HD) DL, and HD UL networks as well as the mean rate utility of legacy nodes with only HD capabilities in a multi-tier FD network. We formulate and solve an optimization problem that aims at maximizing the mean rate utility of the FD network by optimizing the DL and UL user association criteria. We investigate the effects of different network parameters, including the spatial density of BSs and power control parameter. We also investigate the effect of imperfect self-interference cancellation (SIC) and show that it is more severe at UL, where there exist minimum required SIC capabilities for BSs and UEs, for which FD networks are preferable to HD networks; otherwise, HD networks are preferable. In addition, we discuss several special cases and provide guidelines on the possible extensions of the proposed framework. We conclude that DUA outperforms coupled user association, in which UEs associate to the same BS for both UL and DL transmissions.

In-band full-duplex (IBFD) backhauling is a potential technique for wireless backhauling of small cells that allows the use of same spectrum for the backhaul and access links of the small cell base stations (SBSs) concurrently, however, at the expense of backhaul interference and self-interference (SI). This paper investigates the problem of optimal access/backhaul spectrum allocation considering IBFD backhauling, out-of-band full-duplex (OBFD) backhauling (in which the access and backhaul transmissions take place on different spectrum), and the SBSs with the provisioning for hybrid IBFD/OBFD backhauling. We first formulate a problem to maximize the minimum achievable rate (i.e., minimum of the rates in the backhaul link and the access link) at the SBSs in a hybrid IBFD/OBFD setting. The solution of the centralized spectrum allocation problem, which serves as a benchmark for any sub-optimal solution, is provided by transforming the original problem into an epigraph form. As a special case of the formulated problem, we derive closed-form optimal solutions for the access/backhaul spectrum allocation of OBFD backhauling as well as IBFD backhauling. We then propose and comparatively analyze the performance of two distributed backhaul spectrum allocation schemes, namely, maximum received signal power (max-RSP) and minimum received signal power (min-RSP) schemes. For these schemes, we theoretically derive the number of allocated backhaul channels, minimum rate coverage probability, and average achievable rate of each SBS given its distance from the centralized wireless backhaul hub (WBH) for both IBFD and OBFD backhauling. Numerical results reveal that the optimal spectrum allocation rules can significantly vary for IBFD and OBFD backhauling. Optimal OBFD backhauling favors more backhaul spectrum for SBSs located far-away from the WBH. With IBFD backhauling, spectrum allocation for SBSs strongly depends on SI. With the reduction in SI, the optimal backhaul spectrum increases/decreases for nearby/farther SBSs. Simulation results comparing the optimal solution with the distributed spectrum allocation solutions based on max-RSP and min-RSP schemes are also presented.

Using the theory of Poisson cluster process (PCP), this paper provides a framework to analyze multi-cell uplink non-orthogonal multiple access (NOMA) systems. Specifically, we characterize the rate coverage probability of an NOMA user who is at rank m (in terms of the distance from its serving base station) among all users in a cell and the mean rate coverage probability of all users in a cell. Since the signal-to-interference-plus-noise ratio of the mth user relies on efficient successive interference cancellation (SIC), we consider three scenarios, i.e., perfect SIC (in which the signals of m - 1 interferers who are stronger than the mth user are decoded successfully), imperfect SIC (in which the signals of m - 1 interferers who are stronger than the mth user may or may not be decoded successfully), and imperfect worst case SIC (in which the decoding of the signal of the mth user is always unsuccessful whenever the decoding of its relative m -1 stronger users is unsuccessful). To derive the rate coverage expressions, we first characterize the Laplace transforms of the intra-cluster interferences in closed-form considering various SIC scenarios. The Laplace transform of the inter-cluster interference is then characterized by exploiting distance distributions from geometric probability. The derived expressions are customized for an equivalent OMA system. Finally, numerical results are presented to validate the derived expressions. The worst case SIC assumption provides remarkable simplifications in the mathematical analysis and is found to be highly accurate for higher user target rate requirements. A comparison of Poisson point process-based and PCP-based modeling is also conducted.

We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids. Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P2P Electricity Trading system with COnsortium blockchaiN (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.

In multi-tier cellular networks, user performance in both the downlink (DL) and uplink (UL) transmissions depend on the transmit powers of the base stations (BSs) in different network tiers, users' distances, and non-uniform traffic loads of different BSs. In such a network, decoupled UL-DL user association (DUDe), which allows users to associate with different BSs for UL and DL transmissions, can be used to optimize network performance. Again, in-band full-duplex (FD) communication is considered as a promising technique to improve the spectral efficiency of future multi-tier fifth generation (5G) cellular networks. Nonetheless, due to UL-to-DL and DL-to-UL interferences arising due to FD communications, the performance gains of DUDe in FD multi-tier networks are inconspicuous. To this end, this paper develops a comprehensive framework to analyze the usefulness of DUDe in a full-duplex multi-tier cellular network. We first formulate a joint UL and DL user association problem (with the provisioning for decoupled association) that maximizes the sum-rate for UL and DL transmission of all users. Since the formulated problem is a mixed-integer non-linear programming (MINLP) problem, we invoke approximations and binary constraint relaxations to convert the problem into a Geometric Programming (GP) problem that is solved by using Karush-Kuhn-Tucker (KKT) optimality conditions. Given the centralized nature and complexity of the GP problem, we formulate a distributed two-sided iterative matching game and obtain a solution of the game. In this game, the users and BSs rank one another using preference metrics that are subject to the externalities (i.e., dynamic interference conditions). The solution of the game is guaranteed to converge and provides Pareto-optimal stable associations. Finally, we derive efficient light-weight versions of the iterative matching solution, i.e., non-iterative matching and sequential UL-DL matching algorithms. The performances of the solutions are evaluated in terms of aggregate UL and DL rates of all users, the number of unassociated users, and the number of coupled/decoupled associations. Simulation results demonstrate the efficacy of the proposed algorithms over the centralized GP solution as well as traditional coupled and decoupled user association schemes.

We investigate a distributed downlink user association problem in a dynamic small cell network, where every small base station (SBS) obtains its required energy through ambient energy harvesting. On the one hand, energy harvesting is inherently opportunistic, so that the amount of available energy is a random variable. On the other hand, users arrive at random and require different wireless services, rendering the energy consumption a random variable. In this paper, we develop a probabilistic framework to mathematically model and analyze the random behavior of energy harvesting and energy consumption. We further analyze the probability of QoS satisfaction (success probability), for each user with respect to every SBS. The proposed user association scheme is distributed in the sense that every user independently selects its corresponding SBS with the success probability serving as the performance metric. The success probability however depends on a variety of random factors such as energy harvesting, channel quality, and network traffic, whose distribution or statistical characteristics might not be known at users. Since acquiring the knowledge of these random variables (even statistical) is very costly in a dense network, we develop a bandit-theoretical formulation for distributed SBS selection when no prior information is available at users. The performance is analyzed both theoretically and numerically.

Device-to-device (D2D) communications can enhance spectrum and energy efficiency due to direct proximity communication and frequency reuse. However, such performance enhancement is limited by mutual interference and energy availability, especially when the deployment of D2D links is ultra-dense. In this paper, we present a distributed power control method for ultra-dense D2D communications underlying cellular communications. In this power control method, in addition to the remaining battery energy of the D2D transmitter, we consider the effects of both the interference caused by the generic D2D transmitter to others and the interference from all others caused to the generic D2D receiver. We formulate a mean-field game (MFG) theoretic framework with the interference mean-field approximation. We design the cost function combining both the performance of the D2D communication and cost for transmit power at the D2D transmitter. Within the MFG framework, we derive the related Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations. Then, a novel energy and interference aware power control policy is proposed, which is based on the Lax-Friedrichs scheme and the Lagrange relaxation. The numerical results are presented to demonstrate the spectrum and energy efficiency performances of our proposed approach.

We consider a user assignment problem in small cell networks, where small cells obtain the required energy through ambient energy harvesting. We model the network as a competitive market with uncertainty, where small cells, represented as consumers, are willing to maximize their utility scores by selecting users, represented as commodities. Small cells are uncertain about the amount of harvested energy, formulated as natures' state. The solution is the general equilibrium under uncertainty, also called Arrow-Debreu equilibrium. We show that in our setting such equilibrium exists, and is Pareto optimal in terms of expected aggregate network utility. Besides, we use the Walras' tatonnement process to implement equilibrium efficiently.

Distributed resource allocation is considered as a significant feature of future self-organizing wireless networks. On the other hand, full-duplex transmission is an emerging technology which can theoretically double the data rate. and hence enhance the performance of wireless networks significantly. In this work, we address the problem of distributed power control in a two-tier network with full-duplex small cells underlaying one macro cell in a co-channel deployment scenario. We first formulate the corresponding distributed power control problem as a non-cooperative game and then extend it to a repeated game with imperfect public monitoring. The repeated game setting is used as it can withstand cheating. We show the existence and uniqueness of the Nash equilibrium of the formulated non-cooperative game and characterize the set of perfect public equilibrium for the repeated game. A two phase distributed algorithm is then proposed to achieve a power profile which is also a perfect public equilibrium. The power control algorithm is cheat-proof and needs only a small amount of information exchange among network nodes. The effectiveness of the algorithm is shown using numerical results. Our proposed algorithm and analysis are also valid for a half-duplex system as it is a special case of the full-duplex model presented in the paper.

Wireless energy transfer capability and multi-tier network architecture are both envisioned as inherent features of the next generation wireless networks, on account of exponentially increasing low power connected devices and the rising concern about their limited battery life. In this paper, we investigate joint resource allocation problem, for wireless information and energy transfer in small cells overlaid by macrocell. Using scalarization technique of multi-objective programming, we jointly optimize energy harvesting rate and achievable throughput of small-cell users while ensuring that minimum throughput requirement of macrocell user is satisfied. The formulated MINLP problem is converted to convex optimization problem by relaxing some variables and introducing auxiliary variables. We highlight the condition at which the obtained solution is the optimal solution of the original problem. Numerical results are presented to analyze the performance of the proposed resource allocation framework and show the improvement in achieved energy harvesting rate, by comparing with a benchmark scheme.

Wireless information and energy transfer in multitier cellular networks is a new research paradigm in wireless communications. While interference mitigation is one of the major challenges in conventional multi-tier networks, wireless energy harvesting capability considers interference signal as a source of energy. In this paper, we consider simultaneous wireless information and energy transfer in two-tier cellular networks and perform downlink power allocation with two different objectives. To maximize the sum of energy harvesting rate of small cell users, we formulate a linear programming problem whereas to maximize the sum of their information rate, we formulate a non-convex optimization problem. We solve the non-convex optimization problem by using convex-concave procedure and dual decomposition method. Numerical results indicate that the small cell users are exposed to high interference signal when maximum energy harvesting rate is desired and that received interference contributes a large portion of their total harvested energy. The trade-off between information rate and energy harvesting rate is found to be more prominent in terms of the interference signal rather than the power splitting factor since both information rate and energy harvesting rate are maximized when infinitesimally small power is split to the information decoder circuit.

In an underlay cognitive radio network (CRN), in order to guarantee that all primary users (PUs) achieve their target-signal-to-interference-plus-noise ratios (target-SINRs), the interference caused by all secondary users (SUs) to the primary receiving-points should be controlled. To do so, the feasible cognitive interference region (FCIR), i.e., the region for allowable values of interference at all of the primary receiving-points, which guarantee the protection of the PUs, needs to be formally characterized. In the state-of-the-art interference management schemes for underlay CRNs, it is considered that all PUs are protected if the cognitive interference for each primary receiving-point is lower than a maximum threshold, the so called interference temperature limit (ITL) for the corresponding receiving-point. This is assumed to be fixed and independent of ITL values for other primary receiving-points, which corresponds to a box-like FCIR. In this paper, we characterize the FCIR for uplink transmissions in cellular CRNs and for direct transmissions in ad-hoc CRNs. We show that the FCIR is in fact a polyhedron (i.e., the maximum feasible cognitive interference threshold for each primary receiving-point is not a constant, and it depends on that for the other primary receiving-points). Therefore, in practical interference management algorithms, it is not proper to consider a constant and independent ITL value for each of the primary receiving-points. This finding would significantly affect the design of practical interference management schemes for CRNs. To demonstrate this, based on the characterized FCIR, we propose two power control algorithms to find the maximum number of admitted SUs and the maximum aggregate throughput of the SUs in infeasible and feasible CRNs, respectively. For two distinct objectives, our proposed interference management schemes outperform the existing ones. The numerical results also demonstrate how the assumption of fixed ITL values leads to poor performance measures in CRNs.

Recent advancements in self-interference (SI) cancellation capability of low-power wireless devices motivate in-band full-duplex (FD) wireless backhauling in small cell networks (SCNs). In-band FD wireless backhauling concurrently allows the use of the same frequency spectrum for the backhaul as well as access links of the small cells. In this paper, using tools from stochastic geometry, we develop a framework to model the downlink rate coverage probability of a user in a given SCN with massive multiple-input-multiple-output (MIMO)-enabled wireless backhauls. The considered SCN is composed of a mixture of small cells that are configured in either in-band or out-of-band backhaul modes with a certain probability. The performance of the user in the considered hierarchical network is limited by several sources of interference, such as the backhaul interference, small cell base station (SBS)-to-SBS interference, and the SI. Moreover, due to the channel hardening effect in massive MIMO, the backhaul links only experience long term channel effects, whereas the access links experience both the long term and the short term channel effects. Consequently, the developed framework is flexible to characterize different sources of interference while capturing the heterogeneity of the access and backhaul channels. In specific scenarios, the framework enables deriving closed-form coverage probability expressions. Under perfect backhaul coverage, the simplified expressions are utilized to optimize the proportion of in-band and out-of-band small cells in the SCN in the closed form. Finally, a few remedial solutions are proposed that can potentially mitigate the backhaul interference and in turn improve the performance of in-band FD wireless backhauling. Numerical results investigate the scenarios in which in-band wireless backhauling is useful and demonstrate that maintaining a correct proportion of in-band and out-of-band FD small cells is crucial in wireless backhauled SCNs.

We present a joint resource allocation (RA) and admission control (AC) framework for an orthogonal frequency-division multiple access (OFDMA)-based downlink cellular network composed of a macrocell underlaid by a cloud radio access network (C-RAN) of small cells. In this framework, the RA problems for both the macrocell and small cells are formulated as optimization problems. In particular, the macrocell, being aware of the existence of the small cells, maximizes the sum of the interference levels it can tolerate subject to the macrocell power budget and the quality-of-service (QoS) constraints of macrocell user equipments (MUEs). On the other hand, the small cells minimize the total downlink transmit power subject to their power budget, QoS requirements of small cell UEs (SUEs), interference thresholds for MUEs, and fronthaul constraints. Moreover, AC is considered in the resource allocation problem for the small cells to account for the case where it is not possible to support all SUEs. Besides, to allow for the existence of other network tiers, small cells have a constraint on the number of sub-channels that can be allocated. Both optimization problems are shown to be mixed integer nonlinear problems (MINLPs) for which, lower complexity algorithms are proposed that are based on the framework of successive convex approximation (SCA). Numerical results demonstrate the importance of the careful selection of the resource allocation policy at the macrocell and its impact on the performance of small cells. Moreover, we investigate the effect of the different parameters of the RA problem for the C-RAN of small cells on the overall performance of small cells.

This paper lays a mathematical framework for estimating the energy saving of a relay assisting a pair of wireless devices. We derive closed-form expressions for describing the geometrical zone where relaying is energy efficient. In addition, we obtain the probabilistic distribution of the energy saving introduced by relays that are randomly distributed according to a spatial Poisson point process. Furthermore, we present a comparison methodology for fairly evaluating the energy consumption of conventional cellular network from one side and relay-assisted device-to-device communication from another side. Results suggest that a significant energy saving can be achieved when relay-assisted device-to-device communication is adopted for distances below a certain threshold. In order to test the analytical framework, we perform Monte-Carlo simulations and compare the results with those obtained from the mathematical framework.

We propose a code-based discovery protocol for cellular device-to-device (D2D) communications. To realize proximity based services such as mobile social networks and mobile marketing using D2D communications, each device should first discover nearby devices, which have mobile applications of interest, by using a discovery protocol. The proposed discovery protocol makes use of a short discovery code that contains compressed information of mobile applications in a device. A discovery code is generated by using either a hash function or a Bloom filter. When a device receives a discovery code broadcast by another device, the device can approximately find out the mobile applications in the other device. The proposed protocol is capable of quickly discovering massive number of devices while consuming a relatively small amount of radio resources. We analyze the performance of the proposed protocol under the random direction mobility model and a real mobility trace. By simulations, we show that the analytical results well match the simulation results and that the proposed protocol greatly outperforms a simple non-filtering protocol.

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a twostate machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.

The ergodic resource allocation (ERA) problem for uplink transmission in underlay cognitive radio networks (CRNs) is investigated. The objective is to maximize the ergodic sum-rate of secondary users (SUs) considering the unavailability of perfect channel state information (CSI), and subject to transmit power limitations of SUs, and the interference threshold constraint to guarantee the quality of service of primary users. Since with average-based formulation of ERA, the interference threshold constraint and transmit power limitations of SUs do not hold instantaneously, one can replace the average-based constraints in ERA with their outage-based counterparts. For the uncertainty on the CSI values, we utilize the robust optimization theory where the uncertain parameters are modeled as a sum of the estimated value and error which is assumed to be bounded. We then map the considered ERA problems to their robust counterparts. Generally, the robust approaches degrade the performance (e.g., sum rate of SU), as they conservatively consider the error to be in the maximum extent and try to preserve the constrains under any condition of error (worst-case scenario). We aim to moderate this effect by using appropriate models for uncertain parameters, relaxing the worst-case scenario, and stochastically preserving the constraints. Moreover, robust problems are in general non-convex and suffer from high computational complexity due to the existence of uncertain system parameters. Therefore, we use effective suboptimal approaches to solve them with a reasonable complexity. This includes methods based on chance constraint approach as well as an iterative scheme. The proposed solutions provide a trade-off between robustness, performance, and complexity. Simulation results reveal that by using the proposed schemes, stable sum-rate of SUs in the presence of CSI uncertainties can be achieved while the instantaneous power and interference constraints are met with a desired probability.

A novel distributed power control paradigm is proposed for dense small cell networks co-existing with a traditional macrocellular network. The power control problem is first modeled as a stochastic game and the existence of the Nash Equilibrium is proven. Then, we extend the formulated stochastic game to a mean field game (MFG) considering a highly dense network. An MFG is a special type of differential game which is ideal for modeling the interactions among a large number of entities. We analyze the performance of two different cost functions for the mean field game formulation. Both of these cost functions are designed using stochastic geometry analysis in such a way that the cost functions are valid for the MFG setting. A finite difference algorithm is then developed based on the Lax-Friedrichs scheme and Lagrange relaxation to solve the corresponding MFG. Each small cell base station can independently execute the proposed algorithm offline, i.e., prior to data transmission. The output of the algorithm shows how each small cell base station should adjust its transmit power in order to minimize the cost over a predefined period of time. Moreover, sufficient conditions for the uniqueness of the mean field equilibrium for a generic cost function are also given. The effectiveness of the proposed algorithm is demonstrated via numerical results.

Virtualization has been seen as one of the main evolution trends in the forthcoming fifth generation (5G) cellular networks which enables the decoupling of infrastructure from the services it provides. In this case, the roles of infrastructure providers (InPs) and mobile virtual network operators (MVNOs) can be logically separated and the resources (e.g., subchannels, power, and antennas) of a base station owned by an InP can be transparently shared by multiple MVNOs, while each MVNO virtually owns the entire BS. Naturally, the issue of resource allocation arises. In particular, the InP is required to abstract the physical resources into isolated slices for each MVNO who then allocates the resources within the slice to its subscribed users. In this paper, we aim to address this two-level hierarchical resource allocation problem while satisfying the requirements of efficient resource allocation, strict inter-slice isolation, and the ability of intra-slice customization. To this end, we design a hierarchical combinatorial auction mechanism, based on which a truthful and sub-efficient resource allocation framework is provided. Specifically, winner determination problems (WDPs) are formulated for the InP and MVNOs, and computationally tractable algorithms are proposed to solve these WDPs. Also, pricing schemes are designed to ensure incentive compatibility. The designed mechanism can achieve social efficiency in each level even if each party involved acts selfishly. Numerical results show the effectiveness of the proposed scheme.

We propose a distributed opportunistic medium access control (MAC) scheme for maximizing the expected aggregate throughput in a multichannel wireless network such as a clustered orthogonal frequency-division multiple access (OFDMA) network. In our proposed scheme, each user attempts to send only on its best channel and transmits if the best-channel gain is higher than a given threshold, which is dynamically updated depending on previous idle and collision situations. In this way, with our proposed scheme, in a homogeneous system where the channel fading distribution is identical for all users, the best user for each channel is obtained in a distributed manner. We also obtain the optimal values of the thresholds so that the probability of successful transmission is maximized and a minimal number of transmission opportunities are wasted (e.g., due to collision or idle transmissions). In the asymptotic limit of a large number of users and sufficiently long transmission slot duration, we show that, in comparison with the optimal centralized scheme, the throughput loss for our proposed scheme goes to zero. Furthermore, we extend our distributed opportunistic MAC scheme for a homogeneous system to that for a heterogeneous system where the channel fading distribution is heterogeneous across users. Throughput performances and signaling overhead are analyzed for the proposed distributed MAC schemes and compared with those of the existing schemes. Simulation results show that our proposed schemes significantly improve the average aggregate throughput when compared with the existing schemes.

We study the downlink resource allocation problem for simultaneous wireless information and power transfer (SWIPT) in small cells underlaying a macrocell in a two-tier heterogeneous network. For SWIPT, we consider both time-switching and power-splitting approaches. We determine downlink transmit power of small cell base stations along with time-switching/power-splitting variables for SWIPT to jointly optimize energy harvesting rate and achievable throughput of small cell users while ensuring minimum throughput of macrocell user. In the time-switching approach, the resource allocation problem is formulated as a mixed-integer non-linear programming (MINLP) problem. The formulated MINLP problem is solved by relaxing the binary integer constraint and then identifying the condition at which the obtained solution satisfies that constraint. In both the time-switching and power-splitting approaches, in the presence of non-negligible co-tier interference, the formulated problem is solved sub-optimally by iteratively maximizing the minorant of the non-convex objective function. A special case with negligible co-tier interference is considered in the time-switching approach and the optimal solution is obtained by using convex optimization techniques. Numerical results demonstrate significant gain in energy harvesting rate when macrocell users have flexible interference tolerance levels in time-switching approach, highlight the improvement in energy harvesting rate in the presence of co-tier interference signal, and reveal interesting trade-off in achievable throughput and energy harvesting rate.

We propose relay-based wireless-powered uplink cellular networks in which users first harvest energy from RF transmissions of base station/relay nodes and then use that energy for uplink transmission. Given the limited total transmission time and available energy at the relay node, we propose different resource allocation frameworks for the proposed relay-based networks considering two different relay-based harvest-then-transmit scenarios. We first propose iterative algorithm to determine time and relay node power allocation (for downlink wireless charging and uplink data transmission/relaying) for both scenarios. We then perform joint optimal time and power allocation for one scenario. Resource allocation results show that most of the available resources (transmission time and relay node energy) are allocated for wireless energy harvesting which is similar to that observed for downlink simultaneous wireless information and power transfer (SWIPT) in the existing literature. Simulation results on comparison of different relay-based scenarios reveal interesting insights and demonstrate remarkable improvement of different performance metrics of wireless-powered cellular networks in the presence of relay node.

Next generation cellular networks will consist of multiple tiers of cells and users associated with different network tiers may have different priorities (e.g., macrocell-picocell- femtocell networks with macro tier prioritized over pico tier, which is again prioritized over femto tier). Designing efficient joint power and admission control (JPAC) algorithms for such networks under a cochannel deployment (i.e., underlay) scenario is of significant importance. Feasibility checking of a given target signal-to-noise-plus-interference ratio (SINR) vector is generally the most significant contributor to the complexity of JPAC algorithms in single/multitier underlay cellular networks. This is generally accomplished through iterative strategies whose complexity is either unpredictable or of O(M3), when the well-known relationship between the SINR vector and the power vector is used, where M is the number of users/links. In this paper, we derive a novel relationship between a given SINR vector and its corresponding uplink/downlink power vector based on which the feasibility checking can be performed with a complexity of O(B3+ MB), where B is the number of base stations. This is significantly less compared to O(M3) in many cellular wireless networks since the number of base stations is generally much lower than the number of users/links in such networks. The developed novel relationship between the SINR and power vector not only substantially reduces the complexity of designing JPAC algorithms, but also provides insights into developing efficient but low-complexity power update strategies for prioritized multitier cellular networks. We propose two such algorithms and through simulations, we show that our proposed algorithms outperform the existing ones in prioritized cellular networks.

The problem of joint downlink cell association (CA) and wireless backhaul bandwidth allocation (WBBA) in two-tier cellular heterogeneous networks (HetNets) is investigated. Large-scale antenna array is implemented at the macro base station (BS), while the small cells within the macro cell range are single-antenna BSs and they rely on over-the-air links to the macro BS for backhauling. A sum logarithmic user rate maximization problem is studied under the wireless backhaul constraints. Duplex and spectrum sharing with co-channel reverse time-division duplex (TDD) and dynamic soft frequency reuse is considered for interference management in the two-tier HetNet employing large-scale antenna arrays at the macro BS and wireless backhauling for small cells. Two in-band WBBA scenarios, namely, unified bandwidth allocation and per-small-cell bandwidth allocation, are investigated for joint CA-WBBA in the HetNet. A two-level hierarchical decomposition method for relaxed optimization is employed to solve the mixed-integer nonlinear program (MINLP). Solutions based on the General Algorithm Modeling System (GAMS) optimization solver and fast heuristics are also proposed for cell association in the per-small-cell WBBA scenario. It is shown that when all small cells have to use in-band wireless backhaul, the system load has more impact on both the sum logarithmic rate and per-user rate performance than the number of small cells deployed within the macro cell range. The proposed joint CA-WBBA algorithms have an optimal load approximately equal to the size of the large-scale antenna array at the macro BS. The cell range expansion (CRE) strategy, which is an efficient cell association scheme for HetNets with ideal backhauling, is shown to be inefficient when in-band wireless backhauling for small cells comes into play.

We present a joint resource allocation (RA) and admission control (AC) framework for an orthogonal frequency-division multiple access (OFDMA)-based cellular network composed of a macrocell overlaid by small cells in cloud radio access network (C-RAN) architecture. In this framework, the resource allocation problems for both the macrocell and small cells are formulated as optimization problems. The macrocell, being aware of the existence of the small cell tier, maximizes the sum of the interference levels it can tolerate subject to quality-of-service (QoS) constraints. On the other hand, small cells try to maximize the number admitted users while minimizing the total transmit power. Being deployed in a C-RAN architecture, small cells perform RA subject to QoS, interference, and practical fronthaul constraints. Both optimization problems are shown to be mixed integer nonlinear problems (MINLPs) for which, lower complexity algorithms are proposed. Numerical results confirm the importance of the careful selection of the macrocell RA policy. Also, the effect of the fronthaul capacity for small cells and the users QoS requirements are studied.

We consider the problem of minimizing the total downlink transmit power in an orthogonal frequency-division multiple access (OFDMA)-based cellular network composed of a single-antenna macrocell overlaid by multi-antenna small cells deployed in a cloud radio access network (C-RAN) architecture. More specifically, the C- RAN minimizes the total downlink transmit power subject to the quality of service (QoS) constraints for small cell user equipments (SUEs), power budgets of small cells, interference thresholds for macro UEs (MUEs), and practical fronthaul capacity constraints. This problem is a mixed integer nonlinear problem (MINLP). Moreover, the problem can become infeasible which necessitates the employment of some form of admission control (AC). Therefore, relying on the framework of successive convex approximation (SCA), we propose a low-complexity solution for the original non- convex MINLP by solving a series of convex problems, which is guaranteed to converge to a local optimum solution. Numerical results indicate the performance of the proposed formulation and demonstrate the underlying tradeoffs among the SUEs' QoS requirements, number of admitted SUEs, total downlink transmit power, and the available fronthaul capacity.

Recent advancements in the self-interference (SI) cancellation capability of low power wireless devices pave the way of implementing full-duplex (FD) self-backhauling in small-cell networks. FD self-backhauling allows the use of conventional radio access network (RAN) spectrum for backhaul as well as access links concurrently. In this paper, we model and analyze massive MIMO- enabled wireless backhaul networks that are composed of a mixture of small cells, configured either in in-band or out-of-band backhaul mode with a certain probability. We consider a hierarchical network structure to model these networks and characterize the downlink coverage probability of a small cell base station (SBS) for both the in-band and out-of-band backhaul modes. The impact of co-tier and cross-tier backhaul interferences on downlink signal-to-interference ratio (SIR) coverage of small cell users is investigated. Numerical results demonstrate that implementing only either the in-band or out-of-band backhauling solutions may not be useful. Instead, a hybrid system with correct proportion of in-band and out-of-band small cells should be implemented.

Future wireless networks are expected to be highly heterogeneous with the co-existence of macrocells and small cells and they will also provide support for device-to-device (D2D) communication. In such muti-tier heterogeneous systems, centralized radio resource allocation and interference management schemes will not be scalable. In this work, we propose an auction-based distributed solution to allocate radio resources in a muti-tier heterogeneous network. We provide the bound of achievable data rate and show that the complexity of the proposed scheme is linear with the number of transmitter nodes and the available resources. The signaling issues (e.g., information exchange over control channels) for the proposed distributed solution is also discussed. Numerical results show the effectiveness of the proposed solution in comparison with an optimal centralized resource allocation scheme.

In an underlay cognitive radio network (CRN), in order to guarantee protection of primary users (PUs) (i.e., all PUs achieve their target signal-to-interference-plus-noise ratios [SINRs]), the interference caused by all secondary users (SUs) to the primary receiving-points should be controlled. To do so, the feasible cognitive interference region (FCIR), i.e., the region for allowable values of interference at all of the primary receiving-points, which guarantee protecting the PUs, needs to be formally characterized. In the state-of-the-art interference management schemes for underlay CRNs, it is considered that all PUs are protected if the cognitive interference for each primary receiving-point is lower than a maximum threshold, the so called interference temperature limit (ITL) for the corresponding receiving-point. This is assumed to be fixed and independent of ITL values for other primary receiving-points. This corresponds to a box-like FCIR, which is not correct. In this paper, we analytically obtain the FCIR and show that, the FCIR is a polyhedron (i.e., the maximum feasible cognitive interference threshold for each primary receiving-point is not constant, and it depends on that for other primary receiving-points). Therefore, in practical interference management algorithms, it is not proper to consider a constant and independent ITL value for each of the primary receiving-points. This finding would significantly affect the design of practical interference management schemes for CRNs.

The diverse transmit powers of the base-stations (BSs) in a multi-tier cellular network lead to uneven distribution of the traffic loads among different BSs and thus cause underutilization of the available resources at low power BSs. In this context, this paper proposes a channel access-aware (CAA) user association scheme that can simultaneously enhance the system spectral efficiency and balance the traffic loads among different BSs. The CAA scheme is a network-assisted user association scheme that requires the traffic load informations from different BSs in addition to the channel quality indicators. Also, in this paper, we develop a tractable mathematical framework to characterize the spectral efficiency of downlink transmission to a user who associates to a BS using CAA scheme. Numerical results demonstrate the performance gains of CAA scheme over conventional received signal power-based association and biased-received signal power-based association. The derived expressions provide approximate solutions of reasonable accuracy when compared to the results obtained by Monte-Carlo simulations. Moreover, the impact of state-of-the-art almost blank sub-frames (ABS)-based interference coordination scheme on the proposed CAA scheme is also investigated using Monte-Carlo simulations.

We use stochastic geometry to develop a comprehensive modeling framework for K-tier uplink cellular networks with RF energy harvesting from the concurrent cellular transmissions. In the considered system model, channel inversion power control is used and cellular users are equipped with energy storage units. We also use tools from queueing theory, namely, Markov chain analysis, to model the level of stored energy in each user's battery. A successful transmission is assumed only when the amount of energy stored in a user's battery is sufficient to perform channel inversion with a received signal-to-interference ratio (SIR) above a predefined threshold. The performance of the proposed system model is evaluated in terms of the transmission probability, the (SIR) coverage probability, and the overall success probability. Using Poisson point processes (PPPs) enables us to derive simple expressions for these performance metrics in order to obtain insights for network design and optimization. We show the effect of varying the different network parameters such as the spatial density of BSs and the receiver sensitivity. In addition, we discuss several special cases and provide guidelines on the extensions of the proposed framework. We show that the gain of using RF energy harvesting can be highly improved by a proper choice of the network design parameters.

We present a joint sub-channel and power allocation framework for downlink transmission in an orthogonal frequency-division multiple access (OFDMA)-based cellular network composed of a macrocell overlaid by small cells. In this framework, the resource allocation (RA) problems for both the macrocell and small cells are formulated as optimization problems. For the macrocell, we formulate an RA problem that is aware of the existence of the small cell tier. In this problem, the macrocell performs RA to satisfy the data rate requirements of macro user equipments (MUEs) while maximizing the tolerable interference from the small cell tier on its allocated sub-channels. Although the RA problem for the macrocell is shown to be a mixed integer nonlinear problem (MINLP), we prove that the macrocell can solve another alternate optimization problem that will yield the optimal solution with reduced complexity. For the small cells, following the same idea of tier-awareness, we formulate an optimization problem that accounts for both RA and admission control (AC) and aims at maximizing the number of admitted users while simultaneously minimizing the consumed bandwidth. Similar to the macrocell optimization problem, the small cell problem is shown to be an MINLP. We obtain a sub-optimal solution to the MINLP problem relying on convex relaxation. In addition, we employ the dual decomposition technique to have a distributed solution for the small cell tier. Numerical results confirm the performance gains of our proposed RA formulation for the macrocell over the traditional resource allocation based on minimizing the transmission power. Besides, it is shown that the formulation based on convex relaxation yields a similar behavior to the MINLP formulation. Also, the distributed solution converges to the same solution obtained by solving the corresponding convex optimization problem in a centralized fashion.

Wireless device-to-device (D2D) communication underlaying cellular network is a promising concept to improve user experience and resource utilization. Unlike traditional D2D communication, where two mobile devices in the proximity establish a direct local link bypassing the base station, in this work, we focus on relay-aided D2D communication. Relay-aided transmission could enhance the performance of D2D communication when D2D user equipments (UEs) are far apart from each other and/or the quality of D2D link is not good enough for direct communication. Considering the uncertainties in wireless links, we model and analyze the performance of a relay-aided D2D communication network, where the relay nodes serve both the cellular and D2D users. In particular, we formulate the radio resource allocation problem in a two-hop network to guarantee the data rate of the UEs while protecting other receiving nodes from interference. Utilizing time sharing strategy, we provide a centralized solution under bounded channel uncertainty. With a view to reducing the computational burden at relay nodes, we propose a distributed solution approach using stable matching to allocate radio resources in an efficient and computationally inexpensive way. Numerical results show that the performance of the proposed method is close to the centralized optimal solution and there is a distance margin beyond which relaying of D2D traffic improves network performance.

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.

While cognitive radio enables spectrum-efficient wireless communication, radio frequency (RF) energy harvesting from ambient interference is an enabler for energy-efficient wireless communication. In this paper, we model and analyze cognitive and energy harvesting-based device-to-device (D2D) communication in cellular networks. The cognitive D2D transmitters harvest energy from ambient interference and use one of the channels allocated to cellular users (in uplink or downlink), which is referred to as the D2D channel, to communicate with the corresponding receivers. We investigate two spectrum access policies for cellular communication in the uplink or downlink, namely, random spectrum access (RSA) policy and prioritized spectrum access (PSA) policy. In RSA, any of the available channels including the channel used by the D2D transmitters can be selected randomly for cellular communication, while in PSA the D2D channel is used only when all of the other channels are occupied. A D2D transmitter can communicate successfully with its receiver only when it harvests enough energy to perform channel inversion toward the receiver, the D2D channel is free, and the signal-to-interference-plus-noise ratio (SINR) at the receiver is above the required threshold; otherwise, an outage occurs for the D2D communication. We use tools from stochastic geometry to evaluate the performance of the proposed communication system model with general path-loss exponent in terms of outage probability for D2D and cellular users. We show that energy harvesting can be a reliable alternative to power cognitive D2D transmitters while achieving acceptable performance. Under the same SINR outage requirements as for the non-cognitive case, cognitive channel access improves the outage probability for D2D users for both the spectrum access policies. When compared with the RSA policy, the PSA policy provides a better performance to the D2D users. Also, using an uplink channel provides improved performance to the D2D users in dense networks when compared to a downlink channel. For cellular users, the PSA policy provides almost the same outage performance as the RSA policy.

Wireless-powered cellular networks (WPCNs) are currently being investigated to ensure the reliability as well as improved battery lifetime of wireless devices. A WPCN leverages on a centralized base station (BS) that takes care of both wireless information and energy transfer. However, the harvested energy and, in turn, the spectral efficiency of uplink transmission of the users may significantly vary depending on the locations of the users and the channels used for energy and information transfer purposes. To this end, this paper theoretically characterizes the signal-to-noise ratio (SNR) outage zones in a WPCN and comparatively analyzes the performance of three useful configurations of dedicated energy sources that can potentially minimize the SNR outage zones. These configurations are: (i) harvesting energy from and information transfer to a full-duplex BS. This is considered as a baseline configuration; (ii) harvesting energy from symmetrically deployed power beacons (PBs) and information transfer to a conventional half-duplex BS; and (iii) harvesting energy from symmetrically deployed PBs that are colocated with the distributed antenna elements (DAEs) of a conventional half-duplex BS. For all the listed cases, we characterize the SNR outage probability and spectral efficiency of an arbitrarily located user within the cellular region. Based on the derived expressions, we also optimize the distance of the PBs from the BS to minimize the SNR outage probability and provide closed-form solutions for special cases. The optimum distance of the PBs is shown to be a function of the number of PBs and the coverage area of the BS. Numerical results validate the accuracy of the derived expressions, provide design insights related to WPCNs, and reveal the significance of the limited number of optimally placed PBs over a large number of randomly deployed PBs.

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest energy from environmental sources whereas the macrocell base station (MBS) uses conventional power supply. Given a stochastic energy arrival process for the SBSs, we derive a power control policy for the downlink transmission of both MBS and SBSs such that they can achieve their objectives [e.g., maintain the signal-to-interference-plus-noise ratio (SINR); at an acceptable level] on a given transmission channel. We consider a centralized energy harvesting mechanism for SBSs, i.e., there is a central energy storage (CES) where energy is harvested and then distributed to the SBSs. When the number of SBSs is small, the game between the CES and the MBS is modeled as a single-controller stochastic game and the equilibrium policies are obtained as a solution of a quadratic programming problem. However, when the number of SBSs tends to infinity (i.e., a highly dense network), the centralized scheme becomes infeasible, and therefore, we use a mean field stochastic game to obtain a distributed power control policy for each SBS. By solving a system of partial differential equations, we derive the power control policy of SBSs given the knowledge of mean field distribution and the available harvested energy levels in the batteries of the SBSs.

We consider the problem of robust downlink power control in orthogonal frequency-division multiple access (OFDMA)-based heterogeneous wireless networks (HetNets) composed of macrocells and underlaying small cells. A non-cooperative setting is assumed where the macro base stations (MBSs) and small cell base stations (SBSs) compete with each other to maximize their own capacities considering imperfect channel state information. A robust Stackelberg game (RSG) is formulated to model this hierarchical competition where the MBSs and SBSs act as the leaders and the followers, respectively. The formulated RSG can be expressed as an equilibrium program with equilibrium constraints (EPEC). A comprehensive study of this RSG is provided considering various power constraints (e.g., total and spectral mask), various interference constraints (e.g., individual and global), and different uncertainty models (e.g., column-wise and ellipsoidal). We show how the different constraints and uncertainty models change the property of the game (e.g., Nash equilibrium problem (NEP) or generalized Nash equilibrium problem (GNEP)) and accordingly impact the choice of analysis method (e.g., game theory or variational inequality (VI)), solution (e.g., closed-form or numerical), and the design of algorithms and their distributive properties (e.g., totally distributed, semi-distributed, and centralized). A robust Stackelberg equilibrium (RSE) is considered to be the solution and its existence and uniqueness are investigated. Also, algorithms are proposed to arrive at the RSE. Numerical results show the effectiveness of robust solutions in an imperfect information environment.

This paper describes how to establish performance charts for selection of network parameters for effective utilization of a bilateral teleoperated manipulator working under a wireless communication channel. The goal is to construct a set of charts that help researchers and engineers to select appropriate parameters of wireless network setup for a known configuration of environment obstruction. To achieve this goal, a teleoperated setup comprising a master haptic device, a slave manipulator dynamic simulator, and a communication channel emulated using the network simulator version 2 (NS2) simulator is first developed. Next, performance indices are defined to evaluate the quality of position tracking of the slave manipulator end-effector and force tracking of the master haptic. Three indices chosen in this paper are the integral of squared position and force errors, the integral of absolute position and force error, and the amplitude of position and force overshoot. Extensive experiments on the developed setup are then conducted to study effects of time-varying packet loss on the performance of the teleoperated system. The largest mean packet loss, at which the system exhibits satisfactory tracking, is then quantified. This packet loss is used as an indicator to define regions representing the quality of tracking. The effectiveness of the proposed technique is validated by testing a fully instrumented hydraulically actuated system under various real wireless channel scenarios.

Resource reuse schemes in relay-enhanced cooperative orthogonal frequency-division multiple access (OFDMA) networks have been well studied in the literature from an interference mitigation perspective and with cell-partitioning being the basis. In this paper however, we study dynamic intra-cell subcarrier reuse in cooperative OFDMA networks in which the users are allowed to share any subcarrier in the relay links provided that the resultant intra-cell interference is managed; an option not available in non-cooperative networks. Specifically, when the target data-rates of the users are not reachable in a conventional cooperative OFDMA network, dynamic intra-cell subcarrier reuse enables the users to achieve their target data-rates, even when the number of users surpasses the number of subcarriers. We formally define the problem of maximizing the system sum-rate subject to per node power and target data-rate constraints in a cooperative OFDMA single-cell network in which subcarriers are allowed to be reused in the relay links. After analyzing the problem in the dual domain and deriving the performance bounds, we propose a suboptimal resource allocation algorithm where subcarriers and links (relay and/or direct) are assigned to the users. Simulation results show that by employing our resource allocation algorithm, the system sum-rate and the outage ratio are significantly improved, specially in an overpopulated network.

We propose an evolutionary game theory (EGT)-based distributed resource allocation scheme for small cells underlaying a macro cellular network. EGT is a suitable tool to address the problem of resource allocation in self-organizing small cells since it allows the players with bounded-rationality to learn from the environment and take individual decisions for attaining the equilibrium with minimum information exchange. EGT-based resource allocation can also provide fairness among users. We show how EGT can be used for distributed subcarrier and power allocation in orthogonal frequency-division multiple access (OFDMA)-based small cell networks while limiting interference to the macrocell users below given thresholds. Two game models are considered, where the utility of each small cell depends on average achievable signal-to-interference-plus-noise ratio (SINR) and data rate, respectively. Forthe proposed distributed resource allocation method, the average SINR and data rate are obtained based on a stochastic geometry analysis. Replicator dynamics is used to model the strategy adaptation process of the small cell base stations and an evolutionary equilibrium is obtained as the solution. Based on the results obtained using stochastic geometry, the stability of the equilibrium is analyzed. We also extend the formulation by considering information exchange delay and investigate its impact on the convergence of the algorithm. Numerical results are presented to validate ourtheoretical findings and to show the effectiveness of the proposed scheme in comparison to a centralized resource allocation scheme.

We investigate the problem of designing efficient and low-complexity centralized algorithms for joint power and admission control in a cellular cognitive radio network (CRN) which coexists with a primary radio network (PRN) in a spectrum underlay fashion. We first derive a simple one-to-one relation between the signal-to-interference-plus-noise ratio (SINR) vector and its corresponding power vector of all users of the CRN and PRN, and based on this we propose two new admission metrics. Then, in an infeasible system, where the minimum acceptable target-SINRs for all primary and secondary users are not simultaneously reachable, two centralized algorithms are proposed. These algorithms aim at removing the minimal number of secondary users (based on the proposed admission metrics), subject to the constraint that all primary users are supported with their target-SINRs. In an infeasible system, our proposed algorithms outperform other existing algorithms in terms of complexity and secondary users' outage ratio. Furthermore, for a feasible system, where all secondary users can be admitted along with all primary users, by using our derived one-to-one relation between SINR and power vector, we solve the problems of maximizing aggregate throughput and max-min quality-of-service (QoS) for secondary users, both subject to the constraint that all primary and secondary users are supported with their minimum target-SINRs.

This paper characterizes the spectral efficiency of an uplink radio frequency (RF)-powered macrocell network considering harvest-then-transmit protocol such that the macrocell users transmit in the uplink while replenishing the energy from their serving base station (BS) in the downlink. Using the theory of order statistics, a tractable mathematical framework is developed to derive the uplink spectral efficiency and the downlink power consumption resulting due to wireless energy transfer. The framework captures the impact of the locations of the users that are selected for uplink transmission, their channel statistics for information and energy transfer, and different user selection schemes. We first analyze the performance of state-of-the-art greedy and round-robin scheduling schemes in RF-powered cellular networks. Closed-form expressions for the minimum power outage probability (i.e., the probability that the selected user is unable to harvest sufficient power for uplink transmission) are also derived. We then develop modified versions of the conventional user selection schemes that improve the spectral efficiency on a given uplink transmission channel with zero power outage probability (i.e., probability of outage due to insufficient amount of harvested power). The developed schemes are shown to outperform the conventional user scheduling schemes in terms of the throughput and energy harvesting time with a trade-off in fairness among users. The accuracy of the expressions is validated via Monte-Carlo simulations. Numerical results highlight the trade-offs associated with the various user selection schemes as a function of network parameters.

Device-to-device (D2D) communication technology is a promising add-on component for future wireless networks to provide local area services with increased spectrum efficiency and improved user experience. Three modes (i.e., cellular mode, reuse mode, and dedicated mode) can be used for D2D communication. A potential D2D user equipment (UE) can select a communication mode and dynamically adapt the mode selection according to the performance and the cost. This is referred to as the user-controlled mode selection problem. Also, a base station (BS) needs to reserve a spectrum band for the dedicated mode of operation, which we refer to as spectrum partitioning. The optimal spectrum partitioning needs to consider the utility of the BS that depends on the distribution of the users' mode selection, which, in turn, is governed by the spectrum partitioning. To jointly address the problems of spectrum partitioning and user-controlled mode selection (which are cyclically dependent on each other), we propose a dynamic Stackelberg game framework in which the BS and the potential D2D UEs act as the leader and the followers, respectively. Specifically, the adaptive mode selection of potential D2D UEs is formulated as a follower evolutionary game, and an evolutionary stable strategy is considered to be the solution. The dynamic control of spectrum partitioning by the BS is formulated as a leader optimal control problem. We also extend the formulation by considering information delays in control and state. Numerical analysis is performed to evaluate the effectiveness of the proposed framework, which shows that although the mode selection is performed in a distributed and user-controlled manner, the dynamic spectrum partitioning can be viewed as an effective incentive mechanism to drive the user distribution close to the optimal one.

The cognitive radio (CR) paradigm for designing next‐generation wireless communications systems is becoming increasingly popular, and different aspects of it are being implemented in currently available wireless systems. In the last decade, a significant amount of research efforts has been made to solve CR challenges, and several standards related to CR and dynamic spectrum access have been developed. Also, there have been advances in software‐defined radio platforms to implement the CR systems. In this article, we provide a comprehensive survey on the evolution of CR research covering aspects such as spectrum sensing, measurements and statistical modeling of spectrum usage, physical layer aspects such as waveform and modulation design, multiple access, resource allocation and power control, cognitive learning, adaptation and self‐configuration, multihop transmission and routing, and robustness and security in CR networks. Also, state‐of‐the‐art research on the economics of CR networks, CR simulation tools, testbeds and hardware prototypes, CR applications, and CR standardization efforts is summarized. Emerging trends on CR research and open research challenges related to the cost‐effective and large‐scale deployment of CR systems are outlined.

We present a joint resource allocation (RA) and admission control (AC) framework for an orthogonal frequency-division multiple access (OFDMA)-based cellular network composed of a macrocell overlaid by small cells. In this framework, the resource allocation problems for both the macrocell and small cells are formulated as optimization problems. The macrocell RA problem is aware of the existence of the small cell tier. On the other hand, the RA and AC problems for the small cells aim at maximizing the number of admitted users while simultaneously minimizing the consumed bandwidth. These optimization problems are shown to be mixed integer nonlinear problems (MINLPs). Techniques are proposed to obtain either the optimal solution or a bound on the optimal solution with reduced complexity through convex relaxation. Dual decomposition technique is also used to have a distributed solution for the small cell tier. Numerical results confirm that the convex relaxations follow a similar behavior to the MINLP formulations. Also, the distributed solution converges to the optimal solution obtained by solving the corresponding convex optimization problem in a centralized fashion.

We model and analyze a K-tier uplink cellular network with flexible cell association where all transmissions are powered by energy harvesting from ambient interference. Each cellular user transmits data to the corresponding base station (BS) only when the amount of energy harvested is sufficient to perform channel inversion towards the serving BS. Furthermore, the data transmitted can be successfully decoded only when the signal-to-interference-plus-noise ratio (SINR) at the receiver is above a predefined threshold. With flexible cell association, users are not necessarily associated with their nearest BS where a different bias factor is added to each network tier. We use tools from stochastic geometry to evaluate the performance of the proposed system model in terms of the coverage probability of a generic user associated with the k-th tier. We show that energy harvesting can be a reliable source to power cellular users with short-range communication, e.g., small cell users. In addition, we show that energy harvesting can achieve high coverage performance by optimizing different network parameters such as the BS receiver sensitivity as well as the bias factors.

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest their energy from environment sources whereas the macro base station (MBS) uses conventional power supply. Given a stochastic energy arrival process, this paper derives a power control policy for the downlink transmission of both MBS and SBSs such that they can obtain an equilibrium of their own objectives on a long-term basis (e.g., maximizing the transmission rate for SBSs while maintaining the target signal-to-interference-plus-noise ratio (SINR) at the macro users) on a given transmission channel. To this end, we propose a single controller stochastic game and develop a power control policy as a solution of a quadratic programming problem. Numerical results demonstrate the significance of the developed optimal power control policy over the conventional fixed and random power control policies.

In this paper, we exploit tools from stochastic geometry to develop a tractable model for uplink transmissions in single-tier cellular wireless networks with truncated channel inversion power control. Our model gives simple expressions for the outage probability and spectral efficiency which characterize the network performance in terms of the design parameters. In particular, the model reveals a transfer point in the uplink system behavior that depends on the tuple: BS intensity (λ), maximum transmit power of UEs (Pu), and power control cutoff threshold ρo. More specifically, when Puis a tight operational constraint with respect to [w.r.t.] λ and ρo, the uplink performance highly depends on the values of λ and ρo. In contrast, when Puis a non-binding operational constraint w.r.t. λ and ρo, the uplink performance becomes independent of λ and ρo.

We propose a novel Location-Aware multicell Cooperation (LAC) scheme for downlink transmission in OFDMA-based networks. Compared to the traditional multicell cooperation, the proposed scheme uses coordinated multipoint (CoMP) transmission to serve only users with poor signal-to-interference-plus-noise ratio (SINR). On the other hand, users with good SINR conditions are served via multiuser MIMO by a single base station (BS). The proposed scheme uses a joint zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) transmission along with optimized power allocation in a semi-distributed manner to maximize the overall system energy efficiency (i.e., the average data rate per unit power [bps/Watt], or equivalently, average number of successfully transmitted bits per energy unit [bit/Joule]). Numerical results show that the proposed scheme outperforms the scheme that uses cooperation to serve all users, in terms of energy efficiency as well as system capacity and fairness.

We propose an energy-efficient resource allocation scheme for downlink transmission in two-tier Network MIMO OFDMA-based macrocell-femtocell networks where the femto-cells form clusters of equal size. The proposed scheme uses a joint zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) transmission at each network tier to perform allocation of subcarrier and precoding coefficients. Then, power allocation is optimized in order to maximize the total system energy efficiency (i.e., average number of successfully transmitted bits per energy unit [bit/Joule], or equivalently, the average data rate per unit power [bps/Watt]). The macro base stations (MBSs) and the femto base stations (FBSs) in a cluster maximize their energy efficiency in a distributed manner while considering the cross-tier interference and the capacity limitations of backhaul links. The problem of maximizing energy efficiency is formulated as a fractional program and solved by using the Dinkelbach iterative algorithm. Numerical results show that the proposed scheme outperforms the scheme that maximizes the system average capacity, in terms of energy efficiency, and also improves the total system performance in terms of energy efficiency and average system capacity when compared to a single-tier system.

We consider joint subchannel and power allocation for an orthogonal frequency division multiple access (OFDMA)-based cognitive radio network. We formulate the downlink resource allocation problem as a spectral-footprint (bandwidth-power product) minimization problem under interference threshold at primary users, total power and quality of service constraints. The cognitive base station solves this non-convex mixed-integer programming problem iteratively by dividing it into a subchannel allocation master problem and power allocation subproblems. The subchannel assignment problem is solved by applying a modified Hungarian algorithm while the power allocation subproblems are solved by using Lagrangian techniques. Specifically, we propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. The performance of our spectral-footprint minimization technique is compared with the waterfilling power allocation.

Device-to-device (D2D) communication enables the user equipments (UEs) located in close proximity to bypass the cellular base stations (BSs) and directly connect to each other, and thereby, offload traffic from the cellular infrastructure. D2D communication can improve spatial frequency reuse and energy efficiency in cellular networks. This paper presents a comprehensive and tractable analytical framework for D2D-enabled uplink cellular networks with a flexible mode selection scheme along with truncated channel inversion power control. The developed framework is used to analyze and understand how the underlaying D2D communication affects the cellular network performance. Through comprehensive numerical analysis, we investigate the expected performance gains and provide guidelines for selecting the network parameters.

In a two-tier heterogeneous network (HetNet) where femto access points (FAPs) with lower transmission power coexist with macro base stations (BSs) with higher transmission power, the FAPs may suffer significant performance degradation due to inter-tier interference. Introducing cognition into the FAPs through the spectrum sensing (or carrier sensing) capability helps them avoiding severe interference from the macro BSs and enhance their performance. In this paper, we use stochastic geometry to model and analyze performance of HetNets composed of macro BSs and cognitive FAPs in a multichannel environment. The proposed model explicitly accounts for the spatial distribution of the macro BSs, FAPs, and users in a Rayleigh fading environment. We quantify the performance gain in outage probability obtained by introducing cognition into the femto-tier, provide design guidelines, and show the existence of an optimal spectrum sensing threshold for the cognitive FAPs, which depends on the HetNet parameters. We also show that looking into the overall performance of the HetNets is quite misleading in the scenarios where the majority of users are served by the macro BSs. Therefore, the performance of femto-tier needs to be explicitly accounted for and optimized.

For networks with random topologies (e.g., wireless ad-hoc and sensor networks) and dynamically varying channel gains, choosing the long term operating parameters that optimize the network performance metrics is very challenging. In this paper, we use stochastic geometry analysis to develop a novel framework to design spectrum-efficient multi-channel random wireless networks based on the IEEE 802.15.4 standard. The proposed framework maximizes both spatial and time domain frequency utilization under channel gain uncertainties to minimize the number of frequency channels required to accommodate a certain population of coexisting IEEE 802.15.4 networks. The performance metrics are the outage probability and the self admission failure probability. We relax the single channel assumption that has been used traditionally in the stochastic geometry analysis. We show that the intensity of the admitted networks does not increase linearly with the number of channels and the rate of increase of the intensity of the admitted networks decreases with the number of channels. By using graph theory, we obtain the minimum required number of channels to accommodate a certain intensity of coexisting networks under a self admission failure probability constraint. To this end, we design a superframe structure for the coexisting IEEE 802.15.4 networks and a method for time-domain interference alignment.

Small cells overlaid with macrocells can increase the capacity of two-tier cellular wireless networks by offloading traffic from macrocells. To motivate the small cell service providers (SSPs) to open portion of the access opportunities to macro users (i.e., to operate in a hybrid access mode), we design an incentive mechanism in which the macrocell service provider (MSP) could pay to the SSPs. According to the price offered by the MSP, the SSPs decide on the open access ratio, which is the ratio of shared radio resource for macro users and the total amount of radio resource in a small cell. The users in this two-tier network can make service selection decisions dynamically according to the performance satisfaction level and cost, which again depend on the pricing and spectrum sharing between the MSP and SSPs. To model this dynamic interactive decision problem, we propose a hierarchical dynamic game framework. In the lower level, we formulate an evolutionary game to model and analyze the adaptive service selection of users. An evolutionary stable strategy (ESS) is considered to be the solution of this game. In the upper level, the MSP and SSPs sequentially determine the pricing strategy and the open access ratio, respectively, taking into account the distribution of dynamic service selection at the lower-level evolutionary game. A Stackelberg differential game is formulated where the MSP and SSPs act as the leader and followers, respectively. An open-loop Stackelberg equilibrium is considered to be the solution of this game. We also extend the hierarchical dynamic game framework and investigate the impact of information delays on the equilibrium solutions. Numerical results show the effectiveness and advantages of dynamic control of the open access ratio and pricing.

Small cells such as femtocells overlaying the macrocells can enhance the coverage and capacity of cellular wireless networks and increase the spectrum efficiency by reusing the frequency spectrum assigned to the macrocells in a universal frequency reuse fashion. However, management of both the cross-tier and co-tier interferences is one of the most critical issues for such a two-tier cellular network. Centralized solutions for interference management in a two-tier cellular network with orthogonal frequency-division multiple access (OFDMA), which yield optimal/near-optimal performance, are impractical due to the computational complexity. Distributed solutions, on the other hand, lack the superiority of centralized schemes. In this paper, we propose a semi-distributed (hierarchical) interference management scheme based on joint clustering and resource allocation for femtocells. The problem is formulated as a mixed integer non-linear program (MINLP). The solution is obtained by dividing the problem into two sub-problems, where the related tasks are shared between the femto gateway (FGW) and femtocells. The FGW is responsible for clustering, where correlation clustering is used as a method for femtocell grouping. In this context, a low-complexity approach for solving the clustering problem is used based on semi-definite programming (SDP). In addition, an algorithm is proposed to reduce the search range for the best cluster configuration. For a given cluster configuration, within each cluster, one femto access point (FAP) is elected as a cluster head (CH) that is responsible for resource allocation among the femtocells in that cluster. The CH performs sub-channel and power allocation in two steps iteratively, where a low-complexity heuristic is proposed for the sub-channel allocation phase. Numerical results show the performance gains due to clustering in comparison to other related schemes. Also, the proposed correlation clustering scheme offers performance, which is close to that of the optimal clustering, with a lower complexity.

Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control as well as the maximum power limitations for UEs. More specifically, for interference mitigation and robust uplink communication, each UE is required to control its transmit power such that the average received signal power at its serving base station (BS) is equal to a certain threshold ρo. Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold of ρo. We show that there exists a transfer point in the uplink system performance that depends on the following tuple: BS intensity λ, maximum transmit power of UEs Pu}, and ρo. That is, when Puis a tight operational constraint with respect to (w.r.t.) λ and ρo, the uplink outage probability and spectral efficiency highly depend on the values of λ and ρo. In this case, there exists an optimal cutoff threshold ρo*, which depends on the system parameters, that minimizes the outage probability. On the other hand, when Puis not a binding operational constraint w.r.t. λ and ρo, the uplink outage probability and spectral efficiency become independent of λ and ρo. We obtain approximate yet accurate simple expressions for outage probability and spectral efficiency, which reduce to closed forms in some special cases.

Device-to-device (D2D) communication underlaying cellular wireless networks is a promising concept to improve user experience and resource utilization by allowing direct transmission between two cellular devices. In this paper, performance of network-assisted D2D communication is investigated where D2D traffic is carried through relay nodes. Considering a multi-user and multi-relay network, we propose a distributed solution for resource allocation with a view to maximizing network sum-rate. An optimization problem is formulated for radio resource allocation at the relays. The objective is to maximize end-to-end rate as well as satisfy the data rate requirements for cellular and D2D user equipments under total power constraint. Due to intractability of the resource allocation problem, we propose a solution approach using message passing technique where each user equipment sends and receives information messages to/from the relay node in an iterative manner with the goal of achieving an optimal allocation. Therefore, the computational effort is distributed among all the user equipments and the corresponding relay node. The convergence and optimality of the proposed scheme are proved and a possible distributed implementation of the scheme in practical LTE-Advanced networks is outlined. The numerical results show that there is a distance threshold beyond which relay-aided D2D communication significantly improves network performance with a small increase in end-to-end delay when compared to direct communication between D2D peers.

Device-to-device (D2D) communication in cellular networks allows direct transmission between two cellular devices with local communication needs. Due to the increasing number of autonomous heterogeneous devices in future mobile networks, an efficient resource allocation scheme is required to maximize network throughput and achieve higher spectral efficiency. In this paper, performance of network-integrated D2D communication under channel uncertainties is investigated where D2D traffic is carried through relay nodes. Considering a multi-user and multi-relay network, we propose a robust distributed solution for resource allocation with a view to maximizing network sum-rate when the interference from other relay nodes and the link gains are uncertain. An optimization problem is formulated for allocating radio resources at the relays to maximize end-to-end rate as well as satisfy the quality-of-service (QoS) requirements for cellular and D2D user equipments under total power constraint. Each of the uncertain parameters is modeled by a bounded distance between its estimated and bounded values. We show that the robust problem is convex and a gradient-aided dual decomposition algorithm is applied to allocate radio resources in a distributed manner. Finally, to reduce the cost of robustness defined as the reduction of achievable sum-rate, we utilize the chance constraint approach to achieve a trade-off between robustness and optimality. The numerical results show that there is a distance threshold beyond which relay-aided D2D communication significantly improves network performance when compared to direct communication between D2D peers.

This paper takes an analytical approach to solving the optimization problem of finding the power allocation that maximizes the sum-rate of the Gaussian interference channel with any linear power (interference) constraint and proportional rate constraints. It is proved that the sum-rate of the Gaussian interference channel restricted to proportional rate constraints does not have a critical point and the maximum sum-rate subject to said constraints occurs at the boundary of the domain formed by the plane representing the linear power constraint. This is accomplished by using analytic geometry in higher dimensions to show that the curve of intersection of the sum-rate and the proportional rate constraints is always increasing, and intersects the boundary plane representing the linear power constraint at a unique point. A polynomial time (in the number of users) centralized algorithm that finds this point of optimal power allocation is proposed. This is a significant improvement over existing algorithms for related power allocation problems which have exponential time complexity in the number of users. Two distributed algorithms with linear and constant complexities are also presented. Simulation results supporting the analysis and demonstrating the performances of the algorithms are presented.

Multi-tier cellular networks are considered as an effective solution to enhance the coverage and data rate offered by cellular systems. In a multi-tier network, high power base stations (BSs) such as macro BSs are overlaid by lower power small cells such as femtocells and/or picocells. However, co-channel deployment of multiple tiers of BSs gives rise to the problem of cross-tier interference that significantly impacts the performance of wireless networks. Multicell cooperation techniques, such as coordinated multipoint (CoMP) transmission, have been proposed as a promising solution to mitigate the impact of the cross-tier interference in multi-tier networks. In this paper, we propose a novel scheme for Location-Aware Cross-Tier Cooperation (LA-CTC) between BSs in different tiers for downlink CoMP transmission in two-tier cellular networks. On one hand, the proposed scheme only uses CoMP transmission to enhance the performance of the users who suffer from high cross-tier interference due to the co-channel deployment of small cells such as picocells. On the other hand, users with good signal-to-interference-plus-noise ratio (SINR) conditions are served directly by a single BS from any of the two tiers. Thus, the data exchange between the cooperating BSs over the backhaul network can be reduced when compared to the traditional CoMP transmission scheme. We use tools from stochastic geometry to quantify the performance gains obtained by using the proposed scheme in terms of outage probability, achievable data rate, and load per BS. We compare the performance of the proposed scheme with that of other schemes in the literature such as the schemes which use cooperation to serve all users and schemes that use range expansion to offload users to the small cell tier.

The strength of carrier-sense multiple access with collision avoidance (CSMA/CA) can be combined with that of time-division multiple access (TDMA) to enhance the channel access performance in wireless networks such as the IEEE 802.15.4-based wireless personal area networks. In particular, the performance of legacy CSMA/CA-based medium access control scheme in congested networks can be enhanced through a hybrid CSMA/CA-TDMA scheme while preserving the scalability property. In this paper, we present distributed and centralized channel access models that follow the transmission strategies based on Markov decision process (MDP) to access both contention period and contention-free period in an intelligent way. The models consider the buffer status as an indication of congestion provided that the offered traffic does not exceed the channel capacity. We extend the models to consider the hidden node collision problem encountered due to the signal attenuation caused by channel fading. The simulation results show that the MDP-based distributed channel access scheme outperforms the legacy slotted CSMA/CA scheme. The centralized model outperforms the distributed model but requires the global information of the network.

This paper presents a novel framework to derive the statistics of the interference considering dedicated and shared spectrum access for uplink transmission in two-tier small cell networks such as the macrocell-femtocell networks. The framework exploits the distance distributions from geometric probability theory to characterize the uplink interference while considering a traditional grid-model set-up for macrocells along with the randomly deployed femtocells. The derived expressions capture the impact of path-loss, composite shadowing and fading, uniform and non-uniform traffic loads, spatial distribution of femtocells, and partial and full spectral reuse among femtocells. Considering dedicated spectrum access, first, we derive the statistics of co-tier interference incurred at both femtocell and macrocell base stations (BSs) from a single interferer by approximating generalized-K composite fading distribution with the tractable Gamma distribution. We then derive the distribution of the number of interferers considering partial spectral reuse and moment generating function (MGF) of the cumulative interference for both partial and full spectral reuse scenarios. Next, we derive the statistics of the cross-tier interference at both femtocell and macrocell BSs considering shared spectrum access. Finally, we utilize the derived expressions to analyze the capacity in both dedicated and shared spectrum access scenarios. The derived expressions are validated by the Monte Carlo simulations. Numerical results are generated to assess the feasibility of shared and dedicated spectrum access in femtocells under varying traffic load and spectral reuse scenarios.

Base station (BS) sleeping has emerged as a viable solution to enhance the overall network energy efficiency by inactivating the underutilized BSs. However, it affects the performance of users in sleeping cells depending on their BS association criteria, their channel conditions toward the active BSs, and scheduling criteria and traffic loads at the active BSs. This paper characterizes the performance of cellular systems with BS sleeping by developing a systematic framework to derive the spectral efficiency and outage probability of downlink transmission to the sleeping cell users taking into account the aforementioned factors. In this context, a user association scheme is also developed in which sleeping cell users associate to a BS with maximum mean channel access probability (MMAP). The MMAP-based user association scheme adapts according to traffic load and scheduling criteria at the active BSs. We consider greedy and round-robin schemes at active BSs for scheduling users in a channel. We also derive the analytical results for the conventional maximum received signal power (MRSP)-based user association scheme. Finally, we derive the statistics of the received signal and interference power to evaluate the downlink spectral efficiency of a given sleeping cell user. Numerical results provide a comparison between two user association schemes as a function of system parameters and demonstrate the efficacy of the MMAP-based association scheme in non-uniform traffic load scenarios.

ABSTRACT The smart grid is an innovative energy network that will improve the conventional electrical grid network to be more reliable, cooperative, responsive, and economical. Within the context of the new capabilities, advanced data sensing, communication, and networking technology will play a significant role in shaping the future of the smart grid. The smart grid will require a flexible and efficient framework to ensure the collection of timely and accurate information from various locations in power grid to provide continuous and reliable operation. This article presents a tutorial on the sensor data collection, communications, and networking issues for the smart grid. First, the applications of data sensing in the smart grid are reviewed. Then, the requirements for data sensing and collection, the corresponding sensors and actuators, and the communication and networking architecture are discussed. The communication technologies and the data communication network architecture and protocols for the smart grid are described. Next, different emerging techniques for data sensing, communications, and sensor data networking are reviewed. The issues related to security of data sensing and communications in the smart grid are then discussed. To this end, the standardization activities and use cases related to data sensing and communications in the smart grid are summarized. Finally, several open issues and challenges are outlined. Copyright © 2012 John Wiley & Sons, Ltd.

Efficient power and subchannel allocation methods are required for orthogonal frequency division multiple access (OFDMA)-based femtocells to mitigate both co-tier and cross-tier interferences. In this paper, we study the problem of joint power and subchannel allocation in a densely deployed femtocell network with constraints on co-tier and cross-tier interference and minimum datarate requirements. A convex optimization problem is formulated and the solution of the problem is obtained numerically. For this joint resource allocation problem, the effect of clustering (or cooperation) is studied as well, where femtocells are placed in disjoint groups. The performances of distributed resource allocation (i.e., without clustering) and centralized resource allocation (i.e., with only one cluster) are compared against clustering-based (i.e., semi-distributed) resource allocation. Numerical results show that, in a dense environment, with femtocells closely located to each other, the effect of co-tier interference becomes significantly dominant and clustering is a very effective technique in such a densely deployed environment to improve the sumrate capacity of the femtocells.

Topological randomness is an intrinsic characteristic of large scale ad-hoc and sensor networks. For networks with random topologies, choosing the operating parameters that govern the performance metrics is very challenging. Calculating the minimum number of channels required to accommodate a certain population of co-existing star connected networks (SCNs), or quantifying the performance degradation if the minimum number of channels in not available is the main focus of this paper. The main performance metric in our analysis is the self admission failure probability (blocking probability). We relax the single channel assumption that has always been used in the stochastic geometry analysis of random wireless networks. We show that the intensity of the coexisting networks does not increase linearly with the number of channels, and that the rate of increase of the intensity of the coexisting networks decreases with the number of channels. By using graph theory, we bound the number of channels required for accommodating a certain intensity of coexisting SCNs and provide a good initial point for the numerical optimization problem.

Due to the scarcity of the wireless spectrum along with the ever increasing number of cellular wireless users and the associated drastic increase in the data traffic demand, femtocells are envisioned to provide fast, flexible, cost-efficient, and customer driven solutions to offload users from the congested macro access network and enhance the overall system performance. To control offloading and to achieve the required balance of users and traffic served by each network tier, we quantify offloading and discuss different techniques that can be used to offload users from the macro access network to the femto access network, namely, offloading via power control, offloading via femtocell deployment and offloading via biasing. In this paper, we quantify offloading when users connect to the network entity that provides the strongest instantaneous signal power in a Nakagami-m fading environment. To this end, we discuss the merits and drawbacks of each of the offloading techniques.

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.

Hidden node collision is a significant problem in CSMA/CA (Carrier-Sense Multiple Access with Collision Avoidance)-based multihop networks such as wireless sensor networks. The RTS/CTS (Request to Send/Clear to Send)-based handshaking mechanism is not effective to solve this problem in multihop networks. Also, to reduce power-consumption, such a handshaking mechanism is not used in CSMA/CA networks such as the IEEE 802.15.4-based networks. Spatial scheduling of nodes is a classical approach to solve the hidden node problem. We apply this scheduling concept and present a network planning model to mitigate this problem without any control overhead by structuring a CSMA/CA-based wireless network in a cellular layout. For this network model, we derive the required distance between a sender and a receiver so that the minimum required signal-to-interference ratio (SIR) at the receiver can be guaranteed in the worst-case scenario. We also present an analysis to estimate network size for given traffic load under distance-dependent signal attenuation.

Mobile cloud computing is an emerging technology to improve the quality of mobile services. In this paper, we consider the resource (i.e., radio and computing resources) sharing problem to support mobile applications in a mobile cloud computing environment. In such an environment, mobile cloud service providers can cooperate (i.e., form a coalition) to create a resource pool to share their own resources with each other. As a result, the resources can be better utilized and the revenue of the mobile cloud service providers can be increased. To maximize the benefit of the mobile cloud service providers, we propose a framework for resource allocation to the mobile applications, and revenue management and cooperation formation among service providers. For resource allocation to the mobile applications, we formulate and solve optimization models to obtain the optimal number of application instances that can be supported to maximize the revenue of the service providers while meeting the resource requirements of the mobile applications. For sharing the revenue generated from the resource pool (i.e., revenue management) among the cooperative mobile cloud service providers in a coalition, we apply the concepts of core and Shapley value from cooperative game theory as a solution. Based on the revenue shares, the mobile cloud service providers can decide whether to cooperate and share the resources in the resource pool or not. Also, the provider can optimize the decision on the amount of resources to contribute to the resource pool.

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques.

For spectrum sharing and avoidance of mutual interference, carrier-sense multiple access (CSMA) protocols are very popular in distributed wireless networks. CSMA protocols aim to maximize the spatial frequency reuse while limiting the mutual interference and outage. The hard core point process (HCPP) is a very popular tool for modeling and analysis of random CSMA networks. However, the traditional HCPP suffers from the node intensity (and hence the interference) underestimation flaw. Therefore, we propose a modified hard core point process to mitigate this flaw. The proposed modified HCPP is generalized for any fading environment. To this end, we derive a closed-form expression for the intensity of simultaneously active transmitters in a random wireless CSMA network. Then, we derive a closed-form expression for approximating the outage probability experienced by a generic receiver in the network, and subsequently, use it to obtain the transmission capacity of the network. Finally, we show the existence of an optimal carrier-sensing threshold for the CSMA protocol that maximizes the transmission capacity of the network. Simulation results validate the analysis and also provide interesting insights into the design of practical CSMA networks.

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.

Cooperative packet delivery can improve the data delivery performance in wireless networks by exploiting the mobility of the nodes, especially in networks with intermittent connectivity, high delay and error rates such as wireless mobile delay-tolerant networks (DTNs). For such a network, we study the problem of rational coalition formation among mobile nodes to cooperatively deliver packets to other mobile nodes in a coalition. Such coalitions are formed by mobile nodes which can be either well behaved or misbehaving in the sense that the well-behaved nodes always help each other for packet delivery, while the misbehaving nodes act selfishly and may not help the other nodes. A Bayesian coalitional game model is developed to analyze the behavior of mobile nodes in coalition formation in presence of this uncertainty of node behavior (i.e., type). Given the beliefs about the other mobile nodes' types, each mobile node makes a decision to form a coalition, and thus the coalitions in the network vary dynamically. A solution concept called Nash-stability is considered to find a stable coalitional structure in this coalitional game with incomplete information. We present a distributed algorithm and a discrete-time Markov chain (DTMC) model to find the Nash-stable coalitional structures. We also consider another solution concept, namely, the Bayesian core, which guarantees that no mobile node has an incentive to leave the grand coalition. The Bayesian game model is extended to a dynamic game model for which we propose a method for each mobile node to update its beliefs about other mobile nodes' types when the coalitional game is played repeatedly. The performance evaluation results show that, for this dynamic Bayesian coalitional game, a Nash-stable coalitional structure is obtained in each subgame. Also, the actual payoff of each mobile node is close to that when all the information is completely known. In addition, the payoffs of the mobile nodes will be at least as high as those when they act alone (i.e., the mobile nodes do not form coalitions).

We consider the problem of cooperative packet delivery to mobile nodes in a hybrid wireless mobile network, where both infrastructure-based and infrastructure-less (i.e., ad hoc mode or peer-to-peer mode) communications are used. We propose a solution based on a coalition formation among mobile nodes to cooperatively deliver packets among these mobile nodes in the same coalition. A coalitional game is developed to analyze the behavior of the rational mobile nodes for cooperative packet delivery. A group of mobile nodes makes a decision to join or to leave a coalition based on their individual payoffs. The individual payoff of each mobile node is a function of the average delivery delay for packets transmitted to the mobile node from a base station and the cost incurred by this mobile node for relaying packets to other mobile nodes. To find the payoff of each mobile node, a Markov chain model is formulated and the expected cost and packet delivery delay are obtained when the mobile node is in a coalition. Since both the expected cost and packet delivery delay depend on the probability that each mobile node will help other mobile nodes in the same coalition to forward packets to the destination mobile node in the same coalition, a bargaining game is used to find the optimal helping probabilities. After the payoff of each mobile node is obtained, we find the solutions of the coalitional game which are the stable coalitions. A distributed algorithm is presented to obtain the stable coalitions and a Markov-chain-based analysis is used to evaluate the stable coalitional structures obtained from the distributed algorithm. Performance evaluation results show that when the stable coalitions are formed, the mobile nodes achieve a nonzero payoff (i.e., utility is higher than the cost). With a coalition formation, the mobile nodes achieve higher payoff than that when each mobile node acts alone.

Compared to single-hop networks such as WiFi, multihop infrastructure wireless mesh networks (WMNs) can potentially embrace the broadcast benefits of a wireless medium in a more flexible manner. Rather than being point-to-point, links in the WMNs may originate from a single node and reach more than one other node. Nodes located farther than a one-hop distance and overhearing such transmissions may opportunistically help relay packets for previous hops. This phenomenon is called opportunistic overhearing/listening. With multiple radios, a node can also improve its capacity by transmitting over multiple radios simultaneously using orthogonal channels. Capitalizing on these potential advantages requires effective routing and efficient mapping of channels to radios (channel assignment (CA)). While efficient channel assignment can greatly reduce interference from nearby transmitters, effective routing can potentially relieve congestion on paths to the infrastructure. Routing, however, requires that only packets pertaining to a particular connection be routed on a predetermined route. Random network coding (RNC) breaks this constraint by allowing nodes to randomly mix packets overheard so far before forwarding. A relay node thus only needs to know how many packets, and not which packets, it should send. We mathematically formulate the joint problem of random network coding, channel assignment, and broadcast link scheduling, taking into account opportunistic overhearing, the interference constraints, the coding constraints, the number of orthogonal channels, the number of radios per node, and fairness among unicast connections. Based on this formulation, we develop a suboptimal, auction-based solution for overall network throughput optimization. Performance evaluation results show that our algorithm can effectively exploit multiple radios and channels and can cope with fairness issues arising from auctions. Our algorithm also shows promising gains over traditional routing solutions in which various channel assignment strategies are used.

This paper considers the problem of downlink power allocation in an orthogonal frequency-division multiple access (OFDMA) cellular network with macrocells underlaid with femtocells. The femto-access points (FAPs) and the macro-base stations (MBSs) in the network are assumed to compete with each other to maximize their capacity under power constraints. This competition is captured in the framework of a Stackelberg game with the MBSs as the leaders and the FAPs as the followers. The leaders are assumed to have foresight enough to consider the responses of the followers while formulating their own strategies. The Stackelberg equilibrium is introduced as the solution of the Stackelberg game, and it is shown to exist under some mild assumptions. The game is expressed as a mathematical program with equilibrium constraints (MPEC), and the best response for a one leader-multiple follower game is derived. The best response is also obtained when a quality-of-service constraint is placed on the leader. Orthogonal power allocation between leader and followers is obtained as a special case of this solution under high interference. These results are used to build algorithms to iteratively calculate the Stackelberg equilibrium, and a sufficient condition is given for its convergence. The performance of the system at a Stackelberg equilibrium is found to be much better than that at a Nash equilibrium.

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.

A distributed priority-based power and admission control algorithm is presented to address the priority-based gradual removal problem in cellular wireless networks. We assume that there exist two classes of priority for users (high-priority users versus low-priority users) and minimal number of low-priority users should be gradually removed, subject to the constraint that all high-priority users are supported with their target signal-to-interference-plus-noise ratios (SINRs) which is assumed feasible. In our proposed algorithm, each high-priority user rigidly tracks its target-SINR by employing the conventional target-SINR tracking power control algorithm, and each transmitting low-priority user tracks its target-SINR as long as its required transmit power is below a threshold, otherwise it temporarily removes itself. Each removed low-priority user resumes its transmission if the required transmit power to reach its target-SINR goes below a given threshold which is different from the former. Of these two thresholds, whose values are analytically obtained, the former is provided by the base station and the latter is obtained in a distributed manner as a function of the former. We show that the distributed power-update function corresponding to our proposed algorithm has at least one fixed-point which is not unique in general. The convergence point, where our proposed algorithm potentially converges to, depends on initial transmit power levels of users. We also show that our proposed algorithm, at each of its fixed-points, not only provides all high-priority users with their (feasible) target-SINRs but also guarantees that no low-priority user is erroneously removed (i.e., no additional low priority user can be supported along with currently supported users). Furthermore, for the special case of tracking a common target-SINR by all low-priority users, we show that our proposed algorithm minimizes the outage-ratio of low-priority users subject to zero-outage-ratio of high-priority users. Simulation results confirm our analytical developments and show that our proposed priority-based power and admission control algorithm solves the priority-based gradual removal problem efficiently.

The performance of optimal switching adaptive modulation for multiuser amplify-and-forward (AF) relay networks over Nakagami-m fading channel is studied. Moreover, we consider two types of communication: i) relay-assisted communication (RAC): the source transmits its message to best user via relay path, ii) selective communication (SC): the source decides whether to forward the source message to the best user via relay path or direct path by comparing the end-to-end instantaneous signal-to-noise ratio (SNR) at the best user, which is independent of modulation scheme. Specifically, multiuser diversity and adaptive discrete-rate five-mode M-ary quadrature amplitude modulation (M-QAM) with constant transmit power is employed with fixed and optimal switching thresholds. The optimization criterion for switching thresholds is the maximization of spectral efficiency subject to target bit-error-rate. In particular, the detrimental effect of feedback delays in multiuser opportunistic scheduling (which exploits multiuser diversity) and adaptive modulation is quantified. To this end, an exact and an upper bound of the end-to-end SNRs are obtained and used to derive the cumulative distribution function and probability density function for each case. Moreover, exact outage probabilities, and lower bounds for the outage probabilities and average bit error rates, and the average spectral efficiencies are derived in closed-form. Further, we also present the high SNR approximations for outage probabilities and BERs. We observe that, for a particular spectral efficiency, adaptive five-mode M-QAM with optimal switching outperforms fixed switching and provides approximately 2 dB and 2.5 dB gain in average SNR for RAC and SC, respectively. Monte-Carlo simulations are performed to validate the analytical results.

We consider novel cooperative spectrum sensing (CSS) algorithms based on the pattern classification techniques for cognitive radio (CR) networks. In this regard, support vector machine (SVM) and weighted K-nearest-neighbor (KNN) classification techniques are implemented for CSS. The received signal strength at the CR users are treated as features and fed into the classifier to detect the availability of the primary user (PU). Each instance of PU activity (i.e., availability and unavailability) is categorized into positive and negative classes (respectively). In the case of SVM, for minimization of classification errors the support vectors are obtained by maximizing the margin between the separating hyperplane and data. Towards this end, we investigate the effect of different kernels through quantifying in terms of detection probability by representing the receiver operating characteristic (ROC) curves. Furthermore, weighted KNN classification technique is proposed for CSS and the corresponding weights are calculated by evaluating the area under ROC curve of each feature. Our comparative results clearly reveal that the proposed SVM and weighted KNN algorithms outperform the existing state-of-the-art pattern classification-based CSS techniques.

Carrier-sense multiple access (CSMA) protocols coordinate the spectrum access to maximize the spatial frequency reuse and minimize the mutual interference in distributed wireless networks. Since the CSMA protocol correlates the positions of the simultaneously active transmitters, the analytically tractable Poisson point process (PPP) cannot be used to model the spatial distribution of the simultaneously active transmitters. Instead, the hard core point process (HCPP) is widely used to model the spatial distribution of the simultaneously active transmitters. However, the HCPP can be directly applied to CSMA random networks under deterministic channel gains only. In this paper, we integrate the fading and spatial distribution statistics in the analysis of the HCPP, and thus provide a unified framework to capture the intensity of simultaneously active transmitters in a random CSMA wireless network under general fading environments.

We charachterize the random CSMA wireless networks by statistically quantifing the intensity of simultaneously active nodes and the aggregate interference experienced by a generic node in the network. First, starting from a Poisson point process to model the spatial distribution of the network nodes, we propose a modified hard core point process (MHCPP) to model the spatial distribution of the simultaneously active users in a random CSMA network. Our motivation to propose the MHCPP is to mitigate the node intensity underestimation problem of the traditional hard core point process (HCPP). Then, we use the shot noise theory to statistically quantify the interference experienced by a generic node in the network. Closed-form expressions for the intensity of the simultaneously active nodes and the Laplace transform of the probability density function (and hence the moment generating function and the characteristic function), mean, and variance of the approximate aggregate interference are obtained. The accuracy of our model is validated by simulations.

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.

We investigate the problem of distributed scheduling and power control for vertical spectrum sharing in spatial-reuse time division multiple access (STDMA) networks. The objective is to minimize the transmission length (in term of time slots) of secondary users (e.g. users in femtocell networks) subject to the interference-limit constraint for primary users (e.g. users in cellular networks) and quality-of-service (QoS) guarantee of secondary users. This problem is known to be NP-complete. We therefore propose a novel distributed two-stage algorithm based on the distributed column generation method to find the near-optimal solution for the transmission schedule. In the first stage, the dual problem corresponding to the transmission length minimization problem subject to the minimum bandwidth requirement of secondary users, called the restricted master problem, is solved to obtain a dual optimal solution at each secondary transmitter. The dual optimal variables are passed to the second stage to solve the pricing problem. The pricing problem here finds a feasible channel access pattern such that the sum of dual optimal variables is greater than 1 subject to the interference constraints for primary users and the signal-to-interference-plus-noise ratio (SINR) constraints for secondary users so that the solution of the master restricted problem can be improved. We also develop a distributed algorithm for solving the pricing problem based on local measurement at each secondary transmitter and a limited number of message exchanges. The proposed algorithm is compared with previously proposed methods and is evaluated in terms of the schedule length and the number of message exchanges.

This paper evaluates the performance of adaptive modulation in multi-relay networks with selective relaying, under Nakagami-m fading. In the system model, the source decides independently whether to forward the source message to the destination via the best (partial relay selection) relay path or direct path by comparing the end-to-end instantaneous signal-to-noise ratio (SNR) at the destination, which is independent of the modulation scheme. Adaptive discrete-rate M-ary quadrature amplitude modulation with fixed switching thresholds is implemented by dividing the SNR region into five modes. In particular, impact of imperfect (outdated) channel estimation due to feedback delay is quantified for relay selection. We derive the cumulative distribution function for the upper-bound of end-to-end SNR in closed-form. Further, lower-bounds of outage probability and average bit error rate, and upper-bound of spectral efficiency are derived in closed-forms. Monte Carlo simulation results validate our numerical analysis.

Mobile cloud computing aims at improving the performance of mobile applications and to enhance the resource utilization of service providers. In this paper, we consider a mobile cloud computing environment in which the service providers can form a coalition to create a resource pool to support the mobile applications. First, an admission control mechanism is used to provide services of mobile applications to the users given the available long-term reserved resources in a pool. An optimization formulation is introduced to obtain the optimal decision of admission control. Then, for a given coalition of service providers, the revenue obtained from utilizing the resource pool has to be shared among the service providers. A coalitional game model is developed for sharing the revenue. In addition, since the service providers can decide on short-term capacity expansion of the resource pool, a game model is introduced to obtain the optimal strategies of service providers on capacity expansion such that their profits are maximized.

We investigate the distributed robust transmission scheduling and power control problem in a cognitive spatial-reuse time division multiple access (STDMA) network. In particular, we address the problem of minimizing the transmission length (in terms of time-slots) of the secondary links under their minimum quality-of-service (QoS) requirements without violating the maximum tolerable interference limit for the primary receivers. Traditionally, the joint transmission scheduling and power control problem only considers the average link gains; therefore, QoS violation can occur due to improper power allocation with respect to instantaneous channel gain realization. To overcome this problem of QoS violation, our problem formulation takes the channel gain uncertainty into account. Since an optimal solution cannot be obtained due to the NP-completeness of the problem, we propose a novel distributed two-stage algorithm based on the distributed column generation method to obtain the near-optimal solution for the robust transmission schedules in an ad-hoc cognitive radio network. To demonstrate its relative efficiency, our algorithm is compared with previously proposed algorithms. For the proposed algorithm, we also derive the bounds on the probability of signal-to-interference-plus-noise ratio (SINR) constraint violation and the expected number of additional time-slots required to satisfy the traffic demand requirements of secondary links.

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.

ABSTRACT This paper addresses the problem of streaming packetized media data in a combined wireline/802.11 network. Since the wireless channel is normally the bottleneck for media streaming in such a network, we propose that wireless fountain coding (WFC) be used over the wireless downlink in order to efficiently utilize the wireless bandwidth and exploit the broadcast nature of the channel. Forward error correction (FEC) is also used to combat errors at the application‐layer. We analytically obtain the moment generating function (MGF) for the wireless link‐layer delay incurred by WFC. With the MGF, the expected value of this wireless link‐layer delay is found and used by the access point (AP), who has no knowledge of the buffer contents of wireless receivers, to make a coding‐based decision. We then derive the end‐to‐end packet loss/late probability based on the MGF. We develop an integrated ns‐3/EvalVid simulator to evaluate our proposed system and compare it with the traditional 802.11e scheme which is without WFC capability but equipped with application‐ and link‐layer retransmission mechanisms. Through extensive simulations of video streaming, we show that streaming with WFC is able to support more concurrent video flows compared to the traditional scheme. When the deadlines imposed on video packets are relatively stringent, streaming with WFC also shows superior performance in terms of packet loss/late probability, video distortion, and video frame delay, over the traditional scheme. Copyright © 2011 John Wiley & Sons, Ltd.

Defined for low-rate, low-power and short-range applications, IEEE 802.15.4 offers complementary services to IEEE 802.11 and IEEE 802.15.1. However, since IEEE 802.15.4-based wireless personal area networks (WPANs) are very prone to interference, efficient coexistence of IEEE 802.15.4 WPANs in the ISM band is a challenging problem. In this work, we propose a distributed coexistence method for IEEE 802.15.4 operating in the beacon- enabled mode. In this method, each network coordinator learns about the surrounding environment, and schedules its superframe properly to minimize the mutual interference. Using this method, multiple IEEE 802.15.4-based WPANs can colocate in the same logical channel, hence, increasing their coexistence capability in the ISM band. The proposed method considers spatial distribution of the WPANs and a physical interference model. Also, the method does not require any global information about the coexisting WPANs. We evaluate the performance of the proposed method through simulations.

An analytical framework for the link-level end-to-end (e2e) queueing performance evaluation in a multiuser wireless relay network with automatic repeat request (ARQ)-based error control is presented. The e2e system consisting of a base station, a relay station, and multiple users is modeled as a probabilistic tandem of two finite queues for the relay and each user. The transmissions from the users are scheduled in a time-division multiplexing (TDM) fashion, i.e., in each time-slot only one user is in tandem with the relay's buffer with a certain probability. To make the analysis of such system tractable, the finite buffer of the relay is decomposed into smaller non-overlapping portions, each corresponding to an individual user's packets (i.e., per-user queueing). Using the decomposed model, radio link-level performance measures such as e2e packet loss rate, e2e delay and throughput are obtained analytically and compared with simulation results. As an application of this model, a method of obtaining optimum values for selection probabilities to maximize e2e aggregate throughput subject to users' individual delay constraints is presented.

We consider the scheduling and power control problem for spectrum sharing between secondary users and primary users in a spatial reuse time-division multiple access (STDMA) network. The objective is to minimize the transmission length of secondary users in a frame subject to the interference constraints for primary users and the traffic demand of secondary users. The uncertainty of the channel gains is taken into account. Since the power allocation can be improper with respect to the link gain realization, transmissions in the secondary links may fail, and hence, require more time slots. Therefore, traffic demand uncertainty resulting from channel gain variation is also considered. We propose an efficient algorithm based on column generation for robust optimal scheduling and power control for secondary users in presence of channel gain and traffic demand uncertainty. Numerical results show that the proposed algorithm has high computation speed with very low penalty cost when compared to the optimal algorithm. By adjusting the degree of conservatism, we can balance the tradeoff between the robustness and the transmission length of secondary users in a frame.

IEEE 802.15.4 is a popular technology for short-range wireless networking due to the features such as low duty cycle operation, low power consumption, and both contention-based and contention-free transmissions. This standard can be enhanced to provide an optimal medium access mechanism in presence of congestion in the network. We present a Markov decision process (MDP)-based medium access control (MAC) model for IEEE 802.15.4 for the optimal use of contention and contention-free period to minimize energy consumption in repeated transmissions and carrier sensing without degrading latency in packet transmission. The simulation results show that the MDP strategy works more efficiently in presence of congestion when compared to a non- optimal (i.e., traditional) slotted CSMA/CA scheme.

In this paper, the performance of adaptive modulation for multiuser amplify-and-forward relay networks over Nakagami-m fading is studied. Specifically, adaptive discrete rate five-mode M-ary quadrature amplitude modulation is employed with fixed switching thresholds. In particular, the detrimental effect of feedback delays in multiuser scheduling is quantified. To this end, an upper bound of the end-to-end signal-to-noise ratio (SNR) is obtained and used to derive the cumulative distribution function, probability density function and moment generating function. Moreover, lower bounds for the outage probability and average bit error rate, and upper bounds for the spectral efficiency and amount of fading are derived in closed-form. Our lower and upper bounds are asymptotically exact at high SNRs, and hence, provide accurate assessment of the system performance.

We address the problem of designing efficient resource allocation schemes for an OFDMA based multiuser cooperative communication system that uses amplify and forward relaying. With a two-phase relaying protocol, we assume that both source and the relay have fixed power constraints and that the source employs a selective relaying mechanism. That is, the source adaptively decides on which frequency subcarriers relaying has to performed. We first formulate the problem as a capacity maximizing integer programming optimization problem and then propose a heuristic solution that is composed of several subproblems. The first part is to suboptimally allocate subcarriers to different users based on proportional rate fairness. The next part is a two step iterative approach to find relay decisions and power allocation at the source during the first phase and at the relay during the second phase. The last step is a water-filling algorithm to allocate power at the source during the second phase to all non-relaying subcarriers. Simulation results show that this selective relaying mechanism outperforms the always relay case while also providing an approximate proportional rate fairness.

We propose a robust optimization framework for the multiple-access problem in a hospital environment. The users of e-Health applications (referred to as secondary users) coexist with active and passive medical devices (referred to as primary and protected users, respectively) under uncertainty in the channel (i.e. propagation) conditions. In particular, we design robust transmission scheduling and power control methods for secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective of the optimization framework is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference constraints for primary and protected users. In this framework, we model the channel uncertainty as ellipsoidal uncertainty sets and the transmission scheduling and power control are optimized taking this uncertainty into account. Numerical results show that the proposed framework can achieve robust scheduling and power control against channel variations. By adjusting the robustness parameter (i.e. the degree of conservatism), we can balance the tradeoff between robustness and spectrum utilization.

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.

The evolving smart grid will use advanced data communications and networking techniques to improve efficiency and reliability of electric power generation, transmission, distribution, and consumption. Packet loss performance of the data communications networks used in smart grid will have impact on the cost of power supply. In this paper, we model and analyze the impact of packet loss performance on the optimization of cost for power supply in smart grid. To optimize (i.e., minimize) the cost of power supply, the power demand from consumers has to be estimated based on the power usage data, which are transferred from smart meters through data aggregator unit (DAU) to the meter data management system (MDMS). First, we present a model to optimize the cost of power supply given demand uncertainty. Then the probability distribution of power demand is estimated with and without packet loss. Subsequently, we analyze and show how the packet loss increases the cost of power supply. Next, a queueing model is used to quantify the packet loss due to congestion at DAU, and then the transmission rate from the DAU is optimized to minimize the impact of packet loss. The modeling and analysis of packet loss performance presented in this paper is a step toward optimal network design for future smart grid.

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmission requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.

Vehicle-to-roadside (V2R) communications enable vehicular networks to support a wide range of applications for enhancing the efficiency of road transportation. While existing work focused on non-cooperative techniques for V2R communications between vehicles and roadside units (RSUs), this paper investigates novel cooperative strategies among the RSUs in a vehicular network. We propose a scheme whereby, through cooperation, the RSUs in a vehicular network can coordinate the classes of data being transmitted through V2R communication links to the vehicles. This scheme improves the diversity of the information circulating in the network while exploiting the underlying content-sharing vehicle-to-vehicle communication network. We model the problem as a coalition formation game with transferable utility and we propose an algorithm for forming coalitions among the RSUs. For coalition formation, each RSU can take an individual decision to join or leave a coalition, depending on its utility which accounts for the generated revenues and the costs for coalition coordination. We show that the RSUs can self-organize into a Nash-stable partition and adapt this partition to environmental changes. Simulation results show that, depending on different scenarios, coalition formation presents a performance improvement, in terms of the average payoff per RSU, ranging between 20.5% and 33.2%, relative to the non-cooperative case.

The mobile social network (MSN) combines techniques in social science and wireless communications for mobile networking. The MSN can be considered as a system which provides a variety of data delivery services involving the social relationship among mobile users. This paper presents a comprehensive survey on the MSN specifically from the perspectives of applications, network architectures, and protocol design issues. First, major applications of the MSN are reviewed. Next, different architectures of the MSN are presented. Each of these different architectures supports different data delivery scenarios. The unique characteristics of social relationship in MSN give rise to different protocol design issues. These research issues (e.g., community detection, mobility, content distribution, content sharing protocols, and privacy) and the related approaches to address data delivery in the MSN are described. At the end, several important research directions are outlined.

Autonomic communications aim at easing the burden of managing complex and dynamic networks, and designing adaptive, self-turning and self-stabilizing networks to provide much needed flexibility and functional scalability. With the ever-increasing number of multicast applications made recently, considerable efforts have been focused on the design of adaptive flow control schemes for autonomic multicast services. The main difficulties in designing an adaptive flow controller for autonomic multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat outdated and can be harmful to the control operations. To tackle the preceding problem, this article describes a novel, adaptive, and autonomic multicast scheme, the so-called Proportional, Integrative, Derivative plus Neural Network (PIDNN) predictive technique, which consists of two components: the Proportional Integrative plus Derivative (PID) controller and the Back Propagation BP Neural Network (BPNN). In this integrated scheme, the PID controllers are located at the next upstream main branch nodes of the multicast receivers, and have explicit rate algorithms to regulate the receiving rates of the receivers; while the BPNN is located at the multicast source, and predicts the available bandwidth of those longer delay receivers to compute the expected rates of the longer delay receivers. The ultimate sending rate of the multicast source is the maximum of the aforesaid receiving rates that can be accommodated by its participating branches. This network-assisted property is different from the existing control schemes, in that the PIDNN controller can release the irresponsiveness of a multicast flow caused by those long propagation delays from the receivers. By using BPNN, this active scheme makes the control more responsive to the receivers with longer propagation delay. Thus the rate adaptation can be performed in a timely manner, for the sender to respond to network congestion quickly. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support multirate multicast transmission based on feedback of explicit rates, and verify this matching using simulations. Simulation results demonstrate that the proposed PIDNN controller avoids overflow of multicast traffic, and performs better than the existing scheme PNN [Tan et al. 2005] and the multicast schemes based on control theory. Moreover, it also performs well in the sense that it achieves high link utilization, quick response, good scalability, high unitary throughput, intra-session fairness and inter-session fairness.

We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

In wireless personal area networks, such as wireless body-area sensor networks, stations or devices have different bandwidth requirements and, thus, create heterogeneous traffics. For such networks, the IEEE 802.15.4 medium access control (MAC) can be used in the beacon-enabled mode, which supports guaranteed time slot (GTS) allocation for time-critical data transmissions. This paper presents a general discrete-time Markov chain model for the IEEE 802.15.4-based networks taking into account the slotted carrier sense multiple access with collision avoidance and GTS transmission phenomena together in the heterogeneous traffic scenario and under nonsaturated condition. For this purpose, the standard GTS allocation scheme is modified. For each non-identical device, the Markov model is solved and the average service time and the service utilization factor are analyzed in the non-saturated mode. The analysis is validated by simulations using network simulator version 2.33. Also, the model is enhanced with a wireless propagation model and the performance of the MAC is evaluated in a wheelchair body-area sensor network scenario.

We develop performance models for delay-sensitive uplink media streaming over a wireline-cum-WiFi network. Since the wireless channel is normally a bottleneck for such streaming, we modify the traditional 802.11e block acknowledgment (B-ACK) scheme to work with wireless fountain coding (WFC)-a packet-level coding scheme which codes packets in a similar manner to intrasession random network coding but delivers them in a manner similar to fountain coding. By using this modified B-ACK scheme, protocol complexity and wireless link-layer delay are potentially reduced. We analytically quantify this delay and use it to derive end-to-end packet loss/late probabilities when automatic repeat request (ARQ) and forward error correction (FEC) are jointly employed at the application-layer. We develop an integrated ns-3/EvalVid simulator to validate our models and compare them with the case when the traditional 802.11e B-ACK scheme is employed. Through simulations of video streaming, we observe that the modified B-ACK scheme does not always perform better than the traditional B-ACK scheme in terms of end-to-end packet loss/late probability and video distortion under certain conditions of the wireless channel. This observation leads us to propose a hybrid scheme that switches between the modified and traditional B-ACK strategies according to the conditions of the wireless channel and the number of packets to transmit in a block. Via simulations, we show the benefits of the hybrid scheme when compared to the traditional IEEE 802.11e B-ACK scheme under different network settings.

We consider the problem of optimal channel access to provide quality of service (QoS) for data transmission in cognitive vehicular networks. In such a network, the vehicular nodes can opportunistically access the radio channels (referred to as shared-use channels) which are allocated to licensed users. Also, they are able to reserve a channel for dedicated access (referred to as exclusive-use channel) for data transmission. A channel access management framework is developed for cluster-based communication among vehicular nodes. This framework has three components: opportunistic access to shared-use channels, reservation of exclusive-use channel, and cluster size control. A hierarchical optimization model is then developed for this framework to obtain the optimal policy. The objective of the optimization model is to maximize the utility of the vehicular nodes in a cluster and to minimize the cost of reserving exclusive-use channel while the QoS requirements of data transmission (for vehicle-to-vehicle and vehicle-to-roadside communications) are met, and also the constraint on probability of collision with licensed users is satisfied. This hierarchical optimization model comprises of two constrained Markov decision process (CMDP) formulations - one for opportunistic channel access, and the other for joint exclusive-use channel reservation and cluster size control. An algorithm is presented to solve this hierarchical optimization model. Performance evaluation results show the effectiveness of the optimal channel access management policy. The proposed optimal channel access management framework will be useful to support mobile computing and intelligent transportation system (ITS) applications in vehicular networks.

We present a cognitive radio (CR) mechanism for opportunistic access to the frequency bands licensed to a data-centric primary user (PU) network. Secondary users (SUs) aim to exploit the short-lived spectrum holes (or opportunities) created between packet bursts in the PU network. The PU traffic pattern changes over both time and frequency according to upper layer events in the PU network, and fast variation in PU activity may cause high sensing error probability and low spectrum utilization in dynamic spectrum access. The proposed mechanism learns a PU traffic pattern in real-time and uses the acquired information to access the frequency channel in an efficient way while limiting the probability of collision with the PUs below a target limit. To design the channel learning algorithm, we model the CR system as a hidden Markov model (HMM) and present a gradient method to find the underlying PU traffic pattern. We also analyze the identifiability of the proposed HMM to provide a condition for the convergence of the proposed learning algorithm. Simulation results show that the proposed algorithm greatly outperforms the traditional listen-before-talk algorithm which does not possess any learning functionality.

We propose a novel cooperative spectrum sensing algorithm for a cognitive radio (CR) network to detect a primary user (PU) network that exhibits some degree of randomness in topology (e.g., due to mobility). We model the PU network as a random geometric network that can better describe small-scale mobile PUs. Based on this model, we formulate the random PU network detection problem in which the CR network detects the presence of a PU receiver within a given detection area. To address this problem, we propose a location-aware cooperative sensing algorithm that linearly combines multiple sensing results from secondary users (SUs) according to their geographical locations. In particular, we invoke the Fisher linear discriminant analysis to determine the linear coefficients for combining the sensing results. The simulation results show that the proposed sensing algorithm yields comparable performance to the optimal maximum likelihood (ML) detector and outperforms the existing ones, such as equal coefficient combining, OR-rule-based and AND-rule-based cooperative sensing algorithms, by a very wide margin.

We propose a novel subchannel and transmission power allocation scheme for multi-cell orthogonal frequency-division multiple access (OFDMA) networks with cognitive radio (CR) functionality. The multi-cell CR-OFDMA network not only has to control the interference to the primary users (PUs) but also has to coordinate inter-cell interference in itself. The proposed scheme allocates the subchannels to the cells in a way to maximize the system capacity, while at the same time limiting the transmission power on the subchannels on which the PUs are active. We formulate this joint subchannel and transmission power allocation problem as an optimization problem. To efficiently solve the problem, we divide it into multiple subproblems by using the dual decomposition method, and present the algorithms to solve these subproblems. The resulting scheme efficiently allocates the subchannels and the transmission power in a distributed way. The simulation results show that the proposed scheme provides significant improvement over the traditional fixed subchannel allocation scheme in terms of system throughput.

Abstract Mobility management is one of the most challenging research issues for vehicular networks to support a variety of intelligent transportation system (ITS) applications. The traditional mobility management schemes for Internet and mobile ad hoc network (MANET) cannot meet the requirements of vehicular networks, and the performance degrades severely due to the unique characteristics of vehicular networks (e.g., high mobility). Therefore, mobility management solutions developed specifically for vehicular networks would be required. This paper presents a comprehensive survey on mobility management for vehicular networks. First, the requirements of mobility management for vehicular networks are identified. Then, classified based on two communication scenarios in vehicular networks, namely, vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communications, the existing mobility management schemes are reviewed. The differences between host‐based and network‐based mobility management are discussed. To this end, several open research issues in mobility management for vehicular networks are outlined. Copyright © 2009 John Wiley & Sons, Ltd.

This paper considers the downlink power allocation problem in a cellular network where a bi-level hierarchy exists. The network is comprised of the macrocells underlaid with femtocells. The objective of each station in the network is to maximize its capacity under power constraints. The problem is formulated as a Stackelberg game with the macrocell base stations as the leaders and the femtocell access points as the followers. The leaders are assumed to have enough information and foresight to consider the response of the followers while formulating their strategies. To characterize such interaction between leaders and followers, Stackelberg equilibrium is introduced; and it is shown to exist under the assumption of continuity of best response function of the leader sub-game. %For the case of Nash games, the relationship between the upper and lower sub-game equilibrium is explored.

We consider the problem of dynamic content distribution in publish-subscribe mobile social networks. Mobile users subscribe to the content provider. Then, when new content is created, the provider transmits it to subscribers (i.e., mobile users) through the base station. The mobile users who have the social relationship (i.e., mobile users in the same community) can transfer the fresh content when they move and meet each other. From the content provider's perspective, the content transmission policy by the base station can be optimized so that the number of mobile users having the fresh data is maximized subject to the constraint on the maximum waiting time of the new content. An optimization formulation is presented for the content provider to obtain this optimal content transmission policy. Performance evaluation results show that with the optimal content transmission policy, the average number of mobile users having fresh content is larger than that if the new content is transmitted immediately.

With the employment of cognitive radio technology, dynamic spectrum management has the potential to solve the underused spectrum problem. In this paper, we introduce and compare two periodic sensing and transmission schemes for secondary users (SUs) in cognitive radio networks. In an attempt to maximize the exploitation of spectrum opportunities in these two schemes, we analyze three metrics of SUs, namely, channel utility, collision rate and sensing overhead. Optimizations of SU's slot period are analyzed under single-SU single-channel scenario. The proposed schemes are applicable to any i.i.d. ON-OFF channel distribution.

In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%.

IEEE 802.15.4 standard is widely used in wireless personal area networks (WPANs). This standard supports a limited number of guaranteed time slots (GTSs) for time-critical or delay-sensitive data transmission.We propose a GTS allocation scheme to improve reliability and bandwidth utilization in IEEE 802.15.4-based wireless body area sensor networks (WiBaSe-Nets). A knapsack problem is formulated to obtain optimal GTS allocation such that a minimum bandwidth requirement is satisfied for the sensor devices. Simulation results show that the proposed scheme can achieve better GTS utilization and higher packet delivery ratio than the standard IEEE 802.15.4 scheme does.

We consider the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs), where the PU selects a proper set of secondary users to serve as the cooperative relays for its transmission. In return, the PU leases portion of channel access time to the selected SUs for their own transmission. The PU decides the portion of channel access time it will leave for the selected SUs (i.e., the cooperative relays), and the cooperative relays decide their respective power level used to help PU's transmission in order to achieve proportional access time to the channel. We assume that the PU and SUs are rational and selfish, i.e., they only aim at maximizing their own utility. As SU's utility is in term of their own transmission rate and the power cost for PU's transmission, so they will choose a proper power level to meet the tradeoff between transmission rate and power cost. PU will choose a proper portion of channel access time for the cooperative relays to attract them to employ higher power level. We formulate the problem as a non-cooperative game between PU and SUs, and prove that the proposed game converges to a unique Stackelberg equilibrium. By employing an iterative updating algorithm, we can achieve the unique equilibrium point.

We consider the problem of hierarchical bandwidth sharing in dynamic spectrum access (or cognitive radio) environment. In the system model under consideration, licensed service (i.e., primary service) can share/sell its available bandwidth to an unlicensed service (i.e., secondary service), and again, this unlicensed service can share/sell its allocated bandwidth to other services (i.e., tertiary and quaternary services). We formulate the problem of hierarchical bandwidth sharing as an interrelated market model used in microeconomics for which a multiple-level market is established among the primary, secondary, tertiary, and quaternary services. We use the concept of demand and supply functions to obtain the equilibrium at which all the services are satisfied with the amount of allocated bandwidth and the price. These demand and supply functions are derived based on the utility of the connections using the different services (i.e., primary, secondary, tertiary, and quaternary services). For distributed implementation of the hierarchical bandwidth sharing model in a system in which global information is not available, iterative algorithms are proposed through which each service adapts its strategies to reach the equilibrium. The system stability condition is analyzed for these algorithms. Finally, we demonstrate the application of the proposed model to achieve dynamic bandwidth sharing in an integrated WiFi-WiMAX network.

Wireless communications technologies can support efficient healthcare services in medical and patient-care environments. However, using wireless communications in a healthcare environment raises two crucial issues. First, the RF transmission can cause electromagnetic interference (EMI) to biomedical devices, which could critically malfunction. Second, the different types of electronic health (e-Health) applications require different quality of service (QoS). In this paper, we introduce an innovative wireless access scheme, called EMI-aware prioritized wireless access, to address these issues. First, the system architecture for the proposed scheme is introduced. Then, an EMI-aware handshaking protocol is proposed for e-Health applications in a hospital environment. This protocol provides safety to the biomedical devices from harmful interference by adapting transmit power of wireless devices based on the EMI constraints. A prioritized wireless access scheme is proposed for channel access by two different types of applications with different priorities. A Markov chain model is presented to study the queuing behavior of the proposed system. Then, this queuing model is used to optimize the performance of the system given the QoS requirements. Finally, the performance of the proposed wireless access scheme is evaluated through extensive simulations.

The 13 papers in this special issue focus on game theory. The aim of this issue is to bring together the state-of-the-art research contributions that address the fundamentals and sound theoretical models of game theory, and the major opportunities and challenges of applying game theory to solving real problems in industry, biology, medicine, communications, and other disciplines.

We study the end-to-end capacity of a cooperative relaying scheme using OFDM modulation, under power constraints for both the base station and the relay station. The relay uses an amplify-and-forward cooperative relaying technique to retransmit messages on a subset of the available subcarriers. The power used in the base station and the relay station transmitters is allocated in such a manner that the overall system capacity is maximized. The subcarrier selection and power allocation are obtained based on convex optimization formulations and an iterative algorithm. The proposed technique outperforms nonselective relaying schemes over a range of relay power budgets.

A primary ad-hoc network working in parallel with a secondary ad-hoc network is considered. The main challenge in operating cognitive ad-hoc networks is the lack of a centralized controller performing resource allocation for different users in the network. In this paper, a distributed power allocation scheme is considered for secondary users and its performance is analyzed when time average channel gains are substituted for instantaneous channel gains. In this way, it is not necessary to exchange instantaneous channel information; however, users' allocated power will be perturbed. It is of interest to analyze mathematically this perturbation and to show how it affects the network performance. In particular, an upper bound on perturbation of each user's allocated power, rate, and interference caused to a primary receivers by the secondary users is obtained. Then, it is shown that how this perturbation affects the transmission rate and the probability of interference constraint violation by the secondary users.

We propose a wireless access scheme which is based on a channel reservation sharing method for a group of mobile users. This proposed scheme exploits the mobility diversity of the mobile users in order to reduce the cost of wireless access. Another aspect of the proposed scheme is contention resolution among mobile users belonging to the same group in order to access the reserved channel while they are at the same location. A game theoretic model is developed for this wireless access scheme through which the rational mobile users can minimize the cost of wireless access while satisfying their quality-of-service (QoS) requirements (e.g., packet loss rate and average packet waiting time). The proposed game model consists of two interrelated formulations: a coalitional game for channel reservation and a stochastic game for channel access. The stable coalitional structure and equilibrium channel access policy are obtained from this game model.

In this paper, the reflection characteristics of the house flooring construction materials at millimeter-wave (MMW) frequencies are studied. Since this investigation is performed for North American housing, the 57-64 GHz frequency band is considered. In this study, three common flooring materials: hardwood, vinyl and carpet (cut- and loop-pile) are chosen. The reflection characteristics of the aforementioned materials are also measured when the supporting materials, i.e. plywood and underpad, exist. A continues wave (CW) transmit-receive measurement setup with measuring possibility of entire range of 57-64 GHz is employed. The reflection coefficient for all available incident angle and transmission loss for face-to-face antenna direction are measured for the chosen flooring materials. The relative permittivity associated to these materials based on the Fresnel's reflection formula are found. Moreover, the results reveal the frequency dependence of the flooring materials within 57-64 GHz.

We first propose a discrete-time Markov queueing model for a wireless lossy butterfly network which employs opportunistic network coding and dynamic buffer allocation at the bottlenecked relay node. Unlike earlier studies - which assume two or more static buffers at the relay, one for each packet flow - we propose that the relay dynamically allocate buffer space to incoming packets without assuming the static capacities of their respective buffers. Such dynamic buffer allocation in our context thus operates much like two water tanks positioned side by side and connected at the bottom by a static open valve. Motivated by an increasing demand for real-time applications, we show the delay benefit of network coding for a generic wireless lossy butterfly network over classical scheduling schemes such as first-in first-out and round-robin schemes. To improve the performance further, we propose a simple scheduling algorithm, called buffer equalized opportunistic network coding, which operates much like a pair of water tanks with a sliding open valve in the middle to allow packets (water) to move across tanks. We show that the proposed scheme improves in terms of delay over the original model.

This paper considers the problem of optimal and competitive wireless access for data streaming over vehicle-to-roadside (V2R) communication. In a service area, the onboard units (OBUs) in vehicles use wireless access to download streaming data from the roadside units (RSUs). The downloaded streaming data can be stored in proxy buffer for the application to playout. The wireless access can be in reservation or on-demand mode. While the price of wireless access in reservation mode is fixed, that of on-demand mode is determined from the total demand from all OBUs. The OBUs compete with each other for wireless access to a particular RSU. The objective of an OBU is to minimize the cost for wireless access while the quality-of-service (QoS) requirement (e.g., buffer underrun probability) of the streaming application is met. A stochastic game is formulated to model this competitive situation in which OBUs are the players of this game. The strategy of an OBU is the wireless access policy (i.e., the amount of bandwidth to be used for downloading streaming data). The constrained Nash equilibrium is considered to be the solution of this stochastic game. This solution ensures that the cost of each OBU is minimized given the wireless access policies of other OBUs and thus none of the OBUs would unilaterally change its policy for wireless access. In addition, the solution guarantees that the QoS requirement of streaming application is met.

In a heterogeneous wireless network, where many different wireless networks interwork together, call admission control is an important issue to distribute user traffic among different access networks in order to provide quality of service (QoS). In this paper, we present and analyze the performance of a call admission control (CAC) policy for voice and data calls, which is based on one-hop cooperation among the mobile nodes, in a cellular-WLAN interworking scenario. For call-level queueing analysis of the CAC scheme, we use a more general phase-type distribution to model the characteristics of the system. Based on the analytical model, we illustrate the effect of one-hop cooperation on call-level QoS performance (e.g., call blocking probability, call dropping probability).

Mobile multihop relay (MMR) networks based on the IEEE 802.16J standard are able to extend the service area as well as improve the performance of mobile WiMAX networks. We present an optimization framework for jointly optimizing the placement and bandwidth reservation for a relay station in an MMR network. The objective of this framework is to maximize utility of the MMR network service provider. The decision on the placement of the relay corresponds to finding the best location for the relay station under uncertainty about the number of active users in the extended service area of the MMR network. This uncertainty could be due to random connection initiation and termination by the users or due to the random arrivals and departures of the mobile subscriber stations in the extended service area. However, this decision on relay placement may not achieve the highest utility when the users dynamically adapt their decisions on whether to transmit directly to the base station or transmit through the relay station. In this scenario, optimal decision on bandwidth reservation by the relay station needs to be made (over a relatively shorter period of time) which takes the dynamics of users' decision into account. The placement of the relay station (over a relatively longer period of time) can then be optimized based on the optimal bandwidth reservation. A stochastic programming formulation and a Markov decision process formulation are used to obtain the long-term and short- term optimization solutions, respectively.

Remote patient monitoring is an eHealth service, which is used to collect and transfer biosignal data from the patients to the eHealth service provider (e.g., healthcare center). A heterogeneous wireless access-based remote patient monitoring system is presented in which multiple wireless technologies are integrated to support continuous biosignal monitoring in presence of patient mobility. A patient-attached monitoring device with a heterogeneous wireless transceiver collects biosignal data from the sensors and transmits the data through the radio access network (RAN) to the eHealth service provider. In this system, the eHealth service provider reserves wireless bandwidth (or connections) from a network service provider in a proactive manner as well as in an on-demand basis. To determine the optimal number of connections to be reserved pro-actively so that the network access cost is minimized, a stochastic programming problem is formulated considering the randomness of service demand due to the mobility of the patients. Since different biosignal data can have different quality-of-service (QoS) requirements, traffic scheduling is used in the patient-attached device which determines whether to transmit and what to transmit over an available wireless connection. To make the optimal scheduling decision, an optimization problem is formulated as a constrained Markov decision process (CMDP). The objective of this formulation is to minimize the connection cost. The proposed system architecture and the optimization formulations will be useful for the eHealth service provider to provide flexible and cost-effective monitoring service to remote/mobile patients.

We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation.

Efficient and reliable subcarrier power allocation in orthogonal frequency-division multiplexing (OFDM)-based cognitive radio networks is a challenging problem. Traditional waterfilling approach is inefficient for such networks due to the strict requirements on the interference generated to the primary users (PUs). In this paper, we present a solution to an energy-efficient resource allocation problem which maximizes the cognitive radio (i.e., secondary) link capacity taking into account the availability of the subcarriers (and hence the reliability of transmission by cognitive radios) and the limits on total interference generated to the PUs. We consider an energy-aware capacity expression by taking into account another factor called subcarrier availability. Optimizing such an expression saves valuable resources such as battery life by selectively allocating power to underutilized subcarriers. Based on a risk-return model, we formulate a convex optimization problem which incorporates a linear average rate loss function in the optimization objective to include the effect of subcarrier availability. Due to the complex structure of the optimal solution, we propose three suboptimal schemes, namely, the step-ladder, nulling, and scaling schemes. We compare the performances of optimal and suboptimal algorithms with the performance of a classical waterfilling scheme. We conclude that waterfilling, unable to satisfy the interference criterion, performs the worst amongst all the schemes considered in this paper.

We consider the problem of quality-of-service (QoS) provisioning for multiple traffic classes in a MIMO wireless network. This QoS provisioning is posed as a radio resource management (RRM) problem at a wireless node (e.g., a wireless mesh router) with multiple antennas. We decompose this RRM problem into two tractable subproblems, namely, the antenna assignment and the admission control problems. The objective of antenna assignment is to minimize the weighted packet dropping probability for the different traffic classes under constrained packet delay. The objective of admission control is to maximize the revenue of the wireless node gained from the ongoing connections for different traffic classes under constrained connection blocking probability and average per-connection throughput. The decision of antenna assignment is made in a short-term basis (e.g., for every packet transmission interval) while that of admission control is made in a long-term basis (i.e., when a connection arrives). Constrained Markov decision process (CMDP) models are formulated to obtain the optimal decisions on antenna assignment and admission control. To provide efficient channel utilization, the RRM framework considers adaptive modulation at the physical layer which exploits channel state information. Performance evaluation results show that this joint antenna assignment and admission control framework can provide class-based service differentiation while satisfying both the connection-level and packet-level QoS requirements.

Mobile multihop relay (MMR) networks based on the IEEE 802.16j standard are able to extend the service area as well as improve the performance of mobile WiMAX networks. In this paper, we present a relay-centric hierarchical optimization model for jointly optimizing the radio resource management (RRM) and network planning for the relay stations in MMR networks. We consider an in-band relaying system. For a relay station, the RRM problem deals with optimizing the amount of bandwidth reserved from the base station and admission control for the mobile subscriber stations (MSSs) using relay-based transmissions so that the utility of a relay station is maximized. A Markov decision process (MDP) model is formulated to obtain the short-term optimal action of a relay station. Based on the optimal action of each relay station, the network planning problem is solved for a group of relay stations by optimizing the relay placement and base station selection over a longer period of time considering uncertainties in user mobility and traffic load in the network. A chance-constrained assignment problem (CCAP) is formulated to obtain the optimal decisions to maximize the total utility of relay stations under the probabilistic constraint on the total bandwidth usage of the base stations. Numerical results show that the proposed scheme outperforms a static scheme. The proposed radio resource management and network planning framework will be useful for design and optimization of multihop cellular wireless networks in general.

We develop a queuing analytic model to study cross-layer effects on Quality of Service (QoS) performance for downlink transmission in a Vertical Bell Laboratories Layered Space-Time Architecture (V-BLAST) Multiple Input Multiple Output (MIMO) wireless system exploiting multiuser diversity. As in multiuser Single Input Single Output (SISO) systems, multiuser diversity has been shown to have a great potential in improving system throughput in multiuser MIMO systems as well. In a V-BLAST MIMO system, the transmit antennas can carry parallel streams and multiple users can be scheduled for transmissions at the same time. We consider a multiuser diversity scheme that can effectively exploit this extra dimension in multiuser scheduling. To investigate the cross-layer aspect of the performance improvement, we perform a queuing analysis to derive buffer statistics, queuing delay distribution, packet throughput and loss rate experienced in the data link layer when this multiuser diversity technique is deployed in the system. We also present selected numerical results to show how the queuing model can help us to relate these important QoS measures to relevant physical layer and traffic parameters. Usefulness of the developed analytical model is also demonstrated through example applications.

We develop a queueing analytic framework to study the data link layer quality-of-service performance measures for cognitive radio users in an infrastructure-based dynamic spectrum access environment. In order to allocate the available spectrum white spaces among the cognitive radio users in a spectrum overlay scenario, an opportunistic scheduling scheme is considered. The queueing model considers bursty traffic arrival pattern at the cognitive radio user ends, finite buffer size, activity of primary users (i.e., dynamic channel availability), and correlated channel fading. We present a step-by-step procedure to derive the delay distribution, average throughput, and packet loss rate for the cognitive radio users. The proposed framework facilitates cross-layer design for improved QoS experience in cognitive radio networks. Usefulness of the developed analytical model is demonstrated through example applications.

Abstract Orthogonal frequency division multiplexing (OFDM)‐based orthogonal frequency division multiple access (OFDMA) has emerged as a promising transmission technology for next generation wireless systems. In a multiuser scenario, adaptive radio resource allocation can significantly improve the performance of OFDMA systems. In this article, an overview of the major state‐of‐the‐art approaches to adaptive resource allocation in the OFDMA systems is provided. Several open research issues are outlined. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract With the pervasive growth in the popularity of IEEE 802.11‐based wireless local area networks (WLANs) worldwide, the demand to support delay‐sensitive services such as voice has increased very rapidly. This paper provides a comprehensive survey on the medium access control (MAC) architectures and quality of service (QoS) provisioning issues for WLANs. The major challenges in providing QoS to voice services through WLAN MAC protocols are outlined and the solution approaches proposed in the literature are reviewed. To this end, a novel QoS‐aware wireless MAC protocol, called hybrid contention‐free access (H‐CFA) protocol and a call admission control technique, called traffic stream admission control (TS‐AC) algorithm, are presented. The H‐CFA protocol is based on a novel idea that combines two contention‐free wireless medium access approaches, that is, round‐robin polling and time‐division multiple access (TDMA)‐like time slot assignment, and it increases the capacity of WLANs through efficient silence suppression. The TS‐AC algorithm ensures efficient admission control for consistent delay‐bound guarantees and further maximizes the capacity through exploiting the voice characteristic that it can tolerate some level of inconsecutive packet loss. The benefits of the proposed schemes are demonstrated in the simulations results. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract The exponential growth in the demand of voice over internet protocol (VoIP) services along with the increasing demand for mobility in VoIP services has attracted great research efforts towards provisioning of VoIP services in IEEE 802.11‐based Wireless LANs (WiFi networks). We address one of the important research problems, namely, the quality of service (QoS)‐aware efficient silence suppression in the bursty voice traffic, for provisioning VoIP services in WiFi networks. The research works in the recent literature on silence suppression in voice calls have been surveyed categorising them on how the activity arrival is notified to the access point (AP). In most of the recent schemes, notification of uplink activity arrival is done through contention based medium access mechanisms such as the distributed coordination function (DCF). Contention‐based medium access causes non‐deterministic delays, therefore such schemes are not suited to voice traffic which require strict delay bound guarantees. This paper focuses on the schemes which do not use contention based approaches for silence suppression in voice traffic. Analytical performance evaluation and comparison of such schemes is carried out. Two very important performance metrics are modelled mathematically. One is the expected polling overhead time that the schedulers in these schemes can save per voice call during one voice activity cycle as compared to that in the round‐robin polling scheduler. The other is the expected unnecessary wireless channel access delay that a typical first talk‐spurt frame experiences due to the specific design of each scheme. The numerical results of this evaluation lead us to the conclusion whether or not and to what extent each of these schemes is viable. Copyright © 2008 John Wiley & Sons, Ltd.

Efficient and reliable power allocation algorithm in cognitive radio (CR) networks is a challenging problem. Traditional water-filling algorithm is inefficient for CR networks due to the interaction with primary users. In this paper, we consider reliability/availability of subcarriers or primary user activity for power allocation. We model this aspect mathematically with a risk-return model by defining a general rate loss function. We then propose optimal and suboptimal algorithms to allocate power under a fixed power budget for such a system with linear rate loss. These algorithms as we will see allocate more power to more reliable subcarriers in a water-filling fashion with different water levels. We compare the performance of these algorithms for our model with respect to water-filling solutions. Simulations show that suboptimal schemes perform closer to optimal scheme although they could be implemented with same complexity as water-filling algorithm. Finally, we discuss the linearity of loss function and guidelines to choose its coefficients by obtaining upper bounds on them.

Wireless Access in Vehicular Environment (WAVE) technology such as IEEE 802.11pWireless Access in Vehicular Environment (WAVE) technology such as IEEE 802.11p has emerged as a state-of-the- art solution to vehicular communications. The major challenges in WAVE arise due to the fast changing communication environment and short durations of communications due to the mobility. As a result, it is difficult to transmit a large amount of data in such a network for vehicle-to-roadside and/or vehicle-to-vehicle communications. To overcome this problem, we propose a solution based on the idea of BitTorrent used for peer-to-peer networking, and the concept of bargaining game used in game theory. Similar to the distribution of data to peers in BitTorrent, the roadside units (RSUs) randomly distribute the data to the passing vehicles. Then, the on board units (OBUs) on the vehicles with different data, exchange the information among each other using bargaining considering channel adaptations and fairness in their achieved utility. We formulate two optimization problems - one for the RSUs and the other for the OBUs. For OBUs, the bargaining solutions are proposed which are based on three fairness criteria. For RSUs, depending on the traffic pattern, distribution of packets to the OBUs is optimized considering the different priority of the packets so that the overall utilities of the OBUs are maximized. Simulation results show that the proposed schemes can ensure fairness among the OBUs, and adapt to different traffic scenarios with different vehicular traffic intensity. has emerged as a state-of-the- art solution to vehicular communications. The major challenges in WAVE arise due to the fast changing communication environment and short durations of communications due to the mobility. As a result, it is difficult to transmit a large amount of data in such a network for vehicle-to-roadside and/or vehicle-to-vehicle communications. To overcome this problem, we propose a solution based on the idea of BitTorrent used for peer-to-peer networking, and the concept of bargaining game used in game theory. Similar to the distribution of data to peers in BitTorrent, the roadside units (RSUs) randomly distribute the data to the passing vehicles. Then, the on board units (OBUs) on the vehicles with different data, exchange the information among each other using bargaining considering channel adaptations and fairness in their achieved utility. We formulate two optimization problems - one for the RSUs and the other for the OBUs. For OBUs, the bargaining solutions are proposed which are based on three fairness criteria. For RSUs, depending on the traffic pattern, distribution of packets to the OBUs is optimized considering the different priority of the packets so that the overall utilities of the OBUs are maximized. Simulation results show that the proposed schemes can ensure fairness among the OBUs, and adapt to different traffic scenarios with different vehicular traffic intensity.

We present a MAC protocol for opportunistic spectrum access (OSA-MAC) in cognitive wireless networks. The proposed MAC protocol works in a multi-channel environment which is capable of performing channel sensing to discover spectrum opportunities. For this MAC protocol, two channel selection methods are considered which trade the implementation complexity with throughput improvement. We then analyze the saturation throughput performance of the proposed MAC protocol under scenarios where the probabilities for each channel to be available to different secondary flows are the same or different. We then derive analytically the probability of collision of secondary users with primary users due to sensing errors. This analysis can be used, for example, to determine the requirement of sensing accuracy for secondary users, and to design an admission control method. We present numerical results to demonstrate the throughput performance of the OSA-MAC protocol and applications of the proposed analytical model.

Churning of mobile users from one service provider to another is expected to become a common feature when the mobile users have freedom to choose the best wireless service. This churning behavior impacts both the technical and the economical aspects of wireless network design. In this paper, we model the churning behavior of wireless service users by using the theory of evolutionary game. We consider a system model consisting of WLAN hotspots where a wireless user can choose among different WLAN access points based on the performances and/or price. A continuous-time Markov chain model is established to capture the connection arrival and departure processes, as well as the rational and irrational churning behaviors of wireless service users. The evolutionary equilibrium, which is used to compute the average number of users choosing each wireless service, is considered as the solution. Based on this evolutionary game framework, we investigate two different possible pricing schemes, namely, non-cooperative and cooperative pricing schemes, for the wireless service providers. These schemes maximize individual revenue and total revenue, respectively, of the service providers. Performance analysis results are presented for the proposed modeling framework.

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 address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With aBertrand gamemodel, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future.

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.

Fourth generation (4G) wireless networks will provide high-bandwidth connectivity with quality-of-service (QoS) support to mobile users in a seamless manner. In such a scenario, a mobile user will be able to connect to different wireless access networks such as a wireless metropolitan area network (WMAN), a cellular network, and a wireless local area network (WLAN) simultaneously. We present a game-theoretic framework for radio resource management (that is, bandwidth allocation and admission control) in such a heterogeneous wireless access environment. First, a noncooperative game is used to obtain the bandwidth allocations to a service area from the different access networks available in that service area (on a long-term basis). The Nash equilibrium for this game gives the optimal allocation which maximizes the utilities of all the connections in the network (that is, in all of the service areas). Second, based on the obtained bandwidth allocation, to prioritize vertical and horizontal handoff connections over new connections, a bargaining game is formulated to obtain the capacity reservation thresholds so that the connection-level QoS requirements can be satisfied for the different types of connections (on a long-term basis). Third, we formulate a noncooperative game to obtain the amount of bandwidth allocated to an arriving connection (in a service area) by the different access networks (on a short-term basis). Based on the allocated bandwidth and the capacity reservation thresholds, an admission control is used to limit the number of ongoing connections so that the QoS performances are maintained at the target level for the different types of connections.

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.

"Cognitive radio" is an emerging technique to improve the utilization of radio frequency spectrum in wireless networks. In this paper, we consider the problem of spectrum sharing among a primary user and multiple secondary users. We formulate this problem as an oligopoly market competition and use a noncooperative game to obtain the spectrum allocation for secondary users. Nash equilibrium is considered as the solution of this game. We first present the formulation of a static game for the case where all secondary users have the current information of the adopted strategies and the payoff of each other. However, this assumption may not be realistic in some cognitive radio systems. Therefore, we consider the case of bounded rationality in which the secondary users gradually and iteratively adjust their strategies based on the observations on their previous strategies. The speed of adjustment of the strategies is controlled by the learning rate. The stability condition of the dynamic behavior for this spectrum sharing scheme is investigated. The numerical results reveal the dynamics of distributed dynamic adaptation of spectrum sharing strategies.

In a cognitive radio network, frequency spectrum can be shared between primary (or licensed) users and secondary (or unlicensed) users, where the secondary users pay the primary users (or primary service provider) for radio resource usage. This is referred to as spectrum trading. In spectrum trading, pricing is a key issue of interest to primary service providers (i.e., spectrum sellers) as well as to secondary service providers (i.e., spectrum buyers). In a cognitive radio network, pricing model for spectrum sharing depends on the objective of spectrum trading, and therefore, the behaviors of spectrum sellers and spectrum buyers. In this paper, we investigate three different pricing models, namely, market-equilibrium, competitive, and cooperative pricing models for spectrum trading in a cognitive radio environment. In these pricing models, the primary service providers have different behaviors (i.e., competitive and cooperative behaviors) to achieve different objectives of spectrum trading. Specifically, in marketequilibrium pricing model, the objective of spectrum trading is to satisfy spectrum demand from the secondary users, and there is neither competition nor cooperation among primary service providers. In the competitive pricing, the objective is to maximize the individual profit, and there is competition among primary service providers. In cooperative pricing, the objective of spectrum trading is to maximize the total profit, and cooperation exists among primary service providers. We propose distributed algorithms to achieve the pricing solutions of these different pricing models and analyze stability of these distributed algorithms. We perform extensive performance analysis of these pricing algorithms considering different aspects such as profit of the primary service providers, stability region, and impact of number of primary service providers, which reveals interesting insights into the spectrum trading problem.

Next generation wireless systems will provide highspeed wireless connectivity and seamless mobility through the provisioning of heterogeneous wireless access. In such a heterogeneous wireless access environment, mobile users will be able to connect to multiple wireless networks (e.g., IEEE 802.16, cellular, and IEEE 802.11-based networks) operated by different service providers, simultaneously. We address the problem of competitive pricing in such a heterogeneous wireless access network. Each of the wireless access networks is assumed to support two types of connections, namely, premium and best-effort connections. For the premium connections, the price is fixed, while for the best effort connections it is dynamic and depends on the competitive or cooperative behavior of the service providers. A competitive pricing model for best-effort connections is developed based on a noncooperative game formulation. We first consider the case where the prices are offered to the users at the same time (i.e., a simultaneous-play game). Nash equilibrium is considered as the solution of this game. Afterwards, we consider the case where a service provider can offer its price before other providers. This is referred to as a leader-follower game for which Stackelberg equilibrium is considered as the solution. We also consider a cooperative pricing model which maximizes the total revenue of the service providers. A method for revenue sharing is presented for this cooperative pricing model. Numerical studies are carried out to evaluate the performances of the different pricing models.

The IEEE 802.11e standard developed by IEEE 802.11TGe intends to provide the quality of service (QoS) support in IEEE 802.11-based wireless local area networks (WLANs) through the introduction of hybrid coordination function (HCF). The HCF controlled channel access (HCCA) designed as a part of HCF is the medium access mechanism for parameterized QoS and is suitable for multimedia applications requiring hard QoS guarantees. The standard also defines a reference scheduler to complement HCCA in meeting these guarantees. In this paper, we investigate the performance of the reference scheduler described in the standard by using a novel queueing analytic framework. The analysis reveals the performance deficiencies of the reference scheduler. Afterwards, to overcome these deficiencies, we propose a new scheduling scheme, namely, the prediction and optimization-based HCCA (PRO-HCCA), based on the insights gained from the queueing analysis. Simulation experiments show that the proposed scheme overcomes the problems of the reference scheduler and successfully enables the HCCA to fulfill the QoS guarantees for multimedia applications.

Abstract This paper describes the design and implementation of a solar‐powered wireless motion sensor surveillance network. Commercially available systems with similar functionality which exist today have several disadvantages including single points of failure and requires (semi) constant personnel attention as well as an elaborate power system. These systems require a lot of time to set up, they cannot be used in remote areas where a main power supply is unavailable, and are quite costly. Therefore, there is a need to develop a system which is portable, easy to set up, and is energy efficient. The wireless motion surveillance network described in this paper is designed to be portable, economically inexpensive, and energy efficient. The network is created using the IEEE 802.15.4 ZigBee wireless standard by implementing multiple Microchip PICDEM Z nodes. Each node in the network is equipped with a Direction Sensing Infrared Motion Detector (DSIMD) and a solar power unit (SPU). The DSIMD allows for detection of humans and animals alike moving into or out of the network. The system is powered by solar energy that makes it quite adaptable for remote applications. The network is able to cover an area of radius 30 m. By developing a low‐cost system, which is portable, easy to set up, and has an unlimited power supply, this technology is made accessible to a wider range of applications. The implementation of a CMOS camera is discussed at the end which can be used to take a snapshot of the detected object. Copyright © 2007 John Wiley & Sons, Ltd.

We consider QoS-aware spectrum sharing in cognitive wireless networks where secondary users are allowed to access the spectrum owned by a primary network provider. The interference from secondary users to primary users is constrained to be below the tolerable limit. Also, signal to interference plus noise ratio (SINR) of each secondary user is maintained higher than a desired level for QoS insurance. When network load is high, admission control needs to be performed to satisfy both QoS and interference constraints. We propose an admission control algorithm which is performed jointly with power control such that QoS requirements of all admitted secondary users are satisfied while keeping the interference to primary users below the tolerable limit. When 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 max-min fairness criterion as an optimization problem. We show how to transform it into a convex optimization problem so that its globally optimal solution can be obtained. Numerical results show that the proposed admission control algorithm achieves performance very close to the optimal solution. Also, impacts of different system and QoS parameters on the network performance are investigated for both admission control and rate/power allocation problems.

Optimal pricing for dynamic spectrum sharing in "cognitive radio" networks is an open research issue. In this paper, we address the problem of spectrum pricing in a cognitive radio environment in which multiple primary services with spectrum opportunity compete with each other to offer spectrum access to the secondary services. By using an optimal pricing scheme, each of the primary services aims to maximize its profit under quality of service (QoS) constraint. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. For a primary service/user, the QoS degradation is considered as the cost incurred for offering spectrum access to the secondary service/user. For the secondary service, we adopt a utility function to obtain the demand function. With aBertrandgamemodel, we are able to analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., optimal pricing adopted by the primary services). In addition, we present distributed iterative game algorithms to obtain the solution. The stability of the proposed iterative game algorithms in terms of convergence to the Nash equilibrium is studied.

Spectrum trading is a concept used to describe the economics of dynamic spectrum sharing in cognitive radio networks. In this paper, we consider the problem of spectrum trading between primary and secondary services. Multiple markets are established in this trading for the multiple available frequency bands. The primary service is considered as the seller and the secondary service is considered as a buyer in a dynamic spectrum trading market. The spectrum supply from the primary service is derived based on the revenue earned due to spectrum sharing and the cost due to QoS performance degradation of the connections served by the primary service. The spectrum demand of secondary service is obtained based on the utility from spectrum usage. We first obtain the equilibrium pricing which is the point where spectrum supply equals spectrum demand. Then, we consider the case where the spectrum sellers do not offer the equilibrium price. The impact of this disequilibrium pricing is investigated. We model the cases of equilibrium and disequilibrium pricing as feedback control systems in which the spectrum seller and buyer have their own transfer functions. By using classical control theory the stability of the model is analyzed.

Cognitive radio (CR) technology is an innovative radio design philosophy in order to increase the spectrum utilization by exploiting the unused spectrum in dynamically changing environments. Specifically, the CR technology will allow a group of potential users (referred to as secondary users) to access available spectrum resources unused by primary users (PUs) for whom the band has been licensed. The spectrum available for secondary users (SUs) depends on PUs' activity in the licensed spectrum. In this paper, we develop a queuing analytic framework to study important performance measures experienced by SUs in a CR network. We study queuing delay and buffer statistics of SUs' packets by modeling PUs' activity as a two state Markov chain and SUs' channel quality variation as a finite state Markov chain (FSMC). In order to allocate available channels among the SUs, an opportunistic channel allocation scheme is considered. The proposed framework facilitates to design an admission controller for SUs' network in order to maintain a given quality service (QoS) requirement which is specified in the form of statistical delay guarantee.

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.

"Cognitive radio" is an emerging technique to improve the utilization of radio frequency spectrum in wireless networks. In this paper, we consider the problem of spectrum sharing among a primary user and multiple secondary users. We formulate this problem as an oligopoly market competition and use a Cournot game to obtain the spectrum allocation for secondary users. Nash equilibrium is considered as the solution of this game. We first present the formulation of a static Cournot game for the case when all secondary users can observe the adopted strategies and the payoff of each other. However, this assumption may not be realistic in some cognitive radio systems. Therefore, we formulate a dynamic Cournot game in which the strategy of one secondary user is selected solely based on the pricing information obtained from the primary user. The stability condition of the dynamic behavior for this spectrum sharing scheme is investigated.

In this paper, we present a hierarchical bandwidth management and admission control framework for integrated IEEE 802.16/802.11 wireless networks. Developed based on a game-theoretic model, the framework aims to satisfy the quality of service (QoS) requirements of all the users in this integrated network. In particular, at the first level of this hierarchical model, the bandwidth allocation problem among the standalone subscriber stations (SSs) and the WLAN access points (APs)/routers is formulated as a bargaining game. Based on the allocated bandwidth to the SSs, groups of connections in different service types in the standalone SSs cooperate among each other at the second level of the game to share the bandwidth in a fair manner. The admission control for connections from the standalone SSs is devised based on the improvement in total utility of the corresponding service types. For the WLAN connections, estimated traffic load is used by an admission control game to decide whether a new connection from a WLAN node can be admitted or not.

We consider the problem of hierarchical spectrum sharing in cognitive radio environment. In the system model under consideration, licensed service (i.e., primary service) can share/sell available spectrum to an unlicensed service (i.e., secondary service), and again, this unlicensed service can share/sell allocated spectrum to other service (i.e., tertiary service). We formulate the problem of hierarchical spectrum sharing as an interrelated market model in which a multiple-level market is established among the primary, secondary, and tertiary services. We use the concept of demand and supply functions in economics to obtain the partial equilibrium for which all services are satisfied with the shared spectrum size and the charging price. These functions are derived based on the utility of the connections using the different services. In addition, we consider a system for which the global information is not available. Therefore, each service needs to learn and adapt the strategies to reach an equilibrium. Two iterative algorithms (i.e., excess demand-based and successive overrelaxation (SOR)) are proposed. The stability condition for the learning rate is analyzed for these algorithms.

The paper presents a queueing analytical model for radio link level performance analysis in spatial multiplexing multiple-input multiple-output (MIMO) wireless systems using adaptive modulation. In the physical layer, the modulation level is adaptively adjusted according to the channel quality based on quadrature amplitude modulation (QAM). In the medium access control (MAC) layer, the authors consider a time division multiple access (TDMA) scheme with weighted round-robin scheduling. Developed based on a discrete-time Markov chain and vacation queueing model, the analytical model provides various packet-level performance measures. In addition, we present an optimization formulation to obtain users' weights for packet scheduling so that the transmission rate requirements of the users can be satisfied. Afterwards, an admission control scheme is proposed.

A queue-aware power allocation scheme for multiple-output multiple-input (MIMO) system using space time block coding (STBC) is presented. In the the physical layer, along with space-time block coded MIMO, the authors consider adaptive modulation to enhance transmission rate and error performance. To improve data reliability, automatic request (ARQ) is used for retransmission of erroneous packets from the radio link level queue. The proposed power allocation scheme is designed to minimize the cost which is defined as a function of transmit power and packet dropping probability at the radio link level queue. The genetic algorithm is used to obtain the solutions for the optimal parameters of the queue-aware power allocation scheme to minimize radio resource usage while meeting the quality-of-service (QoS) requirements for data traffic. A queueing analytical model is presented to investigate the link level performances (e.g., average queue length, packet dropping probability, throughput, and average delay) under different physical layer parameter setting.

The IEEE 802.11e standard developed by IEEE 802.11TGe intends to provide the quality of service (QoS) support for the popular 802.11 based wireless local area networks (WLAN) through the introduction of hybrid coordination function (HCF). The HCF controlled channel access (HCCA) designed as a part of HCF is the medium access mechanism for parameterized QoS and is suitable for multimedia applications requiring hard QoS guarantees. The standard also defines scheduling and admission control schemes to complement HCCA in meeting these guarantees. However, the reference scheduler has several limitations which render it incapable of providing QoS guarantees to many multimedia applications. In this paper, we propose a new scheduling scheme which overcomes these limitations to provide critical performance guarantees. Simulation experiments show that the proposed scheme successfully enables the HCCA to fulfill the QoS guarantees for wide variety of multimedia applications.

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 novel queuing analytical framework for the performance evaluation of a distributed and energy-aware medium access control (MAC) protocol for wireless packet data networks with service differentiation. Specifically, we consider a node (both buffer-limited and energy-limited) in the network with two different types of traffic, namely, high-priority and low-priority traffic, and model the node as a MAP (Markovian arrival process)/PH (phase-type)/1/K nonpreemptive priority queue. The MAC layer in the node is modeled as a server and a vacation queuing model is used to model the sleep and wakeup mechanism of the server. We study standard exhaustive and number-limited exhaustive vacation models both in multiple vacation case. A setup time for the head-of-line packet in the queue is considered, which abstracts the contention and the back-off mechanism of the MAC protocol in the node. A nonideal wireless channel model is also considered, which enables us to investigate the effects of packet transmission errors on the performance behavior of the system. After obtaining the stationary distribution of the system using the matrix-geometric method, we study the performance indices, such as packet dropping probability, access delay, and queue length distribution, for high-priority packets as well as the energy saving factor at the node. Taking into account the bursty traffic arrival (modeled as MAP) and, therefore, the nonsaturation case for the queuing analysis of the MAC protocol, using phase-type distribution for both the service and the vacation processes, and combining the priority queuing model with the vacation queuing model make the analysis very general and comprehensive. Typical numerical results obtained from the analytical model are presented and validated by extensive simulations. Also, we show how the optimal MAC parameters can be obtained by using numerical optimization

We present a novel integrated analytical framework for analyzing the quality-of-service (QoS) performance measures in a wireless mobile multimedia network. The framework integrates physical, radio link, and network layer parameters and protocols to analyze the call-level and packet-level performances. In the network layer, call admission control (CAC) is responsible for deciding whether an incoming call can be accepted or not so that the performances of the ongoing calls do not deteriorate below the acceptable level. Also, an adaptive channel allocation (ACA) scheme is used to maximize the utilization of the radio resources. In the data link layer, queue management and error control are used for non-real-time loss-sensitive traffic. In the physical layer, a finite state Markov channel (FSMC) is used to model channel fading, and adaptive modulation is used for rate adaptation according to channel quality. Various call-level and packet-level QoS measures for real-time, non-real-time, and best-effort traffic are obtained. The analytical results are validated by extensive simulations. Examples of the applications of the presented analytical framework are also provided

A queuing analytical model is presented to investigate the performances of different sleep and wakeup strategies in a solar-powered wireless sensor/mesh network where a solar cell is used to charge the battery in a sensor/mesh node. While the solar radiation process (and, hence, the energy generation process in a solar cell) is modeled by a stochastic process (i.e., a Markov chain), a linear battery model with relaxation effect is used to model the battery capacity recovery process. Developed based on a multidimensional discrete-time Markov chain, the presented model is used to analyze the performances of different sleep and wakeup strategies in a sensor/mesh node. The packet dropping and packet blocking probabilities at a node are the major performance metrics. The numerical results obtained from the analytical model are validated by extensive simulations. In addition, using the queuing model, based on a game-theoretic formulation, we demonstrate how to obtain the optimal parameters for a particular sleep and wakeup strategy. In this case, we formulate a bargaining game by exploiting the trade-off between packet blocking and packet dropping probabilities due to the sleep and wakeup dynamics in a sensor/mesh node. The Nash solution is obtained for the equilibrium point of sleep and wakeup probabilities. The presented queuing model, along with the game-theoretic formulation, would be useful for the design and optimization of energy-efficient protocols for solar-powered wireless sensor/mesh networks under quality-of-service (QoS) constraints

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.

VVe present a unified analytical model for service differentiation in a broadband packet-switched wireless network (e.g., IEEE 802.16) with two types of traffic, namely, the quality-of-service (QoS)-sensitive traffic and the best-effort traffic. While fair scheduling is used to allocate radio resources between these two traffic types, connection admission control (CAC) is employed to limit the number of ongoing connections for QoS-sensitive users so that the performance requirements for the QoS-sensitive traffic can be satisfied. An analytical model is developed assuming a work conserving traffic scheduling in the radio link layer and it also considers multi-rate transmission at the physical layer. Both the packet-level and the connection-level performance measures are obtained. Finally, we apply the concept of utility to obtain user satisfaction as a function of the QoS measures obtained from the analytical model. An optimization formulation is also presented from which the near-optimal solutions for scheduling and CAC parameters can be obtained. The proposed analytical model would be useful for performance analysis and engineering of next-generation broadband wireless networks

We investigate the packet-level and the connection-level performances in a power-constrained (e.g., solar-powered) wireless mesh router with packet scheduling and admission control. The system model under consideration is compatible with the IEEE 802.16a standard with orthogonal frequency division multiple access (OFDMA) air interface. In the medium access control (MAC) layer, transmission frames are grouped into super-frames and the allocation of subchannel and number of frames to each connection (i.e., scheduling) is performed to satisfy the traffic requirement of each connection and also the power supply constraint at a mesh router/802.16a base station (BS). A queueing analytical model based on discrete-time Markov chain (DTMC) is used to analyze the packet-level performances. Based on this scheduling, router capacity in terms of the maximum number of ongoing connections is obtained. Subsequently, a threshold-based admission control method is proposed for both relay and local connections in a router so that connection-level performances are satisfied. To this end, we optimize the admission control over multiple time periods in which the amount of supplied power and the traffic load at a mesh router are time-dependent.

Fourth generation (4G) wireless networks will provide seamless high bandwidth connectivity with quality-of-service (QoS) support to mobile users where a mobile will be able to connect to several wireless access networks simultaneously. In such a scenario, bandwidth allocation to a mobile from different types of networks will depend on the traffic load characteristics in each access network. In this paper, we formulate the bandwidth allocation problem in a 4G heterogeneous wireless network as an oligopoly market competition. In an oligopoly market, a few firms provide service/product to the customers. Here, we model the firms as the different types of networks offering bandwidth to the connections in order to maximize the system utility. A Cournot game is used to model this market competition and Nash equilibrium is considered to provide a stable solution. We propose two algorithms, namely, iterative and search algorithms, to obtain the solution. Based on the proposed bandwidth allocation algorithm, we present an admission control mechanism to ensure that the QoS of new and ongoing connections are maintained at the target level.

We investigate connection-level, packet-level, and energy-saving performances in a power constrained (e.g., solar-powered) OFDM/TDMA wireless mesh network. When a mesh router goes to the sleep mode to reduce energy consumption, packet-level performances (as observed by a mesh client) will be degraded since in sleep mode, a mesh router can neither receive nor transmit packets. An analytical model is developed to analyze the relationship among the packet-level and the connection-level performances. Based on the analytical model, the system performance is analyzed in terms of system utility. To this end, an optimization problem is formulated by using a Markov decision process to choose the optimal policy on sleep management and maximum number of admissible connections at a mesh router so that the system utility is maximized.

A queueing analysis of a distributed and energy-aware medium access control (MAC) protocol is presented for wireless ad hoc networks with service differentiation. We consider a battery-powered node in the network with two different types of traffic, and model the node as a MAP/PH/1/K non-preemptive priority queue with multiple vacations to model the sleep and wakeup mechanism of the MAC layer as the server of the queues. Taking into account the non-saturation case for the queueing analysis of the MAC protocol, using phase-type distribution for both the service and the vacation processes, and combining the priority queueing model with the vacation queueing model make the analysis very general and comprehensive.

This paper presents a mathematical model which represents packet-level performance in a wireless network using amplify-and-forward cooperative diversity. This model takes into account a bursty traffic arrival pattern at the source node as well as an error recovery mechanism based on a general automatic repeat request (ARQ) protocol. We derive not only the expectation but also the distribution of packet delivery delay. Using the model, we quantify throughput/delay improvement for increasing SNR and/or cooperative nodes. For an additional cooperative node, we quantify the amount of SNR that can be reduced (i. e., SNR saving) without degrading system performance. As an application of the proposed model, we also demonstrate how to determine the minimum number of cooperative nodes to satisfy a certain level of quality of service (QoS) requirements.

We present a radio resource management framework for the IEEE 802.16-based OFDM/TDD wireless mesh networks. The major components of the framework, namely, subchannel allocation, admission control, and route selection schemes are developed so that the quality of service (QoS) can be guaranteed on a per-connection basis. We formulate a tandem queueing model to obtain the in-connection performance measures such as average delay and packet data unit (PDU) dropping probability for relay connections in an end-to-end basis. The same formulation can be used to obtain the performance measures such as the average transmission delay for a PDU and the PDU dropping probability at a mesh-node (i.e., IEEE 802.16 base station) for local connections. Two subchannel allocation algorithms, namely, the optimal and the iterative subchannel algorithms, are proposed. The admission control and route selection schemes for relay connections are based on the performance measures obtained from the tandem queueing model. In particular, a new connection is accepted if the packet delay requirements corresponding to that connection can be satisfied and the route that provides the smallest delay is selected for routing packets through the network to the Internet gateway. The performances of the proposed radio resource management schemes are evaluated by simulations.

One of the most important features of the evolving fourth generation (4G) wireless networks is the capability of a mobile station to connect to several wireless access networks simultaneously. This introduces new challenges in bandwidth allocation among mobiles since the load characteristics of different networks must be taken into account to design efficient resource allocation algorithms. In this paper, we present bandwidth allocation and admission control algorithms based on bankruptcy game which is a special type of an N-person cooperative game. A coalition among the different wireless access networks is formed to offer bandwidth to a new connection. The stability of the allocation is analyzed by using the concept of the core and the amount of allocated bandwidth to a connection in each network is obtained by using Shapley value. Subsequently, an admission control algorithm is proposed. Numerical results are presented to demonstrate the behaviors of the proposed algorithms.

In this paper, we present a fuzzy logic-based admission control algorithm for orthogonal frequency division multiple access (OFDMA)-based broadband wireless networks. The system under consideration is compatible with the IEEE 802.16 standard in the TDD-OFDMA mode of operation. The proposed admission control algorithm considers various traffic source parameters (i.e., normal rate, peak rate and probability of peak rate) and packet-level delay requirements for the traffic to decide whether an incoming connection can be accepted or not. We formulate a queueing model to investigate the impacts of physical layer parameters (e.g., channel quality and number of allocated subchannels) on the radio link layer performances (e. g., average queue length, delay and throughput). The inference rules for resource allocation in the proposed fuzzy logic admission control are defined based on these queueing performance measures. The performance of the proposed admission control algorithm is analyzed by simulations and also compared to those of the traditional schemes.

Although the medium access control (MAC) protocol and the physical layer are well defined in IEEE 802.16 standard, bandwidth allocation (BA) and connection admission control (CAC) remain as open research issues. In this paper, we present a joint adaptive bandwidth allocation and connection admission control method for real-time and non-real-time polling services in the IEEE 802.16-based broadband wireless networks which use adaptaive modulation and coding at the physical layer. This method is based on an optimization-based approach where the in-connection (i.e., packet-level) performances (i.e., delay and transmission rate for real-time and non-real-time polling services, respectively) are used as cost functions and decision criteria for allocating bandwidth and for accepting or blocking a new connection, respectively. A queueing model is used to analyze transmission delay and transmission rate under adaptive modulation and coding. Binary integer programming is used to obtain the solutions for the optimization formulation. Typical performance results demonstrate the superiority of the proposed scheme over traditional static and adaptive band-width allocation schemes.

We present a novel analytical framework to investigate the performances of different sleeping strategies in a wireless sensor network where a solar cell is used to charge the battery in a sensor node. While the energy generation process (i.e., solar radiation) in a solar cell is modeled by a stochastic process (i.e., a Markov chain), a linear battery model with relaxation effect is used for the battery capacity recovery process. Average queue length, packet dropping and packet blocking probabilities and packet delay distribution at each node are the major performance metrics. Developed based on a multi-dimensional discrete-time Markov chain, the presented model can be used to analyze the performances of different sleep and wakeup strategies at each node (e.g., strategies based on available battery capacity, channel state, solar radiation condition and queue length, and hybrid of these conditions). The numerical results obtained from the analytical model are validated by extensive simulations. The presented model would be useful for designing and optimizing sleeping strategies in a solar powered sensor network under energy and QoS constraints.

The IEEE 802.11e draft standard currently being developed by IEEE 802.11TGe proposes to enable the much needed Quality of Service (QoS) support for the popular 802.11 based wireless local area networks (WLAN) through the introduction of Hybrid Coordination Function (HCF). The HCF Controlled Channel Access (HCCA) designed as a part of HCF is the medium access mechanism for parameterized QoS and is suitable for multimedia applications requiring hard QoS guarantees. The draft standard also defines scheduling and admission control schemes to complement HCCA in meeting these guarantees. However, most of the popular multimedia applications generate Variable Bit Rate (VBR) traffic that brings challenge to the HCCA and its scheduler and admission controller design. This paper introduces a novel queueing analytic framework that will be useful to analyze the performance of HCCA in provisioning required QoS for VBR traffic applications. The analysis also provides important insights that could be useful to improve the HCCA scheduler and admission controller designs.

We investigate connection-level and packet-level quality-of-service (QoS) performances in a solar-powered wireless mesh network when a connection admission control (CAC) mechanism is used at each mesh node. A queueing analytical model is developed to evaluate the various performance measures at a mesh node considering a general sleep and wakeup mechanism and constrained power supply at that node. Based on this queueing model, we demonstrate how an optimization problem can be formulated to obtain the optimal sleep and wakeup parameters or the CAC threshold so that the desired QoS performance can be achieved under constrained power supply.

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.

In this paper, we propose an adaptive bandwidth allocation (BA) and connection admission control (CAC) mechanism based on game theory for polling services in IEEE 802.16 broadband wireless networks. A noncooperative two-person general-sum game is formulated where the base station and a new connection are the players of this game. The game formulation provides not only the decision on accepting or rejecting a connection, but also the amount of bandwidth allocated to a new connection (if admitted). A queueing model considering adaptive modulation and coding (AMC) in the physical layer is used to analyze quality of service (QoS) performances, namely, delay and throughput performances, respectively, for real-time and non-real-time polling services. This queueing model is used by the proposed bandwidth allocation and admission control game to ensure that the payoffs for both the base station and the new connection are maximized. The performance of the proposed scheme is evaluated by simulation and compared with that of traditional admission control with static and adaptive bandwidth allocation.

We present a queuing-theoretic and optimization-based model for radio resource management in IEEE 802.16-based multiservice broadband wireless access (BWA) networks considering both packet-level and connection-level quality-of-service (QoS) constraints. Specifically, we model and analyze two approaches, namely, the optimal and the iterative approaches, for joint bandwidth allocation (BA) and connection admission control (CAC). To limit the amount of bandwidth allocated to each service type, for both these approaches, the total available bandwidth is shared among the different types of services using a complete partitioning approach. While, for the optimal approach, an assignment problem is formulated and solved, a water-filling mechanism is used for the iterative approach. The latter incurs significantly less computational complexity compared to the former while providing similar system performances. To analyze the connection-level performance measures such as connection blocking probability and average number of ongoing connections, a queuing model is developed. Then, an optimization formulation is used to obtain the optimal threshold settings for complete partitioning of the available bandwidth resource so that the connection-level QoS for the different services can be maintained at the target level while maximizing the average system revenue. To analyze the packet-level performance measures such as the packet delay statistics and transmission rate (or throughput), a queuing analytical model is developed which considers adaptive modulation and coding (AMC) at the physical/radio link layer. In summary, the queuing-theoretic and optimization-based model for joint BA and CAC provides a unified radio resource management solution for the IEEE 802.16-based broadband wireless access networks

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 paper presents a novel Markov-based model for analyzing the end-to-end transmission of a batch of packets in a multihop wireless network using multirate transmission. The end-to-end reliability of this transmission (in terms of the number of packets delivered to the destination node) is controlled through different types of automatic repeat request (ARQ)-based error control mechanisms implemented at each node. For a batch of packets, we derive complete statistics (i.e., probability mass function) for end-to-end latency and the number of packets successfully delivered to the destination node. Typical numerical results obtained from the model are validated by means of simulation. These results reveal the trade-off between end-to-end latency and end-to-end reliability, which would be an important issue in designing and engineering multihop wireless networks. Also, we demonstrate the usefulness of the proposed analytical model in predicting the latency and the reliability performances of TCP (transmission control protocol) in a multihop wireless scenario

IEEE 802.16 standard defines the air interface specifications for broadband access in wireless metropolitan area networks. Although the medium access control signaling has been well-defined in the IEEE 802.16 specifications, resource management and scheduling, which are crucial components to guarantee quality of service performances, still remain as open issues. In this paper, we propose adaptive queue-aware uplink bandwidth allocation and rate control mechanisms in a subscriber station for polling service in IEEE 802.16 broadband wireless networks. While the bandwidth allocation mechanism adaptively allocates bandwidth for polling service in the presence of higher priority unsolicited grant service, the rate control mechanism dynamically limits the transmission rate for the connections under polling service. Both of these schemes exploit the queue status information to guarantee the desired quality of service (QoS) performance for polling service. We present a queuing analytical framework to analyze the proposed resource management model from which various performance measures for polling service in both steady and transient states can be obtained. We also analyze the performance of best-effort service in the presence of unsolicited grant service and polling service. The proposed analytical model would be useful for performance evaluation and engineering of radio resource management alternatives in a subscriber station so that the desired quality of service performances for polling service can be achieved. Analytical results are validated by simulations and typical numerical results are presented.

A queuing analytical model is presented to evaluate call-level and packet-level quality of service (QoS) metrics in the uplink of a voice/data cellular code division multiple access (CDMA) network. In this model, a threshold-based call admission control (CAC) is used to limit the number of admitted calls in a cell and also to prioritize handoff calls over new calls. The transmission rates for data calls can be adjusted to accommodate more voice and/or data calls while satisfying the minimum signal-to-interference ratio (SIR)/ transmission rate requirement. Also, automatic repeat request (ARQ)-based error control is used for improved reliability of data packets. Call-level performance measures for both voice and data calls and packet-level performance measures specifically for data calls can be obtained from the analytical model. The interdependencies among call-level and packet-level QoS metrics are investigated under different CAC, rate adaptation, and error control parameter settings. To this end, the level of users' satisfaction (or user utility) is formulated as a function of the QoS metrics and an optimization formulation is presented to obtain the local-optimal system parameters

In a distributed multihop mesh/relay network (e.g., wireless ad hoc/sensor network, cellular multihop network), each node acts as a relay node to forward data packets from other nodes. These nodes are often energy-limited and also have limited buffer space. Therefore, efficient power saving mechanisms (e.g., sleeping mechanisms) are required so that the lifetime of these nodes can be extended while at the same time the quality of service (QoS) requirements (e.g., packet delay and packet loss rate) for the relayed packets can be satisfied. In this paper, we present a novel queueing analytical framework to study the tradeoff between the energy saving and the QoS at a relay node. Specifically, by modeling the bursty traffic arrival process as a MAP (Markovian arrival process) and the packet service process as having a phase-type (PH) distribution, we model each node as a MAP/PH/1 nonpreemptive priority queue. The relayed packets and the node's own packets form two priority classes and the medium access control (MAC)/physical (PHY) layer protocol in the transmission protocol stack acts as the server process. Moreover, we use a phase-type vacation model for the energy-saving mechanism in a node when the MAC/PHY protocol refrains from transmitting in order to save battery power. Two different power saving mechanisms due to the standard exhaustive and the number-limited exhaustive vacation models (both in multiple vacation cases) are analyzed to study the tradeoff between the QoS performance of the relayed packets and the energy saving at a relay node. Also, an optimization formulation is presented to design an optimal wakeup strategy for the server process under QoS constraints. We use matrix-geometric method to obtain the stationary probability distribution for the system states from which the performance metrics are derived. Using phase-type distribution for both the service and the vacation processes and combining the priority queueing model with the vacation queueing model make the analysis very general and comprehensive

In this paper, we develop a novel framework for analyzing radio link level performance for opportunistic scheduling with automatic repeat request (ARQ)-based error control in multi-rate wireless networks. The multi-rate transmission is assumed to be achieved through adaptive modulation and coding (AMC) to adjust the transmission rate according to the channel condition. The residual error effect due to each AMC setting is counteracted by means of a limited persistence ARQ protocol. The novelty of the proposed analytical framework lies in the fact that we are able to derive complete statistics (in terms of probability mass function) for both short-term and long-term performance measures such as system throughput, per-flow throughput, inter-success delay under both uncorrelated and correlated wireless channels. These performance measures can also be obtained in case of non-identical channels for different users. Analytical results are validated through simulations and the impacts of channel behavior on the different radio link level performance metrics are investigated.

This paper models and analyzes the performances of a class of ARQ (automatic repeat request) protocols in a multi-hop wireless data network. The performance metric here is the number of transmissions required for successful delivery of a packet over a multi-hop path. By using a discrete-time Markov model, the distribution for the total required number of transmissions is modeled as phase type distribution. The effects of different network parameters-such as packet error rate in each hop, maximum number of allowable retransmissions at each hop and retransmission probability at each hop-on the required total number of transmissions are investigated. The novelty of this model is that the probability mass function (pmf) for the number of transmissions required for successful end-to-end delivery of a packet can be easily obtained under different hop-level error control policies. Using the pmf, the tradeoff between transmission energy and percentage of data delivery (i.e., reliability) in a multi-hop path can be analyzed. The analytical model is validated by simulations. While the proposed analytical framework is general enough to capture the impact of any MAC (medium access control) mechanism at each hop, we specifically present typical performance results under IEEE 802.11 DCF (distributed coordination function) MAC

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

Abstract We propose a framework for mobility‐aware pro‐active low energy (MAPLE) clustering in ad hoc mobile wireless networks. Most of the clustering approaches proposed in the literature primarily focus on the algorithmic aspects of clustering without considering the practical implementation issues and these are often reactive in nature. The proposed approach addresses the problem of clustering in a medium‐access control framework and enables pro‐active and energy‐efficient clustering by exploiting the node mobility information. In particular, for pro‐active clustering we introduce a method to exploit the link level information to estimate the mobility pattern of the wireless nodes and the cost of using a wireless link in terms of required transmission power. A channel reservation technique is used to reduce the number of contentions among the nodes while accessing the channel during cluster formation. Simulation results show that the proposed framework results in superior clustering performance in terms of stability, load distribution, and control overhead compared to other clustering approaches proposed in the literature. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents an analytical framework to calculate the probability mass function (pmf) of channel access delay in IEEE 802.11 distributed coordination function (DCF) medium access control (MAC) mechanism. The access delay is defined as the time between a station chooses a new backoff value and the time it is able to access the channel for data packet transmission. Using a Markov process, the access delay is modeled as having phase-type distribution. Since the back-off is frozen when the channel is sensed busy, the access delay distribution is observed to be composed of non-continuous clusters. The envelope of the pmf as well as the envelope of each cluster resemble hyper-exponential distribution. While the proposed model is flexible enough to accommodate any distribution of MAC data frame length, the numerical results presented in this paper are for the fixed-length data frames. The model would be useful in many aspects such as queueing analysis and/or designing energy-efficient MAC protocols compatible with the IEEE 802.11 DCF standard

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

In this paper, we consider the case where non-linear traffic bounds are provided for traffic sources which share a link operating under a generalized processor sharing discipline. We consider the problem of searching for parameters for token bucket traffic shapers which provide linear bounds for the non-linear traffic bounding function in order to make use of results for traffic delay bounds which require a linear traffic bounding function, expressed in the form of token bucket shaper parameters. We formulate an optimization problem to obtain the parameters (i.e., bucket size and token generation rate) with the objective of minimizing a delay bound for a particular traffic source. This method can be used iteratively to obtain good delay bounds for a number of sources. Some typical numerical results obtained from the optimization model are presented. We also propose an alternate method, which we refer to as the composite delay envelope method.

We present a queueing analytical model to evaluate call-level and packet-level performances for uplink transmission of data calls in a voice/data cellular CDMA network. In the call-level, call admission control (CAC) is used to ensure that the cell is not overloaded and also to prioritize the handoff calls over the new calls. We assume finite queueing at the mobile to buffer the data packets for uplink transmission. The transmission rates for data calls can be adjusted to accommodate more voice and/or data calls while satisfying a minimum signal-to-interference (SIR)/rate requirement for voice/data calls. Call-level performance measures (i.e., new call blocking and handoff call dropping probabilities) for both voice and data calls and packet-level performance measures (i.e., queue throughput, packet dropping probability and delay) specifically for data calls can be obtained from our model. Impacts of the call-level parameter settings on the packet-level performance measures are investigated and typical numerical results are presented

We present queueing analysis for two connection admission control (CAC) schemes for OFDM wireless networks. The first one is a threshold-based CAC scheme in which admissibility for a new connection is determined based on a certain threshold and the number of ongoing connections. The second scheme uses the information on queue status to determine the probability to accept an incoming call. The connection-level and the packet-level performance measures for both CAC schemes are obtained from the queueing analytical models. Typical numerical results based on the proposed model are presented which provide interesting insights on the behavior of these two different CAC schemes. Also, simulation results are presented to validate the analytical results

Although the medium access control (MAC) signaling has been well-defined in the IEEE 802.16 specifications, resource management and scheduling, which are crucial components to guarantee QoS performances, still remain as open issues. In this paper, we propose a scheme for queue-aware uplink bandwidth allocation in a subscriber station (SS) for polling services (real-time and non-real-time) in IEEE 802.16 broadband wireless networks. The proposed scheme adaptively allocates bandwidth in order to control queue length at a target level so that the requirements for delay and PDU dropping probability can be met. We present a queueing analytical framework to analyze the proposed scheme from which various QoS performance measures can be obtained. The proposed analytical model is validated by simulations and typical numerical results are presented

In this paper, we analyze the radio link level queueing performance of a OFDM/TDMA network under Rayleigh fast fading channel. The probability mass function of transmission rate is derived analytically. A vacation queueing model is used to capture the data transmission corresponding to a particular user. The probability transition matrix for the queue of that particular user is formulated as quasi-birth-and-death process, and a matrix geometric method is used to obtain the queue-length distribution. The proposed model is verified by extensive simulations. Also, we demonstrate how the model can be used for admission control so that the radio link level queueing performances for the different users can be satisfied

We propose a model for analyzing the QoS performance measures in a cellular wireless mobile network with call admission control (CAC) and adaptive channel allocation (ACA). The proposed model considers cross-layer (physical/radio link/network) parameters to analyze both the call-level and the packet-level performances. In the network layer, call admission control (CAC) is responsible for deciding whether an incoming call can be accepted or not such that the performances of the ongoing calls do not deteriorate below the acceptable level. Also, an adaptive channel allocation (ACA) scheme is used to maximize the utilization of the radio resources. In the physical layer, adaptive modulation is used to adjust the packet transmission rate according to channel quality. Different QoS measures such as new call blocking probability, handoff call dropping probability, radio link level packet dropping probability and average packet delay are obtained. The analytical results are validated by extensive simulations. We also present typical applications of the proposed analytical model

We present an analytical model to analyze latency for reliable end-to-end batch transmission in a multi-hop wireless network using multi-rate transmission at each hop. The end-to-end reliability is achieved through hop-level error recovery based on an automatic repeat request (ARQ) protocol with unlimited persistence. The multi-rate transmission in the radio link is achieved through adaptive modulation and coding (AMC). We derive complete statistics (i.e., probability mass function) for the end-to-end latency. The analytical results are validated through simulations. The proposed model would be useful to analyze and optimize reliable end-to-end protocol (i.e., transport layer protocol) performance in multi-hop wireless networks.

We present a novel model for analyzing end-to-end transmission of a batch of packets in a multi-hop wireless network with automatic repeat request (ARQ)-based error control mechanism implemented at each node. For a batch of packets, we derive complete statistics (in terms of probability mass function) for end-to-end latency and the number of packets successfully delivered to the destination node. The analytical model is validated by means of simulation. Typical numerical results obtained from the model reveal the trade-off between end-to-end latency and reliability which would be an important issue in design and engineering of multi-hop wireless networks. The presented analytical model would be useful is analyzing and optimizing flow control and congestion control protocols in multi-hop wireless networks such as sensor networks.

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 present a Markov-based model to analyse both connection-level and packet-level quality-of-service (QoS) measures under fair scheduling in differentiated services wireless networks. In our model, two queues are used - one for QoS sensitive traffic and the other for best effort traffic. Connection admission control (CAC) is applied to the QoS sensitive traffic to avoid performance degradation, while there is no connection admission control for the best effort traffic. We obtain connection-level QoS measures (e.g., connection blocking probability) and packet-level QoS measures (i.e., mean number of packets in queue and mean packet delay), and we investigate their inter-dependencies. Typical numerical results show that the connection admission control method can affect the packet-level QoS performance significantly. The proposed analytical model would be useful for design, analysis, and optimization of differentiated services (DiffServ) wireless IP networks.

We propose an analytical model for call-level transient performance analysis in cellular mobile networks with time-varying traffic patterns. We consider both static and adaptive bandwidth allocation. Based on the transient analysis, we develop a threshold-based adaptive call admission control scheme in which a threshold is used to control the admission of new calls and thereby achieve the desired call-level quality of service (QoS) in the network. Results from numerical examples based on the transient analysis are presented. These results are validated by simulation results.

Provisioning quality of service (QoS) in Web servers has gained immense importance because Web servers are a major part of the Internet. To deliver the pledged QoS, Web service providers need control over the allocation of the resources in their Web servers. Control is also necessary for reaching the optimal resource allocation through proper service differentiation. In this paper, we propose and investigate an analytic approach that enables the service providers to deploy a differentiated service policy that offers this control. The proposed service policy is configurable by tunable control parameters. We devise the relationships between the performance measures and these parameters by adopting a unique queuing theoretic approach. Once these relationships are established, we describe how these parameters can be set to their most appropriate values depending on the objectives of the service providers. We illustrate the usefulness of our approach by conducting the analysis on a real Web trace.

This paper presents an optimization-based approach to solve the wireless fair scheduling problem under a multirate time division multiple access (TDMA)-based medium access control (MAC) framework. By formulating the fair scheduling problem as an assignment problem, the authors propose the optimal radio channel allocation for multirate transmission (ORCA-MRT) algorithm for fair bandwidth allocation in wireless data networks that support MRT at the radio link level. The key feature of ORCA-MRT is that while allocating transmission rate to each flow fairly, it keeps the interaccess delay bounded under a certain limit. The authors investigate the performance of the proposed ORCA-MRT scheduler in comparison to another recently proposed multirate fair scheduling algorithm. They also propose two channel prediction models and perform extensive simulations to investigate the performance of ORCA-MRT for different system parameters such as channel state correlation, number of flows, etc.

This paper addresses the problem of dynamic rate and power adaptation for forward link data transmission using high-order modulation and multicode formats in cellular wideband code division multiple access (WCDMA) networks. A novel framework for dynamic joint adaptation of modulation order, number of code channels (hence transmission rate), and transmission power is proposed for downlink data transmission in a cellular WCDMA system where different users have similar frame error rate (FER) requirements. Based on a general downlink signal-to-interference ratio (SIR) model, the problem of optimal dynamic rate and power adaptation is formulated, for which the rate and power allocation can be found by an exhaustive search. Two heuristic-based dynamic rate and power allocation schemes are proposed. Performance of dynamic joint rate and power adaptation under the proposed framework is evaluated for a random micromobility model using computer simulations. Also, an analytical approach to evaluate the throughput performance of dynamic rate and power adaptation using high-order modulation and multicode formats is presented.

This letter models and analyzes an adaptive radio link level error control protocol using Reed–Solomon codes for wireless networks. Results show that the proposed dynamic rate adaptive strategy provides a much improved throughput relative to a conventional type-I and type-II hybrid-automatic repeat request (ARQ) protocols.

where he taught and conducted research in both Antenna Arrays for Cellular Networks and ATM/MPLS Networks.During this period, he was also a consultant to Iran Telecommunications Research Center and directed various projects funded by private sector.Dr Valaee'

The paper models and analyzes the end-to-end performance in a multi-hop wireless data network for a class of hop-level ARQ (automatic repeat request) protocols. The performance metric is the number of transmissions required for successful delivery of a packet over a multi-hop path. By using a discrete time Markov model, the distribution for the required number of transmissions is modeled as a phase type distribution. The paper investigates the effects of different network parameters, such as packet error rate in each hop, maximum number of allowable retransmissions at each hop and retransmission probability at each hop, on the required total number of transmissions. The novelty of this model is that the probability density function (pdf) for the number of transmissions required for successful end-to-end delivery of a packet can easily be obtained from this model. Using the pdf, different quality of service (QoS) performance metrics, such as end-to-end delay, transmission energy and percentage of data delivery, can be calculated. The numerical results obtained from this model are observed to be very close to those obtained from simulation for end-to-end TCP (transmission control protocol) and reliable UDP (user datagram protocol) flows, which demonstrates the usefulness of this model for analyzing end-to-end protocol performance across multi-hop wireless links.

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.

In this paper, we present a model to analyze the performance of multi-service cellular wireless networks in terms of the new call blocking and the handoff call dropping probabilities when the call arrival rates depend on the time of the day. A Markov modulated Poisson process (MMPP) is used to represent the different rates in each time period. To capture the multiple classes of services (e.g., voice, data, video) we use a multi-dimensional Markov model where each dimension represents the number of users in each class. For each class of users a fixed number of channels (guard channels) are reserved for handoff call. The model can be also used for other types of channel reservation schemes such as fractional guard channel or thinning scheme. A recursive algorithm is applied to obtain the steady state probabilities for the system states which reduces the computational complexity. Simulation results are presented to show the accuracy of the proposed model.

We propose a framework for mobility-aware pro-active low energy (MAPLE) clustering in ad hoc mobile wireless networks. Most of the clustering approaches proposed in the literature primarily focus on the algorithmic aspects of clustering without considering the practical implementation issues, and these are often reactive in nature. The proposed approach addresses the problem of clustering in a medium-access control framework and enables pro-active and energy-efficient clustering exploiting the node mobility information. In particular, for pro-active clustering, we introduce a method to exploit the link level information to estimate the mobility pattern of the wireless nodes and the cost of using a wireless link in terms of required transmission power. A channel reservation technique is used to reduce the number of contentions among the nodes while accessing the channel during cluster formation. Simulation results show that the proposed framework results in superior clustering performance in terms of control overhead, average number of link failures and load distribution compared to other clustering approaches proposed in the literature, such as the LCC-LID (least cluster change lowest ID)-based clustering.

The paper presents a novel dynamic bandwidth estimation mechanism for improving TCP (Transmission Control Protocol) performance in wired-cum-wireless networks. The key idea is to measure continuously the bandwidth used by a TCP flow by monitoring the rate of returning acknowledgements (ACKs) and the round-trip time (RTT) values. The distinguishing feature of this mechanism (compared to other mechanisms such as that in TCP Westwood) is that it exploits the burstiness pattern of ACK arrivals and estimates the available bandwidth more accurately. In the proposed mechanism, the bandwidth sample is calculated by distributing a burst of ACKs over an off period based on the degree of congestion and burstiness in the network. The estimation technique is robust against burstiness of ACK arrival and type of loss (e.g., wireless loss, congestion loss). A new variant of TCP New-Reno based on this adaptive bandwidth estimation technique is referred to as TCP Prairie. Simulation results obtained using ns-2 reveal that TCP Prairie provides significant throughput performance improvement over TCP New-Reno and TCP Westwood under congestion and/or wireless loss scenarios. Also, compared to TCP Westwood, TCP Prairie is observed to be more friendly towards TCP New-Reno.

This paper presents an optimization-based approach to solve the wireless fair-queuing problem under a TDMA (time division multiple access)-based MAC (medium access control) framework. By formulating the fair scheduling problem as an assignment problem, we propose ORCA-MRT (optimal radio channel allocation for multi-rate transmission) for fair bandwidth allocation in wireless data networks which support multi-rate transmission at the radio link level. The key feature of ORCA-MRT is that while allocating transmission rate to each flow fairly it keep's the inter-packet transmission delay bounded under a certain limit. We investigate the performance of the proposed ORCA-MRT scheduler in comparison to another recently proposed multi-rate fair scheduling algorithm. We also propose two channel prediction models and perform extensive simulation to investigate the performance of ORCA-MRT in terms of different system parameters such as channel state correlation, number of flows, etc.

Dynamic radio link adaptation is a key component in WCDMA/3G wireless networks to improve the spectral efficiency while meeting the radio link level QoS (quality of service) requirements, such as the BER (bit error rate) requirements, for the different wireless services. Again, performance of an end-to-end protocol, such as TCP (Transmission Control Protocol), depends on the performance of the underlying radio link adaptation technique. A multilayer modeling of the WCDMA/3G radio interface is therefore necessary to understand better the interlayer protocol interactions and identify suitable transport and radio link layer mechanisms to improve TCP performance in a wide-area cellular WCDMA/3G network. We present such a multilayer system model to analyze TCP performance under joint rate and power adaptation with constrained BER requirements for downlink data transmission in a cellular VSF (variable spreading factor) WCDMA/3G network. To this end, we present the outline of a more general model which can completely describe the interaction between TCP and the radio link adaptation and error control protocol.

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.

A set of centralized burst-level cell scheduling schemes, namely, First Come First Served with Frame Reservation (FCFS-FR), FCFR-FR+, Earliest Deadline First with Frame Reservation (EDF-FR), EDF-FR+, and Multitraffic Dynamic Reservation (MTDR), are investigated for transmission of multiservice traffic over time division multiple access (TDMA)/time division duplex (TDD) channels in wireless ATM (WATM) networks. In these schemes, the number of time slots allocated to a virtual circuit (VC) during a frame-time is changed dynamically depending on the traffic type, system traffic load, the time of arrival (TOA)/time of expiry (TOE) value of the data burst and data burst length. The performances of these schemes are evaluated by computer simulation for realistic voice, video and data traffic models and their quality-of-service (QoS) requirements in a wireless mobile multimedia network. Both the error-free and the correlated fading channel conditions are considered. Simulation results show that the EDF-FR+ and MTDR schemes outperform the other schemes and can provide high channel utilization with predictive QoS guarantee in a multiservice traffic environment even in the presence of bursty channel errors. The EDF-FR+ scheme is found to provide better cell multiplexing performance than the MTDR scheme, Such a scheme would be easy to implement and would also result in a power conservative TDMA/TDD medium access control (MAC) protocol for broadband wireless access. Burst-level cell scheduling schemes such as EDF-FR+ can be easily adapted as MAC protocols in the emerging differentiated services (DS) enhanced wireless Internet protocol (IP) networks.

To improve the spectral efficiency while meeting the radio link level quality of service requirements such as the bit-error-rate (BER) requirements for the different wireless services, transmission rate and power corresponding to the different mobile users can be dynamically varied in a cellular wideband code-division multiple-access (WCDMA) network depending on the variations in channel interference and fading conditions. This paper models and analyzes the performance of transmission control protocol (TCP) under joint rate and power adaptation with constrained BER requirements for downlink data transmission in a cellular variable spreading factor (VSF) WCDMA network. The aim of this multilayer modeling of the WCDMA radio interface is to better understand the interlayer protocol interactions and identify suitable transport and radio link layer mechanisms to improve TCP performance in a wide-area cellular WCDMA network.

Optimal dynamic rate allocation among mobile stations for variable rate packet data transmission in a cellular wireless network is an NP-complete problem; therefore, suboptimal solutions to this problem are sought for. In this paper, three novel suboptimal dynamic rate adaptation schemes, namely, peak-interference-based rate allocation, sum-interference-based rate allocation, and mean-sense approximation-based rate allocation, are proposed for uplink packet data transmission in cellular variable spreading factor wide-band code division multiple access (WCDMA) networks. The performances of these schemes are compared to the performance of the optimal dynamic link adaptation for which the rate allocation is found by an exhaustive search. The optimality criterion is the maximization of the average number of radio link level frames transmitted per frame time under constrained signal-to-interference-plus-noise ratio (SINR) at the base station receiver. Two different error control alternatives for variable rate packet transmission environment are presented. We demonstrate that the dynamic rate adaptation problem under constrained SINR can be mapped into the radio link level throughput maximization problem with integrated rate and error control. Performance evaluation is carried out under random and directional micromobility models with uncorrelated and correlated long-term fading, respectively, in a cellular WCDMA environment for both the homogeneous (or uniform) and the nonhomogeneous (or nonuniform) traffic load scenarios.

This paper addresses the problem of dynamic link adaptation under multiple quality-of-service (QoS) constraints in cellular wideband code-division multiple-access (WCDMA) systems. A novel dynamic joint rate and power adaptation framework is proposed for downlink data transmission in a multicell variable spreading factor (VSF) WCDMA system where the different classes of users have different signal-to-interference ratio (SIR) requirements. Based on a general downlink SIR model, the problem of optimal dynamic rate and power adaptation under multiple SIR constraints is also formulated, for which the rate and power allocation can be found by an exhaustive search. Two schemes, namely, near-optimal and suboptimal schemes, are proposed for implementation-friendly dynamic rate and power adaptation. Performance of dynamic joint rate and power adaptation under the proposed framework is evaluated under random micro-mobility model with uncorrelated long-term fading and a directional micro-mobility model with correlated long-term fading in a cellular WCDMA environment.

This paper models and analyzes the performance of opportunistic scheduling under constrained frame error rate (FER) requirement for downlink data transmission in cellular CDMA networks using high-order modulation and multicode formats. The effects of multipath-induced interference and imperfect self-interference cancellation on single-transmission are specifically modeled. Based on the proposed framework the performances of the several opportunistic schedulers are evaluated using Monte Carlo simulation considering both bursty and nonbursty downlink data traffic flows.

This paper addresses the problem of dynamic rate and power adaptation for forward link data transmission using high-order modulation and multicode formats in cellular wideband code division multiple access (WCDMA) networks. A novel framework for dynamic joint adaptation of modulation order, number of code channels (hence transmission rate) and transmission power is proposed for downlink data transmission in a cellular WCDMA system where the different users have similar frame error rate (FER) requirements. Based on a general downlink signal-to-interference ratio (SIR) model, the problem of optimal dynamic rate and power adaptation is formulated, for which the rate and power allocation can be found by an exhaustive search. Two heuristic-based dynamic rate and power allocation schemes are proposed. Performance of dynamic joint rate and power adaptation under the proposed frame-work is evaluated under random micro-mobility model using computer simulations.

In this paper, the problem of fair scheduling in a wireless network is formulated as an assignment problem and an optimal radio channel allocation (ORCA) strategy is proposed for fair bandwidth allocation in a centralized manner simulation results show that the performance improvement due to ORCA can be significant compared to other wireless fair-queuing mechanisms proposed in the literature such as WPS (wireless packet scheduling).

A novel dynamic joint rate and power adaptation framework is proposed for downlink data transmission in a multicell variable spreading factor (VSF) WCDMA system where the different classes of users have different signal-to-interference ratio (SIR) requirements. Based on a general downlink SIR model, the problem of optimal dynamic rate and power adaptation under multiple SIR constraints is also formulated, for which the rate and power allocation can be found by an exhaustive search. Performance of the dynamic joint rate and power adaptation under the proposed framework is evaluated under random micro-mobility model with uncorrelated long-term fading and a directional micro-mobility model with correlated long-term fading in a cellular WCDMA environment for both homogeneous (or uniform) and the non-homogeneous (or non-uniform) traffic load scenarios.

Dynamic rate adaptation for uplink data transmission in a cellular multidimensional multicode (MDMC) direct-sequence code-division multiple-access packet data network is modeled and analyzed. An analytical framework is developed to evaluate the performances of radio link level dynamic rate adaptation schemes under multipath fading and log-normal shadowing. The radio link level throughput under optimal dynamic rate adaptation (having exponential computational complexity) and different heuristic-based suboptimal rate adaptation schemes can be assessed under the presented analytical framework. The performance of MDMC signaling is compared with that of the single-code variable spreading factor (VSF) signaling. To this end, based on an equilibrium point analysis of the system in steady-state, a base station-assisted and mobile-controlled dynamic rate adaptation scheme is presented.

This paper proposes a novel dynamic joint rate and power control procedure for downlink data transmission in a multicell variable spreading factor wideband code-division multiple-access (WCDMA) system where the different users have similar quality-of-service requirements in terms of the signal-to-interference ratio (SIR). Two variations of the dynamic joint rate and power allocation procedure, namely, Algorithm-1 and Algorithm-2, are presented. The performances of these two schemes are compared to the performance of the optimal dynamic link adaptation for which the rate and power allocation is found by an exhaustive search. The optimality criterion is the maximization of the total radio link level capacity (or sum-rate capacity) in terms of the average number of radio link level frame transmitted per adaptation interval under constrained SIR and power limit in the base station transmitter. The proposed schemes have linear time complexity as compared to the exponential time complexity of the optimal scheme and achieve better radio link level throughput fairness compared to the optimal link adaptation scheme with a moderate loss in total throughput. Performance evaluation is carried out under random and directional micromobility models with uncorrelated and correlated long-term fading, respectively, in a cellular WCDMA environment for both the homogeneous (or uniform) and the nonhomogeneous (or nonuniform) traffic load scenarios.

We propose a measurement-based dynamic random access (RA) code assignment procedure for prioritized packet data transmission in wideband code-division multiple access (WCDMA) networks. This dynamic adaptation process is based on analytical performance results derived for random packet access under Rayleigh fading in WCDMA networks. The performance of the proposed measurement-based RA code assignment procedure with three different adaptation methods is evaluated by using computer simulations. The performance of the proposed scheme is compared with those of a retransmission control-based and static channel allocation-based prioritized packet access scheme. An integrated (physical layer and link layer) delay-throughput performance model is presented for finite population RA WCDMA systems. The proposed dynamic RA code assignment procedure can be used in an adaptive quality of service (QoS) framework for dynamically adjusting the QoS of prioritized RA data traffic in the evolving WCDMA-based differentiated services wireless Internet protocol networks.

Abstract In this article, a comprehensive survey of the medium access control (MAC) approaches for wireless mobile ad hoc networks is presented. The complexity in MAC design for wireless ad hoc networks arises due to node mobility, radio link vulnerability and the lack of central coordination. A series of studies on MAC design has been conducted in the literature to improve medium access performance in different aspects as identified by the different performance metrics. Tradeoffs among the different performance metrics (such as between throughput and fairness) dictate the design of a suitable MAC protocol. We compare the different proposed MAC approaches, identify their problems and discuss the possible remedies. The interactions among the MAC and the higher layer protocols such as routing and transport layer protocols are discussed and some interesting research issues are also identified. Copyright © 2003 John Wiley & Sons, Ltd.

This paper models and analyzes the performance of TCP (transmission control protocol) under joint rate and power adaptation with constrained BER requirements for downlink data transmission in a multi-cell VSF (variable spreading factor) WCDMA system. The performance of TCP in a wide-area Internet environment is evaluated by using computer simulations considering user mobility, short-term fading (i.e., multipath fading) and long-term fading (i.e., shadowing). The motivation is to explore the inter-layer protocol interactions and to identify suitable transport and radio link layer mechanisms to improve wireless TCP performance in a cellular WCDMA environment.

In this paper, a measurement-based dynamic RA (Random Access) code assignment procedure Is proposed for prioritized packet data transmission in WCDMA (Wideband Code Division Multiple Access) networks. This dynamic assignment process is based on analytical performance results derived for random packet access under Rayleigh fading in WCDMA networks. The performance of the proposed measurement-based RA code assignment procedure with three different adaptation methods is evaluated using computer simulation for bursty data packet arrival patterns. The performance of the proposed scheme is compared to those of a retransmission control-based and static channel allocation-based prioritized packet access schemes. The proposed scheme can be used in an adaptive QoS (Quality of Service) framework for dynamically adjusting the QoS of prioritized random access data traffic In the evolving WCDMA-based DS (Differentiated Services) wireless IP (Internet Protocol) networks.

Optimal dynamic rate allocation among mobile stations for variable rate packet data transmission in a cellular wireless network is an NP-complete problem; therefore, sub-optimal solutions to this problem are sought. Again, interference calculation is non-trivial in the case of a packet-switched cellular CDMA network with heterogeneous traffic load in different cells. In this paper, a sub-optimal two-step dynamic rate selection procedure is proposed for uplink packet data transmission in cellular WCDMA (wideband code division multiple access) networks. A novel 'mean-sense' approach for inter-cell interference calculation is employed assuming homogeneous traffic load in the different cells. Two different error control alternatives for this variable rate packet transmission environment are presented and their performances are analyzed for three different channel models. The performance of the proposed two-step rate selection procedure is fairly close to that of the optimal rate allocation found through exhaustive search.

A unified time-division multiple access (TDMA) based centralized wireless access scheme is proposed for performing the statistical multiplexing of bursty data sources in a wireless packet data network. This scheme combines dynamic bandwidth allocation with admission control and packet conditioning (at the mobile stations) to provide fair bandwidth distribution among bursty data flows with different profile rates (or subscription levels) in an error-prone environment. The dynamic bandwidth allocation policy is credit-based and both the burst-level and the packet-level bandwidth allocations are considered. The performance of the scheme is evaluated using computer simulations for different total subscription levels, for different compositions of flows with different profile rates, and for different channel quality with different channel-error correlation patterns. The simulation results show that the throughput variability among flows with the same level of subscription is considerably small except for long range dependent flows with very high traffic burstiness. The relative throughput fairness among flows with different profile rates can also be achieved. The post facto loss and delay values (i.e., observed average packet delay and average packet loss values) for the flows depend on the corresponding delay tolerance limits of the data bursts, TDMA frame-length, and the wireless link utilization level. The energy efficiency of the wireless access scheme is evaluated in terms of the average transmitter usage time and the average receiver usage time in the mobile stations for both the burst-level and the packet-level bandwidth allocation. The proposed scheme can be used in an adaptive quality-of-service (QoS) framework for dynamically adjusting the QoS for flows in order to accommodate wireless channel errors and user mobility.

The performance implications of retransmission diversity packet combining on the RLC (radio link control)/MAC (medium access control) layer and transport layer protocol performance are investigated for three different heuristic-based RLC/MAC layer access control schemes in a CDMA S-ALOHA network under frequency selective Rayleigh fading. The transport layer protocol implements a two-level error recovery mechanism for reliable data transmission. Two different transport layer timer control mechanisms are considered. Implications of some physical layer parameters on system performance are discussed. It is observed that, for two-level error recovery through a reliable transport protocol, the achieved throughput depends on the transport protocol timer control mechanism and a suitable mechanism can be identified for an underlying RLC/MAC layer access control scheme and a particular physical layer design.