Unmanned aerial vehicles (UAVs) are emerging as key enablers for adaptive connectivity in future sixth-generation (6G) networks, offering high mobility, flexible deployment, and enhanced line-of-sight coverage. However, the open nature of wireless communication and the elevated positioning of UAVs as a relay expose them to severe security threats, including jamming and eavesdropping. In this work, we propose a secure downlink communication framework where a UAV relay flying at a fixed altitude serves multiple ground users while contending with an active jammer and a passive eavesdropper. To enhance physical layer security, we jointly optimize the UAV’s trajectory and transmit power over a discrete time horizon to maximize the cumulative secrecy rate. The resulting non-convex optimization problem is solved using both a successive convex approximation (SCA) method and deep reinforcement learning (DRL). Simulation results demonstrate the optimized trajectories in various user topologies and shed light on the relationship between secrecy energy efficiency and maximal transmit power of the UAV relay as well as the number of users.

We investigate digital twin-assisted task offloading in unmanned-aerial-vehicle (UAV)–mobile edge computing (UAV-MEC) networks with energy harvesting. Digital twin technology leverages a real-time simulated environment to optimize UAV-MEC networks. Considering unpredictable mobile edge computing (MEC) environments and low-power Internet of Things (IoT) devices, we propose a digital twin-assisted task offloading scheme in UAV-MEC networks with energy harvesting. The goal is to minimize latency and maximize the number of associated IoT devices by optimizing UAV placement and IoT device association. The constraints on computing, caching, energy harvesting, latency, and maximum number of IoT devices an UAV can serve are considered. To solve the formulated problem, we employ a branch-and-bound algorithm to obtain optimal results. We also solve the optimization problem using the relaxed heuristic algorithm. In addition, we propose a difference of convex penalty-based algorithm to solve the problem with reduced computational complexity. This approach provide efficient alternatives to obtain near-optimal solution. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithm and validate the benefits of leveraging digital twin technology in UAV-MEC networks with energy harvesting.

Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of Quality of Experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure toward communication service providers (CSPs). In the sixth-generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to Intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intent-driven closedloop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in the OpenAirInterface platform and realizing the CL control and management with a near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

Unmanned Aerial Vehicle (UAV) has emerged as a promising technology to provide wireless signals from air to the ground users in specific scenarios such as earthquakes, tsunamis and other disasters. The performance of the UAV is degraded when the signals are blocked by obstacles in dense urban scenarios. To address this issue and enhance the signal quality available to the ground users, Reconfigurable Intelligent Surface (RIS) has emerged as a new technological paradigm. It offers an intelligent configuration for the signal propagation environment by redirecting the signals to the users. In this article, we solve a non-convex optimization problem of RIS-assisted UAV network by jointly optimizing the RIS phase shift and 3D trajectory of UAV to maximize the energy efficiency of a rotatory-wing UAV. The considered optimization problem is solved using Deep Reinforcement Learning (DRL) based techniques in an on-line fashion to reduce the computational complexity. We leverage Twin-delayed Deep Deterministic Policy Gradient (TD3) to solve the problem by considering the UAV trajectory as a set of continuous actions. For comparison, we also use the Soft Actor-Critic (SAC), Deep Deterministic Policy Gradient (DDPG) and Double Deep Q-Network (DDQN) for continuous and discrete optimization of the UAV trajectory, respectively. Extensive simulations show that the TD3 outperforms all the considered DRL techniques with the highest energy efficiency and throughput, and the lowest propulsion energy.

This paper proposes an${i}$terative${w}$ater-${f}$illing algorithm (IWF) for the${e}$nergy-${e}$fficiency (EE) maximization problem of the multi-user${m}$ultiple-${i}$nput and${m}$ultiple-${o}$utput (MIMO)${b}$roadcast${c}$hannel (BC). This algorithm is termed as IWF-EE-BC and has two levels of operations. The inner level computes solutions, by an algorithm, is named as${w}$ater-${f}$illing for the EE of the BC, which is implemented within a single iteration, with the short name: WF-EE-BC1. The solutions by WF-EE-BC1 are the optimal solutions of the auxiliary energy-efficiency maximization problems. Each term of the added throughput part in these auxiliary problems is decoupled in power variables for all users. Then the outer level determines when to output a good solution to the considered problem, based on the results obtained by the inner level. The considered problem has complex-valued matrix optimization variables, beyond the range of the optimization problems whose optimization variables are often real-valued variables. Particularly, it is a${s}$emi-${d}$efinite${o}$ptimization problem (SDO) with a more complicated form of the objective function, over the field of complex numbers. Since existing results on optimization algorithms, including SDO ones, cannot guarantee convergence of IWF-EE-BC, the novel fixed point method is designed and used. Overcoming these difficulties, this paper obtains convergence of IWF-EE-BC, with efficiency.

The sixth generation (6G) mobile communication system is expected to utilize millimeter wave and THz frequency bands, in addition to RF bands. The propagation characteristics and ranges of these bands vary vastly while the multi-band users supposedly experience seamless coverage, high throughput and consistent Quality of Service. Heterogeneous base stations (BSs) equipped with these multiple technologies shall be fairly loaded for this. SINR based approaches tend to assign more users to RF channels while starving other bandwidth rich mediums. In this paper, we propose an algorithm to improve the performance of multi-band 6G networks by optimizing the user association to heterogeneous BS to maximize the cumulative data rate while ensuring an acceptable transmission power and fair load balancing among the BSs. The optimization problem is solved using the Lagrangian method. Simulation results show an improved cumulative throughput and fairness.

Abstract The rampant deployment of data distribution service (DDS) as middle‐ware service providers for industrial network platforms has been widely investigated. All DDS‐based node discovery protocols establish communication with its intended target systems by accessing matched endpoints/nodes information. These matched endpoints/nodes information is usually embedded with the system’s programmable control plane network which acts as the conveyor vehicle. The introduction of software defined networking (SDN) is to characterize the control plane from embedded data plane. The DDS implements the simple discovery protocol (SDP) as its inherent node discovery protocol. Deploying DDS for data packet exchange in server‐based collaborative distributed networked control (DNC) systems has gained traction. The current automatic discovery protocol (ADP) based on SDP is fraught with real‐time limitations such as high memory consumption and poor packet transmission. This work presents novel memory‐efficient automatic discovery data distribution service (MAD‐DDS) with enhanced threshold bloom filters (ETBF) where ETBF stores transmission packets at simulation end‐nodes. The packet is further adjusted using optimized binarization and decision thresholds inside ADP, hence guaranteeing memory reduction. The testbed computation recorded significantly improved quality of service (QoS) whereas numerical results depict significant decline in memory consumption with consistent packet transmission rates that produces increased computational capacity.

Major device nodes within the Internet of Things (IoT) system collects and store information in bit forms of 0’s and 1’s regardless of its repetition. The nodes do not possess the capability of processing redundant data information except for outright rejection/replacement of packets of similar sizes. This becomes a research problem since high volumes of packet redundancy are prevalent owing to repetitive information. Many optimal solutions have been provided to reprocess redundant packets and to store them in edge server for accessibility by other connected IoT systems and networks servers. To do so, major IoT platforms implements tier-based network layers which primarily aid seamless communication among nodes. These network layers perform near-similar tasks of guaranteeing packet sensing and exchange although at often-higher energy requirement. To mitigate the energy concerns, packets are clustered and compressed, allowing exchange of essential information only. But the approach continues to present heavy packet-losses and/or redundancies. In this article, two-tier layered network—where packet exchange is conducted at the top layer in order to lower energy consumption and promote system-reliability is investigated. All packets are first segregated into multiple clusters using the Voronoi cell-based correlation cluster formation (VC3F) technique. Cluster heads (CHs) are identified by their multipath (M-Score) value, thus, assuming sole responsibility of redundant packet removal within each cluster. The redundant packets are then moved to the edge-tier layer using optimized multiobjective flower pollination (MO-FPO) routing, and finally processed using hybrid models of novel fast-fully connected neural network (F2CNN) accelerator and Lempel–Ziv–Welch (LZW) data compression. The F2CNN and LZW models are harmonized to further collectively explore potential benefits of both models. These benefits include the neural capability to work on the sensitivity level of the packets determined by the packet classification and validation approaches. The system is evaluated with detailed experimental investigations where higher system throughput, packet delivery ratio (PDR), end-to-end delay, and system reliability are corroborated.

As a technology for 6G wireless communications, Intelligent Reflecting Surfaces (IRSs) are considered as a promising solution to boost the network capacity, spectrum and coverage in multiusers’ downlink communication systems. The users in blockage and cell edge areas can utilize this technology for data transfer purpose. In this paper, a machine learning-based policy optimization for downlink communication in distributed IRS aided multiple-input single-output (MISO) systems is proposed. Three categories of users are considered, namely, users who can utilize only the direct links, blockage area users who can utilize only the IRS links, and cell edge or poor link quality of users who can utilize both the direct and IRS links. The sum rate maximization problem is formulated to derive the optimal policy (i.e. communication link, IRS selection, power allocation and reflection coefficients) for those users, considering the IRS selection, link quality, power allocation and IRS reflection constraints. The proposed methods to achieve the optimal policy include reinforcement learning-based model with binary decision tree-based user categories, maximum posterior probability-based IRS selection, fractional programming method-based power and IRS coefficient allocation, and value function-based policy optimization. Through simulations, the sum data rate and energy efficiency performances of different categories of users are obtained and discussed.

We propose an Iterative Water-Filling algorithm for Energy-Efficiency maximization problem of the Multi-User Multiple-Input Multiple-Output Multiple-Access-Channel (MU-MIMO-MAC) system, named as IWF-EE-MIMO. The algorithm can be regarded as two levels of operations. The inner level of IWF-EE-MIMO aims at computing the solution to each user of the family, while other users keeping their previous power allocation, in turn. The outer level of IWF-EE-MIMO aims at determining when to accept a good solution to the considered problem based on the results obtained by the inner level. For our designed algorithms in this paper, WF-EE-MIMO1, as the subordinate algorithm, is only used for the inner level. IWF-EE-MIMO, as the main algorithm, computes the solution to the considered problem. Note that IWF-EE-MIMO includes WF-EE-MIMO1, to avoid confusion. The considered problem is of non-linear fractional semi-definite optimization in complex-valued matrix optimization variables, beyond the range of standard (semi-definite) optimization theory. Thus, the optimality condition of the considered maximization problem needs to be created. Under this creation, for the considered problem, convergence or optimality of IWF-EE-MIMO is obtained with its efficiency.

Satellite networks can help extend wider communication coverage and provide more types of services; and introducing service function chain (SFC) to satellite networks can enhance their flexibility and scalability. However, this highly challenges the complexity and efficiency of network service management. In this work, we first present an intent-driven satellite network service management architecture. It provides a user-oriented programmable and customizable service provisioning mechanism, which can improve the flexibility and efficiency in service delivery and provisioning. Furthermore, we elaborate an intent-driven SFC deployment scheme, which is termed as ISFC. The presented ISFC is with the intent parsing, network function virtualization infrastructure point of presence selecting, and the optimal service function path generation. Finally, we provide the ISFC deployment algorithm. And the simulation results show that the presented ISFC scheme can well satisfy user’s requirements with much lower delay.

This paper proposes an optimal admission and placement stochastic controller that inserts security and depend-ability in the operational aspects of edge-cloud system under a 5G deployment. The proposed mechanism uses the frame-work of Semi-Markov Decision Making Process (SMDP) and seeks for an optimal policy that efficiently allocates the virtual resources to secure and run the services across the cloudified infrastructure. Driven by a new latency-oriented cost structure, the optimal controller achieves a dependable and secure operation by optimally balancing the service requests between the edge and the cloud system taking into account the service profile, the workload, and the traffic load. A structural analysis of the optimal policy reveals its implementation friendliness while a cloudnomics analysis shows that the optimal cost can be further optimized by fine tuning the parameters of the proposed cost structure.

This article considers an ultradense heterogeneous network (UDHN) consisting of cache-enabled unmanned aerial vehicles (UAVs) and Internet of Things mobile devices (IMDs) receiving their requested contents via the power domain nonorthogonal multiple access (PD-NOMA) protocol. Employing the fast global${K}$-means (FGKMs) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as nonlegitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3-D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is nonconcave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the interior-point method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional most popular caching (MPC) strategy substantially.

This article considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet-of-Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the 3-D UAV placements, transmit power, and cloud resources. This represents a challenging, nonconvex, and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems, namely, 2-D UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global$K$-means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.

Cloud firewalls stand as one of the major building blocks of the cloud security framework protecting the Virtual Private Infrastructure against attacks such as the Distributed Denial of Service (DDoS). In order to fully characterize the cloud firewall operation and gain actionable insights on the design of cloud security, performance models for the cloud firewall become imperative. In this article, we propose a multi-dimensional Continuous-Time Markov Chain model for the cloud firewall that takes into account the burstiness and correlation features of the legitimate and malicious data traffic. By adopting the Markov-Modulated Poisson process (MMPP) and the Interrupted Poisson Process (IPP), we identify the workload conditions under which the cloud firewall might be subject to a loss of availability. Furthermore, by comparing the IPP and Poisson attacks, we numerically verify that the cloud firewall is inherently vulnerable to a burstiness-aware attack which might seriously compromise its operation. Additionally, we characterize the joint harmful impact of burstiness and correlation on the cloud firewall that might lead to performance degradation. Finally, we design an elastic cloud firewall by proposing a MMPP-driven load balancing procedure that provisions virtual firewalls dynamically while fulfilling a Service Level Agreement (SLA) latency specification.

In this paper, an energy saving scheme is presented whereby unused virtual machines (VMs) in distributed edge devices enter a sleep mode taking into account the quality of service (QoS) experienced by mobile users. The virtual machines in the system under consideration form a shared VM pool assisted by the software-defined networking (SDN) technology. The energy saving optimization problem aims to minimize the number of active VMs without violating the QoS requirements, and is solved using the square-root staffing rule and the Halfin-Whitt function. In addition, a traffic management strategy in overloaded edge devices is carried out to maximize the number of accommodated users taking into account the fronthaul capacity which impacts task migration among edge devices. Results show the effectiveness of the proposed scheme in saving remarkable amount of energy while satisfying the QoS requirements. It is also shown that the VM sleeping scheme exhibits higher energy saving and less task migration compared to the entire edge device sleeping scheme due to its enhanced flexibility.

In this paper, we propose an environment perception framework for autonomous driving using state representation learning (SRL). Unlike existing Q-learning based methods for efficient environment perception and object detection, our proposed method takes the learning loss into account under deterministic as well as stochastic policy gradient. Through a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC), we focus on uninterrupted and reasonably safe autonomous driving without steering off the track for a considerable driving distance. To ensure the effectiveness of the scheme over a sustained period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. The results obtained through simulations on DonKey simulator show the effectiveness of our proposed method by examining the variations in policy loss, value loss, reward function, and cumulative reward for `VAE+DDPG' and `VAE+SAC' over the learning process.

With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimize the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges on which data to cache and where to cache these data. The process of deciding the cache contents involves multiple objectives regarding the content popularity, contact duration between UT and SBSs, communication ranges between UT and SBSs caches, and contact probability between UT and SBSs. In this paper, we propose a new strategy on cache placement decisions for mobile edge networks based on binary classification technique. The aim is to formulate the cache placement as a classification problem that is solved using machine learning techniques in order to define an optimal decision boundary on cache or not cache decisions. Simulation results show that the performance of cache placement algorithms using classifier based learning techniques can achieve higher hit rate than other algorithms.

Retransmission of user's data at partner node in repetition based cooperative protocols decreases bandwidth efficiency and imposes overhead on cooperative signalling. In this paper we propose two full-duplex distributed cooperative protocols for OFDM systems, that improve BER performance and decrease overhead without any hardware changes in comparison with the conventional cooperative protocols. In the proposed full-duplex distributed cooperative system, the partner node retransmits a part of the information on high quality subcarriers that belong to the user's data instead of all received ones. An extensive Monte Carlo simulation study is presented to reveal the superior performance of the distributed decoding system as compared to that of the conventional cooperative schemes for the OFDM scheme.

This article addresses a real-time monitoring mechanism of vital signs of patients in a three-tier Internet of Medical Things-based software-defined-wireless body area network. The challenging issues are energy efficiency, interference, delay, emergency conditions, and reliability. We propose a two-tier scheduling algorithm in which Walsh Hadamard codes are employed to avoid the interference and decrease the delay and energy consumption in tier I. To schedule the transmission of different patients in tier II [i.e., the transmissions between hubs and access points (APs)], we propose a fair nonorthogonal multiple access-based scheduling algorithm, which jointly considers the channel state, energy consumption, and delay. Some processing tasks of the proposed algorithm are executed by local edge servers connected to APs and managed by a central SD-controller in tier III. Consequently, the transmission delay and energy consumption considerably decrease and the effective throughput increases. The algorithm takes the precedence of some information over other sensed data into account by employing the emergency index. The simulations results illustrate the advantages of the proposed algorithm in terms of energy consumption, network delay, and effective throughput, and the superior performance over other benchmark schemes.

This article investigates the problem of synchronous randomized neighbor discovery with directional antennas. Due to the long tail effect, it will take long time to discover the last few neighbors, which increases overall neighbor discovery time. This effect is due to small proportion of remaining undiscovered neighbors. Moreover, improper choices of reception probabilities make the discovery even worse. In this article, a cognitive framework is proposed to minimize the expectation of neighbor discovery time. We present a scheme in which reception probabilities are dynamically adjusted. We consider an ideal scenario and a practical scenario. In an ideal scenario where perfect information about the number of neighbors is available, reception probabilities are adjusted according to the number of neighbors. A method of dynamic programming is used to recursively calculate the optimal reception probabilities. In an actual scenario where perfect information about number of neighbors is unavailable, a neighbor estimation method based on maximum-likelihood estimation is executed before probability adjustment. Simulation results show that when perfect information about neighbor is available and total transmission probability is within a proper range (between 0.1 and 0.2), the average neighbor discovery time can be significantly reduced (by 38% to 43%, respectively) compared with an existing probability-fixed scheme. With imperfect information, the scheme also works well and realizes appreciable reduction in average neighbor discovery time compared with existing self-adaptive schemes.

In this paper, a power minimization problem using base station sleeping is proposed for heterogeneous cloud radio access networks (H-CRANs) taking into account the computing delay constraints. In the proposed system, which is modeled using M/M/k queues, the edge device coexists with the small base station (SBS) to provide computing capabilities beside the central cloud. In general, the SBS sleeping is governed by the availability of resources provided the macro base station (MBS) which is in charge of accommodating offloaded users from sleeping SBSs. However, switching off lightly loaded SBSs can impose significant burdens on cloud servers. Here, the proposed sleeping scheme allows SBSs serving more computing tasks to remain active in order to fulfill the task completion deadlines requested by mobile users and to keep the cloud response time within a predefined limit. In other words, the proposed scheme aims to save power by undertaking a centralized selection of active and sleeping SBSs taking into account the delay constraints of both cloud and mobile devices. First, we consider a disjoint cloud-edge system, where computing services can be provided by either the cloud or the edge device, and aim to minimize the number of active SBSs. The problem is formulated as a 0-1 knapsack problem with SBS utilization considered as the weight while the ratio of computing tasks to all incoming tasks is considered as the value of that SBS. In this problem, which is solved using dynamic programming, SBSs processing less computing tasks are given higher values; and as a result, higher chance to sleep compared to others. Second, a shared computing system is proposed whereby active SBSs (edge devices) contribute to the total computing capability. Here, an exhaustive search approach is used to achieve the optimal power saving. We also proved that the shared computing system performs better in terms of response time compared to the disjoint system depending on the number of active SBSs.

This work proposes an optimal security risk management mechanism to holistically minimize the risks of a Denial of Service (DoS) attack and Service Level Agreement (SLA) violations that might unfold at the 5G edge-cloud ecosystem. Using the Semi-Markov Decision Process framework, a cyber risk-aware controller is designed to optimally decide on the admission, placement, and migration of a service taking into consideration a user taxonomy and the service requirements. A new cost structure that balances the targeted security risks as well as the cost and the reward of a secure service provisioning is introduced to pave the way for a safe edge-cloud operation. To proactively restrict the population of untrusted users, we consider security controls in the form of a linear and an exponential cost functions and show that the former represents a more flexible and profitable pathway for a Mobile Network Operator to operate at the expense of an inflated security risk while the latter leads to the opposite outcome. Results show that the baseline mechanism might violate the SLA and expose the edge and the cloud to a DoS attack in levels that are 102, 1012, and 1014times higher than those of the proposed controller.

In this article, we present a data obfuscation technique for smart meter data based on additive correlated noise. This noise is used to mask the data transmitted by different users to a third party, resulting in protection against eavesdroppers, but at the same time enabling the accurate recovery of statistics of the original data for use by the energy supplier. We analyze the proposed technique by studying its obfuscation performance and accuracy of statistics recovered from the masked data as a function of noise correlation with the users' data. Finally, we identify deep learning techniques such as generative adversarial networks for data obfuscation using correlated noise, and show preliminary results to demonstrate their performance in this scenario.

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation-intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending the data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this article, an MEC scenario in the 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the nonorthogonal multiple access protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power-latency tradeoff, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation. Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power-latency tradeoff of the joint optimization of communication and computing resources and the superior performance over other benchmark schemes.

In this paper, an edge device sleeping mechanism is proposed to save energy in cooperative edge computing networks. Energy saving in the proposed scheme is obtained by implementing a software- defined networking (SDN)-assisted interactive On/Off operation on edge devices taking into account the quality of service (QoS) experienced by end-users. First, an optimization problem is formulated to reduce the number of active edge devices under the queueing probability constraint. Herein, edge devices are modeled as M/M/k queueing systems, whereas the square-root staffing rule is used to maintain the queueing probability below desired levels. Then, a load balancing mechanism is carried out to reduce the variations in resource utilization among edge devices. To this end, a discrete-time Markov chain (DTMC)-based algorithm is implemented to achieve the intended load balancing. Results show the effectiveness of the proposed scheme in achieving energy saving and maintaining the queueing delay at controlled levels.

In this paper, we investigate the problem of distributed ferrying transmission in UAV-assisted device-to-device (D2D) communication networks. When drones are performing tasks with given trajectories, terrestrial communication devices can select them for loading data opportunistically, and then drones will offload the data to corresponding receivers in the appropriate later time. For the dynamic multi-device network, there are composite optimization problems including competition of drone selection, time allocation of data loading and offloading, as well as limited channel access. Due to the distributed feature, devices share resources through independent perception and decision making. Therefore, a dynamic hierarchical game is designed for the problem of joint UAV allocation and channel access. Specifically, a predictable dynamic matching market is constructed to address the problem of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. Based on the game model, a distributed hierarchical algorithm is proposed and the property of convergence is discussed. Simulation results confirm that the effective selection of data ferrying approach can improve the transmission performance significantly, while unreasonable optimization approaches may lead to the decline of the transmission performance.

Software-defined networking (SDN) is a key enabling technology of industrial Internet of Things (IIoT) that provides dynamic reconfiguration to improve data network robustness. In the context of smart grid infrastructure, the strong demand of seamless data transmission during critical events (e.g., failures or natural disturbances) seems to be fundamentally shifting energy attitude toward emerging technology. Therefore, SDN will play a vital role on energy revolution to enable flexible interfacing between smart utility domains and facilitate the integration of mix renewable energy resources to deliver efficient power of sustainable grid. In this regard, we propose a new SDN platform based on IIoT technology to support resiliency by reacting immediately whenever a failure occurs to recover smart grid networks using real-time monitoring techniques. We employ SDN controller to achieve multifunctionality control and optimization challenge by providing operators with real-time data monitoring to manage demand, resources, and increasing system reliability. Data processing will be used to manage resources at local network level by employing SDN switch segment, which is connected to SDN controller through IIoT aggregation node. Furthermore, we address different scenarios to control packet flows between switches on hub-to-hub basis using traffic indicators of the infrastructure layer, in addition to any other data from the application layer. Extensive experimental simulation is conducted to demonstrate the validation of the proposed platform model. The experimental results prove the innovative SDN-based IIoT solutions can improve grid reliability for enhancing smart grid resilience.

In this paper, a sparse code multiple access (SCMA)-based edge computing scheme is proposed for Internet-of-Things (IoT) systems. The aim of implementing SCMA, which is a nonorthogonal multiple access resource allocation technique, is to improve network connectivity and maximize data rate provision. The proposed edge-IoT system is investigated under different SCMA configurations to explore the various performance aspects such as connectivity, throughput, task completion time, and complexity. First, the problem is formulated as a data rate maximization problem for SCMA-based heterogeneous networks under power constraints. Then, the problem is subdivided into a power allocation problem, which is solved using the water filling approach, and a codebook allocation problem that is solved using a heuristic algorithm. The results show that the SCMA scheme can significantly improve the IoT performance compared to the conventional orthogonal frequency-division multiple access resource allocation scheme in terms of connectivity, throughput, and task completion time provided that SCMA configurations are suitable with IoT processing capabilities to avoid undesired detection latency.

Cloud radio access network (CRAN) is a promising approach to provide ubiquitous and on demand access to future Internet of Things (IoT) networks. The existing CRANs assume a single cloud which suffers from computational complexity and signaling latency to support massive number of IoT devices in large scale network deployments. This paper focuses on the scheduling of IoT devices in a multicloud IoT network scenario. This paper considers the downlink of an IoT network consisting of multiple clouds, each coordinates a cluster of several base stations (BSs) allowing joint signal processing. The transmit frame of each BS is composed of several resource blocks (RBs). The multiple clouds are linked to the central cloud which performs scheduling of IoT devices and synchronization of transmit frames. The work models the IoT devices to RBs assignment problem considering the intercloud and intracloud interference. The optimization problem maximizes the overall network utilization under practical network constraints. Further, this paper also proposes a low complexity heuristic algorithm to solve the constraint resource allocation problem in linear time. Complexity analysis of proposed algorithm is carried out and simulations results for a number of IoT network scenarios demonstrate that proposed solution is numerically accurate and performs close to the optimal solution.

Emerging device centric communication technologies such as device to device (D2D) communication, devices to device (Ds2D) communication and multi- homing (MH) D2D have been considered as essential part of future 5G networks as well as internet of things (IoT). The device centric communication offers enhanced cellular data rates, high spectral efficiency, reduced latency, improved fairness, better energy efficiency and extended coverage; however, the battery life of end devices is crucial to fully reap benefits of this technology. In this article we propose a new method for device centric communication in IoT system, where multiple source IoT devices can send data to multiple destination IoT devices using multiple interfaces. This method is called devices to devices (Ds2Ds) communication. A tree search algorithm is proposed to select the optimal source IoT devices, destination IoT devices and radio interfaces. The results of proposed Ds2Ds communication are benchmarked against Ds2D and MH- D2D. Extensive simulation has been carried out to compare energy efficiency per source device. The simulation results show the superiority of Ds2Ds over Ds2D in terms of energy efficiency, which, in turn implies better throughput. Ds2DS is superior to MH-D2D in terms of energy consumption per source device, a very good and promising requirement for green communication.

Device to device (D2D) communications are emerging as an essential part of technological solutions to boost data rates in the next generation networks. Cognitive radio (CR) opportunistically utilises spectrum to boost spectral efficiency. CR‐assisted D2D networks will bring the benefits of both D2D as well as CR together in futuristic cellular networks. This study proposes to opportunistically use TV spectrum white spaces. A joint user selection, mode assignment, and power allocation in CR‐assisted D2D networks can definitely yield higher data rates. The proposed study maximises data rate together with users' selection fulfilling various users' power, base station's transmit power, quality of service, and interference related thresholds. This problem is mixed integer non‐linear programming and considered non‐deterministic polynomial time (NP)‐complete. Due to the discrete variables in the problem, finding an optimal solution with the help of an exhaustive search algorithm (ESA) becomes very challenging. The problem gets exponentially complex with the increasing number of user pairs. Thus, the need of another method becomes imperative that yields near optimal solution. Mesh adaptive direct search (MADS) algorithm is considered for solution in the CR‐assisted D2D network resource management problem. Simulation results using MADS yield near optimal solution confirming the suitability of MADS for CR‐assisted D2D networks.

Extensive deployment of heterogeneous small cells in cellular networks results in ultra-dense small-cell networks (USNs). The USNs have been established as one of the vital networking architectures in the 5G to expand system capacity and augment network coverage. However, intensive deployment of cells results in a complex interference problem. In this paper, we propose a distributed multi-domain interference management scheme among cooperative small cells. The proposed scheme mitigates the interference while optimizing the overall network utility. In addition, we jointly investigate OFDMA scheduling, TDMA scheduling, interference alignment (IA), and power control. We model small cells' coordination behavior as an overlapping coalition formation game. In this game, each base station can make an autonomous decision and participate in more than one coalition to perform IA and suppress intra-coalition interference. To achieve this goal, we propose a distributed joint interference management (JIM) algorithm. The proposed algorithm allows each small-cell base station to self-organize and interact into a stable overlapping coalition structure and reduce interference gradually from multi-domain, thus achieving an optimal tradeoff between costs and benefits. Compared with existing approaches, the proposed JIM algorithm provides appreciable performance improvement in terms of total throughput, which is demonstrated by simulation results.

Cloud-radio access network (C-RAN) is regarded as a promising solution to manage heterogeneity and scalability of future wireless networks. The centralized cooperative resource allocation and interference cancellation methods in C-RAN significantly reduce the interference levels to provide high data rates. However, the centralized solution will not be scalable due to the dense deployment of small cells with fractional frequency reuse by small cells, causing severe inter-tier and inter-cell interference turning the resource allocation and user association into a more challenging problem. In this paper, we propose an auction based distributed resource allocation method (ADRA) for a two-tier OFDM based C-RAN system. We investigate a joint user association, radio resource and power allocation problem for small cells underlying a macro C-RAN system. First, we establish a queueing model in C- RAN. We then formulate an optimization problem for joint user association and resource allocation with the aim to minimize mean response time. Resource allocation, interference and queueing stability constraints are considered in the optimization problem. To solve this problem, we propose a distributed method where small cell users and small cell base stations jointly participate using the concept of auction theory. The ADRA method is evaluated via simulations by considering the different ratio of bandwidth utilization.

As the multitude and complexity of the cloud market increases, the evaluation and selection of cloud services becomes a burdensome task for the users. With the extraordinary rise of available services from various Cloud Service Providers (CSPs), the role of cloud brokers has become more and more important. This paper proposes an optimal cloud broker model to address the challenge of optimally allocating multiple cloud system resources to multiple mobile user's requests with different requirements. The cloud brokering mechanism is formulated as a Semi-Markov Decision Process (SMDP) model under the average system cost criteria. The overall system cost takes into consideration the cost of occupying computing resources, the communication costs, the request traffic, as well as various security risk degrees and resource requirements from the various mobile users. Through minimizing the overall system cost, the optimal resource allocation policy is calculated by means of the Value Iteration Algorithm. Some analysis are conducted and numerical results are presented, demonstrating the feasibility of the proposed cloud broker design.

In this paper, a user association and bandwidth allocation approach is proposed for heterogeneous networks (HetNets) using fuzzy logic controllers. Due to the heterogeneous nature of user demands, we categorize the incoming mobile users into low, medium, or high based on their data rate requirements. Similarly, the bandwidth utilization in a given small cell base station (SBS) is quantified and evaluated. Based on the per user demand and bandwidth availability, the controller decides whether a particular user should be associated with that SBS or offloaded to the macro base station (MBS). Moreover, the controller adjusts the fraction of bandwidth allocated to each user based on users data rate requirement and availability of resources towards maximizing the total data rate in the network. The proposed scheme, which is performed by SBSs in a distributed manner, is investigated and compared with two other approaches; namely, the best signal-to-interference-plus-noise ratio (SINR) which is considered as the baseline approach in the literature, and a greedy-based approach where priority in association is given to users demanding higher data rates. Our approach shows promising results regarding the improvement of data rate, bandwidth utilization, and blocking ratio, with an increased number of offloaded users.

The architecture of cloud radio access networks (C-RANs) is envisioned as an attractive paradigm of 5G that takes advantages of both centralized baseband and coordinated multi-point(CoMP) processing in radio access networks. In C-RAN the data rate provisioning can be significantly improved due to the fractional frequency reuse performed by small cells. The dense deployment of small cells, however, incurs severe inter-tier and inter-cell interference turning the user association into a more challenging problem. Moreover, multi-cell association problem occurs in CoMP and control/user planes (C/U planes) splitting based C-RAN system where users are associated with more than one cells to support joint-transmission and reception method. In this paper, we consider multi-cell user association approach taking into account the data rate and aggregated interference of mobile users. We propose the posterior probability based user association and power allocation (P2UPA) method that depends on prior knowledge of the channel state information (CSI). The objective of the proposed method is to maximize the sum data rate of small cell users while maintaining the constraints of aggregated interference, power consumption, and data rate among small cell users. Finally, the sum data rate and energy efficiency performance of P2UPA are evaluated through simulations.

Device-to-Device (D2D) communications underlaying cellular communications has been explored in the literature for a while since the benefits of enhanced sum throughput and more efficient spectrum usage have been proven very promising through the activation of direct transmissions between a pair of devices. To achieve better performance in terms of energy preservation, we consider introducing energy harvesting (EH) mechanism into the traditional D2D model. Our aim is to maximize sum throughput for D2D users without compromising the QoS performance of cellular users (CUs) in an EH-aided communications model. D2D transmissions will only be activated at the beginning of a time slot if there remains enough energy, which is set as a lower threshold, for one-slot data transmission in the batteries of D2D users. Otherwise, it will switch into energy harvesting mode until the energy level in the batteries rises back to an upper threshold. The formulated optimization problem is a nonlinear mixed integer problem. Since it is mathematically challenging to get an optimal solution, we aim for a suboptimal solution with an iterative joint resource block and power resource allocation algorithm. Then we compare this heuristic algorithm with a simplified version where the constraints to make sure that every D2D user has at least one RB for communications are slighted. Numerical simulation results show that energy harvesting mechanism can efficiently power D2D communications underlaying cellular networks. They also corroborate higher sum throughput under different parameter settings of our first proposed approach.

The problem of distributed and dynamic sensing user selection in cognitive systems is studied in this paper, where channel sensing consumes resources and users behavior is distributed. Since users can obtain the channel state from the fusion center, if there are other users sensing the channel, users may enjoy the results sensed by others rather than sense the channel themselves. Such selfish behavior decreases both network utility and individual rewards. Inspired by the social expectation that no one should always enjoy the fruits of others' labor and that one should provide compensation after obtaining a benefit, we propose a distributed sensing compensation algorithm in this paper. The main concept of this algorithm is that after a channel is accessed successfully, users must sense the channel as a compensation for enjoying others' sensing results. The system state probabilities are obtained using Markov chain analysis. We show that there are always an optimal or near optimal number of users sensing the channel and hence, the near-optimal performance is achieved on average using the proposed algorithm. Additionally, the algorithm achieves good fairness performance with respect to the sensing cost. It is further shown that the proposed algorithm is not only suitable for static scenarios but also adaptable for dynamic scenarios with a changing active user set.

Multimedia cloud data center is the core component of the multimedia cloud networks. In the cloud data center, software, service and storage visualizations are realized through the deployment of virtual machines (VMs). The reliability of multimedia service depends on the reliability of the data center as well as the reliability of the deployment and redundancy model of VMs. In this poster, we consider four deployment scenarios for VMs such as parallel, triple modular, triple modular/simplex, K-out-N redundancy model from the reliability point of view. The reliability performance using these redundancy models is presented and compared in this poster.

This study examines subcarrier and optimal power allocation in orthogonal frequency division multiple access based 5G device‐to‐device (D2D) networks. To improve spectrum efficiency, D2D users share same subcarriers with the legacy users using underlay approach. In this approach, it is challenging to design an efficient subcarrier and power allocation method for D2D networks which guarantees the quality of service requirements of legacy users. Therefore, the key constraint is to check the interference condition among D2D and legacy users while allocating the same resources to D2D users. In this study, the authors propose a throughput efficient subcarrier allocation (TESA) and geometric water‐filling based optimal power allocation (GWFOPA) method for multi‐class cellular D2D systems. First, the TESA method selects subcarriers and allocates power equally for D2D users according to their service classes while maintaining interference and data rate constraints. Then, the GWFOPA method is applied to optimise power in a computationally effective way. The objective of TESA and GWFOPA method is to maximise the data rate of each class while maintaining interference constraint and fairness among the D2D users. Finally, the authors present simulation results to evaluate performance of TESA and GWFOPA in terms of throughput, user data rate, and fairness.

Fifth generation (5G) communication networks can achieve high spectral efficiency using sparse code multiple access (SCMA) scheme when large number of users are trying to transmit their data simultaneously. The sparsity of SCMA codewords offers the possibility of applying a low‐complexity message passing algorithm as an alternative to maximum likelihood detector. However, the requirement of densely deployed 5G users is to opportunistically explore new frequencies via cognitive features to overcome spectrum scarcity challenges. In this study, spectrum sensing enables cognitive radio capabilities for the SCMA system applied in millimetre wave (mmWave) 5G communications. Proposed cognitive SCMA system can sense the spectrum holes and adapt the transmission in order to utilise the available subcarriers. Besides, wavelet packet transform based techniques are used instead of conventional Fourier‐based spectrum sensing (FSS) and orthogonal frequency‐division multiple access (OFDMA). Wavelet packet spectrum sensing offers more accurate estimation of frequency and power compared with FSS. On the other hand, wavelet packet multiple access is more flexible and robust against interference compared with OFDMA. The simulation results verify that the proposed method can significantly improve the performance of SCMA system in terms of probabilities of false alarm and detection, and symbol error rate.

This paper proposes an iterative detector for uncoded OFDM signals in cooperative networks, where the information symbols are simply partitioned through a time-domain matrix at the OFDM transmitter. We analytically show that our proposed iterative detector at the partner node converges and can completely recover the user's data from its partitioned version, if sufficient redundancy is inserted in the user's data. For efficient use of the redundancy in the user's data, a coded cooperative transmission based on superposition modulation is proposed. Additionally, a closed-form input-output relationship for the partitioning and reconstruction algorithm in the proposed cooperative scenario is derived. We also obtain closed-form expressions for symbol error rate performance of the proposed coded cooperative scenario over Rayleigh frequency-selective fading channels. Numerical comparisons shed light on the relative merits of the proposed coded cooperation under various inter-user and uplink channel conditions.

This paper investigates the problem of dynamic spectrum access for canonical wireless networks, in which the channel states are time-varying. In the most existing work, the commonly used optimization objective is to maximize the expectation of a certain metric (e.g., throughput or achievable rate). However, it is realized that expectation alone is not enough since some applications are sensitive to fluctuations. Effective capacity is a promising metric for time-varying service process since it characterizes the packet delay violating probability (regarded as an important statistical quality-of-service index), by taking into account not only the expectation but also other high-order statistic. Therefore, we formulate the interactions among the users in the time-varying environment as a non-cooperative game, in which the utility function is defined as the achieved effective capacity. We prove that it is an ordinal potential game which has at least one pure strategy Nash equilibrium. Based on an approximated utility function, we propose a multi-agent learning algorithm which is proved to achieve stable solutions with dynamic and incomplete information constraints. The convergence of the proposed learning algorithm is verified by simulation results. Also, it is shown that the proposed multi-agent learning algorithm achieves satisfactory performance.

Ultra-dense networks can further improve the spectrum efficiency (SE) and the energy efficiency (EE). However, the interference avoidance and the green design are becoming more complex due to the intrinsic densification and scalability. It is known that the much denser small cells are deployed, the more cooperation opportunities exist among them. In this paper, we characterize the cooperative behaviors in the Nash bargaining cooperative game-theoretic framework, where we maximize the EE performance with a certain sacrifice of SE performance. We first analyze the relationship between the EE and the SE, based on which we formulate the Nash-product EE maximization problem. We achieve the closed-form sub-optimal SE equilibria to maximize the EE performance with and without the minimum SE constraints. We finally propose a CE2MG algorithm, and numerical results verify the improved EE and fairness of the presented CE2MG algorithm compared with the non-cooperative scheme.

With the ever increasing data and computational demands from mobile users, heterogenous wireless networks (HetNets) and mobile cloud computing (MCC) have been advocated as a promising solution to meet these demands. Insufficient bandwidth is one of the most important challenges being faced by a successful implementation of the MCC technology due to heavy data traffic. The MCC implementation on HetNet increases the bandwidth available to each base station (BS) by frequency reuse. In this paper, a novel data storage method for big data files is proposed, along with a data correction technique to deal with the issue of failure of a data chunk retrieval. The proposed algorithm exploits the multiple paths that are available between a user and the cloud storage system in a MCC-HetNet environment. Since the bottleneck in the MCC is the wireless link between the user equipment (UE) and the BS, we have implemented the algorithm on the wireless links between the UE and the BS. The simulated results show that the proposed method outperforms the conventional data storage method between the mobile device and the cloud system.

In order to meet requirements of local services in next generation cellular networks, Device to device (D2D) communication is gaining popularity as strategy to maximize overall system throughput. This paper presents a joint resource allocation (JRA) technique with the aim to maximize system throughput while observing the limitations of power and interference. The optimization problem presented is mixed integer, non linear and NP- hard. Increasing discrete variables in the problems, enhances the computational complexity exponentially and exhaustive search becomes formidable task. The problem is solved using mesh adaptive search (MADS) algorithm. It is shown that proposed strategy is suitable for solution of such kind of combinatorial problem. Computational convergence towards solution with adequate iterations shows efficacy of the proposed strategy. System throughput maximization results of simulations show suitability of the suggested scheme in this paper compared to other techniques.

The rapid growth of modern wireless applications results in spectrum and energy scarcity. Cognitive radio (CR) technology is pivotal to resolve spectrum shortage. However, energy consumption is a critical issue in CR networks (CRNs) due to the unique functionality of spectrum sensing. Besides RF energy harvesting has come up as a potential solution to provide energy to CR devices. In this paper, we first provide an overview of spectrum sensing in CRNs with RF energy harvesting. Then, we propose a framework for multi-band spectrum sensing in CRNs with RF energy harvesting. We formulate a problem to optimize sensing time for throughput maximization while protecting primary users and keeping a minimum level of residual energy. Simulation results show the performance of multi-band cooperative spectrum sensing in terms of average throughput and energy harvested.

In machine-type communication (MTC) over LTE cellular network, resource allocation problem becomes a challenging issue as MTC devices compete with LTE users for the same radio resources. Compared to the LTE users, MTC devices generate more uplink traffic requests and signalling which results in congestion arises in uplink transmission when a large number of devices send connection requests simultaneously. In this paper, we consider the resource allocation problem for MTC over LTE networks in which LTE users, MTC devices, and relay nodes co-exist. Firstly, we derive an analytical model which detects overload condition in the base station (eNB) and estimates available resources for MTC devices. We propose a relay-assisted radio resource allocation (R3A) scheme for MTC devices which utilize dynamic access class barring method in overload situations when the number of resource blocks are less than the MTC devices. In the case when the number of MTC devices is less than the available resources than we use relay nodes to maximize the throughput of MTC system. Numerical results demonstrate the significance of proposed R3A method. The results are evaluated in terms of access success probability, access drop percentage, and MTC channel capacity.

It is our pleasure to write the Editorial for the Special Issue on Evolution and Development of 5G Wireless Communication Systems. Upon conclusion of fourth generation (4G) cellular network standardization tasks a few years ago, the direction of research has started to shift systematically towards fifth generation (5G) communication systems. The difference between 4G and 5G is not limited to the increased throughput and performance. 5G systems are supposed to be flexible to accommodate heterogeneous traffic and devices, and various applications with different quality-of-service (QoS) requirements. Particularly, the goal is to take full benefit of advances in technology including cloud computing, Internet of Things (IoT), ultra-dense networks, massive MIMO, device-to-device communication, pervasive and social computing. In order to meet stringent goals, 5G communication systems build upon the evolution of the existing technologies and the development of the new technologies mentioned above. The Special Issue contains 11 papers, each paper covers the subject from different prospective, and thus, offer readers a holistic view of different research challenges currently under investigation by research communities. The papers can be grouped under following topics: C. Hua et al. present a paper entitled “Wireless backhaul resource allocation and user-centric clustering in ultra-dense wireless networks”. It considers optimization of resource allocation in wireless backhaul links and user-centric clustering in the access links. The objective is to maximise the weighted sum rate of all users under the backhaul resource constraints. An iterative algorithm is proposed to solve the transformed problem based on its special property. Simulation results show that the proposed algorithm outperforms other existing schemes under different network settings. Z. Wang et al. present a paper entitled “Interference pricing in 5G ultra-dense small cell networks: a Stackelberg game approach” which models the scenario as a Stackelberg game, where the macrocell base stations (MBS) act as the leader and all small cell base stations (SCBSs) as followers. Simulation results show the correctness of the analysis and the significant benefits when the power control and channel allocation are jointly considered in the proposed schemes. Z. Kaleem et al. present “Public safety users’ priority-based energy and time-efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long-term evolution-advanced systems”, which proposes a time and energy-efficient contention-resolving device discovery resource allocation (TEECR-DDRA) scheme that has the capability to enhance the success ratio for discovery of D2D users by reducing collisions among users. Moreover, the proposed TEECR-DDRA scheme has the ability to prioritise PS users to meet their QoS and latency requirements. System-level simulations show that the proposed TEECR-DDRA scheme performs remarkably well under D2D network. M. T. Gul et al. present a paper entitled “Merge-and-forward: a cooperative multimedia transmissions protocol using RaptorQ codes”, proposing a cooperative multimedia transmission protocol based on a novel merge-and-forward relaying and the best relay selection (RS) schemes. Moreover, to combat the packet loss for enhanced and reliable video delivery, they adopt application layer forward error correction scheme which is based on the most improved and advanced version of fountain codes (i.e., RaptorQ codes). They evaluate the performance of the proposed scheme in terms of decoding failure probability, decoding overhead, peak signal-to-noise ratio, and mean opinion score. K. Yang et al.'s paper “Edge aware cross-tier base station cooperation in heterogeneous wireless networks with non-uniformly-distributed nodes” investigates the cross-tier base station (BS) cooperation in non-uniform heterogeneous networks where the distribution of pico BSs (PBSs) is modelled as Neyman–Scott cluster process. The authors propose an edge aware cross-tier cooperation scheme to improve the performance of edge hotspot users that have weaker signal-to-interference-plus noise ratio (SINR). Stochastic geometry is utilised to derive the SINR and energy efficiency performance of the proposed scheme, which is compared with other classical schemes such as full cooperation (FC) and traditional non-cooperation scheme. Y. Cai et al. present “Secure transmission in the random cognitive radio networks with secrecy guard zone and artificial noise” which proposes a simple and decentralised secure transmission scheme by jointly incorporating the secrecy guard zone and artificial noise in cognitive radio networks. Numerical results show how the system parameters affect the achievable maximum secrecy throughput, the optimal transmission power and the optimal power allocation between the information-bearing signal and the artificial noise. Y. Sun et al. present “Local altruistic coalition formation game for spectrum sharing and interference management in hyper-dense cloud-RANs” and investigate the spectrum sharing and interference management in hyper-dense cloud radio access networks (C-RANs). The authors formulate this problem as a local altruistic coalition formation game (LACF) with externalities. The authors propose a distributed coalitional formation algorithm based on modified recursive core to obtain the final stable coalition partition. Furthermore, the system stability, convergence and complexity of the proposed algorithm are analysed. W. Chang et al. paper “Effects of non-uniform quantisation on the interference mitigation using multi-cell multiple-input and multiple-output coordinated beamforming” proposes a low complexity cumulative distribution function (CDF)-based non-uniform quantisation method with a limited number of feedback bits for applying more quantisation levels to represent feedback CSI, which occurs with higher probability. The simulation results proved the higher transmission rate, particularly in cases with fewer feedback bits. D. Liu et al.'s paper “Self-organising multiuser matching in cellular networks: a score-based mutually beneficial approach” studies the self-organising user assignment problem for the multi-user cooperation network. Furthermore, the multi-user assignment problem is formulated as a one-to-one matching game, in which idle users and active users rank one another individually based on their own preference. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results. D. C. Araújo et al.'s paper “Massive MIMO: survey and future research topics” presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research. R. Sun et al. present the paper “Transceiver design for cooperative nonorthogonal multiple access systems with wireless energy transfer”. The paper considers an energy harvesting-based cooperative non-orthogonal multiple access (NOMA) system. Transmitter beamforming, power splitter and receiver filter are jointly designed to maximise rate with the predefined QoS constraint of weaker node and the power constraint of node which simultaneously sends independent signals to a stronger node and weaker node. Since the problem is non-convex, they propose an iterative approach to solve it. Moreover, a zero-forcing based low-complexity solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission. All of the papers in Special Issue show that 5G systems can support the specialized use cases which are not supported by the current access systems. In addition, authors investigated the issues related to backhaul for 5G systems and latency reduction. The integration of new technologies with the evolved current systems bring tremendous improvement in 5G systems. Alagan Anpalagan received the B.A.Sc., M.A.Sc., and Ph.D. degrees in electrical engineering from the University of Toronto, Toronto, ON, Canada. In 2001, he joined the Department of Electrical and Computer Engineering, Ryerson University, Toronto, where he was promoted to Full Professor in 2010. He served the department as the Graduate Program Director (2004–2009) and the Interim Electrical Engineering Program Director (2009–2010). He directs a research group working on radio resource management and radio access and networking areas within the WINCORE Lab. During his sabbatical (2010–2011), he was a Visiting Professor with Asian Institute of Technology and a Visiting Researcher with Kyoto University, Kyoto, Japan. His industrial experience includes working at Bell Mobility, Nortel Networks, and IBM Canada. He has coauthored three edited books, namely, Design and Deployment of Small Cell Networks (Cambridge University Press, 2014), Routing in Opportunistic Networks (Springer, 2013), and Handbook on Green Information and Communication Systems (Academic Press, 2012). His current research interests include cognitive radio resource allocation and management, wireless cross-layer design and optimization, cooperative communication, machine-to-machine communication, small cell networks, and green communications technologies. Dr. Anpalagan has served as an Associate Editor of the IEEE Communications Surveys & Tutorials since 2012 and Springer Wireless Personal Communications since 2009. Adnan Shahid received the B.Eng. and the M.Eng. degrees in computer engineering with communication specialization from the University of Engineering and Technology, Taxila, Pakistan in 2006 and 2010, respectively, and the Ph.D degree in information and communication engineering from the Sejong University, South Korea in 2015. He is currently working as a Postdoctoral Researcher at iMinds/IBCN, Department of Information Technology, University of Ghent, Belgium. From Sep 2015 – Jun 2016, he was with the Department of Computer Engineering, Taif University, Saudi Arabia. From Mar 2015 – Aug 2015, he worked as a Postdoc Researcher at Yonsei University, South Korea. From Aug 2012 – Feb 2015, he worked as a PhD research assistant in Sejong University, South Korea. From Mar 2007 – Aug 2012, he served as a Lecturer in electrical engineering department of National University of Computer and Emerging Sciences (NUCES-FAST), Pakistan. He was also the recipient of the prestigious BK 21 plus Postdoc program at Yonsei University, South Korea. He is a member of IEEE and actively involved in various research activities. He is also serving as an Associate Editor at IEEE Access Journal and Annals of Telecommunication Journal. His research interests includes the next generation wireless communication and networks with prime focus on resource management, interference management, cross-layer optimization, self-organizing networks, small cell networks, device to device communications, machine to machine communications, 5G wireless communications, etc. Waleed Ejaz (S’12, M’14, SM‱16) is a Senior Research Associate at the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada. Prior to this, he was a Post-doctoral fellow at Queen's University, Kingston, Canada. He received his Ph.D. degree in Information and Communication Engineering from Sejong University, Republic of Korea in 2014. He earned his M.Sc. and B.Sc. degrees in Computer Engineering from National University of Sciences & Technology, Islamabad, Pakistan and University of Engineering & Technology, Taxila, Pakistan, respectively. He worked in top engineering universities in Pakistan and Saudi Arabia as a Faculty Member.His current research interests include Internet of Things (IoT), energy harvesting, 5G cellular networks, and mobile cloud computing. He is currently serving as an Associate Editor of the Canadian Journal of Electrical and Computer Engineering and the IEEE ACCESS. In addition, he is handling the special issues in IET Communications, the IEEE ACCESS, and the Journal of Internet Technology. He also completed certificate courses on Teaching and Learning in Higher Education from the Chang School at Ryerson University. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in the Centre for Communication Systems Research (CCSR) at the University of Surrey, UK. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has lead role in a number of multimillion international research projects including the new physical layer work area for 5G innovation centre at Surrey. He has supervised 17 successful PhD graduates and published over 150 peer-reviewed research papers including more than 20 IEEE Journals. His research interests include the derivation of information theoretic performance limits, energy efficient design of cellular system and learning/self-organizing techniques for optimization of cellular system operation. He is a senior member of IEEE and a Fellow of Higher Education Academy (FHEA), UK. Kandeepan Sithamparanathan has a PhD from the University of Technology, Sydney and is currently with the School of Electrical and Computer Engineering at RMIT University. He is also a NICTA Researcher at the NICTA Victoria Research Laboratory (VRL, Melbourne). In the past he had worked with the National ICT Australia (Canberra Research Laboratory) and CREATE-NET (Trento). Kandeepan served as one of the Vice Chairs for the IEEE Technical Committee on Cognitive Networks (TCCN) and has published a book together with Dr Andrea Giorgetti from the University of Bologna, Italy, titled ‘Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation and Localization’, published by Artech House (Boston). He currently Chairs the IEEE VIC Communication Society Chapter and is a Senior Member of the IEEE. He was awarded as one of the best IEEE Reviewers by the IEEE Communications Society. Kandeepan has published around ninety peer reviewed journal and conference papers. He has chaired several IEEE workshops and other conferences. His research interests are in 5G communications, cognitive radios and signal processing techniques. Yuhua Xu received his B.S. degree in Communications Engineering, and Ph.D. degree in Communications and Information Systems from College of Communications Engineering, PLA University of Science and Technology, in 2006 and 2014 respectively. He has been with College of Communications Engineering, PLA University of Science and Technology since 2012, and currently as an Assistant Professor. His research interests focus on opportunistic spectrum access, learning theory, game theory, and distributed optimization techniques for wireless communications. He has published several papers in international conferences and reputed journals in his research area. He served as Associate Editor for Wiley Transactions on Emerging Telecommunications Technologies and KSII Transactions on Internet and Information Systems. In 2011 and 2012, he was awarded Certificate of Appreciation as Exemplary Reviewer for the IEEE Communications Letters. He was selected to receive the IEEE Signal Processing Society's (SPS) 2015 Young Author Best Paper Award, and the Funds for Distinguished Young Scholars of Jiangsu Province in 2015.

Millimetre‐wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand‐fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet‐based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet‐based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier‐based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet‐based system compared with its Fourier‐based counterpart over the 60 GHz mmWave channel.

In this study, the authors consider two multiple‐antenna transceivers exchanging information through a relay‐assisted network using a single‐carrier communication scheme. The authors assume that the propagation delay in different relaying path is negligible and the relay nodes are synchronous. As a result, the end‐to‐end multipath channel is not frequency‐selective (time‐dispersive) and hence, the successive arriving signals at the transceivers do not interfere with each other. Otherwise, inter‐symbol‐interference (ISI) will be inevitable and cyclic insertion and removal matrices will be required to combat ISI. In such a two‐way network, the relay nodes harvest energy from the surrounding environment and utilise this energy to forward their received messages using a harvest‐then‐forward protocol. For different receiver diversity combining techniques, the authors design an optimal relay beamforming to maximise the quality of the received signals at the transceivers subject to the energy casualty constraint at the relay nodes (the energy consumed for transmission of each block cannot exceed the accumulative harvested energy). For each diversity combining technique, a closed‐form solution is obtained for the optimal signal‐to‐noise ratio (SNR) that shows how adjusting the data transmission rate of the transceivers and the amount of energy harvested at the relays affects the received SNR.

Spectrum auction is one of the most effective solutions to allocate the spectrum resource following the market rules and has attracted much attention from both academia and industry. However, most of the existing studies assume that the spectrum buyers' demands are homogeneous and the interference relationship is fixed without any change with the variation of spectrum. Furthermore, the economical efficiency of auction outcome has not drawn enough attention. That motivates us to design an auction scheme to jointly consider the multi-demand of buyers, heterogeneous spectrum, and economical efficiency. In this paper, we propose a novel overlapping coalition formation-based double auction, called VERACITY, to address this problem. The auctioneer groups the conflict free buyers into the same coalition and allows a buyer to join multiple coalitions based on the heterogeneous demand. Dynamic overlapping coalition formation implemented by the auctioneer is to find the approximately optimal coalition structure corresponding to the economical efficiency outcome, i.e., maximizing the social welfare. Furthermore, we prove that VERACITY is individually rational, budget balanced, truthful, and economically efficient. Simulation results are presented to show the convergence and effectiveness of the proposed VERACITY.

In this paper, we consider a multichannel cognitive radio network, where cooperative secondary users have heterogeneous sensing ability in terms of their sensing accuracy. We employ a group-based cooperative spectrum sensing (CSS) scheme in which cooperating secondary users are grouped such that different groups are responsible for sensing different channels. In this group-based CSS scheme, channels sharing the same cooperating users are scheduled to sense in different sensing rounds. In this work, we propose adaptively assigning the heterogeneous cooperating secondary users to different groups to maximize the throughput efficiency while maintaining a predefined sensing accuracy. To this end, we analytically derive a closed-form expression for the throughput efficiency in terms of the average opportunistic throughput and average sensing overhead. We also formulate the throughput efficiency maximization problem for heterogeneous secondary users as a nonlinear binary programming problem, which is computationally intractable. We then propose three efficient adaptive assignment heuristics that perform the assignment of users to groups and the assignment of those groups to the sensing rounds such that the throughput efficiency is maximized. Simulation results demonstrate that our proposed assignment heuristics can achieve near optimal performance with low computational complexity and can also improve the throughput efficiency significantly compared to the existing nonadaptive assignment and sequential CSS schemes.

Extensive deployment of small cells in heterogenous cellular networks introduces both challenges and opportunities. Challenges come with the reuse of the limited frequency resource for improving spectral efficiency, which always introduces serious mutual inter- and intracell interference between or among small cells and macrocells. The opportunities refer to more potential chances of inter- and intratier cooperations among small cells and macrocells. Energy efficiency will be a critical performance requirement for future green communications, especially when small cells are densely deployed to enhance the quality of user's experience. We exploit the potential cooperation diversities to combat the interference and energy management challenges. To capture the complicated interference interaction and also the possible coordination behavior among small cells and macrocells, this paper proposes a novel bargaining cooperative game (BCG) framework for energy efficient and interference-aware power coordination in a dense small cell network. In particular, a new adjustable utility function is employed in the BCG framework to jointly address both the spectral efficiency and energy efficiency issues. Using the BCG framework, we then derive the closed-form power coordination solutions and further propose a joint interference-aware power coordination scheme (Joint) with the considerations of both interference mitigation and energy saving. Moreover, a simplified algorithm (Simplified) is presented to combat the heavy signaling overhead, which is one of the significant challenges in the scenario of extensive deployment of small cells. Finally, numerical results are provided to illustrate the effectiveness of the proposed Joint and Simplified schemes.

Abstract Device‐to‐device (D2D) communications are being considered a way forward to achieve higher data rate targets for futuristic wireless networks. D2D introduces interference among cellular users and D2D users. A joint resource allocation (JRA) strategy in cellular network with D2D functionality can definitely enhance overall data rate. The strategy under consideration maximizes the overall data rate of cellular network besides meeting threshold of power and interference constraints. The JRA is a class of mixed integer non‐linear constraint optimization problems and is NP hard. Because of discrete nature of variables in the problem, optimal solution performs extensive search of integer variables, and problem becomes exponentially complex with the increasing number of user pairs. In this paper, mesh adaptive direct search algorithm is applied to solve the aforementioned problem. The algorithm is suitable for complex problems of combinatorial nature to solve the JRA strategy in D2D. The proposed algorithm converges to optimal solution within acceptable computational iterations. Simulation results of system capacity and interference also demonstrate the suitability of the proposed approach viz‐a‐viz other algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Heterogeneous cellular networks (HetNets) have emerged as a new promising paradigm to further enhance capacity, where multiple types of low power smallcells are overlaid in a high power macrocell. They provide more opportunities to explore the potential cognition and cooperation diversities to improve the spectral efficiency. On the other hand, energy efficiency is a critical performance metric, which deserves more attention from academia, industry, and standardization, in particular, in scenarios where smallcells are densely deployed. In this paper, a systematic architecture is presented to efficiently utilize network resources and thus improve the overall energy efficiency. The architecture is referred to as OCRT because it combines a multi‐tier energy efficiency considerations of operators, core networks, radio access networks and terminals (e.g., OCRT). Furthermore, a corresponding triply‐cycle‐based functional structure is proposed for the OCRT to make various interactions between the corresponding functional entities clear. An implementation scheme of OCRT based on cognitive information interaction cycle and an energy efficiency‐aware protocol is presented. Finally, a use case of the presented OCRT green design is provided for energy efficiency optimization in a cognition‐and‐cooperation‐characterized HetNet. Copyright © 2015 John Wiley & Sons, Ltd.

A major problem faced by machine type communication (MTC) devices in machine to machine (M2M) communication is the congestion and traffic overloading when incorporating into LTE Advanced networks. In this paper, we present an approach to tackle this problem by providing an efficient way for multiple access in the network and minimizing network overload. We consider the random access network (RAN) between the LTE base stations and MTC devices in the cell. We propose an unsupervised learning algorithm, based on Q-learning, as a means of base station selection scheme where MTC devices continuously adapt to changing network traffic and decide which base station is to be selected on the basis of QoS parameters. Simulation results demonstrate that the proposed algorithm helps MTC devices achieve better performance and, therefore, enhances the M2M communication performance.

Cooperative communications take full advantage of the broadcast nature of wireless channels and create spatial diversity, thereby achieving tremendous improvement in the network performance. This paper presents an energy-efficient cooperative MAC (EECO-MAC) protocol using cooperative communication and power control techniques in mobile ad hoc networks. The best partnership selection algorithm, which takes energy consumption into consideration is proposed to select the optimal cooperative helper for the cooperative transmission. Through exchanging control packets, the optimal transmission power is allocated for senders to transmit data packets to receivers. In order to reduce the influence induced by interference, space-time back off and time-space back off algorithms are proposed. Simulation results show that EECO-MAC consumes less energy and prolongs the network lifetime compared to IEEE 802.11 DCF and Coop MAC.

Power consumption analysis is the first step in the process of dimensioning the cell size for any mobile network. Therefore, improving power utilization emerges as one of the major challenges to the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) Release 12 coordinated multipoint (CoMP) transmission and reception scheme. This paper proposes new models for heterogeneous deployment of CoMP macrocells overlaid with the emerging neighborhood femtocell network in order to meet the power efficiency requirements. Two case studies are mathematically analyzed: Firstly, neighborhood femtocells are allocated at the macrocell edge line in order to reduce the ultimate range of CoMP transmission while coverage is extended using low power femtocell transmissions. Secondly, femtocells are deployed at selected CoMP intra-cell regions in order to improve neighborhood coverage; transmissions are coupled between femtocells and CoMP to provide the necessary converge across the cell area and transfer connections from macrocell to low power femtocells. A comparative study is performed to show the power efficiency obtained through scaling the network area at both cell-edge and sub-cell areas. The analysis show new strategies for deploying small cells under CoMP macrocell umbrella in order to minimize the power consumption figures compared to only macrocell based network.

In this paper, we propose an optimal power allocation scheme that minimizes the energy per bit of worst user (i.e., the user with highest energy per bit) in a multiuser wireless communication network. The problem of determining energy efficient power allocation to improve the worst user is a constrained non-convex nonlinear fractional programming problem. We propose an iterative energy efficient power allocation algorithm that guarantees optimal solution. We use parametric equivalent formulation to get the optimal solution. Numerical solutions obtained using simulations are presented and compared with equal power allocation scheme.

Energy efficiency is one of the most critical design issues in wireless sensor networks (WSNs). Recently, RF energy transfer emerges as a promising solution to enhance energy efficiency. In RF energy transfer, energy is supplied to wireless networks through dedicated energy transmitters. WSNs can be equipped with the RF energy charging capabilities such that the WSNs are referred as wireless rechargeable sensor networks. This paper aims to 1) optimally place the energy transmitters and 2) determine optimal number of energy transmitters in WSNs with RF energy transfer. For optimal placement of energy transmitters, a trade-off between maximum energy charged in the network and fair distribution of energy is studied. We present a mechanism by defining a utility function to maximize both total energy charged and fairness. For optimal number of energy transmitters, an optimization problem is formulated and solved while satisfying the constraint on minimum energy charged by each sensor node. Simulation results illustrate the performance of WSNs with RF energy transfer in terms of average energy charged, fairness, and optimal number of energy transmitters.

Cellular based M2M systems generate massive number of access requests which create congestion in the cellular network. The contention-based random access procedures are designed for cellular networks which cannot accommodate a large number of M2M traffic. In this paper, a contention-based slotted Aloha random access procedure for M2M network is first analyzed using different performance metrics. The impact of massive M2M traffic over cellular traffic is studied based on different arrival rate, random access opportunity and throughput. Then, an analytical model of selecting a base station (eNB) along with load balancing is developed. Finally, a dynamic access class barring method and RAN level congestion control mechanism by selecting the appropriate eNB is presented and evaluated with M2M traffic.

We study the energy-optimal subcarrier power allocation for OFDM-based cognitive radio (CR) networks. A CR transmitter communicates with CR receivers on a channel borrowed from licensed primary users (PUs) when PUs' transmission are detected absence on those channels. Due to non-orthogonality of the transmitted signals in the adjacent bands, both the PU and the secondary user (SU) cause mutual-interference to each other. We assume that the statistical channel state information between the cognitive transmitter and the primary receiver is known. The secondary transmitter maintains a specified statistical mutual-interference limits for all the PUs communicating in the adjacent channels. We propose iterative method based on Dinkelbach theorem using parametric objective function for the fractional programming problem. We show analytically that under a special case, the optimal power allocation follows waterfilling algorithm. Numerical results are given to show the effect of different parameters on the energy-efficiency.

In this work, a bi-directional network is considered where two single-antenna transceivers communicate with each other through multiple energy harvesting relay nodes. This communication is accomplished using a single-carrier scheme by exchanging the signal blocks between the transceivers. Since the distances between the transceivers through different relaying paths are not the same, the propagation delay of each path is different than the others. As a result, at sufficiently high data rates, the end-to-end multi-path channel is time-dispersive (frequency-selective) and inter-symbol-interference (ISI) is not avoidable at the both transceivers (in our single-carrier scheme the received signal blocks experience inter-block-interference (IBI)). A relay selection scheme is proposed to combat such an IBI by switching on the relay nodes that contribute to a single tap of the end-to-end channel and turning off the remaining relays. For each tap of the channel, an optimal relay beamformer is designed in order to maximize the transmission rate when the energy consumed by the relays to transmit a signal block is restricted to the harvested energy so far and a minimum desired signal-to-noise ratio (SNR) at the both transceivers is guaranteed. The performance of such an energy harvesting network design is illustrated by presenting the simulation results.

Low power wireless sensors, personal wireless hub (PWH) and receivers can reduce the workload of the paramedic staff in a hospital. In this paper, we use multiple PWHs to transfer sensor data to the main central controller, which helps the wireless sensor devices. A well designed multiple PWH assignment and power control scheme can reduce the electromagnetic and in-band interference induced to the other medical devices in the hospital. We propose a framework and low complexity algorithm for interference aware joint power control and multiple PWH assignment (IAJPCPA) in a hospital building with cognitive radio capability. The proposed IAJPCPA is a non-convex mixed integer non-linear optimization problem (NC-MINLP) which is generally NP-hard. We present an efficient PWH assignment and power control scheme for IAJPCPA. We also propose an upper bound on the IAJPCPA that converts the non-convex problem into a convex optimization problem. We examine the effect of different system parameters.

Heterogeneous networks (HetNets), where multiple low power small cell eNodeBs (SeNBs) are overlaid on the coverage of a high power macrocell eNodeB (MeNB), serve as promising paradigm to enhance spectral efficiency of future cellular wireless networks. To capture the complicated interference interaction and also the coordination behavior among MeNB and SeNBs, this paper proposes a bargaining cooperative game (BCG) framework for interference-aware power coordination in a HetNet. In particular, a new adjustable utility function is employed in the BCG framework to jointly address the spectral and energy efficiencies as well as to achieve the optimal tradeoff between them. We then derive the closed-form power coordination solutions and further propose an interference-aware power coordination scheme with the considerations of both interference mitigation and energy saving. Finally, the numerical results are provided to illustrate the convergence property and efficiency of the proposed power coordination scheme.

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.

Abstract A utility‐based distributed data routing algorithm is proposed and evaluated for heterogeneous wireless sensor networks. It is energy efficient and is based on a game‐theoretic heuristic load‐balancing approach. It runs on a hierarchical graph arranged as a tree with parents and children. Sensor nodes are considered heterogeneous in terms of their generated traffic, residual energy and data transmission rate and the bandwidth they provide to their children for communication. The proposed method generates a data routing tree in which child nodes are joined to parent nodes in an energy‐efficient way. The principles of the Stackelberg game, in which parents as leaders and children as followers, are used to support the distributive nature of sensor networks. In this context, parents behave cooperatively and help other parents to adjust their loads, while children act selfishly. Simulation results indicate the proposed method can produce on average more load‐balanced trees, resulting in over 30%longer network lifetime compared with the cumulative algorithm proposed in the literature. Copyright © 2014 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) have received significant research focus due to their widespread applicability in military, health care, agriculture, environment monitoring applications. These applications require WSNs to be self-organized and mobile. Clustering techniques are particularly important in the success of WSN implementations especially when sensor nodes are moving at different speeds. Many existing clustering algorithms assume that sensor nodes move at the same speed which is not true in many practical cases. Thus, in this paper, we introduce a velocity-based clustering algorithm and implement a relay placement technique in order to maintain seamless network connectivity. Simulation results show that the packet loss rate of the proposed algorithm is much lower than the existing LEACH and HEED clustering protocols.

We apply multiple imputations particle filter (MIPF) to deal with non-linear state estimation problem in the presence of missing data. We use imputations to replace the missing data. We present the convergence analysis of MIPF and show that it is almost surely convergent.We also present examples with a nonstationary growth model and dual-sensor bearing-only tracking, which demonstrate that MIPF can effectively deal with missing data in nonlinear problems.

In this paper, we propose an adaptive group-based cooperative spectrum sensing scheme to maximize the spectrum efficiency without degrading the sensing accuracy. We consider cooperating secondary users with non-identical energy thresholds and average signal-to-noise ratio which will be represented by non-identical local probabilities of detection and false alarm. We formulate the throughput efficiency maximization problem for non-identical secondary users subject to a minimum requirement on the sensing accuracy. To solve the formulated problem, we propose an adaptive user and group assignment algorithm that minimizes the sensing overhead and maximizes the throughput efficiency. Simulation results demonstrate that our proposed algorithm can effectively improve the throughput efficiency with guaranteed sensing accuracy as compared to the non-adaptive and sequential cooperative sensing schemes.

In this paper, we apply the cross entropy optimization (CEO) to the problem of joint multiple relay assignment and source/relay power allocation (JMRAPA) in green cooperative cognitive radio (GCCR) networks. We use shared-band amplify and forward relaying for cooperative communication in the JMRAPA problem. The proposed JMRAPA maximizes the total rate and minimizes the greenhouse gas emissions in GCCR networks. It is a non-convex combinatorial optimization problem and is NP-hard. We propose to use concave upper bound that makes it a convex problem. The effectiveness of the proposed CEO-based method is shown through simulation results.

We investigate the optimal allocation of power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying technique. The power allocation in DF relaying for green cooperative cognitive radio with an objective of maximising energy‐efficiency is a constraint non‐linear non‐convex fractional programming problem. The optimisation needs to satisfy the primary users interference constraints and secondary users outage constraints. The authors present the optimal power allocation in DF relaying by transforming the constraint non‐linear non‐convex fractional power allocation problem into a concave fractional programme by using Charnes–Cooper transformation. The authors also present an iterative algorithm that uses parametric transformation and guarantees ε ‐optimal convergence. The convergence of the iterative algorithm is proved and numerical results obtained for cooperative cognitive radio network are presented with different network parameter settings.

The authors study the opportunistic spectrum access techniques for hybrid overlay–underlay cognitive radio networks. A secondary user (SU) chooses a channel, transmission mode and adjusts its power so that the interference limit is not crossed and its throughput is maximised. The authors assume that multiple primary user (PU) channels are available and the SU conducts spectrum sensing to access the channels. The objective is to maximise the throughput by switching between the overlay and underlay transmission modes. Using finite‐horizon partially observable Markov decision process framework, the authors first study the optimal policies, where the PU is assumed to be in busy, concurrent or idle state, and the SU either stays idle or transmits with any of the two designed power levels. Although the PU's states are hidden, their activity statistics, transmission ranges and interference thresholds are assumed to be known. Via Monte Carlo simulation, the authors evaluate the performance of physical layer optimal policy (PLOP) and cross‐layer policy (CLAP) and compare them with a fully observable optimal policy. The beliefs in each slot for both policies are updated using the forward algorithm based technique. Simulation results show that the proposed CLAP is more throughput efficient than the conventional PLOP.

In this study, the authors propose a reliability‐based cooperative decision fusion scheme which considers the reliability of the secondary users (SUs') local decisions when making a final decision at the fusion centre in cognitive radios. The authors use past information about the local and global decisions to estimate the reliability of the sensing decision obtained from each SU and then reflect this difference in reliability in the weighting of each SU's decision. The authors formulate the problem of minimising the probability of sensing error at the fusion centre, subject to a limit on the network probability of detection, as a constrained non‐linear integer programming problem. To solve this problem, the authors implement an iterative solution based on the generalised non‐linear Lagrangian relaxation. Simulation results show that our proposed solution can achieve optimal results with zero duality gap using only a few number of iterations. Results also demonstrate that the proposed reliability‐based fusion scheme provides performance improvement, in terms of the minimum probability of sensing error, when compared to the OR and AND fusion schemes. This improvement is more pronounced as the number of users increases since by assigning weights differently to users, the multiuser diversity gain is better exploited.

Strategic bargaining cooperative games have found extensive applications to resource management in wireless networks. In this survey, basics of a strategic bargaining game and solution concepts are firstly presented. Geometrical interpretations are introduced to better understand real meanings of them. Then, the authors survey the applications of various strategic bargaining games for the emerging wireless networks, where the authors concentrate on several interesting problems based on their previous systematic studies: (i) distributed resource management design for cognitive radio networks based on geometrical interpretation of the cooperative solution; (ii) asymmetric bargaining modelling for green communications; (iii) a unified utility tradeoff design between spectral and energy efficiency in heterogeneous cellular networks; (iv) the cooperative rate splitting game for Long Term Evolution‐coordinated multi‐point system; and (v) a general bargaining formulation with different tradeoffs between efficiency and fairness. In addition, the authors survey the applications of strategic bargaining games to cooperation incentive mechanism, bargaining game on capacity region of interference channel and multiuser and multimedia applications. Finally, challenges and potential research direction are summarised in this work.

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.

ABSTRACT In wireless sensor networks, achieving load balancing in an energy‐efficient manner to improve the network lifetime as much as possible is still a challenging problem because in such networks, the only energy resource for sensor nodes is their battery supplies. This paper proposes a game theoretical‐based solution in the form of a distributed algorithm for constructing load‐balanced routing trees in wireless sensor networks. In our algorithm, load balancing is realized by adjusting the number of children among parents as much as possible, where child adjustment is considered as a game between the parents and child nodes; parents are considered as cooperative players, and children are considered as selfish players. The gained utility by each node is determined by means of some utility functions defined per role, which themselves determine the behavior of nodes in each role. When the game is over, each node gains the maximum benefit on the basis of its utility function, and the balanced tree is constructed. The proposed method provides additional benefits when in‐network aggregation is applied. Analytical and simulation results are provided, demonstrating that our proposed algorithm outperform two recently proposed benchmarking algorithms [1, 2], in terms of time complexity and communication overhead required for constructing the load‐balanced routing trees. Copyright © 2012 John Wiley & Sons, Ltd.

Recent advances in body area network technologies such as radio frequency identification and ham radio, to name a few, have introduced a huge gap between the use of current wireless sensor network technologies and specific needs of some important wireless sensor network applications such as medical care, disaster relief, or emergency preparedness and response. In these types of applications, the mobility of nodes can occur, leading to the challenge of mobility handling. In this paper, we address this challenge by prioritizing transmissions of mobile nodes over static nodes. This is achieved by using shorter contention windows in reservation slots for mobile nodes the so-called backoff technique combined with a novel hybrid medium access control MAC protocol the so-called versatile MAC. The proposed protocol advocates channel reuse for bandwidth efficiency and management purpose. Through extensive simulations, our protocol is compared with other MAC alternatives such as time division multiple access and IEEE 802.11 with request to send/clear to send exchange, chosen as benchmarks. The performance metrics used are bandwidth utilization, fairness of medium access, and energy consumption. The superiority of versatile MAC against the studied benchmark protocols is established with respect to these metrics. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we investigate the optimal allocation of the power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying techniques. The power allocation in DF relaying for green cognitive radio with objective of maximizing energy efficiency is a constraint nonlinear nonconvex fractional programming (CNNFP) problem. We present the optimal power allocation in DF relaying by transforming the CNNFP power allocation problem into a concave fractional program by using Charnes-Cooper transformation. We also present an iterative ε-optimal solution for the CNNFP problem using Dinkelbach algorithm. The convergence of the iterative algorithm is proved and numerical solutions obtained using simulations for DF cooperative communications are presented.

This paper investigates the problem of distributed channel selection for interference mitigation in cognitive radio networks (CRNs) with block-fading channels, using a game-theoretic solution. Specifically, the channel gains are blockfixed in a slot and change randomly in the next slot. Existing algorithms, which are originally designed for static channels, can not converge in the presence of time-varying channels. We formulate this problem as a non-cooperative game with random payoffs, in which the utility of each player (CR user) is defined as the expected weighted experienced interference. This game is proved to be a potential game with the network utility, the expected weighted aggregate interference, serving as the potential function. Then, we propose a stochastic learning automata based distributed channel selection algorithm, with which the CR users learn the desirable channel selections from their action-payoff history. It is analytically shown that the proposed learning algorithm converges to pure strategy Nash equilibrium (NE), which maximizes the network utility globally or locally, without information exchange. Moreover, simulation results show that it achieves higher normalized transmission rate.

In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy‐efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non‐linear non‐convex optimisation problem. The authors first transform the energy‐efficient maximisation problem into a parametric optimisation problem and then propose an iterative power allocation algorithm that guarantees ε ‐optimal solution. A proof of convergence is also given for the ε ‐optimal algorithm. The proposed ε ‐optimal algorithm provide a practical solution for power allocation in energy‐efficient cognitive radio networks. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithms are investigated.

This study presents a new technique for resource allocation in multiuser orthogonal frequency division multiplexing systems. The goal is to maximise the minimum data rate available to any user while minimising the total transmitted power. The strength Pareto evolutionary algorithm (SPEA‐2) is used to achieve this goal. The SPEA‐2 algorithm solves the contradicting multiple objectives by evaluating individual's fitness value based on the number of external non‐dominated individuals that dominate it and then searching the solution space to minimise this fitness value. Most of the existing multi‐objective solutions, for the problem under consideration, have used binary coded chromosomes which restricted the number of users to be in power of two only. This limitation is overcome in the proposed scheme by using an integer coded chromosome. The population density information is also incorporated into the fitness function to refine the search. Simulation results indicate that the proposed algorithm achieves higher data rates as compared with previous algorithms. Furthermore, the proposed scheme allocates both subcarriers and bits jointly, without being computationally expensive. The faster convergence of the algorithm to near‐optimal value, as compared with previous algorithms is indicative of its reduced complexity, which is attributed to the modification in the power objective.

This article investigates the problem of distributed channel selection in opportunistic spectrum access (OSA) networks with partially overlapping channels (POC) using a game-theoretic learning algorithm. Compared with traditional non-overlapping channels (NOC), POC can increase the full-range spectrum utilization, mitigate interference and improve the network throughput. However, most existing POC approaches are centralized, which are not suitable for distributed OSA networks. We formulate the POC selection problem as an interference mitigation game. We prove that the game has at least one pure strategy NE point and the best pure strategy NE point minimizes the aggregate interference in the network. We characterize the achievable performance of the game by presenting an upper bound for aggregate interference of all NE points. In addition, we propose a simultaneous uncoupled learning algorithm with heterogeneous exploration rates to achieve the pure strategy NE points of the game. Simulation results show that the heterogeneous exploration rates lead to faster convergence speed and the throughput improvement gain of the proposed POC approach over traditional NOC approach is significant. Also, the proposed uncoupled learning algorithm achieves satisfactory performance when compared with existing coupled and uncoupled algorithms.

This article investigates the problem of distributed channel selection for opportunistic spectrum access systems, where multiple cognitive radio (CR) users are spatially located and mutual interference only emerges between neighboring users. In addition, there is no information exchange among CR users. We first propose a MAC-layer interference minimization game, in which the utility of a player is defined as a function of the number of neighbors competing for the same channel. We prove that the game is a potential game with the optimal Nash equilibrium (NE) point minimizing the aggregate MAC-layer interference. Although this result is promising, it is challenging to achieve a NE point without information exchange, not to mention the optimal one. The reason is that traditional algorithms belong to coupled algorithms which need information of other users during the convergence towards NE solutions. We propose two uncoupled learning algorithms, with which the CR users intelligently learn the desirable actions from their individual action-utility history. Specifically, the first algorithm asymptotically minimizes the aggregate MAC-layer interference and needs a common control channel to assist learning scheduling, and the second one does not need a control channel and averagely achieves suboptimal solutions.

This paper combines two emerging research areas: green communications and cognitive radio. A green cognitive radio network must be accountable for its energy expenditure. Energy expenditure of a cognitive base station is reduced by maximizing the bits/Joule energy efficiency (EE) of its transmissions. Any high complexity solution to this optimization problem will spend too much energy in computation. This paper presents a low complexity solution to the problem of finding the power allocation that maximizes the EE, while limiting the interference to the primary users and meeting the users' minimum rate requirements. The objective function of the optimization problem is not concave. Charnes-Cooper Transformation is applied to the problem to convert it into a concave program. KKT conditions were analyzed instead of the Lagrangian dual in lieu of low complexity solutions. A power allocation procedure that branches into two main cases depending on the channel gains is proposed. In the first case, an exact solution is obtained by solving a single non-linear equation that produces a common water level. In the second case, a near optimal solution in closed form is given. Simulation results supporting the analytical green solutions are presented.

We investigate cross-layer rate adaptation techniques for a secondary user (SU) equipped with a finite buffer in green cognitive radio networks. We assume that the activity statistics of the licensed primary users (PUs) channels are independent and identically distributed, and a SU detects their states using a spectrum sensing method. We formulate the problem as an infinite-horizon partially observable Markov decision process (POMDP). The policy is obtained using maximum-likelihood heuristic policy (MLHP) technique. We assume that the transition probabilities of the PUs and the fading channel between the SU's transmitter and receiver are known, but the exact PU's states are hidden. By tracking belief of the hidden states, the SU takes decision on the rate and corresponding power to minimize energy consumption with constraints on delay and bit error rate. Numerical results are given to show the performance of the proposed MLHP, which is found to perform very close to fully observable optimal policy. We also show pointer to choose design parameter so that the scheduler becomes the most energy-efficient for given quality of service requirements of the application.

We investigate the spectrum access and power adaptation techniques in a cognitive radio network to optimize throughput of a secondary user with specified sensing error limit. Using partially observable Markov decision process framework, we first study the optimal policies, where the primary user is assumed to be in busy, concurrent or idle state, and the secondary user either stay idle or transmits with any of the two designed power level. The collision is avoided with proper reward choices. Although the primary user's states are hidden, their activity statistics, ranges of transmission, and interference thresholds are assumed to be known. The instantaneous optimal policy for each time-slot is then obtained for the current belief of the states obtained through channel sensing. We also propose a forward algorithm based technique that updates belief using the sensor output in the first slot and then using the acknowledgment feedback in the subsequent time-slots in a frame. Simulation results show that the proposed cross-layer technique is more throughput efficient than the physical layer optimal case, specially when the primary user activity is slowly varying and/or frame size is smaller.

This paper envisions a near future in which the proliferation of wireless ad hoc networks in urban centers causes excessive spectrum pollution on currently allocated unlicensed bands. One solution for this problem is for the operators to lease freshly released spectrum from the regulators and sublease it to agencies in major cities. We consider one such operator who divides an urban area into regions and subleases spectrum with the condition that the interference measured at boundary points should not exceed a threshold. The subleasing pricing structure has a fixed part, as well as a variable part that discounts the price based on the margin between the interference threshold and the actual interference. The slope of the variable part is called the discount rate and is determined by a competition that is modeled as a game within a game. For a fixed discount rate, the competition between the customers forms a strategic game. The end result of this game becomes the input to the Stackelberg game between the customers as a whole on the one side and the operator on the other side. We derive the mild condition under which the strategic game of the customers has a unique Nash equilibrium, and obtain an explicit closed form solution for the equilibrium point. This result is then used to derive the best response of the operator and the optimum (Stackelberg equilibrium) discount rate the operator would want to offer. Numerical results obtained through simulations that support the analysis are also provided.

Cooperative relaying techniques have been shown to provide spatial diversity in fading wireless environment. As a result, they increase link reliability, provide higher capacity, reduce transmit power, and extend transmission range as opposed to non-cooperative transmission (direct transmission). Although the use of cooperative relaying has been proven to achieve those gains in the absence of interference (single flow), it is not clear how much gain we can expect from using cooperative relaying in multi-flow scenario specifically the total network capacity. The network capacity in multi-hop multi-flow settings is severely affected by interference between links and, this effect increases when the cooperative relaying is imposed. In this paper, we use a clique based capacity analysis to investigate the performance gain (loss) on network capacity for wireless ad hoc networks by using cooperative relaying. It is observed that the throughput drops significantly when cooperative links are imposed in the network.

In cognitive radio networks, it is important to effectively use the under-utilized spectrum resources without affecting the primary users. In an underlay system, secondary users are allowed to share the channel simultaneously with primary users (with the restriction on interference level) but not in an overlay system. In this article, we consider a system where a secondary user can switch between overlay and underlay modes of operation in order to improve its throughput with limited sensing capability (i.e. sensing only one channel at a time). The results based on Markov chain analysis are satisfactorily verified using Monte-Carlo simulation. It is found that proper selection of transmission mode can provide greater improvement in throughput for a secondary user. The mode selection depends on the transition characteristics of primary users and the throughput ratio between the two modes of operation.

We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.

Practical implementation of balanced data routing algorithms in WSNs is challenging because of the heterogeneity among nodes inherited from the physical world in forms of different amount of nodal traffic, residual energy, data transmission rate and bandwidth. As the main concern in sensor networks is preserving nodes' energy, such algorithms should balance energy depletion among nodes by carefully considering the impact of aforementioned heterogeneities to prolong the network lifetime. In this paper, a distributed energy balanced algorithm for data gathering and routing is proposed aiming to construct energy balanced routing trees in a network that contains heterogenous nodes. For this purpose, a game theoretical approach in which nodes can be selfish or cooperative players based on their roles in the network. Utility functions use local information of nodes and they are defined in a way that, while each node in selfish mode tries to achieve the most individual benefit, it implicitly helps to construct a balanced tree for the entire network. Evaluation and simulation results show noticeable improvement in generating more energy balanced routing trees, resulting longer network lifetimes compared to similar work in the literature.

The power allocation that maximizes the sum rate of transceivers operating in the same frequency band is a difficult non-convex problem. In our earlier work, we proved that for transceivers operating under a total power constraint, the maximum sum rate occurs at the boundary of the feasible set formed by the hyper plane representing the power constraint. This finding is nontrivial considering that we are dealing with an interference limited system. In this paper, we study the convex structure of the sum rate on the boundary of the power constraint hyper plane. For two transceivers, we prove that the sum rate is always convex on the line created by the power constraint equality. In the case of three transceivers, we identify a region in the middle of the plane created by the power constraint equality, where the sum rate is concave. This is significant because it is in the middle of the boundary plane that the power allocation can be expected to be fair to all three users. We also provide a power allocation protocol and an algorithm that distribute the power among the transceivers with fairness. Simulation results are provided to support the theorems proven in the paper as well as to demonstrate the convergence of the algorithm to the global maximum sum rate. Results of the algorithm are compared with solutions based on Game theory.

This paper combines Convex analysis and Game theory to investigate the problem of maximizing the sum rate of transceivers operating in the same frequency band. In our earlier work, we proved that for transceivers operating under a total power constraint, the power distribution that maximizes the sum rate lies on the boundary of the feasible set formed by the power constraint. In this paper, we first prove that for two users, the sum rate is convex on the boundary formed by the line segment representing the power constraint, and the maximum sum rate is achieved when all the power is allocated to one of the users. Obviously, such a power allocation is unfair to the other user. We consider a scenario in which the first user is willing to sell some of the power allocated to him to the second user. We use Stackelberg Game theory to analyze this scenario and prove the existence of a unique competitive equilibrium. We derive the best response functions, and determine the optimum price the first user must charge and the optimum amount of power the second user should buy at this price. We also use simulation to obtain the best response functions and the equilibrium point, and demonstrate their agreement with our analytical results.

In cognitive radio networks, it is important to effectively use the under-utilized spectrum resources without affecting the primary users. In an underlay system, cognitive users are allowed to share the channel simultaneously with primary users with the restriction on interference level but not in an overlay system. In this paper, we consider a system where cognitive users can switch between overlay and underlay modes of operation in order to improve their throughput. The results, based on Markov chain analysis, are satisfactorily verified using Monte-Carlo simulation. It is shown that proper selection of transmission mode can provide greater improvement in throughput for cognitive users. If a primary user occupies the channel for a longer (shorter) period, then the system should allow the cognitive users to choose underlay (overlay) mode for throughput advantage. The exact strategy (or mode) switching thresholds can be found from our analysis and it depends primarily on the transition probabilities of the primary users and throughput ratio during underlay/overlay transmission.

In this paper, the problem of mobility handling in wireless sensor network (WSN) is studied with a simple priority backoff technique. To incorporate this technique for stationary and mobile sensor nodes, a novel hybrid MAC protocol called VMAC is designed with a fixed frame length. VMAC combines the advantages of schedule-based MAC for energy savings and contention-based MAC for short transmission delays. To exploit the bandwidth in the network, channel reuse is encouraged and is readily integrated into the protocol. Simulation results using ns2 demonstrate that VMAC with certain frame lengths are suited for selected topologies, but the frame length of one provides sufficient performance. It is also shown that the energy consumption of VMAC is roughly one-third lower compared to pure schedule-based protocol while the average delay is about two-fold less than that of contention-based protocol in one-hop communication scenarios with frame length of one; meaning that VMAC performs very well in short-range communication. The backoff technique is also shown to be fair when nodes contend for medium access and it is even resourceful in speeding up hardware address resolution and routing.

The downlink subcarrier allocation in a cooperative multiuser system with an amplify-and-forward relaying is studied for OFDM systems. We use the cooperation coefficient (Γ) from the literature as a basis for subcarrier allocation. Mean of the coefficient is first derived for Rayleigh faded channel. Then, a well known subcarrier allocation algorithm namely Max-Min is modified to make use of Γ. In this algorithm, the base station (BS) allocates the subcarriers to the users based on the combination of the direct and weighted indirect subcarrier gains. How to weigh the indirect path depends on the amount of information about Γ available at BS. Three different scenarios, each with varying level of implementation complexity, are considered: (a) knowledge of the instantaneous Γ at BS, (b) knowledge of the mean of Γ at BS, and (c) an estimate of the mean of Γ at BS. Finally, the performance of the Γ-modified Max-Min algorithm is evaluated for the three scenarios using Monte-Carlo simulation. It is shown that (i) the proposed algorithm outperforms the non-cooperative counterpart in terms of total throughput, (ii) the throughput gain moderates with larger group size showing the group diversity behavior in opportunistic communication and (iii) using the mean of the coefficient, instead of instantaneous coefficient, in cooperative subcarrier allocation provides comparable performance in Rayleigh faded channel environment, giving implementation advantage to the mean-based implementation of the Γ-modified Max-Min algorithm.

Though power allocation has been studied extensively in the literature, as energy efficiency becomes more important in “green” wireless communication, we revisit to study the differences in SNR vs BER-based power allocation (PA) for relay communication when the direct link is severely faded. The average SNR and average BER expressions are first derived for an amplify-and-forward relaying protocol with Rayleigh fading channels. Based on the derived expressions at the destination through two-hop communication, closed-form expressions for optimum transmit power are obtained at the source and the relay. It is observed that the higher the total transmit power is, the bigger portion of it should be allocated to the relay independent of its location. For a given end-to-end BER, optimally selected relay's location is relatively more closer to the source if BER-based PA is employed than that of the SNR-based PA. It is also noted that the BER-based power allocation scheme can achieve 1dB performance improvement over the SNR-based counterpart when relay is centrally located and is in the higher transmit power regime resulting in energy saving, for a two-hop wireless system with no avail of direct communication between transceivers.

Cooperative spectrum sensing is employed in cognitive radio networks to reliably detect the primary users' transmissions by fusing the sensed data of individual secondary users. In this paper, we propose a new weighted fusion scheme, in which the reliability of the secondary users' local decisions are considered when making a final decision at the fusion center. We use past information about the local and global decisions to estimate the reliability of the sensing decision obtained from each secondary user. This difference in reliability is reflected in the weighting of each secondary user's decision when combined at the fusion center. Simulations are provided to compare the performance of the proposed scheme to the OR, AND, Equal Weight and Signal-to-Noise Ratio (SNR) based Weight fusion schemes. Results show that our proposed scheme provides performance improvement for cooperative spectrum sensing when compared to the other fusion schemes.

Cooperative spectrum sensing is employed in cognitive radio network to reliably detect the primary users' transmissions by fusing the sensing data of individual secondary users. In this paper, we study the performance of cooperative spectrum sensing, in terms of the system probability of detection, when the secondary users' local decisions are correlated. We use a correlation model that is indexed by a single parameter and fix the fusion rule to one of three decision rules which are the OR, AND and Majority Voting rules. Our results show that the performance of cooperative spectrum sensing degrades with the increase in correlation between the secondary observations for all the fusion rules considered. We also show that, whether the OR or Majority Voting rule is superior depends mainly on the correlation index. When the secondary users' local decisions are independent, the Majority Voting rule outperforms the OR and AND fusion rules. However, as the correlation between the local decisions increases, the OR fusion rule outperforms the other two rules. Also, as the correlation index increases, for the same system probability of false alarm, higher signal-to-noise ratio is required to be received at the secondary users to achieve the same system probability of detection for all the fusion rules considered.

Abstract For emerging wireless mesh networks, multicode multicarrier CDMA(MC‐CDMA) based technology is one of the most viable candidates. We perform stochastic modelling of the queues at the mobile station for uplink communication with multicode multicarrier CDMA system with two types of traffic, namely real‐time and non‐real‐time. Each traffic is assigned its own codes. However, the non‐real‐time traffic is allowed to use codes assigned to real‐time traffic, when real‐time traffic is not using its codes. Based on the probability of bit error for a multicode MC‐CDMA system, we first compute the probability of packet error. The packet in error will be inserted into the queue until it successfully gets through to the receiver. The packet arrival process at the input queue is modelled as Markov modulated Poisson processes (MMPP). The QoS performance in terms of packet loss for real‐time traffic and the occupancy distribution for non‐real‐time traffic is evaluated using matrix geometric techniques. We present numerical results for low and high load of real‐time traffic with varying loads of non‐real‐time traffic. We observe the binomial tweaking feature of occupancy distribution at higher loads due to batch departures. Copyright © 2009 John Wiley & Sons, Ltd.

The power allocation that maximizes the sum rate of transceivers operating in the same frequency band is a difficult non-convex problem. Lack of a convex structure excludes the direct application of Lagrangian dual techniques as the duality gap might not be zero. This paper advances current knowledge by introducing three significant steps in finding a solution. First, we show that for transceivers operating under a total power constraint, the maximum sum rate occurs at the boundary of the feasible set formed by the hyper plane representing the power constraint. This conclusion is nontrivial considering that we are dealing with an interference limited system. Second, we prove that the duality gap is zero for this problem, despite the lack of concavity of the objective. We do this by showing that the maximum sum rate is concave in the power constraint. Third, we propose an iterative algorithm that finds the optimal power allocation by solving the dual problem. Simulation results are provided to support the theorems proven in the paper as well as to demonstrate the convergence of the algorithm to the global maximum sum rate. Results of the algorithm are also compared with solutions based on Game theory.

This paper presents a simple synchronous distributed power allocation algorithm that maximizes the total transmission rate of a number of radios operating in an unlicensed band, with either a total power constraint or individual power constraints. The redistribution of power by the algorithm also results in a fairer rate distribution. The algorithm is not based on Game theory or Lagrangian dual. Rather it uses the sensitivity of each user's rate to changes in the power levels of all users in the system, to steer the power distribution towards the global maximum sum rate. The algorithm's complexity scales with the number of users in the system. Simulation results demonstrate that the algorithm does converge to the global maximum sum rate and, at the same time, redistributes the power among the users to achieve a more equitable rate distribution. Results of the algorithm are also compared with solutions based on Game theory.

Modulation recognition systems have to be able to correctly classify the incoming signal's modulation scheme in the presence of noise. This paper addresses the problem of automatic modulation recognition of digital communication signals using support vector machines (SVM). Three digital modulation schemes have been considered and four features have been used as inputs to the SVM. A fuzzy multi-class classification method has been proposed and the overall accuracy of 77.0% at signal-to-noise ratio (SNR) of 10dB has been achieved.

For a class of the important problems that seek to maximize the sum rate of the multi-user multiple input multiple output multiple access channel (MIMO MAC) and compute the corresponding optimal input distribution, we developed a more efficient algorithm for solving this class of the problems compared with currently known algorithms. The performance result of this new algorithm indicates that the proposed algorithm overcomes some of the weaknesses of other algorithms. One of the key weaknesses that it overcomes is that the well-known iterative water-filling algorithms cannot utilize the machinery of parallel computation, owning to their inherent structure defects. Not only does the proposed algorithm sufficiently utilizes the machinery of parallel computation, it also shows faster convergence compared with previous research results. Numerical results show that the same properties of the proposed algorithm are also effective for finding the optimal input policy due to its simplicity and fast convergence.

Cooperative diversity techniques have recently received a lot of attention due to their ability to provide spatial diversity in fading wireless environment, thus increases link reliability, provides higher capacity and reduces transmit power for the same level of performance. In this paper we study a joint problem of relay selection, power allocation and routing in multi-hop wireless ad hoc networks based on cooperative transmission. In particular, an optimal routing strategy is proposed to minimize the end-to-end total transmission power subject to end-to-end target rate. An ad-hoc routing strategy is proposed to find an optimal route (in terms of total transmit power minimizing) for decode-and forward strategy based on the well known Dijikstra algorithm which can be easily implemented in distributed way. Simulation results show that the proposed strategy has great improvement in terms of power saving compared with traditional non-cooperative shortest path algorithms, by more than 70% in some simulation scenario.

In this paper, we address the problem of spectrum sharing where wireless (competitive) operators coexist in the same frequency band. First, we model this problem as a strategic non-cooperative game where operators simultaneously share the spectrum according to the Nash equilibrium (N.E). Given a set of channel realizations, several Nash equilibria exist which render the outcome of the game unpredictable. Second, the inter-operator spectrum sharing problem is reformulated as a hierarchical power allocation game, where one of the operators (i.e., primary) poses as a leader and the other operator (i.e., secondary) as a follower. Using backward induction, the Stackelberg equilibrium (S.E) is reached where the best response of the secondary operator is taken into account upon maximizing the primary operator's payoff. It turns out that the Stackelberg approach yields better payoffs for operators compared to the classical greedy water-filling approach. Furthermore, to reach Pareto-efflcient boundaries, the spectrum sharing problem is formulated as a repeated game, where players interact over a longer period of time and learning from each other's strategies. Numerical results provide a comparison between the non-cooperative, hierarchical and centralized approach.

In this paper, we propose a cooperative communication system where every link adapts to the channel condition without any feedback, by using a recently proposed adaptive demodulation (ADM) scheme. In ADM, the receiver demodulates only a subset of the bits from a Raptor coded Gray mapped M-QAM received symbol, where the number of demodulated bits depends on the channel condition. The ADM scheme, which was originally proposed for point to point communication, determines the most reliable bits using approximate decision regions obtained through a binary composite hypothesis test in the AWGN environment. In contrast, we prove a simple theorem that identifies the most reliable bits in a Gray mapped M-QAM received symbol in a straight forward and exact manner, and use it in every receiver of our cooperative communication system to adaptively extract the appropriate bits. Performance results demonstrate that our ADM based Raptor coded cooperative communication system is robust enough to realize diversity order of two, even under adverse channel conditions in various links.

One of the fundamental requirements of cognitive radio networks is to reliably detect the presence of licensed primary users. Therefore, spectrum sensing should be performed prior to allowing unlicensed users to access the vacant licensed bands. Multiple secondary users can cooperate to increase the reliability of spectrum sensing. Previous work on wideband spectrum sensing showed that multiband joint detection, which jointly detects the signal energy over multiple frequency bands, is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. In this paper, we investigate the integration of basic wideband spectrum sensing with both data (soft) and decision (hard) fusion techniques to improve the performance in the presence of multiple secondary users. We formulate the optimization problem for the multiband joint detection when cooperation is used for both hard and soft decision approaches. Numerical results show the significant improvement in the performance, in terms of the aggregate opportunistic throughput and false alarm probability, achieved by using cooperative sensing. Also, better performance was achieved when the data fusion approach was used.

Dynamic subcarrier allocation improves the performance of OFDM systems by exploiting multi-user diversity. Fairness index is a parameter which indicates how fairly the sub-carriers are allocated among the users in a system. A greedy sub-carrier allocation algorithm optimizes the system performance in terms of throughput, but it sacrifices the instantaneous fairness. In this paper, we define a new term called "asymptotic fairness". It is shown that for a small number of users greedy subcarrier allocation algorithm leads to a normalized fairness index close to unity after a few channel realizations; therefore, if the users of the same group can wait for a few OFDM symbols, they all can get almost the same data rate. To generalize the idea for larger number of users, we have proposed grouping of the users into smaller group sizes. The proposed subcarrier allocation algorithm allocates the subcarriers in two steps: group-allocation and user-allocation. Group-allocation is performed to maintain fairness among different groups by using a fairness-oriented subcarrier allocation algorithm such as max-min algorithm. In the user-allocation step, the subcarriers are allocated to the users within the group using the greedy algorithm to maximize the throughput. The proposed algorithm is specifically suitable for non-real-time applications. According to the required average fairness index in the system and the maximum allowable waiting time, it is possible to find the proper group size in the proposed two-step subcarrier allocation.

In this paper, different power allocation schemes between the source and the cooperating relay nodes are analyzed and numerically evaluated for a two-hop decode-and-forward OFCDM based relay network. A cooperative power allocation ratio ¿ (=source power/total power) is defined and BER performance is evaluated for different values of ¿ in the relay network. It is shown that there exists an optimal power allocation ratio for different operating environment such as source-to-relay channel gains and time-frequency spreading factors. It is reported that (a) When all three channels (source-to-relay, source-to-destination and relay-to-destination) have equal gains, power ratio is found to be ¿ ¿ 0.8 (i.e., 80% and 20% of the total power is distributed among source and relay node respectively). The BER performance degrades at a faster rate when ¿ increases above the optimal value than the decrement at higher Eb/N¿. (b) For a network with stronger source-to-relay link, the optimal ¿ remains invariant at higher Eb/N¿for equal channel gain case; however, the optimal power ratio moves toward lower value of ¿ at lower Eb/N¿. (c) The optimal ¿ remains almost the same with different time-frequency spreading factors.

We propose a novel opportunistic access scheme for cognitive radios that considers the channel gain as well as the predicted idle channel probability. In contrast to previous works where a cognitive user vacates a channel only when that channel becomes busy, our scheme requires the cognitive user to switch to the channel with the next highest idle probability if the current channel's gain is below a certain threshold. We derive the threshold values that maximize the long term throughput for various transition probabilities and compare our throughput results to those obtained using a non-channel adaptive scheme. We also provide simulation results that validate our analytical results.

In this paper, we first propose a modified adaptive subcarrier allocation algorithm for a two-hop decode-and-forward OFCDM relay system and then its BER performance analysis is formulated and numerically evaluated via Monte-Carlo simulation. The effect of considering the CSI of source-base station and source-relay links are evaluated in a cooperative diversity system. Results show that the allocation of subcarriers based on source-relay link CSI provides better BER performance at higher Eb/Noand at lower Eb/No, both the source-relay and source-base station links need to be considered. We also introduce a parameter called cooperative channel state information, C-CSI-(0 ¿ v ¿ 1), that weighs in end-to-end SINR and hence impacts the subcarrier allocation in OFCDM systems. From our numerical simulation, we notice that the cross-over Eb/Nopoint (around which frequency spreading gives better performance than time spreading) moves towards the lower Eb/Nowhen v approaches 1; that is, when the subcarrier allocation is done giving more weight to source-base station link rather than the source-relay link. We also observe a relationship between the cross-over point (Eb/Noin dB) and the C-CSI, which provides additional flexibility in operating environment for OFCDM systems.

In this article, subcarrier and power allocation schemes are proposed and analyzed for different scenarios for a two-hop decode-and-forward OFCDM based relay network. In subcarrier allocation, the effect of considering the channel state information (CSI) of source-base station and source-relay link are evaluated in a cooperative diversity system. Results show that allocation of subcarriers based on source-relay node CSI provides better BER performance at higher Eb/No, and at lower Eb/No, both the source-relay and source-base station links need to be considered. From our numerical simulation, we also noticed that the cross-over Eb/No, point (around which frequency spreading gives better performance than time spreading) moves towards the lower Eb/No, when the subcarrier allocation is done giving more weight to source-base station link rather than the source-relay link which provides additional flexibility in operating environment for OFCDM systems. In power allocation, a cooperative power allocation ratio lambda (=source node power/total power) is defined and BER performance is evaluated for different values of lambda in the relay network. It is found that there exists an optimal power allocation ratio for different operating environment such as source-to-relay channel gains and time-frequency spreading factors. It is reported that: (a) when all three channels (source-to-relay, source-to-destination and relay-to-destination) have equal gains, power ratio is found to be lambda ap 0.8 (i.e., 80% and 20% of the total power is distributed among source and relay node respectively). The performance degrades at much faster rate when lambda increases above the optimal value at higher Eb/No. On the other hand, the performance remains almost the same when the decrement in lambda is less than the optimal value. (b) For a network with stronger source-to-relay link, the optimal lambda remains almost the same as the case with equal channel gains at higher Eb/No; however, the optimal power ratio moves toward lower value of lambda of 0.65 at lowerb/No. (c) The optimal lambda remains almost the same with different time-frequency spreading factors.

In this paper, a Carrier Frequency Offset (CFO) correction scheme is proposed for a downlink Variable Spreading Factor (VSF) OFCDM system based on the maximum likelihood principle. We first derive the likelihood function for VSF- OFCDM system with CFO, and then propose to use a gradient algorithm to estimate and minimize the effect of CFO in a tracking mode. The maximal ratio combining receiver is considered for detection. Our results show that the BER performance in the low SNR environment can be improved significantly with few number of iterations.

Dynamic resource allocation improves the performance of multiuser OFDM systems by exploiting multiuser diversity. Cooperative diversity is a technique where multiple users share their resources to realize a spatial diversity gain through cooperation. In this paper, the problem of downlink subcarrier allocation in a cooperative multiuser system is investigated. We assume a single-cell case where the base station has perfect knowledge of subchannel gains and all the mobile users are paired in cooperative groups. The mobile users in one cooperative group relay their partner data stream which is received from base station using a time division protocol. Based on the capacity contribution from the relaying terminal, a new parameter called cooperation coefficient is introduced. Considering the cooperation among users in assigning the subcarriers, a new subcarrier allocation algorithm is proposed. The performance of the proposed algorithm is then evaluated for different values of cooperation coefficients and is shown to maintain the same level of fairness but higher data rates compared with a similar algorithm which does not consider cooperation.

In this paper, the effect of frequency offset on the performance of downlink OFCDM systems with variable spreading factors is investigated. The bit error rate of downlink VSF-OFCDM is analyzed taking into account the effect of carrier frequency offset when subcarrier grouping is used. An analytic expression of the SINR for downlink OFCDM with frequency offset using BPSK modulation is derived for the case of maximal ratio combining receiver. Numerical results show that, when the total spreading factor is fixed to 32, the VSF-OFCDM system with higher frequency domain spreading factor is more sensitive to frequency offset than that with lower frequency domain spreading factor. Our results also show that, as the number of users increases, the degradation of the BER performance is more pronounced with the higher frequency domain spreading factor since more subcarriers are present in each group.

Wireless relay has recently gained a lot of interest in the research community. It was suggested to use space-time block codes in the cooperative relaying system to increase the spectral efficiency of the relaying protocol. Using space-time codes in the relaying scheme also provides the diversity benefits of multiple antenna techniques. In this paper, a new method of distributed space-time coding based on Alamouti codes with one regenerative relay called "on-channel distributed space-time block coding" is introduced and analyzed. By a slight modification in the proposed scheme, "recursive on-channel relaying" technique is proposed which is shown to have better performance. After showing theoretically that the proposed methods achieve spatial diversity gain, performance of them is then evaluated via Monte-Carlo simulation. It is shown that the proposed space-time coded relaying protocols perform better in moderate to high SNR, compared to repetition-based relaying protocols, and requires 3 dB less SNR to achieve a BER of 10-4.

This paper proposes an adaptive modulation algorithm for orthogonal frequency and code division multiplexing (OFCDM) system to increase the spectral efficiency without sacrificing the BER performance under different spreading factors. The proposed algorithm is used with an adaptive subcarrier allocation technique which assigns users to subcarriers to minimize the overall BER of the system. A fixed threshold is used to switch between modulation levels depending on the estimated SINR in each group. A spectral efficiency of 3.2 bits per symbol is obtained for a target BER of 10-2. BCH (511, 385) coding with rate 3/4 used to accommodate a lower target BER of 10-3yields a spectral efficiency of 2.8 bits per symbol. The proposed algorithm provides an increase in spectral efficiency than using BPSK only, without increasing the total transmit power.

We propose an interference detection technique for detecting time varying jamming signals in spread spectrum communication systems. The technique is based on discrete polynomial phase transform (DPPT), where the jamming signal is synthesized from the modulated spread spectrum signal using the DPPT. The technique has shown good performance under low interference conditions with 2dB SJR, when correlation coefficient between the synthesized chirp signal and the reference chirp is 0.9. The computational complexity of the proposed technique is low compared to other techniques such as Hough-Radon transform. This interference detection technique can be applied for different interference excision methods in military and wireless communication applications.

In wireless multiuser OFDM systems, dynamic resource allocation has been shown to improve the performance by exploiting the multiuser diversity. In this paper we propose a subcarrier allocation algorithm to increase the total data rate for the downlink of a variable bit rate multiuser OFDM system with proportional rate constraints subject to bit error rate and total transmit power. We assume that the channel is quasi-static where the channel status does not vary within each transmission block and the base station has perfect knowledge of subchannel gains. The proposed algorithm is based on prioritizing the critical (most sensitive) user in the system and the variance of the subchannel gains for each user is used to define the sensitivity of the user to the subcarrier allocation. Simulation results show that this algorithm achieves higher capacity with acceptable proportional fairness compared to the previous suboptimal solutions proposed by Rhee et al. in [9] and Shen et al. in [12].

Orthogonal frequency code division multiplexing (OFCDM) has recently been introduced as a candidate for 4th generation wireless systems. This signalling scheme utilizes code spreading in the time and frequency domains simultaneously to improve frequency diversity and minimize MAI simultaneously. As a result, it is capable of outperforming multicarrier CDMA systems that employ one-dimensional spreading. In this paper, a novel adaptive subcarrier allocation algorithm is developed for OFCDM to improve the overall BER performance for all spreading configurations. This algorithm assigns users to subcarrier groups that provide favorable fading characteristics, while simultaneously reducing the amount of interference caused to other users. The proposed algorithm is shown to provide a performance improvement ranging from 1.5 dB with 2times16 spreading, to 7 dB with 16times2 spreading. The algorithm is also shown to maintain or improve the BER floor for each OFCDM spreading configuration.

Orthogonal frequency and code division multiplexing (OFCDM) is a promising emerging technique for the fourth generation cellular system. In this paper, we propose an adaptive modulation algorithm for OFCDM in order to increase the spectral efficiency without sacrificing the bit error rate (BER) performance. The proposed algorithm is used with an adaptive subcarrier allocation technique which assigns users to subcarriers that produce the best signal to interference and noise ratio (SINR) characteristics while producing minimal multiple access interference to other users. We examine a fixed threshold adaptation algorithm to switch between modulation levels depending upon the estimated SINR. The performance of adaptive modulation in a Rayleigh fading channel for different BER targets is evaluated. The proposed algorithm is shown to provide an increase in throughput and spectral efficiency than using BPSK only. An increase of 47% and 63% in throughput corresponding to a spectral efficiency of 1.47 and 1.63 bits per symbol can be obtained for a target BER of 1% and 10% respectively without increasing the total transmit power.

Multicarrier system such as orthogonal frequency division multiplexing (OFDM) is considered as a promising candidate for wireless networks that support high data rate communication. In this paper, we investigate the performance of a multi-user OFDM system under imperfect synchronization which is caused due to noise, Doppler shift and frequency selective fades in the channel. Monte Carlo analysis indicates an average of 22% loss in the number of allocatable subcarriers under imperfect synchronization as compared to perfect synchronization. As the number of users is increased, the average number of available subcarriers remains moderately constant, but the minimum and maximum number of allocatable subcarriers varies significantly. Based on empirical modelling, we characterize the available number of subcarriers as a Poisson random variable. In addition, we determine the percentage decrease in the total number of allocatable subcarriers under varying channel parameters. The results indicate 19% decrease in the number of available subcarriers as average AWGN power is increased by 10 dB; 44% decrease as the Doppler frequency is varied from 10 Hz to 100 Hz; and 56% decrease as the fading gain is varied from 0 dB to -30 dB. Hence, determining the availability of subcarriers under imperfect synchronization is essential in optimization process of radio resource allocation for multiuser systems.

The problem of base station antenna assignment (BSAA) with minimum mobile transmit power (MTP) is studied for CDMA networks with fixed overlapping sector antenna architecture (FOSAA) where more than one co-located antenna is used to cover any space in the network. It is first noted that the non-FOSAA has limitations in switching users between in-cell sectors and also out-of-cell sectors in moderately-loaded networks. It is then shown that by employing overlapping sectors in FOSAA, we can exploit the flexibility of assigning a user to one of possibly many potential antenna to effectively support the nonuniform azimuthal traffic. It is also shown that the BSAA problem with minimum MTP is a special case of a general problem that was solved by Hanly and Yates. The process of dynamic cell sectoring is differentiated twofold as cell-breathing (CB) and cell-slicing (CS) and the latter can be viewed as being azimuthal discrete counterpart of the former radial scheme. The hybrid scheme, CB+CS, offers better performance in terms of minimum total MTP in a FOSAA system. Simulation results demonstrate the flexibility and effectiveness of the FOSAA system in nonuniform angular traffic

We propose an adaptive prioritizing soft handoff algorithm for concurrent handoff requests aiming at a same cell. A predicted set, an adaptive priority profile jointly exploiting the impact of required handoff power and call holding time have been developed to realize the proposed algorithm. A link-layer scheduler residing in each base station to ensure the desired operation of the prioritizing procedure is also designed. Numerical results are acquired through comparing the proposed algorithm with no-prioritizing scenario and performance gain is obtained in terms of handoff dropping probability, average guard power utilization, and average guard power efficiency, by no less than 24%, 5%, respectively for the first two criteria, and some amount for the last.

Optical fiber based wireless access schemes receive renewed attention with the popularity of hot-spots. They increase capacity, QoS and support wideband multimedia services and have the possibility of utilizing existing fiber infrastructure. However, link design in a fiber-wireless system needs careful consideration of many factors. There are two signal to noise ratios involved, the optical SNR (OSNR) and the electrical SNR. These two form the cumulative SNR in the concatenated fiber-wireless channel. The OSNR is a function of the modulation index m, E/O, O/E conversion losses and, the fiber length. There is a 39 dB loss due to E/O and O/E conversion only in resistively matched wideband links and the OSNR rapidly decreases with fiber length. The cumulative SNR at the mobile unit decides the QoS and cell size. This SNR depends on OSNR, wireless channel path loss and the optical receiver amplifier gain. In this paper, we study the relationships between critical design parameters, such as maximum radio and optical link losses, cumulative and optical SNR and, optical amplifier gain in a fiber-based wireless system.

In this paper, a packet scheduling scheme based on real-time channel conditions and avoidance of dominant inter-cell interferers is proposed and studied. This scheme is implemented by the distribution of tags by receivers among transmitters. We focus on reverse links of a CDMA/TDM system where for every slot, each base station issues M(/spl ges/1) tags to M mobile users (MU) based on the ranked reverse link gains. The number of tags thus issued depends on the expected traffic, the number of cells and the propagation conditions in the network. Two types of tags can be issued: eg, hard and soft tags. A MU will transmit only if it receives a tag from its home base station (BS) and no hard tag from any other BS. Hence, in our scheme, MU that not only have stronger channel pins to their respective home BS but also cause relatively lower inter-cell interference are scheduled for transmissions. This scheme is hence a cooperative approach whereby good-neighbor behaviour is exhibited. Soft-tagging offers flexibility in allowing MU that cause moderate inter-cell interference to transmit under certain conditions such as longer starved time. Simulation results in shadow and a Rayleigh failing environment are presented to show that our scheme outperforms the conventional scheme by 30% in transmit bit energy when M = 10 in a 9-cell network with 100 MU uniformly distributed.

We study the reverse link performance of a cellular CDMA network that employs fixed overlapping sector antenna architecture (FOSAA) in a hot-spot environment. Simulation results demonstrate the flexibility and effectiveness of the FOSAA in non-uniform angular traffic. The CDF statistics of received SIR are presented for different congestion levels in hot-spot sectors. A scheme with FOSAA can yield on the average 0.8 dB SIR increase over a scheme without FOSAA, when 60% of the mobiles is concentrated in a hot-spot sector in a conventional 3-sector cell. The performance measures such as the mobile transmit power and the antenna selection and sector coverage are also studied.

The problem of base station antenna assignment (BSAA) with minimum mobile transmit power (MTP) is studied for CDMA networks that employ fixed overlapping sector antenna architecture (FOSAA). It is noted that the non-FOSAA has limitations in switching users between in-cell sectors and also out-of-cell sectors in moderately loaded networks. It is then shown that by employing overlapping sectors in FOSAA, we can exploit the flexibility of assigning a user to one of possibly many potential antenna to effectively support the non-uniform angular traffic. It is also proven that the problem of selecting a set of antenna from a pool of overlapping antenna and assigning the users to them in FOSAA with minimum MTP is a special case of a general problem that was solved by Hanly (1995) and Yates (1995). The process of dynamic cell sectoring is differentiated two-fold as cell-breathing (CB) and cell-slicing (CS) and the latter can be viewed as azimuthal counterpart of the former radial scheme. The hybrid scheme, CB+CS, is shown to yield the optimal solution in minimum total MTP in a CDMA/FOSAA system. The performance results for the total MTP and the received signal quality are reported. As the congestion level increases, the difference in SIR performance between CB and CS schemes becomes more apparent with the latter outperforming the former. The performance results also show that on average, the CB scheme requires about 30% more power than in CB+CS, when 60% of the mobiles are concentrated in a hot-spot sector in a conventional 3-sector cell.

A novel combined rate, power and cell (R/P/C) control scheme that gracefully implements congestion control, is proposed and studied. We consider the reverse link in a CDMA network with a high degree of traffic fluctuations spatially with time, as would be the case in future wireless systems. Our scheme attempts to reduce the high interference power variations among cell-site antennas that can exist in a real network. The transmission rates of those users in the congested (non-congested) cells are decreased (increased) providing the required average throughput among users; hence, the proposed scheme is appropriate for delay insensitive applications. We consider the minimization of the average transmit bit energy (/spl Theta/) subject to maintaining individual target E/sub b//I/sub 0/ for each user. Two algorithms, one directly minimizing /spl Theta/ and the other indirectly using measured pilot power, are given. Both algorithms select the optimal cell-site if forward and reverse link gains are equal; however, the latter is decentralized and uses only local measurements and amenable for practical implementation. Simulation results show that a 20% reduction in transmit bit energy can be achieved on average using our schemes over the scheme with no rate control.