High computational power consumption is a significant problem in a Proof-of-Work blockchain. Restricting the total mining power or the number of miners is a possible solution to save computational power consumption. However, reducing the mining power or the number of miners raises concerns about fairness and security. In this paper, we propose a stochastic lightweight blockchain called SLChain in which a random subset of miners is selected to mine the next block. In the proposed SLChain, the power consumption is significantly reduced while maintaining fairness among miners, robustness to multiple types of attacks, and block time consistency. Furthermore, the proposed SLChain can mitigate selfish mining attacks. We derive an analytical model to calculate the rewards earned by selfish miners. Simulations are conducted to study the accuracy of the proposed analytical model. From the numerical results, we found that the profitable threshold of selfish mining attacks increases from 25% to 29.29% in the proposed SLChain. In summary, the proposed stochastic lightweight blockchain can simultaneously reduce computational power consumption, maintain fairness and security levels, ensure block time consistency, and mitigate selfish mining attacks simultaneously.

Chain-based routing protocols such as PEGASIS reduce redundant transmissions in Wireless Sensor Networks (WSNs), yet their static link structures and greedy neighbor selection often result in long communication links, uneven energy depletion, and premature network failure. Achieving both globally optimized chain construction and energy-balanced routing under diverse network deployments therefore remains a significant research challenge. To address these limitations, this paper proposes GWO-PEGASIS, a scalable two-stage optimization framework that integrates K-means clustering for spatial partitioning with the Grey Wolf Optimizer (GWO) to construct globally optimized intra- and inter-cluster chains under connectivity constraints. In the first stage, K-means forms spatially compact clusters based on node density, initial energy, and spatial distribution. In the second stage, GWO enables adaptive chainhead selection, balanced multihop forwarding, and improved spatial load distribution. Extensive simulations validate the effectiveness of the proposed method: GWO-PEGASIS shortens total chain length by 10.46% relative to PEGASIS, more than doubles the network lifetime, and further improves lifetime by 11.2% and 14.6% over EB-PEGASIS-SCL and MC-CRITIC-KM, respectively. It also achieves the lowest per-round energy consumption, the most balanced residual-energy distribution, and the highest throughput among all evaluated protocols. These results demonstrate that GWO-PEGASIS offers a robust, energy-efficient, and scalable routing solution for WSNs, highlighting the strong potential of swarm-intelligence-driven optimization for addressing complex topology design challenges in resource-constrained environments.

This study explores the optimization of transmission strategies focusing on energy efficiency within a network comprising ground-based beyond-diagonal reconfigurable intelligent surfaces (BD-RIS), a.k.a RIS 2.0, and multiple unmanned aerial vehicles (UAVs). The motivation behind this work stems from the critical need to enhance energy efficiency in next-generation wireless networks, where the integration of UAVs and RIS technologies presents both opportunities and challenges. Specifically, while UAVs offer flexible deployment and improved coverage, their limited battery life and the complex interference environment in multi-user networks necessitate innovative solutions for sustainable operation. Each UAV is designed to serve its corresponding user group, with each group utilizing unique subcarriers to maintain orthogonality and employing a rate-splitting multiple access (RSMA) strategy within each group. The primary objectives of this work are to optimize: 1) the allocation of BD-RIS elements to groups, 2) the phase rotations of BD-RIS, 3) the common rate allocation in RSMA, 4) UAV trajectories, and 5) the design of precoders. To achieve these objectives, we formulate an optimization problem under the framework of mixed-integer nonlinear programming (MINLP), with a focus on maximizing energy efficiency. Our proposed solution combines generalized Benders decomposition (GBD), a manifold-based algorithm, and successive convex approximation (SCA). GBD decomposes the MINLP into primal and master sub-problems, which are iteratively solved. To efficiently address variable coupling in the primal problem, we adopt a block coordinate descent (BCD) method and employ the Riemannian conjugate gradient (RCG) technique for phase rotation. SCA addresses the remaining challenges in the primal problem, while a two-stage approach simplifies the optimization process. Simulations confirm the significant energy efficiency improvements achieved by the proposed method.

Emerging Blockchain-empowered Federated Learning (BCFL) technology combines the decentralized security of blockchain with the privacy protection of federated learning. BCFL addresses the issue of single points of failure in centralized systems, making it an increasingly popular solution. However, current consensus mechanisms, such as Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), lead to challenges such as high computational costs and limited scalability. This paper proposes a Batch Zero-Knowledge Proof-based practical Byzantine fault-tolerant (BZ-BFT) consensus mechanism for BCFL to enhance efficiency and reliability. By integrating Zero-Knowledge Proof (ZKP), our approach enables the verification of the primary node's proposal without revealing information from other network nodes, thereby ensuring the credibility of the aggregated results. To address the high computational overhead associated with ZKP, we present a batch quantization preprocessing technique called BatchZKP. Our proposed BZ-BFT reduces initialization, proof generation, and verification time by$97.81 \%, 70.0 \%$, and 47.64 %, respectively, significantly boosting BCFL system efficiency and reliability. Additionally, our approach reduces communication complexity from$O\left(n^{2}\right)$to$O(n)$and enhances Byzantine fault tolerance to${1/2}$.

The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RIS) in sixth-generation (6G) networks offers significant potential for enhancing integrated sensing and communication (ISAC) systems. Motivated by the need for real-time adaptability, spectrum efficiency, and intelligent wireless infrastructure, this paper investigates a UAV-RIS-enabled ISAC framework capable of meeting the dual demands of sensing and communication in complex environments. A key challenge in such systems lies in dynamically balancing resource allocation, UAV trajectory planning, and interference management. To address this, we propose a joint optimization framework in which a UAV simultaneously conducts sensing operations and serves multiple ground users in obstructed environments. The formulated problem involves a non-convex trade-off between maximizing sensing signal-to-noise ratio (SNR) and communication throughput. To solve it, we develop a multi-agent deep reinforcement learning (MADRL) solution. Three collaborative agents independently optimize UAV trajectories using Deep Deterministic Policy Gradient (DDPG), RIS phase shifts using Twin Delayed DDPG (TD3), and beamforming matrices using Proximal Policy Optimization (PPO). These agents learn optimal policies under dynamic channel conditions and mobility constraints. Simulation results show that the proposed approach improves sensing SNR by 22% and communication rates by 20% compared to baseline methods. With equal weight factors for sensing and communication, the system achieves 95% of the maximum theoretical sensing SNR while ensuring a minimum communication rate of 1 bps/Hz, validating its effectiveness in real-time ISAC environments.

This paper proposes an adaptive stabilization control mechanism by using deep reinforcement learning (DRL) for hovering drones that have to execute a surveillance task for a long time. For long-endurance flights, we design and implement a buoyancy-aided autonomous aerial vehicle (AAV) that can use buoyancy lift to decrease the weight and increase the battery capacity so that the flight time can be significantly extended. However, the balloons of the buoyancy-aided AAV can cause “an inverted pendulum effect” and an instability issue on the drone attitude because the increased surface is easily affected by the gusty wind. We propose a buoyancy-aided adaptive stabilization control (BAASC) method with the DRL to stabilize the attitude and extend the flight time of the quadrotor-based buoyancy-aided AAV. This proposed model can immediately control the speeds of all rotors to balance the attitude based on the current state of the drone. Therefore, the degree of swing can be stabilized, and the inverted pendulum effect can be eliminated. The experimental results reveal that the designed buoyancy-aided AAV with the proposed BAASC scheme can effectively stabilize the attitude to extend the flight time by 112.8% compared with a nonbuoyancy-aided AAV under a gusty wind disturbance.

Grid connected and solar powered base stations (BSs) acting as distributed energy sources are increasingly becoming a popular solution to mobile operators. These networks experience double stochasticity due to the space-time variations in energy harvest and BS traffic. Hence, accurate and efficient green energy outage estimation in such networks is a challenging task. In this work, we propose a complutationally efficient cooperative energy transfer based distributed energy bank strategy to alleviate green energy outage and design energy sustainable networks. We first develop low-complexity Markovian frameworks to estimate green energy outage in a standalone BS without energy cooperation (WEC) and a multi-BS energy-cooperative (EC) setting, respectively. For the WEC system, we present a computationally efficient three-state discrete time Markovian statistical model, while the multi-BS EC framework is characterized by a two-state Markov model. The energy outage is studied as a function of capital expenditure (CAPEX), manifesting engineering insights from a service provider's perspective. Subsequently for the EC framework, we formulate a CAPEX optimization problem by jointly optimizing the BS cluster size and solar provisioning on individual BSs. Our results demonstrate that the proposed EC framework alleviates the green energy outage significantly, providing computational efficiency gains and CAPEX savings over the state-of-art approaches.

In this paper, we present an optimal uplink transmission strategy to maximize the energy efficiency for Internet of Things (IoT) assisted by reconfigurable intelligent surface (RIS). To quickly and accurately obtain the optimal transmission power of the IoT devices and the phase shift vector of the RIS, we develop a self-supervised double deep Q network (SSDDQN) learning algorithm. We demonstrate that the SSDDQN-based method achieves energy efficiency comparable to global-optimal mathematical methods while significantly reducing execution time during inference. Additionally, numerical results show that SSDDQN reduces training time costs by 33.3% compared to traditional DDQN.

Reconfigurable Intelligent Surface (RIS) is a transformative technology using the passive beamforming capabilities to create the programmable wireless environments. However, the control overhead is a challenging issue. The system needs to deliver the high-precision reflection coefficients to the RIS controller, for adjusting the phase shifts of the numerous RIS elements. Therefore, we propose a Spirograph-based discrete beamdirection strategy, which selects a set of non-uniformly distributed beam directions based on the generic patterns generated by the Spirograph (SG). We evaluate the achievable data rate and the number of controlling bits of SG-based method. We compare the SG-based method, the ideal continuous phase-shift method, and the SISO method. Simulation results show that with the properly-selected discrete beam directions, the SG-based method can achieve a comparable data rate as the ideal method, while having a lower control overhead.

Realizing energy sustainability is a key theme in sixth generation communications. Along with the emphasis on energy efficiency, operator revenue has emerged as a crucial aspect to make the networks scalable. In this paper, we propose a high altitude platform station (HAPS) aided and power grid connected green communication framework. To design green network, the proposed framework aims to offload excess users with the solar powered terrestrial macro base station (tMBS) to the HAPS mounted MBS (hMBS) in the event of high traffic or low energy harvest. The solar powered tMBS utilizes the power grid connectivity purely for energy selling rather than energy procurement. The inherent communication and energy networks in the proposed system are studied and modeled jointly as a six state discrete time Markov chain. The paper also provides analytical bounds on the solar provisioning required at the hMBS for radio access network functions. The proposed framework is compared with a without offloading and grid energy procurement based competitive state of art, in terms of network quality of service (QoS) and annual operator profit. Our simulation based performance studies demonstrate that the proposed framework under limited hMBS offloading capability offers gains compared to the competitive state of the art, up to 21% enhanced network QoS and 64% increased operator profit.

In the post-fifth generation (5G) era, escalating user quality of service (QoS) strains terrestrial network capacity, especially in urban areas with dynamic traffic distributions. This paper introduces a novel cooperative unmanned aerial vehicle relay-based deployment (CUD) framework in satellite air-ground integrated networks (SAGIN). The CUD strategy deploys an unmanned aerial vehicle-based relay (UAVr) in an amplify-and-forward (AF) mode to enhance user QoS when terrestrial base stations fall short of network capacity. By combining low earth orbit (LEO) satellite and UAVr signals using cooperative diversity, the CUD framework enhances the signal to noise ratio (SNR) at the user. Comparative evaluations against existing frameworks reveal performance improvements, demonstrating the effectiveness of the CUD framework in addressing the evolving demands of next-generation networks.

The development of integrated satellite-terrestrial networks has gained significant attention from both industry and academia in recent years, owing to their potential for delivering low latency, high dependability, strong resilience, ubiquitous connectivity and global broadband coverage services. However, due to the ever-changing nature of satellite topology and the complexity of diverse integrated satellite-terrestrial networks, routing requests is challenging. In this paper, the vehicle movement is uncertain introducing the intermittent connectivity related to vehicles. Therefore, we propose a distributionally robust optimization (DRO) model to minimize, under uncertain latency probability distributions, the expected worst-case overall task routing delay from source to target user equipment through satellite constellation. The model addresses undetermined uploading and downloading latency between automobiles, satellites, and user equipment by employing the Wasserstein ambiguity set, allowing for unpredictable vehicle mobility and intermittent connections. By reformulating the problem into a tractable form, we determine the optimal routing path for task uploading, satellite constellation, and task downloading. Ultimately, the performance of the proposed DRO model demonstrates the model’s ability to address the challenges of integrated satellite-terrestrial network routing.

Traffic light control plays a crucial role in intelligent transportation systems. This paper introduces Temporal Difference-Aware Graph Convolutional Reinforcement Learning (TeDA-GCRL), a decentralized RL-based method for efficient multi-intersection traffic signal control. Specifically, we put forward a new graph architecture using each lane as a node for considering intersection relations. Additionally, we propose two new rewards by considering temporal information, namely Temporal-Aware Pressure on Incoming Lanes (TAPIL) and Temporal-Aware Action Consistency (TAAC), which enhance learning efficiency and time-interval sensitivity. Experimental results on five datasets show the superiority of TeDA-GCRL over state-of-the-art methods by at least 9.5% in average travel time.

Renewable power provisioning of the base stations (BS) in addition to the traditional power grid connectivity presents an interesting prospect towards realizing green future network services. Designing such dual-powered systems is challenging due to the presence of space-time varying stochasticity in traffic and green energy harvest at each BS. These traffic and green energy imbalances result in non-optimal network green energy utilization and thus resulting in a higher grid energy purchase to the mobile operator. In this paper, we present a novel coverage adjustment and sharing of energy (CASE) framework that exploits the user traffic load and green energy availability imbalances across the networked BSs towards maximizing the operator profit and designing energy sustainable system. The profit maximization problem is formulated considering the networked BSs to have the flexibility of load aware coverage adjustment and green energy sharing capability among themselves, in addition to trading energy with the grid. The proposed CASE framework first leverages the spatio-temporal traffic and energy inhomogeneities and performs load management for maximizing user quality of service (QoS). The CASE strategy then distributes the residual energy imbalance across the BSs and maximizes the utilization of temporal green energy harvest across the BSs. The proposed strategy is compared with only coverage adjustment, only sharing of energy, and a benchmark without CASE based framework. Our simulation results indicate significant improvement in user QoS and operator profit, up to 18% and 39% respectively at high skewness scenario, in addition to fully utilizing the green energy potential in the network.

By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency communications (URLLCs) and enhanced mobile broadband (eMBB) multiplexing system. This study investigates the delay and backlog violation behavior of URLLCs and eMBB multiplexing systems supported by different technologies, e.g. RIS and DF relay, for different scheduling policies of static priority, nonpreemption, and earliest deadline first. A tight analysis approach based on the Martingale theory was proposed to evaluate the serviceability of URLLC and eMBB multiplexing systems. On this basis, the Martingale theory analyzes the delay and backlog bounds by transforming the arrival and service processes into exponential forms of the moment generating function. Furthermore, this study derives the closed-form expression of delay and backlog bound for the URLLCs and eMBB multiplexing in two-hop heterogeneous communication networks. Numerical results demonstrate that the proposed Martingale-based tightly analytical method outperforms the state-of-the-art classic stochastic network calculus for evaluating delay and backlog violation in URLLC and eMBB multiplexing systems.

Backscatter communications (BC) have recently emerged as a promising technology for sixth-generation (6G) networks with device-to-device (D2D) communications to support energy-efficient Internet of Things communications. This paper investigates the power allocation problem for energy-efficient 6G networks with backscatter-enabled D2D (BC-D2D) communications. To this end, we formulate the power allocation problem as a biobjective optimization problem that jointly maximizes the sum data rate and minimizes the power consumption of the networks, subject to energy-harvesting, reflection coefficient (RC) and power constraints. To solve this problem, suboptimal RCs are first analytically obtained based on the energy-harvesting constraints of the BC-D2D transmitters. Next, the biobjective optimization problem is transformed into a convex, single-objective power allocation optimization problem using the weighted sum approach, which is then solved using convex optimization. Results show that the proposed scheme outperforms the baseline schemes in energy efficiency under scenarios with different numbers of cellular user equipment and BC-D2D pairs.

Due to the ability to provide low latency, high dependability, and worldwide broadband coverage services, the development of integrated satellite-terrestrial networks has attracted significant interest from both industry and academia over the past few decades. However, the dynamic satellite topology, heterogeneous, expansive, and intricate properties of the integrated satellite-terrestrial network make routing tasks difficult. In this research, we design the distributionally robust optimization (DRO) model with the objective of minimizing the estimated worst-case total task routing delay from the source mobile devices to the matching target mobile devices under an uncertain probability distribution. Taking into account the unpredictable vehicle movement and discontinuous connection between vehicles and mobile devices, the indeterminate offloading and downloading from automobiles to satellites and mobile devices, respectively, are captured by the Wasserstein ambiguity set. Then, we are able to determine the optimal route for task uploading, routing throughout the satellite constellation, and downloading. Finally, experimental results demonstrate that our proposed model has a lower and more robust latency than that of robust optimization (RO) strategy.

The conventional routing protocol considers several local transmission factors in a single node for the many-to-one packet transmission. These factors lead to rapid energy consumption in some specific nodes, thereby generating energy holes to reduce network lifetime. In this article, a novel energy-balanced power-efficient gathering in sensor information systems (EB-PEGASISs) is proposed to improve energy efficiency and utilization for wireless sensor networks (WSNs). In the original power-efficient gathering in sensor information system (PEGASIS), the protocol constructs a chain-based network with the locally shortest distance rather than the globally optimal network length. To improve this, two construction algorithms are proposed to achieve the shortest network length in the EB-PEGASIS, including centralized formation and distributed construction schemes. In the centralized algorithm, the chain length of each starting node is computed, and the minimum chain length can be subsequently determined in the following packet transmission phase. In order to reduce formation complexity, a distributed method uses the$K$-means clustering algorithm to partition the network into clusters. In particular, it forms a subchain in each cluster and connects each subchain into the final shortest chain. Additionally, to balance the energy consumption among all nodes in the packet transmission phase, Jain’s fairness index for residual battery capacity is designed to minimize the fluctuation of energy consumption among nodes in a network. As a result, not only energy efficiency is achieved with the shortest chain length but also energy utilization is balanced for packet transmission. Ultimately, simulation results validate that the network lifetime of the EB-PEGASIS is about 2.46 times the original PEGASIS and 3.36 times the random projection-polar coordinate-chain (RPC) protocol for WSNs.

In traditional wireless sensor networks (WSNs), packets are mainly transmitted in a multihop routing manner. The multihop transmission, however, leads to a hotspot problem in the sink connectivity area (SCA), and the overall network efficiency is reduced due to the quick battery power exhaustion of nodes in that area. This article proposes a novel balanced routing algorithm with transmission range adjustment (BRATRA) to address the network efficiency problem, including the energy efficiency and utilization issues. First, a balanced routing strategy is designed to deal with the SCA load imbalance problem. With the shortest balanced path, the amounts of forwarding packets for the nodes in the SCA and all the other intralayers become more even. From the perspective of power equilibrium in each routing path, each node then determines its accurate transmission radius according to the derived formula and performs power control to realize the even power utilization between interlayers, thereby prolonging the overall network lifetime. Performance evaluation validates that the proposed BRATRA strategy can achieve efficient power utilization in each intralayer and double the network lifetime as compared to the Dijkstra routing strategy. Additionally, it yields better power utilization fairness among nodes, and on average only 5% of battery power is unused for all network nodes, resulting in a network lifespan ten times larger than that using a conventional strategy.

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design.

Integrating unmanned aerial vehicles with RIS (UAV–RIS) can offer ubiquitous deployment services in communication-disabled areas, but is limited by the on-board energy of the UAVs. In this paper, a novel energy harvesting (EH) scheme on top of the UAV–RIS system, called EH-RIS scheme, is developed for the next generation high performance wireless system. The proposed EH-RIS scheme extends the simultaneous wireless information and power transfer (SWIPT) system by splitting the passive reflected arrays on the geometric space for transporting information and harvesting energy simultaneously. However, pedestrian mobility, and rapid channel changes post challenges to efficient resource allocation in wireless systems. Thus, a robust deep reinforcement learning (DRL)-based algorithm is developed to improve the proposed EH-RIS scheme for guaranteeing the quality of service (QoS) under dynamic wireless environments. The simulation results demonstrate the effectiveness and efficiency of the proposed robust DRL-based EH-RIS system, which not only outperform the existing state-of-the-art solutions but also approach to the performance of the exhaustive search method.

Achieving self-sustainability has been one of the key challenges in designing smart grid connected residential systems. Solar enabled and power grid connected, dual-powered residential systems is an attractive solution, but it is not carbon free and cost optimal for the end user. This paper proposes temporal energy cooperation among the dual-powered residences as a potential cost and energy efficient solution. Through this paper, we present a microgrid based, multi-residence cooperative energy transfer mechanism to offset the power grid dependency. The developed analytical framework characterizes the green energy storage as a discrete time Markov model and aims to exploit the temporal residential load variations, towards designing self-sustainable systems at a much lower capital expenditure (CAPEX). Our simulation results capture the variation of optimum residence cluster size as a function of energy sharing price and load skewness to become cost profitable. The results also demonstrate a significant reduction in CAPEX, achieved through the proposed energy cooperative framework, over a non-cooperative residential system.

Multi-agent reinforcement learning (MARL) has pre-viously been employed for efficient spectrum sharing among co-operative connected vehicles. However, we show in this paper that existing MARL models are not robust against non-cooperative or malicious agents (vehicles) whose spectrum selection strategy may cause congestion and reduce the spectrum utilization. For example, a selfish (non-cooperative) agent aims to only maximize its own spectrum utilization, irrespective of the overall system efficiency and spectrum availability to others. We investigate and analyze the MARL-based spectrum sharing problem in connected vehicles including vehicles (agents) with selfish or sabotage strategies. We then develop a theoretical framework to consider the selfish agent, and study various adversarial scenarios (including attacks with disruptive goals) via simulations. Our robust MARL approach where “robust” agents are trained to be prepared for selfish agents in testing phase achieves more resiliency in the presence of a selfish agent and even a sabotage one; achieving 6.7%~20% and 50.7% ~ 138% higher unicast throughput and broadcast delivery success rate over regular benign agents, respectively.

This paper investigates the symbiotic radio (SR) system supported by reconfigurable intelligent surfaces (RIS) to provide shared spectrum. SR Stakeholders share the same infrastructure and spectrum resources, but with different quality of service (QoS) requirements. The objective of this study is to develop a low complexity and global optimization algorithm to maximize the energy efficiency (EE) of the secondary receiver (SRx) and under a required signal-to-interference-plus-noise ratio (SINR) constraint for the primary receiver (PRx). Specifically, we formulate the joint optimization of phase shift, transmission power control, and reflection element scheduling of the RIS-assisted SR system as a nonconvex mixed-integer nonlinear program (MINLP) problem. Then, we relax the nonconvex MINLP problem into an equivalent convex MINLP problem. To this end, we propose an efficient and effective method based on the accelerated generalized Benders decomposition (GBD) algorithm to solve the global-optimal and fast convergence goals. Simulation results show that the proposed GBDbased approach efficiently improves the EE by 41.94% compared to the successive convex approximation (SCA).

Satellite communication is one primary structure of the future wireless systems. Many corporations and academic institutions are committed to this research, e.g., StarLink. Their aim is to construct a constellation of satellite networks covering the whole world. Thanks to this design, our mobile devices can connect to the Internet anywhere in the world without being restricted by the coverage area of base stations. However, given that the constellation of satellites around the Earth is a system that varies in time, algorithms must adapt to the dynamic topology. To solve the problem, in this paper we investigate how to send data from the base station to the target satellite with minimal delay. We systematically formulate the transmission limitations, uplink, and downlink transmission delay. With well defined limits and delays on space-terrestrial transmissions, we can set rewards and punishments by mathematical analysis to simulate the agent in deep reinforcement learning (DRL), which can explore all choices in the system and optimize the routing algorithm. Finally, the simulation results corroborate a fully functional space-terrestrial network constellation to simulate the actual situation.

Reconfigurable intelligent surface (RIS) is a promising technology for energy efficient wireless communications and has drawn significant attention recently. Combining unmanned aerial vehicle with RIS (UAV-RIS) can provide on-demand deployment services in dynamic scenarios. However, reaping the benefits of UAV-RIS will be limited by the energy of the battery-powered UAV. To enhance the endurance of UAV-RISs, we develop a novel energy harvesting scheme for simultaneous wireless information and power transfer (SWIPT), resource allocation, and energy harvest from impinging radio-frequency (RF) signals. Different from the exist works, the proposed scheme creatively splits the passive reflected arrays on geometric space for transporting information and harvesting energy simultaneously. Furthermore, a deep deterministic policy gradient (DDPG) scheme is designed to continuously allocate UAV-RIS’s resources on both the time and space domains to maximize the total harvested energy, while guaranteeing the communication quality for each user. As shown by our simulation results, the proposed UAV-RIS SWIPT system improves performance significantly over the benchmark.

With the explosive growth of low earth orbit (LEO) satellite networks, such as Starlink, satellite communication has lower latency and can achieve high-speed transmission than before. However, the time-variant topology during all network lifetimes makes the routing problem in the LEO satellite networks challenging. Therefore, in this paper, we propose the deep reinforcement learning-based satellite routing (DRL-SR) method to tackle the multi-commodity flow routing problem in the LEO satellite networks. Given the current state of the satellite network environment, the satellite operation center will determine how to route the requests to the matching destinations. Particularly, the single agent in our DRL-SR approach can determine the multiple next hops as actions for all the corresponding requests each timeslot. Finally, simulation results show that our proposed algorithm yields lower latency than the shortest path approach.

Typically, wireless sensor networks (WSNs) use limited-capacity batteries that cannot be recharged or replaced. In general, designing an energy-efficient routing protocol has a significant impact on prolonging the network lifetime. In this article, a novel distributed 2-hop cluster-routing protocol (D2CRP) is introduced to achieve energy efficiency in WSNs. In the cluster formation phase, each node obtains the information of its neighbor nodes within the 2-hop range to form the 2-hop cluster in a fully distributed manner. The transmission distance and residual energy are jointly considered to determine the energy-efficient cluster head (CH) in each 2-hop cluster. After the CH is generated, each member node can transmit packets to its 1-hop neighbor or the CH directly. To reduce the overall transmission distance for intercluster communication, multiple chains can be formed among CHs via their adjacent CHs that are closer to the base station (BS). As a result, energy-efficient intracluster and intercluster routing can be achieved for packet transmission. In addition, the optimal cluster number of 2-hop clustering is formulated and derived to minimize the energy consumption for both intracluster and intercluster communications. Simulation results show that the optimal cluster number of D2CRP can be achieved and D2CRP is effectively improved on the performances of network lifetime, energy consumption, and packet transmission than the other four state-of-the-art competitive routing protocols, including low-energy adaptive clustering hierarchy (LEACH), R-LEACH, power-efficient gathering in sensor information system (PEGASIS), and two-tier distributed fuzzy logic-based protocol (TTDFP).

Over the last few years, drone base station (DBS) technology has been recognized as a promising solution to the problem of network design for wireless communication systems, due to its highly flexible deployment and dynamic mobility features. This article focuses on the 3-D mobility control of the DBS to boost transmission coverage and network connectivity. We propose a dynamic and scalable control strategy for drone mobility using deep reinforcement learning (DRL). The design goal is to maximize communication coverage and network connectivity for multiple real-time users over a time horizon. The proposed method functions according to the received signals of mobile users, without the information of user locations. It is divided into two hierarchical stages. First, a time-series convolutional neural network (CNN)-based link quality estimation model is used to determine the link quality at each timeslot. Second, a deep$Q$-learning algorithm is applied to control the movement of the DBS in hotspot areas to meet user requirements. Simulation results show that the proposed method achieves significant network performance in terms of both communication coverage and network throughput in a dynamic environment, compared with the$Q$-learning algorithm.

In this article, a balanced multipath routing and hybrid transmission approach (BMRHTA) is proposed to effectively alleviate the imbalance of the forwarding load in a sink connection area (SCA) and prolong the network lifecycle for wireless sensor networks (WSNs). To achieve the energy efficient and balanced WSNs, three design issues, including the multipath, multihop, and single-hop transmissions, are jointly optimized to maximize the overall network lifecycle. First, the path load aggregation phenomenon in the SCA, which makes the forwarding packet load unevenly distributed among hotspots, is examined. In order to achieve the load balance in SCA, multiple shortest balanced paths are generated in the BMRHTA model. In the first stage, two uncorrelated shortest paths are discovered from each node to the sink and the optimal path selection cycle can be determined to achieve the SCA load balance. Afterward, a network equilibrium policy is offered to resolve the optimal transmission period of energy balance via hybrid transmission. As a result, the balanced shortest paths, the path selection cycle and the transmission period can be determined in the network formation phase to avoid the excessive load concentration in the subsequent maintenance phase. Simulation results show that the joint two uncorrelated balanced routing and the proposed network equilibrium policy can nearly quadruple the network lifecycle extension, as compared to a conventional node power policy. Also, the proposed BMRHTA achieves better performance than the current state-of-the-art competitive approaches in terms of energy efficiency and lifecycle.

In this article, a distributed topology control approach with hierarchical self-routing (DTC-HSR) is presented for Bluetooth low-energy (BLE) networks. First, the conventional star piconet is replaced by the designed mesh-ring subnet with better throughput and lower delay. To achieve the goal of load balancing design, two phases, including the leader selection and the topology construction are executed in the proposed approach. In the leader selection phase, each master node discovers its adjacent slave nodes to determine a leader master as a coordinator. In the topology construction phase, the local mesh-ring subnet is first formed and then the global mesh-ring subnets are interconnected into the desired DTC-HSR topology. To form the local mesh-ring subnet, each leader master computes the desired number of piconets with even link connectivity and distributes the piconet connection information for each node to form a mesh-ring subnet. In addition, each master node connects with the other local mesh-ring subnets via its associated bridge nodes, including slave nodes, intrabridges, and interbridges to create the definitive DTC-HSR scatternet. Afterward, a hierarchical self-routing strategy is jointly employed for the DTC-HSR to efficiently deliver routed packets through different mesh-ring subnets. Simulation results demonstrate that the DTC-HSR topology with the even connectivity feature outperforms the dual-ring tree (DRT) and cluster-based mesh (CBM) approaches in terms of network transmission and energy efficiency performances. The DTC-HSR configuration thus achieves efficient topology construction and hierarchical self-routing for load balancing in BLE networks.

Designing solar-enabled and power grid connected, ‘dual-powered’, cellular networks is challenging due to the double stochasticity arising from energy harvest and user traffic, resulting in spatio-temporally varying traffic-energy imbalances. Improper strategy to optimize the power grid connectivity results in generation of significant carbon footprint. In this paper, we present an analytical framework to mathematically capture the traffic-energy imbalances in such a dual-powered network and propose to improve the temporal network energy utilization by exploiting these imbalances through a cooperative energy sharing mechanism among the base stations (BSs), via the grid infrastructure itself. The cooperative communication system is designed and optimized independently from two perspectives, namely, grid energy procurement and carbon emission minimization (in carbon free ‘energy producer’ mode) and operator revenue maximization (in ‘energy prosumer’ mode). The energy producer mode involves the BSs, without the flexibility to procure energy and acting as distributed energy source to the power grid. The energy prosumer mode provides additional flexibility of grid energy procurement to the BSs in addition to energy sharing and selling. For a given capital expenditure (CAPEX), both the optimization problems are reformulated into convex quadratic problems and closed form expressions for the optimal quanta of energies to be shared/procured through/from the grid are obtained. The optimal CAPEX for the proposed modes of network operation are obtained via linear revenue maximization problem formulation. The results demonstrate that the proposed cooperative energy framework significantly improves the temporal network energy utilization, thereby reducing the grid energy procurement and providing significant revenue gains compared to the state-of-art.

Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wireless networks. In this paper, we present a reinforcement learning (RL) based random linear network coding scheme for drone-aided cellular systems to address eavesdropping. In this scheme, the network coding policy, including the encoded packet number, the packet and power allocation, is chosen based on the measured jamming power, previous transmission performance and BS channel states. A virtual model generates simulated experiences to update Q-values besides real experiences for faster policy optimization. We also propose a deep RL version and design a hierarchical architecture to further accelerate the policy exploration and improve the anti-eavesdropping performance, in terms of the intercept probability, the latency, the outage probability and the energy consumption. We analyze the computational complexity, drone deployment, secure coverage area and the performance bound of the proposed schemes, which are verified via simulation results.

Providing coverage for flash crowds is an important application for drone base stations (DBSs). However, any arbitrary crowd is likely to be distributed at a high density. Under the condition for each DBS to serve the same number of ground users, multiple DBSs may be placed at the same horizontal location but different altitudes and will cause severe co-channel interference, to which we refer as the coverage overlapping problem. To solve this problem, we then proposed the data-driven 3D placement (DDP) and the enhanced DDP (eDDP) algorithms. The proposed DDP and eDDP can effectively find the appropriate number, altitude, location, and coverage of DBSs in the serving area in polynomial time to maximize the system sum rate and guarantee the minimum data rate requirement of the user equipment. The simulation results show that, compared with the balanced$k$-means approach, the proposed eDDP can increase the system sum rate by 200 percent and reduce the computation time by 50 percent. In particular, eDDP can effectively reduce the occurrence of the coverage overlapping problem and then outperform DDP by about 100 percent in terms of system sum rate.

The number of devices connected to the Internet of Things (IoT) is growing at an enormous rate globally. In the next generation networks, distributed fog computing deployments at the network edge can provide computing resources to the users, especially for latency-sensitive applications. Further, the heterogeneous needs of the fifth generation (5G) networks demand the virtualization of network functions, termed as network function virtualization (NFV). Therefore, an integrated NFV and fog computing resource allocation framework for IoT is of prime importance. Accordingly, in this paper, we model the interactions between the data service operators (DSOs) and the authorized data service subscribers (ADSSs) as an equilibrium problem with equilibrium constraints (EPEC), and utilize the alternating direction method of multipliers (ADMM) as a large-scale optimization tool to obtain solutions. This results in the optimization of resource pricing for the DSOs and the amount of resources to be purchased by the ADSSs. Moreover, we propose a many-to-many matching based model to allocate the fog node (FN) resources according to the VNF resource requirements of the ADSSs. Simulation results show the effectiveness of our proposed approach in achieving efficient resource allocation in NFV enabled IoT fog computing.

A recent line of research focuses on crowd density estimation from RGB images for a variety of applications, for example, surveillance and traffic flow control. The performance drops dramatically for low-quality images, such as occlusion, or poor light conditions. However, people are equipped with various wireless devices, allowing the received signals to be easily collected at the base station. As such, another line of research utilizes received signals for crowd counting. Nevertheless, received signals offer only information regarding the number of people, while an accurate density map cannot be derived. As unmanned aerial vehicles (UAVs) are now treated as flying base stations and equipped with cameras, we make the first attempt to leverage both RGB images and received signals for crowd density estimation on UAVs. Specifically, we propose a novel network to effectively fuse the RGB images and received signal strength (RSS) information. Moreover, we design a new loss function that considers the uncertainty from RSS and makes the prediction consistent with the received signals. Experimental results show that the proposed method successfully helps break the limit of traditional crowd density estimation methods and achieves state-of-the-art performance. The proposed dataset is released as a public download for future research.

Solar enabled and grid connected “dual-powered” base stations (BSs) have developed as a cost effective solution to network operators. While these networks prevent energy outages and are effective in providing seamless operation for catering to the user quality of service (QoS), they still rely significantly on the power-grid, generating carbon footprint. In this paper, we propose a profitable energy sharing based cooperative framework to reduce the grid energy consumption and to facilitate better utilization of network energy. Traffic-energy imbalances in a dual-powered network often result in some BSs being energy deficient due to spatio-temporal variation of traffic. In such an event, it is proposed that the energy deficient BS, through the proposed energy cooperation (EC) framework, interact with the other networked BSs and requests them to share the deficient energy or a portion of it at a much lower price than the grid price via grid itself. We compare the proposed EC model with a without energy cooperation (WEC) model, where the BSs do not interact with one another and release energy above their storage capacity only to the power-grid for selling. Our results demonstrate that for a two BS network, the EC model provides a significant reduction in grid consumption up to 50% with a 38% gain in operator's revenue at 80% traffic skewness.

This study investigates how base stations (BSs) apply network coding to protect the downlink data and how drones relay the coded packets to resist active eavesdropping that performs jamming to induce the BS to raise the transmit power and thus steal more data. We present a drone-aided network coding framework for secure downlink transmission, which incorporates a random linear network coding algorithm to encode the BS messages against active eavesdropping. This framework designs a model-based reinforcement learning to choose the BS network coding and transmission policy based on the jamming power sent by the active eavesdropper, the previous transmission performance, and the BS channel states without the prior knowledge of the drone-eavesdropper channel states. The learning parameters such as the Q-values are updated by the real experiences in the downlink transmission process besides the simulated experiences that are generated from the virtual model in the designed Dyna architecture. Simulation results show that our proposed scheme outperforms the benchmarks in terms of the intercept probability, the transmission performance, and the BS energy consumption.

This research presents a new multichain routing strategy named the destination-oriented routing algorithm (DORA). The proposed design generates a new multichain routing scheme to transmit packets for energy-balanced WSNs. Based on the multichain routing, the optimal transmission distance between any two nodes is derived by the mathematical analysis model. With the optimal distance, the design criterion is to select the furthest forwarding node within the communication range and the direction to the sink, thus forming the multichain structures by accurate distance, direction, and multiple paths. With the shorter chain routing, unnecessary energy loss can be reduced to prolong the global network lifespan. The simulation results demonstrate that the proposed DORA doubles the network lifespan, compared with that of conventional PEGASIS and improves 60% lifespan on the RPC protocol.

This study proposes a multiple mesh-ring (MMR) topology construction with a hybrid routing strategy for Bluetooth low energy (BLE) networks. To achieve the load-balancing network, three design phases, including leader selection, role decision, and scatternet formation, are executed to generate even MMR configurations. First, each master discovers its adjacent slaves to determine a coordinator. Second, the coordinator computes the desired number of piconets with even scatternet link connectivity and distributes the scatternet connection information for each master. Finally, each designated master connects with its associated nodes, including slaves, intrabridges, and interbridges to create the link balanced MMR scatternet. To jointly design an energy-efficient routing protocol for the MMR topology, a hybrid routing strategy is deployed to perform the shortest path routing inside the mesh-ring subnet, and self-routing through different mesh-ring layers. Simulation results demonstrate that the balanced MMR topology leads to a significant gain in terms of network transmission performance and network lifetime when compared to the conventional dual-ring tree (DRT) and cluster-based mesh (CBM) approaches for BLE networks.

This study presents a novel nonuniform power formation algorithm (NUPFA) for Bluetooth low-energy (BLE) networks. With two stages, NUPFA constructs the scatternet in a distributed manner. In the first stage, the master and slave nodes discover each other using low-level power to form an initial scatternet. In the second stage, each master alternatively scans or advertises using normal power to interconnect dual links among any two piconets until the whole scatternet is constructed. Using nonuniform power configuration, an energy-efficient scatternet is generated, since packet transmission power can be reduced in each piconet to minimize overall power consumption. With additional scatternet links, more routing paths can be utilized to reduce the path length and enhance network efficiency. simulation results show that compared with the existing cluster-based on-demand routing protocol (CORP) and Bluetooth Mesh schemes, the NUPFA protocol achieves superior energy efficiency for packet transmission. Also, the dual link features can effectively reduce packet latency, thereby improving the overall performance of BLE mesh networks.

We investigate massive access in the Internet-of-Things (IoT) relying on multi-pair two-way amplify-and-forward (AF) relay systems using massive multiple-input multiple-output (MIMO). We utilize the approximate message passing (AMP) algorithm for joint device activity detection and channel estimation. Furthermore, we analyze the achievable rates for multiple pairs of active devices and derive the closed-form expressions for both maximum-ratio combining/maximum-ratio transmission (MRC/MRT) and zero-forcing reception/zero-forcing transmission (ZFR/ZFT)-based beamforming schemes adopted at the relay. Moreover, to improve the achievable sum rates, we propose a low-complexity algorithm for optimizing the pilot length L. Our simulation results verify the accuracy of the closed-form expressions of the MRC/MRT and ZFR/ZFT scenarios. Finally, the proposed pilot-length optimization algorithm performs well in both the MRC/MRT and ZFR/ZFT scenarios.

Latent tree model (LTM) is a probabilistic tree-structured graphical model, which can reveal the hidden hierarchical causal relations among data contents and play a key role in explainable artificial intelligence. However, because current LTM modeling techniques are only suitable for single-content variable, the applications of LTMs are somewhat limited. Toward this end, a multilayer LTM (ML-LTM) is first presented to deal with the hierarchical clustering issues of multicontent variables. Second, we further develop an ML-LTM-based multicontent recommendation system. Our experiment results show that the proposed ML-LTM can achieve 90% recommendation accuracy, but the current LTM can only has 20%. Third, we propose an incremental update approach for ML-LTM that can save five-sixth updating time comparing with the whole-model retraining approach for achieving the same recommendation accuracy.

Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light on real-world neuromonitoring technologies. However, human variability in the EEG activities hinders the development of practical applications of EEG-based BCI. To tackle this problem, many transfer-learning techniques perform supervised calibration. This kind of calibration approach requires task-relevant data, which is impractical in real-life scenarios such as drowsiness during driving. This study presents a transfer-learning framework for EEG decoding based on the low-dimensional representations of subjects learned from the pre-trial EEG. Tensor decomposition was applied to the pre-trial EEG of subjects to extract the underlying characteristics in subject, spatial, and spectral domains. Then, the proposed framework assessed the characteristics to obtain the low-dimensional subject representations such that the subjects with similar brain dynamics can be identified. This method can leverage the existing data from other users, and a small number of data from a rapid, non-task, unsupervised calibration from a new user to build an accurate BCI. Our results demonstrated that, in terms of prediction accuracy, the proposed low-dimensional subject representation-based transfer learning (LDSR-TL) framework outperformed the random selection, and the Riemannian manifold approach in cognitive-state tracking, while requiring fewer training data. The results can greatly improve the practicability, and usability of EEG-based BCI in the real world.

Recently, the development of Unmanned Aerial Vehicle (UAV) has been nearly matured and widely used in various fields. The combination of UAV and communication technologies, such as UAV Base Station (UAV-BS), can significantly increase the flexibility and scalability of the overall communication networks to provide more efficient communication services. While the UAV-BS improves the network service efficiency, the quality of services (QoS) in the air-to-ground communication link is highly affected unless the right users are unknown. In this paper, we propose the learning-based downlink user selection algorithm. The 3D downlink channel can be fast identified to judiciously select the users subset. In our proposed framework, we combine the k-means clustering and Convolutional Neural Network (CNN) that can increase the estimation accuracy of 3D wireless channels to enhance the communication service efficiency of the UAV-BS network. The field measurement results show that proposed method can achieve an average bit error rate (BER) of 3.56x10−7, which is better than the distance-based selection scheme that has an average of BER 2.88x10−3. The feasibility and effectiveness of the proposed method in real environment are proved, experimentally.

Solar-powered and power grid connected green cellular networks are becoming attractive due to low carbon footprint and cost-effectiveness in providing uninterrupted service. In this paper, we analyze the performance of such dual-powered multi-cell network in presence of skewed traffic load across the different base stations (BSs). Cell coverage is decided at the network design stage based on long-term average traffic intensity across the various regions of a multi-cell coverage area. In presence of dynamically-changing skewness of traffic loads across different cells, we propose to adjust the cell coverage to accommodate the traffic and energy availability imbalance in the cells, while the demand for residual energy deficiency for serving the customers is fulfilled through the power grid connectivity. Network service provider's cost with the proposed coverage adjustment based strategy is compared with that of the conventional approach where the individual BSs do not undergo any cell coverage adjustment and seek to provide the maximum network performance. Our analysis and simulation-based performance results demonstrate, that the network performance as well as monetary gains of the service provider are significantly higher with our proposed strategy. For example, at a moderate (30%) traffic skewness, the proposed strategy offers about 4% gain in operator's annual profit, while serving about 8% more users on average at the peak hour. At a very high (80%) skewness, these numbers are respectively about 50% and 39%.

With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. To solve the above problems, edge computing has become a promising solution. In this paper, we propose a new algorithm for processing probabilistic skyline queries over uncertain data streams in an edge computing environment. We use the concept of a second skyline set to filter data that is unlikely to be the result of the skyline. Besides, the edge server only sends the information needed to update the global analysis results on the cloud server, which will greatly reduce the amount of data transmitted over the network. The results show that our proposed method not only reduces the response time by more than 50% compared with the brute force method on two-dimensional data but also maintains the leading processing speed on high-dimensional data.

Wireless and mobile technologies, as well as their users, are growing rapidly as the Internet of Things (IoT) products, such as sensor-network technologies, mobile devices, and supporting applications, become widely dispersed. Owing to the dynamic changes in the wireless networks and the exponential growth of the IoT products, which make it difficult to locate large quantities of users and devices, providing accurate tracking and trajectory predictions in open and highly condensed wireless networks is extremely difficult. An adaptive and scalable system is required to offer accurate location-based services (LBSs) for the success of IoT. To enhance the attainment of IoT, we propose a hybrid of principal component analysis (PCA) and gated recurrent unit (GRU) algorithms for mobility predictions in a wireless urban area. During the system development processes, we first collect an LTE signal from three unmanned aerial vehicle base stations (UAV-BSs), the Wi-Fi signal strength from each reachable Wi-Fi access points (APs), and channel information from the Wi-Fi signal media. We then apply PCA to reduce the number of Wi-Fi features and to decrease signal noise. Next, we train the GRU algorithm to develop models that can predict the mobility of IoT device users. Finally, we evaluate the tracking and trajectory models. To evaluate the proposed techniques, we compare the common parameters of the GRU with those of other deep learning types. The proposed technique provides plausible and state-of-the-art results for mobility predictions of IoT devices in a wireless environment.

In the emerging Internet-of-Things (IoT) paradigm, the lifetime of energy-constrained devices (ECDs) cannot be ensured due to the limited battery capacity. In this article, unmanned aerial vehicles (UAVs) are served as carriers of wireless power chargers (WPCs) to charge the ECDs. Aiming at maximizing the total amount of charging energy under the constraints of the UAVs and WPCs, a multiple-period charging process problem is formulated. To address this problem, bipartite matching with one-sided preferences is introduced to model the charging relationship between the ECDs and UAVs. Nevertheless, the traditional one-shot static matching is not suitable for this dynamic scenario, and thus the problem is further solved by the novel multiple-stage dynamic matching. Besides, the wireless charging process is history dependent since the current matching result will influence the future initial charging status, and consequently, the Markov decision process (MDP) and Bellman equation are leveraged. Then, by combining the MDP and random serial dictatorship (RSD) matching algorithm together, a four-step algorithm is proposed. In our proposed algorithm, the local MDPs for the ECDs are set up first. Next, using the RSD algorithm, all possible actions can be presented according to the current state. Then, the joint MDP is built based on the local MDPs and all the possible matching results. Finally, the Bellman equation is utilized to select the optimal branch. Finally, simulation results demonstrate the effectiveness of our proposed algorithm.

The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.

This paper study the payload data communication of multi-UAVs system, and propose the system architecture. Considering the asynchronous interference of multi-UAVs communication, the filtered-OFDM is introduced to suppress the out-of-band emission and mitigate the inter-subband interference. According to simulation results, the proposed multi-UAVs system based on F-OFDM that is superior to OFDMA scheme in asynchronous interference suppression and spectral efficiency. Finally, we valid the multi-UAVs communication system work well under the considered air-to-ground channel.

This paper applies Machine Learning (ML) to predict the quality of Air-to-Ground (A2G) links performance for Unmanned Aerial Vehicles Base Stations (UAV-BSs) services. UAV-BSs can instantly identify the status of the current 3D wireless channel in an unknown environment without relying on previous statistical channel modeling. The proposed method that employs the unsupervised learning clustering technology applying to A2G channel modeling in 3D wireless communication scenarios. As environment changing, the proposed method can derive the 3D temporary channel model based on collected RSS data and analyzing. To evaluate the proposed method, the simulation data and measurement data are used to co-verify the performance. As the results shown, the RMSE of conventional statistical channel model and proposed temporary channel model are very similar. The similarity achieves about 91.8% both of the simulation and experimental environments to verify the accuracy and feasibility of our proposed method, and that provides more fast and effective of 3D channel modeling approach.

In this paper, we consider an Unmanned Aerial Vehicle (UAV)-assisted cellular system which consists of multiple UAV base stations (BSs) cooperating the terrestrial BSs. In such a heterogeneous network, for cellular operators, the problem is how to determine the appropriate number, locations, and altitudes of UAV-BSs to improve the system sumrate as well as satisfy the demands of arbitrarily flash crowds on data rates. We propose a data-driven 3D placement of UAV-BSs for providing an effective placement result with a feasible computational cost. The proposed algorithm searches for the appropriate number, location, coverage, and altitude of each UAV-BS in the serving area with the maximized system sumrate in polynomial time so as to guarantee the minimum data rate requirement of UE. The simulation results show that the proposed approach can improve system sumrate in comparison with the case without UAV-BSs.

To cope with the exponential growth of demand, ultra dense networks (UDNs) are a promising technology in future mobile networks. With small cells densely deployed in networks, how to allocate wireless resources in UDNs efficiently becomes a challenging research topic. In this paper, we concentrate on the distributed user-centric clustering and base station (BS) mode choose problem in UDNs. We formulate a combinatorial optimization problem, with the throughput maximization and power consumption minimization jointly considered in the optimization object. In order to reduce the complexity of the problem, we decompose the original problem into two subproblems in terms of user-centric clustering and BS mode choose, and then solve those subproblems by the max-sum algorithm in sequence. The proposed algorithm can be conducted in a distributed way, and the computational complexity grows linearly with the network size. Simulation results show that the performance of proposed algorithm approaches the performance of the exhaustive algorithm well, and outperforms the conventional algorithm significantly.

In moving train networks, two-hop architecture is adopted to improve users experience by reducing the interaction between on-board users and base stations on the train route. In addition, edge networking have emerged as a solution for bringing services to the proximity of the users. However, deploying two-hop and edge networks do not guarantee a continuous service delivery for train users. When a large number of users transit from the train to the land, they experience service interruption due to control signalling storm and backhaul latency. In this paper, we propose a holistic edge service management system to provide mobile service continuity. The contribution of this paper is twofold. First, we develop an enhanced handover scheme that reduces control signals by handling user mobility at the edge. Second, we develop a pre-copy migration scheme that eliminates backhaul latency by relocating containerized applications to the user proximity across edge train networks. Our experimental results show that the two proposed solution can reduce the control signals and migration downtime by 50% and 36%, respectively.

With the inertial measurement unit (IMU) and motor technology improvement, the unmanned aerial vehicles (UAVs) become more accessible for different applications. Among the fixed-wing and the rotary- wing UAV, the former is advantageous in having no runway and small craft size which are more attractive to users. The rotary-wing UAVs is used to provide real-time monitor and control on interest targets for ground users. However, the effects of propellers are not addressed in existing rotary-wing UAV channel models. In this paper, we propose a simple air-to-ground radio communication channel for rotary-wing UAVs considering the propellers. Analysis and results are presented based on the model. The results show the maximum of Doppler shifts for all simulated frequencies exceed 300Hz, the requirement of the LTE, and there is potential risks adapt the current communication system directly for serious time-variant multipath and Doppler frequency shift channel issues.

Because of SDN centralization nature, denial of service (DoS) attacks have become a prominent concern. In the OpenFlow (OF) protocol, the transport layer security (TLS) protocol is recommended to secure the control channel. Unfortunately, the tasks involved in the proper configuration of a secured TLS are very challenging. Even worse, TLS is made an optional mode of communication in OF. As a consequence, some OF-enabled switches and controllers do not adopt TLS. In this paper, we develop a lightweight authentication mechanism, called Hidden Authentication (HiAuth), to protect SDN controller against DoS attacks. HiAuth legitimizes SDN forwarding devices by hiding authentication information into the header of control channel packets. Our experimental results prove that HiAuth is lightweight and can not only mitigate DoS attacks, but also provide high undetectability to the attacker.

This paper proposes an enhanced secure pseudonym scheme to protect the privacy of cloud data in Internet of Drones (IoD). Nowadays, drones equipped with cameras can provide surveillance and aerial photography applications. Unlike the video devices, personal drones with high mobility can track and follow an individual, causing both the identity and location privacy issues. However, IoD devices cannot implement complex cryptographic schemes because of limited computing power. To this end, we develop a secure light-weight network coding pseudonym scheme. Our designed two-tier network coding can decouple the stored IoD cloud data from the owner's pseudonyms. Therefore, our proposed network coding-based pseudonym scheme can simultaneously defend against both outside and inside attackers. We implement our proposed two-tier light-weight network coding mechanism when facing untrusted cloud database. Compared to the computationally secure hash-based pseudonym scheme, our proposed scheme achieves the highest unconditional security level, but also can reduce more than 90% of processing time as well as 10% of energy consumption.

A range-skyline query (RSQ) is the combination of range query and skyline query. It is one of the practical query types in multicriteria decision services, which may include the spatial and nonspatial information as well as make the resulting information more useful than skyline search when the location is concerned. Furthermore, continuous RSQ (CRSQ) is an extension of RSQ that the system continuously reports the skyline results to a query within a given search range. This paper focuses on the RSQ and CRSQ within a specific range on Internet of Mobile Things (IoMT) applications. Many server-client approaches for CRSQ have been proposed but are sensitive to the number of moving objects. We propose an effective and noncentralized approach, distributed CRSQ process (DCRSQ process), for supporting RSQ and CRSQ in mobile environments. By considering the mobility, the proposed approach can predict the time when an object falls in the query range and ignore more irrelevant information when deriving the results, thus saving the computation overhead. The proposed approach, DCRSQ process, is analyzed on cost and validated with extensive simulated experiments. The results show that DCRSQ process outperforms the existing approaches in different scenarios and aspects.

Extracting the valuable features and information in big data has become one of the important research issues in data science. In most Internet of Things (IoT) applications, the collected data are uncertain and imprecise due to sensor device variations or transmission errors. In addition, the sensing data may change as time evolves. We refer an uncertain data stream as a dataset that has velocity, veracity, and volume properties simultaneously. This paper employs the parallelism in edge computing environments to facilitate the top-k dominating query process over multiple uncertain IoT data streams. The challenges of this problem include how to quickly update the result for processing uncertainty and reduce the computation cost as well as provide highly accurate results. By referring to the related existing papers for certain data, we provide an effective probabilistic top-k dominating query process on uncertain data streams, which can be parallelized easily. After discussing the properties of the proposed approach, we validate our methods through the complexity analysis and extensive simulated experiments. In comparison with the existing works, the experimental results indicate that our method can improve almost 60% computation time, reduce nearly 20% communication cost between servers, and provide highly accurate results in most scenarios.

With the rapid growth of Internet of Things (IoT), integrating a variety of IoT can result in novel applications. However, IoT devices are often deployed in an open environment where IoT are inclined to be malfunctioned. Although data reliability can be achieved by data recovery with conventional replication, the communication between IoT is susceptible to eavesdropping. Therefore, in this paper, we study the eavesdropping prevention of data repair in IoT environments based on network coding. We theoretically derive the relation between security level and storage in heterogeneous IoT systems. To further reduce the repair bandwidth, we exploit recurrent neural network for the storage failure prediction. Under the condition when failure probability and workloads of storage devices are considered, two allocation algorithms are proposed to avoid data repair. Finally, we show the relation between storage cost and reliability with different numbers of IoT devices. Experimental results manifest that the proposed allocation algorithms can outperform the baseline case by 18.4% in terms of the security level.

Software defined networking (SDN) enables network function programmability for ease of configuration and maintenance, and also allows network administrators to change traffic rules on the fly. However, denial of service (DoS) attacks pose security challenges on the centralized control plane of SDN. Although the transport layer security (TLS) can help secure the control plane, it is computationally intensive, complex to configure, and not mandatory in OpenFlow protocol. In this paper, we present a lightweight authentication solution, called hidden authentication (HiAuth), to protect the SDN controller by hiding the identities of the forwarding devices into the control packets via efficient bitwise operations. HiAuth is the first to incorporate information hiding techniques into OpenFlow to provide security against DoS attacks. HiAuth exploits the IP identification field of IPv4 and the transaction identification field of OpenFlow in two authentication schemes. The experimental results show that HiAuth can effectively mitigate intruder DoS attacks and provide high undetectability to attackers.

In this paper, we present the overflow problem of a network coding storage system (NCSS) when the encoding parameters and the storage parameters are mismatched. The overflow problem of the NCSS occurs because the network-coded encryption yields extended coded data, resulting in high storage and processing overhead. To avoid the overflow problem, we propose an overflow-avoidance NCSS scheme that takes account of security and storage requirements in both encoding and storage procedures. We provide the analytical results of the maximum allowable stored encoded data under the perfect secrecy criterion. The design guidelines to achieve high coding efficiency with the lowest storage cost are also presented.

With the rapid growth of Internet of Things (IoT), the idea of integrating a variety of IoTs has been proposed to support a variety of novel applications. However, IoT devices are often deployed in an open environment in which IoTs are inclined to be malfunctioned. Although data reliability can be achieved by data recovery with conventional replication, the communication between IoTs is susceptible to eavesdropping. Since most of the current work focuses on designing the architectures for IoTs under the specific scenarios, the eavesdropping prevention for heterogeneous IoT systems remains unexplored. Therefore, in this paper, we consider using the network-coding-based distributed storage systems for security and show that repair bandwidth can be reduced by increasing storage per node. Moreover, we theoretically derive the relation between repair bandwidth and storage in heterogeneous IoT systems. Finally, we show the relation between storage cost and reliability with regard to different amounts of IoT devices.

In the software defined networks (SDN), the flow table of an OpenFlow switch is usually implemented by the ternary content addressable memory (TCAM). The TCAM has limited capacity which may cause the flow table overflow problem. To address the problem, related works perform a rule replacement scheme according to either the estimated next- packet arrival time of each flow or the hit count of each rule; however, it may remove a rule whose packets will soon arrive at a switch, especially when the network is unstable. This paper proposes a novel TCAM management scheme named in-switch rule caching and replacement (IRCR) to better deal with the flow table overflow problem. The IRCR replaces a rule according to the expected number of incoming matched flows (EIMF), which can be derived from the inter-arrival time distribution models of the corresponding flows. The uniqueness of the IRCR is that it is designed as an in-switch application to avoid link delays between controllers and switches. Using either captured real network traffic or uniform distribution traffic, the IRCR outperforms related works for both exactly matched rules and wildcard rules in terms of hit ratios. In addition, the IRCR has little overhead to the switch.

Pilot contamination (PC) is known as a dominant factor for the performance of time-division duplex massive MIMO systems. Most of the existing studies of PC assumed perfect inter-cell synchronization, which is too costly to achieve or even impossible in practice. In this paper, we consider the unsynchronized scenario, in which the reused pilot signals from other cells are subject to timing misalignment, and propose a method for pilot sequence design for reducing the mean square error (MSE) of the linear least squares (LS) channel estimation. An analytic MSE formula is first derived, which is a very complicated function of the pilot coefficients. To ease analysis, an upper bound on the MSE is then derived. Through minimization of this upper bound, an analytic solution is obtained. Computer simulations are used to illustrate the performance of the proposed pilot sequence.

This paper proposes a traffic-aware load balancing scheme for machine-to-machine (M2M) networks using software-defined networking (SDN). Load balancing techniques are essential for M2M networks to relieve the heavy loading caused by bursty traffic. Leveraging the capability of SDN to monitor and control the network, the proposed load balancing scheme can satisfy different quality of service requirements through traffic identification and rerouting. Experimental results show that the proposed scheme can reduce service response time up to 50% compared to the non-SDN load balancing scheme.

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants' priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS), and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association, and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness, and QoS performance compared with several baseline schemes.

Software-Defined Networking and Network Function Virtualization technologies are utilized to construct service chains cheaply and elastically. In Mobile Edge Computing platform, cloud servers can be deployed near to base stations. Migrating service functions of service chains to edge can reduce network latency or save unnecessary bandwidth consumption between edge and cloud datacenter. However, the control plane latency of SDN/NFV Mobile Edge Computing Platform is subject to the scalability of flow table in SDN switches due to flow table overflow problem. In this paper, we proposed Hash-based Group Management scheme to reduce the number of maintained flow entries by assigning users into groups and design a hash-based structure to efficiently maintain the group information. In our simulation results, the proposed scheme can reduce the number of maintain flow entries and table overflow ratio to improve the control plane latency.

In future 5G communication systems, the mm-wave transmission is considered as a key solution to fulfill the growing demands in mobile traffic. To overcome the high propagation loss experienced at high frequencies, mm-wave transmission relies heavily on highly directional antennas. Because directional transmission is sensitive to position changes, the existing handover strategies which only consider the instantaneous received signal strength fails to provide robust connections for moving users. In this paper, we propose an improved handover strategy for mm-wave mobile networks taking the connection robustness issue into account. The connection robustness is evaluated in terms of the effective beam coverage probability, which is defined as the probability that a user stays in the coverage area of the same beam after a certain time interval. We show that the proposed handover strategy can reduce handover frequency up to 35% with negligible throughput loss compared to the existing signal strength based scheme.

The limited size of the flow table, usually implemented by ternary content addressable memory (TCAM), may result in the flow table overflow problem, which becomes severer in software defined networks (SDN). To conquer this problem, this paper proposes an in-switch dynamic flow aggregation (IDFA) mechanism, which can be dynamically triggered. In the IDFA, redundant flow entries are inserted to speed up flow aggregation convergence time. The uniqueness of the proposed IDFA is that it resides in an OpenFlow switch, instead of acting as an application in an SDN controller, which avoids link delays between the controller and switches. This paper also proposes two novel techniques, degradation and repermutation, to aggregate flows effectively while keeping semantic equivalence. Evaluation results show that, the average compression ratio of the IDFA in a fat-tree topology under a LAN (random IP) environment is 26.3% (43%) better than that of the Flow Table Reduction Scheme (FTRS), a representative related work. The average flow aggregation convergence time of the IDFA in the fat-tree topology is 84% shorter than that of the FTRS. In addition, the occurrence probability of flow table overflow in the fat-tree topology is 26.71 % lower than that of the FTRS.

This paper proposes a resource reservation scheme to reduce random access delay for Ultra-Reliable and Low-Latency Communication (URLLC) traffic. URLLC has been proposed as one of the key usage scenarios in the fifth generation (5G) of mobile networks. The primary requirement for providing URLLC services is the delay in the control plane being shorter than 10 ms. Clearly, it is challenging to achieve such the stringent delay requirement since the bursty URLLC requests can cause network congestion in the random access phase. To reduce the random access delay, we propose to reserve random access resources for URLLC during the initial random access transmission. The design criteria of random access and an analytical model for different reservation policies are presented in this paper. Simulation results show that the existing random access scheme fails to meet the delay requirement for URLLC traffic, but our proposed reservation scheme can meet the 10 ms delay requirement with 95% confidence.

This paper investigates the impact of request aggregation on delay performance in group-based machine type communications (MTC). To alleviate the network congestion caused by concurrent transmissions from massive MTC devices, group-based MTC with request aggregation are shown to be one of the effective schemes. In group-based MTC, the devices are grouped into clusters with a dedicated cluster head (CH) which aggregates the requests sent by the cluster members. Hence, the amount of concurrent transmissions for connection establishment to the base station (BS) can be reduced. However, how to determine the amount of aggregated requests in one CH-to-BS transmission, namely aggregation factor, is still an open issue. To this end, we formulate an optimal aggregation problem to minimize the request delay subject to a drop ratio constraint. Based on our analytical and simulation results, we present the performance curves to relate the aggregation factor and the request delay as well as the drop ratio. Consequently, we suggest the optimal aggregation factors for MTC applications requiring ultra-low latency and ultra-reliable delivery.

Commercial OpenFlow-enabled legacy switches implement flow tables with Ternary Content Addressable Memory (TCAM) to perform high-speed flow matching process. However, the size of TCAM-based flow table is restricted to few thousands of entries due to manufacturing cost and high power consumption. In our previous research, we proposed Flow Entry Agent to integrate Off-Chip tables to increase the storage capacity without modifying Application Specific Integrated Circuit (ASIC). In this paper, we propose an efficient matching mechanism for Off-Chip tables. Simulation results show that our mechanism significantly increase the efficiency of flow matching process.

With the great potential in flexible network provisioning adapted for various quality of service (QoS) requirements, software-defined network (SDN) has been considered as one of the most promising network architectures in the next generation mobile networks. However, the rule management in SDN-enabled mobile networks becomes a challenging task due to rapid topology changes caused by high user mobility. One primary issue is how to handle the continuous cache misses in the new routing path during a handoff process. To resolve the cache missing issue and reduce its handoff delay, different prefetching approaches have been proposed. However, the pre-installed rules in current prefetching approaches will incur extra delay in the core network, which cannot be ignored. In this paper, based on network calculus theory, we develop an analytical handoff delay performance model for different prefetching approaches in SDN- enabled mobile networks. The derived delay bounds of prefetching approach in different network environments are validated by simulations. We show that the presented network- calculus-based delay analysis methodology can be an effective and interesting analytical approach for evaluating ultra low delay communications systems in 5G wireless.

Scheduler plays an important role for Long Term Evolution-Advanced (LTE-A) system to achieve high throughput performance. Existing research work on scheduler design did not fully consider non-full buffer capacity at the base station (BS) owing to the fluctuation on data volume. In this paper, we proposed a quality-of-service (QoS) guaranteed channel-aware (QGCA) scheduler with the consideration of BS buffer status to improve system capacity. The proposed QGCA scheme fully considers the required components for LTE-A scheduler, including resource grouping along with modulation and coding scheme. Moreover, we proposed an adaptively channel-aware resource grouping (CARG) method within the QGCA scheduler for improving the performance of existing resource grouping schemes. Numerical results show that our proposed QGCA scheduler and CARG scheme outperform conventional methods, especially under non-full buffer and delay-sensitive VoIP traffic.

In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.

In this paper, we present a systemic approach to provide deterministic delay guarantee for dynamic service chaining in software defined networking (SDN). The delay performance of service chaining in SDN is affected by signaling message exchange in control plane and packet transmissions in data plane, respectively. First, we develop an analytical method to characterize the delay performance of control plane when handling traffic with different priorities according to network calculus (NC) and queuing theory. Second, taking into account the estimated delay in control plane, we propose a novel service traversal mechanism to calculate the optimal traversal path for the service chain. We demonstrate that NC delay analysis can provide deterministic quality of service (QoS)-guaranteed service chaining for any specified delay requirements, whereas theoretical queueing delay analysis can only provide statistical QoS guarantee. In summary, the proposed NC delay analysis can help to understand the network design for a future delay sensitive Internet in which deterministic latency must be guaranteed.

Compared with current distributed networks, Software defined networking is an innovative technology to manage networks by a centralized controller with global visibility. OpenFlow is one kind of SDN protocols. It provides flow-level controllability of network traffic to simplify the management of the administrators of data center. Flow control rules used by OpenFlow in switch flow table are called flow entries. Modern commodity switches use TCAM to implement flow table because of its high matching speed. However, the general TCAM size of switch only accommodates up to thousands of flow entries due to its high cost-to-density ratio and high power consumption. It indicates that the maximal number of flow that can be controlled is restricted to the TCAM size of switch. That leads to several scalability issues, especially in a data center. In this paper, we propose a topology based flow entry management (TFEM) technique, which is a scalable and memory-efficient solution by exploiting the hierarchical topology characteristic to aggregate the uses of flow entries for most of the small flows in a data center. With the proposed downlink and uplink separated flow tables structure, our results show that TFEM can reduce the average number of flow entries by 50%~78% for fat-tree topology against the traditional SDN without any switch hardware modification.

Software defined network (SDN) is an emerging network architecture which offloads the control logic of the network from the underlying forwarding devices to a centralized controller. This centralized control intelligence software defines the behavior of the network. However, the programmability and centralization of the SDN architecture introduce potential security concerns. In this paper, we first investigate the threats of denial of service (DoS) attacks on the SDN control channel. Then, we evaluate the impact of DoS by simulating a DoS attack against the network controller. Our results show that it is possible to exhaust the controller resources in the absence of an authentication mechanism. Finally, we propose a lightweight information hiding authentication mechanism to prevent DoS attacks in the SDN control channel.

In recent years, the wireless local area networks (WLAN) access points (APs) are being deployed rapidly in the offices and the campuses to satisfy the quality of service (QoS) requirements such as network bandwidth. However, many empirical studies show that a large fraction of idle WLAN resources result in the significant energy losses. To reduce the energy consumption without violating the QoS requirement, we propose a QoS-aware AP energy saving mechanism using software defined network (SDN). We leverage the capability of the SDN controller to dynamically monitor the network condition information to estimate the network bandwidth requirement of the devices and then manage the network forwarding to provide seamless handover. Our experimental results show that the proposed scheme can effectively reduce the number of power-on WLAN APs while still providing QoS guarantee, such as network bandwidth and handover cost.

This paper proposes an analytical approach to modeling and analyzing the coexisting transmission performance in a multi-tier heterogeneous network with multiple radio access technologies (Multi-RATs). The coexistence issue is a special phenomenon uniquely existing in a multi-RAT network and it is rarely studied in prior works on heterogeneous wireless network. To simply characterize the coexisting transmission impacts in a multi-RAT network, in this paper we consider a three-tier heterogeneous network using the RATs of L and U, where RAT-L is adopted by the access points (APs) in the first two tiers, APs in the third tier only use RAT-U and the APs in the second tier can opportunistically use RAT-U. The opportunistic CSMA/CA protocol is used by the APs contending the RAT-U channel and the channel access probabilities for the RAT-U AP in two different tiers are derived under the proposed user association scheme with random weights. The coexisting coverages of RAT-L and RAT-U are defined and found, respectively. Numerical results show that the proposed modeling and analyzing approach can be well applied to evaluate the coexistence transmission performance while LTE and WiFi APs both access the unlicensed band.

In this paper, we present a big-data self organizing network (Bi-SON) framework aiming to optimize energy efficiency of ultra-dense small cells. Although small cell can enhance the capacity of cellular mobile networks, ultra-dense small cells suffer from severe interference and poor energy efficiency. The self organizing network (SON) can automatically manage and optimize the system performance. However, current SON-enable mechanisms mostly focus on indoor femtocells. Our proposed Bi-SON suggests a data flow framework from data collection, analysis and optimization to reconfiguration. We adopt the statistics analysis approach to determine the optimal system parameters to improve the energy efficiency of a huge number of outdoor small cells. The Bi-SON mechanism periodically collects the management data of small cells, e.g. transmission power, reference signal receiving power and the number of users per cell. We find that simple sorting and filtering data analysis from huge number of small cells can already effectively find the almost optimal solution. Our simulation results show that Bi-SON can improve throughput and energy efficiency by 50% and 135% respectively, compared to the scheme without energy saving approach.

In this paper, we present a close-form expression for the downlink throughput of a massive MIMO system with finite antenna elements in a multi-celluar systems. In practice, the performance of massive MIMO system is limited by the number of antenna elements. From the operator aspect, the cost of massive MIMO is a major concern. Thus, a massive MIMO system with finite antenna elements is of interest. On the other hand, as the number of antenna elements is not so large, the impact of co-channel interference from neighboring cells become a critical issue that should be taken into account. We consider the time-division duplexing (TDD) multi-celluar systems. Our system model accounts for the effects of channel estimation, pilot contamination, 2D channel and 1D antenna array, and our simulations take 3D channel and 2D antenna array into consideration in addition. We derive approximations of achievable rates with linear precoders, and validate by simulations. Our results can provide an evaluation framework for designing a practical massive MIMO system to find the performance tradeoff between the number of antenna elements and cost.

Cognitive radio (CR) systems can enhance the licensed spectrum efficiency by finding and allocating the underutilized channels of the primary users to the secondary users. After scanning a wide range of spectrum. A general CR system needs to utilize all the available channels of various bandwidths. Whenever the active primary users appear, the secondary users shall return the borrowed channels, causing interruption even during a transmission period. The behavior of changing the operating channels is called hopping mode in this paper. In this paper we propose an improved preemptive resume priority (PRP) M/G/1 queueing network model for such a general CR system, aiming to characterize the impacts of hopping-mode behaviors and various bandwidth on the delay performance of the secondary users. We further propose a probability-based various-bandwidth channel selection scheme to reduce the overall system time for the hopping-mode secondary users with multiple interruptions, where the overall system time is defined as the sum of transmission time and waiting time. Our analytical results, validated by simulations, show that the proposed probability-based various-bandwidth channel selection scheme can improve the overall system time by 20% compared to the existing methods.

This paper thoroughly explores the fundamental interactions between cell association, cell load, and throughput in a green (energy-efficient) small cell network in which all base stations form a homogeneous Poisson point process (PPP) of intensity λBand all users form another independent PPP of intensity λ∪. Cell voidness, usually disregarded due to rarity in cellular network modeling, is first theoretically analyzed under generalized (channel-aware) cell association (GCA). We show that the void cell probability cannot be neglected any more since it is bounded above by exp(-λ∪/λB) that is typically not small in a small cell network. The accurate expression of the void cell probability for GCA is characterized and it is used to derive the average cell and user throughputs. We learn that cell association and cell load λ∪/λBsignificantly affect these two throughputs. According to the average cell and user throughputs, the green cell and user throughputs are defined respectively to reflect whether the energy of a base station is efficiently used to transmit information or not. In order to achieve satisfactory throughput with certain level of greenness, cell load should be properly determined. We present the theoretical solutions of the optimal cell loads that maximize the green cell and user throughputs, respectively, and verify their correctness by simulation.

Network coding is an important cloud storage technique, which can recover data with small repair bandwidth and high reliability compared to the existing erasure coding and replication methods. However, regardless of which data recovery technique is used, the repaired data in a geographically distributed cloud storage system are easy to be eavesdropped at the transmission link between the local datacenter and its remote backup site. This kind of network security issue is called link eavesdropping in this paper. For a network coded cloud storage system, we propose a systematic design methodology to determine the important data recovery system parameters for any specified security level. Through analysis, we present the performance curves to relate the remote repair bandwidth and the number of coded data fragments. Consequently, all the important system parameters of a network coded data recovery system, including the number of storage nodes and the link capacity between the datacenter and the backup site, can be precisely designed for satisfying different security level requirements.

The fundamental interactions between cell association, cell throughput and load in a green (energy-efficient) small cell network are exploited in this paper. Due to user-centric cell association, a small cell network severely suffers the void cell problem. We propose a weighted channel-aware cell association (WCCA) scheme and derive the average cell throughput under this scheme that characterizes the per-cell throughput with the void cell impact. We show that the average throughput is significantly affected by WCCA and cell load. In order to reflect whether the energy of a base station is efficiently used to transmit information or not, the green cell throughput, which is defined based on the average cell throughput, is derived and it indicates that cell load should be properly determined in order to achieve the satisfactory green cell throughput. The theoretical solution of the optimal cell load that maximizes the green cell throughput is found and verified its correctness by simulation.

With the prosperity of e-commerce, on-line vendors use recommendation systems in different fields. Classic recommendation algorithms are designed assuming that data is stationary and will not change over time. However, since the scale and variability of data are growing gradually, these methods will encounter the issues of the memory deficient and the out-of-date model, which degrade the recommendation accuracy intensively. In addition, retraining the whole model for every new arrival record results in high complexity. In this paper we propose a light-weight adaptive updating method to overcome these issues. Comparing with the explicit feedback recommendation, which asks the customers to express their opinions on the recommended items, the implicit feedback recommendation is easier to collect and non- intrusive way. However, the dynamic time-variant system with implicit feedback has not been seen in the literature. In this paper, we propose a real- time incremental updating algorithm (RI-SGD) to deal with time-variant systems based on the implicit feedback. We compare our method with methods that retraining the whole model and show that our method costs less than 1% of the retraining time with a competitive accuracy.

This paper studied the fundamental modeling defect existing in Poisson-distributed cellular networks in which all base stations form a homogeneous Poisson point process (PPP) of intensity λBand all users form another independent PPP of intensity λU. The modeling defect, hardly discovered in prior works, is the void cell issue that stems from the independence between the distributions of users and BSs and “user-centric” cell association, and it could give rise to very inaccurate analytical results. We showed that the void probability of a cell under generalized random cell association is always bounded above zero and its theoretical lower bound is exp (-λU/λB) that can be achieved by large association weighting. An accurate expression of the void probability of a cell was derived and simulation results validated its correctness. We also showed that the associated BSs are essentially no longer a PPP such that modeling them as a PPP to facilitate the analysis of interference-related performance metrics may detach from reality if the BS intensity is not significantly large if compared with the user intensity.

In this paper, how to optimally deploy base station density in a small cell network with energy-efficient power control was investigated. Base stations (BSs) and users form two independent Poisson point processes (PPPs) in the network. Since user-centric cell association may lead to void cells that do not have any users, the power of each BS is controlled in either all-on or on-off mode depending on whether its cell is void or not. The average cell rates for each power control mode are first found and their corresponding energy efficiency is also characterized. The optimal BS density that maximizes the energy efficiency under a given user density is theoretically proved to exist and its value can be found numerically. Both analytical and simulated results indicate that on-off power control is significantly superior to all-on power control in terms of energy efficiency if BSs are deployed based on their optimal energy-efficient density.

In this paper we propose a traffic-aware networking technique by using software-defined networking (SDN) to precisely and promptly identify video streaming packets. We performed experiments on our SDN testbed. Compared with the existing deep packet inspection (DPI) method, the proposed SDN-enabled traffic-aware packet routing technique can reduce the latency by 75% and increase the success rate for traffic identification up to 138%.

In this paper, we propose a low-complexity distributed channel-oriented channel allocation scheme (COCAS) to improve the link reliability of users and the spectrum efficiency of femtocells for multi-antenna orthogonal frequency-division multiple access (OFDMA)-based femtocell systems. By two steps of comparisons on channel gains of resource blocks and antennas, the COCAS can jointly allocate suitable antennas and resource blocks for transmission. We also investigate the impacts of channel allocation and multiple antennas on link reliability of users and spectrum efficiency of femtocells. Simulation results show that the proposed COCAS can achieve higher spectrum efficiency for femtocells than the random channel allocation scheme with a given number of antennas equipped on the femtocell base station, under the link reliability requirement.

Cognitive radio (CR) is concretely embodied in hierarchical cellular systems by deploying an underlying microcellular system to reuse the underutilized spectrum of a macrocellular system. One of the key challenges to the success of hierarchical cellular systems is to manage the intercell interference between the macrocell and the microcell and to maximize the spectrum efficiency. In this paper, antenna beamforming, power allocation, and multiuser scheduling are jointly designed to opportunistically utilize the macrocell's uplink spectrum in serving multiple secondary microcellular users concurrently. The joint design of antenna beamforming, power allocation, and scheduling with the objective of maximizing the sum rate is indeed a mixed-integer nonlinear programming NP-hard problem. The proposed simpler iterative subgradient projection and semidefinite programming approach can obtain better performance than the conventional zero-forcing beamforming. Furthermore, unlike the optimal singular value decomposition (SVD) beamforming that requires all users to have channel knowledge at the receiver for cooperation, the proposed joint design methodology requires no channel knowledge at the receiver and can outperform the SVD beamforming without user scheduling. When considering both implementation complexity and performance enhancement issues, the proposed joint power allocation, multiuser scheduling, and antenna beamforming technique can help provide important insights into the design of interference management techniques for hierarchical CR systems.

Due to the exploded demand for mobile traffic and diverse service type, flexible mobile networks with high capacity is drawing lots of research efforts. Distributed large scale (DLS) MIMO has superiority over centralized large scale MIMO and separated small cells due to lower signal loss to users and central control. In this paper, we present a self-optimization method for Cloud RAN based DLS MIMO. The method includes network dimensioning and antenna clustering with adaptability to the migration of hardware/virtualization technology and user over time. Other advantages are easing the process for dimensioning and optimization, requiring fewer antennas, and less demand for controller & baseband computing power due to the characteristic of low complexity.

In this paper, we propose a cloud-assisted network coded packet retransmission approach to reduce the number of packet retransmission in wireless multicasting. It is shown that the efficiency of packet retransmission can be significantly improved if we include network topology information (e.g., Network connectivity) during the encoding process of network coding. We leverages the capability of software-defined networking (SDN) to dynamically monitor and control the entire network and design a network topology based network coded packet retransmission (NTNCPR) mechanism. The proposed NTNCPR mechanism can easily calculate the good packet combination based on network topology information.

To relieve the heavy loading caused by burst machine-to-machine (M2M) traffic and also satisfy various quality of service (QoS) requirements, load balancing techniques are often introduced in M2M networks. In recent years, software-defined networking (SDN) has shown the possibility of improving load balancing technique. In this paper, we propose traffic-aware load balancing mechanism for M2M networks using SDN. The proposed mechanism can satisfy different QoS requirements of M2M traffic by instant traffic identification and dynamic traffic rerouting, which leverage the capability of SDN to dynamically monitor and control the entire network.

Coverage analysis of a small cell network is very crucial since small cell deployment will dominates the topology of a cellular in the future. The majority of prior work on the coverage probability is studied based on a simple and consistent Rayleigh fading models in a Poisson-distributed cellular network in order to avoid analytical intractability. In this paper, we study the coverage probability problem in a Poisson small cell network with much more general channel impairments. First a neat expression of the coverage probability with compose Rayleigh fading and log-normal shadowing is derived and it discloses two important facts - the coverage performance is not improved by deploying more base stations and it is significantly weakened by shadowing. Then we find the coverage probability with low complexity for the case that a dual-slop path loss is used, the desired signal experiences Nakagami-m fading and interference signals undergo Rayleigh fading. It is able to more practically reflect the coverage performance of a user.

Small cell networks are the key to the success of the next generation wireless systems because of the potential huge capacity gain, but pose many challenges on managing the inter-cell interference from huge number of small cells and that from macrocells. Recently, an interesting technique, called interference alignment (IA), was proposed to align the mutual inter-cell interference (ICI) in the same sub-space by designing the beamforming vector among the coordinated cells. Because of the reduced ICI, the system capacity of an IA system can be improved. Nevertheless, the methods of selecting a group of users for the IA system has been rarely seen in the literature, especially in the macrocell/small cells hierarchical overlaying networks. In this paper, we investigate the effects of pairing users and base stations in an IA-based hierarchical overlaying macrocell/small cells networks. Furthermore, we design an IA scheme that can not only reduce the inter-cell interference among small cells, but also avoid interfering with the overlaying macrocell. Comparing to the conventional IA scheme based on the random pairing for users and base stations, the sum rate of the proposed joint user scheduling and IA beamforming design is 30% and 64.5% improved for the cell edge users with the SNR-based and SINR-based pairing for the users and base stations.

In this paper, we present an interference-aware slot allocation technique to improve system performance of the hierarchical picocell/macrocell system, where low-power picocells are underlying a high-power macrocell. Although a hierarchical picocell/macrocell system can improve capacity, it also poses a challenging inter-cell interference (ICI) issue. To control the ICI between pico-cells and macro-cells, one enhanced inter-cell interference coordination (eICIC) technique, called almost blank subframes (ABS), was proposed in the 3rd Generation Partnership Project (3GPP) Long-Term Evolution-Advanced (LTE-A) system. Nevertheless, the current ABS mechanism in 3GPP only defines the fixed ABS ratio, which is not very spectrum efficient and can affect the macrocellular system performance. In this paper, we propose an interference-aware ABS ratio adaptation technique to enhance the total spectrum efficiency of the hierarchical picocell/macrocell system. Compared with the current fixed ABS ratio approach, the proposed method can improve the spectral efficiency of pico-cells and macro-cells by 13% and 7% at the cell edge, respectively.

Advertisements and coupon forwarding among mobile devices and social platforms become significant in recent years. However, when users are in the crowded places where the network connection is unstable such as shopping malls, forwarding of advertisements to target users in limited time is challenge. Thus, we propose an interest-aware opportunistic advertising by mining social and consuming information system (ISC) and algorithm. In ISC system, we combine social relationship and consuming information to find out the consumers who have similar interest. ISC algorithm adopts the result of ISC system to reduce the delay of broadcasting advertisement and increase the advertisement effect. We also implement and evaluate the performance of ISC system and algorithm on mobile devices and the ONE simulator. Our experiment results show that ISC algorithm is cost effective and intensively magnify the advertising effeteness with many customers under uncertain network and time-limited environments.

The device-to-device (D2D) communications can improve the spectrum efficiency with low power and effectively offload the traffic from the macro-eNB. However, the macro/femto/D2D heterogeneous networks face a complicated three-tier interference issue. The completely centralized resource management is not applicable in such three-tier heterogeneous networks. Therefore, we propose a network-assisted device-decided (NADD) scheme to jointly select the channels and adjust the transmission power for D2D devices. In the NADD method, the macro-eNB broadcasts resource allocation instruction to D2D devices. According to the resource allocation instruction and the device's communication quality, D2D devices autonomously and dynamically select the suitable channels and adjust the transmission power. Simulation results show that NADD scheme can significantly improve the total throughput and ensure the link reliability of macrocell users, comparing to both the fixed power scheme and the network-decided (ND) power control scheme.

For security concerns, a security traversal service can route data flows through a sequences of security devices (middleboxes). In this paper, we identify the problem of delay guarantee in security traversal and propose a scheme to dynamically change the security traversal path. To provide deterministic delay guarantee with minimum virtual machine (VM) and transmission cost, we model this security traversal path determination as a constrained shortest path problem (CSP) and propose an optimal security traversal with middlebox addition (OSTMA) mechanism. Besides, we implement the proposed OSTMA mechanism in an OpenFlow network by designing a centralized security traversal controller to dynamically monitor the network condition information and reconfigure the security traversal path. Our experimental results show that the proposed dynamic security traversal scheme can still achieve delay requirements for network topology changes and burst traffic.

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. We analyze the achievable rate loss due to channel state information (CSI) mismatch arising from both channel estimation and quantization feedback. We first derive an upper bound to characterize the effect of imperfect CSI, and then present a limited feedback strategy for both the two-hop channels in the relay system. This newly presented feedback strategy reveals the relationship among the CSI quantization levels, the transmit power, and the pilots for channel estimation. An optimized pilot design at the base station and the relay is also presented by applying the derived bounds for the two-hop system.

We consider the cloud storage systems with data stored in two geographically different datacenters for remote backup. In such system, inter-data center communication is established for data repair when storage nodes fail in the data center. Since the repairing data are transmitted over the Internet, the communication between the datacenters can become susceptible to eavesdropping. This problem is especially crucial in network coding-based distributed storage systems because more repair bandwidth and repair links are required, compared to conventional replication. In this paper, we show that remote repair bandwidth can be reduced by increasing storage per node and derive the tradeoff curves between remote repair bandwidth and storage. Moreover, we show that there exist another tradeoff for storage cost and reliability for different amount of remote and local storage nodes.

Communication networks such as cellular phone networks are quite likely to be severely damaged during a large-scale of disaster, making SOS message dissemination to rescue authorities extremely difficult. In this paper, we first propose a FM radio-based emergency communication service through the integration of FM transmitters into smartphones. FM radio provides a number of advantages such as longer propagation length and less susceptible to obstacles, making it suitable for broadcasting SOS messages. Besides, we design Morse code-based SOS message dissemination using FM transmitters (MCSOS-FM) and its corresponding communication procedure for the emergency communication service in order to improve communication range, victim localization and evacuation route planning. The combination of FM radio and Morse code is completely compatible with the current radio system equipped by rescue workers, and thus increases the possibilities of successful receiving and recognizing SOS messages. Our experiment result based on smartphone implementation shows that the proposed emergency communication service is cost effective and energy efficient with relatively large communication range.

Cognitive radio (CR) technology can be been applied in hierarchical cellular systems to allow concurrent transmissions for licensed (primary) and unlicensed (secondary) users to improve spectrum utilization. The major challenge of hierarchical CR systems is to manage the inter-cell interference between the primary and secondary systems. In the paper, we first present an optimal multiple-input multiple- output (MIMO) orthogonal frequency division multiplexing (OFDM) uplink transmission scheme for hierarchical CR systems that can maximize the sum rate of the underlay secondary CR users, and prevent the primary systems from the severe interference of the secondary systems. The main contribution of this work is to propose a methodology to transform the signal to interference plus noise ratio (SINR) maximization problem of the secondary system into a quasi-convex form, thereby obtaining the optimal beamforming solution using a simple bisection method. Furthermore, we proposed a sub-optimal user scheduling algorithm in the secondary system to effectively mitigate the interference to the primary system.

In this paper, we propose a sensors-assisted rescue service architecture to integrate rescue schemes for different purposes, including disaster prediction, evacuation planning, and emergency broadcast. In the proposed architecture, multiple-sensed mobile devices are designed to provide a personalized situational awareness, thereby further enhancing the flexibility and efficiency of rescue services. Reliability and scalability of rescue services are improved by leveraging the dynamical resource provision of cloud computing. The proposed rescue service architecture is implemented to show the advantages of power efficiency and scalability of the proposed rescue service architecture.

In this paper, we present amodeling technique of using Jackson's network theorem to characterize the performance of mashup multiple servers in cloud computing environments. The key challenge in providing new mashup mobile applications in cloud computing, such as the real-time location-based services, is to evaluate the overall delay resulting from integrating multiple cloud servers. Furthermore, the number of virtual machines (VM) will affect the quality of service (QoS) for mobile applications with various traffic loads. However, an effective analytical model to characterize both the effects of integrating multiple cloud servers and scalable VMs is rarely seen in the literature. The proposed multi-cloud mashup analytical model can calculate the service waiting time for various numbers of VMs and different arrival rates. Through simulations and analysis, we show that for various numbers of VMs and traffic loads, the proposed model can accurately predict the breakpoint where the waiting time in mashup cloud servers will sharply increase. Hence, the proposed mashup multi-cloud analytical model can facilitate the management of resource in future cloud data centers.

In this article, we consider a scalability problem associated with software-defined datacenter, of which the unicast/multicast routing states is proven to be NP-complete. We introduce an efficient multiple membership query algorithm, called Scalar-pair Vectors Routing and Forwarding (SVRF), based on the prime theory such as Chinese Remainder Theorem (CRT). Our proposed algorithm simply calculates corresponding output ports of each multicast group by dividing a common scalar-pair with a group-specific key, within pseudo-polynomial time. The result is then used to make a forwarding decision within few cycles through a hardware accelerator. Compared to Bloom filter, our algorithm can achieve remarkable performance in terms of memory consumption, processing time, hardware cost, and 100% delivery accuracy, while applying for a large number of large-scale distinct flows (including unicast and multicast) in a large-scale datacenter networks. Our work may be applied to various research areas of computer science and networking.

The articles in this special section focus on new applications that are supported by cloud computing.

This paper proposes a new hybrid cyclic delay diversity (HCDD) scheme for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Unlike the existing double space time transmit diversity (DSTTD) and stacked cyclic delay diversity (SCDD) schemes that are suitable for integer multiplexing rates, the proposed HCDD scheme can achieve arbitrary non-integer multiplexing rates. Our results demonstrate that the HCDD scheme can provide flexibility in selecting the required number of transmitting antennas and in adjusting diversity and multiplexing gains to match various users requirements. The advantages of the proposed HCDD, including the flexible rate assignment and less complex receivers, are presented in applications of scalable video broadcasting (SVB).

The design of wireless local area networks (WLANs) with enhanced throughput performance have attracted significant amounts of attention in recent years. Based on the IEEE 802.11n standard, frame aggregation is considered one of the major factors to improve the system performance of WLANs from the medium access control (MAC) perspective. In order to fulfill the requirements of high throughput performance, feasible design of automatic repeat request (ARQ) mechanisms becomes important for providing reliable data transmission. In this paper, two MAC-defined ARQ protocols are proposed to consider the effect from frame aggregation for the enhancement of network throughput. An aggregated selective repeat ARQ (ASR-ARQ) scheme is proposed which incorporates the selective repeat ARQ scheme with the consideration of frame aggregation. On the other hand, for worse channel quality, the aggregated hybrid ARQ (AH-ARQ) mechanism is proposed to further enhance the throughput performance by adopting the Reed-Solomon (RS) block code as forward error correction (FEC) scheme. Novel analytical models for both the ASR-ARQ and AH-ARQ protocols are established with the consideration of interfering wireless stations. Simulations are conducted to validate and compare the proposed ARQ mechanisms based on the service time distribution and system throughput. Numerical evaluations show that the proposed AH-ARQ protocol can outperform the other schemes under worse channel condition; while the ASR-ARQ scheme is superior to the other mechanisms under better channel condition.

In cloud computing environments, users can rent virtual machines (VMs) from cloud providers to execute their programs or provide network services. While using this kind of cloud services, one of the biggest problems for the users is to determine the proper number of VMs to complete the jobs considering both budget and time. In this paper, we propose a resource prediction framework (RPF), which can help users choose the minimum number of virtual machines to complete their jobs within a user specified time constraint. In order to verify the feasibility of RPF, we have done three case studies, namely parallel frequent pattern growth (FP-Growth), parallel K-means, and Particle Swarm Optimization (PSO). FP-growth, K-means and PSO are data intensive algorithms. These algorithms are typically executed repeatedly with different execution parameters to find the optimal results. When evaluating RPF by these algorithms in cloud environments, we have to modify them to parallel versions. The evaluation results indicate that RPF can successfully obtain the minimum number of VMs with acceptable errors. According to our case studies, the proposed RPF can be adopted by data intensive jobs by providing flexibility to both end users and cloud system providers.

This paper presents a novel cross-layer forwarding scheme, called UNified unicast and multiCast LabEling (UNCLE), for mobile ad hoc networks (MANETs). Unlike traditional unicast and multicast forwarding schemes requiring two different protocol stacks, the proposed UNCLE forwarding method requires only one single protocol stack, and thus avoid the need of distinguishing unicast frames from multicast frames. Furthermore, the header of the stateless UNCLE forwarding scheme is designed based on a simple modulo approach, resulting in much lower protocol overheads than the traditional stateless unicast Dynamic Source Routing (DSR) and the Differential Destination Multicast (DDM) schemes. In addition to low forwarding latency the proposed UNCLE is very scalable for various network sizes and the multicast group sizes without table lookup in layers 2 and 3. Therefore, the UNCLE forwarding scheme can facilitate the deployment and management for providing both unicast and multicast services significantly.

This paper presents an improved layer-2 routing algorithm, called dynamic and disjoint edge node divided spanning tree (D2ENDIST), to overcome the issues of the single path route and unbalanced link utilization in cloud datacenters. D2ENDIST consists of two key schemes: (1) disjoint ENDIST routing and (2) reroute by dynamic reweights. The former scheme can provide multi-path routes, thereby reducing traffic congestion. The latter scheme can balance the traffic load and improve the link utilization. Our experimental results show that the proposed scheme can enhance system throughput by 25% subject to the constraint of very short failure recovery time compared to the existing ENDIST scheme.

The idea of cognitive radio (CR) has embodied concretely in hierarchical cellular systems by deploying an underlying microcellular system to reuse the underutilized spectrum licensed by an macrocellular system. The fundamental challenges for successfully realizing such hierarchical systems are to manage the intercell interference between the macrocell and microcell and to pursue the goal of maximizing the spectrum recycling efficiency. In this paper, we jointly consider antenna beamforming, power allocation, and multiuser scheduling for the microcellular system to opportunistically utilize the uplink spectrum of the macrocell and to concurrently serve multiple secondary users in the downlink. With the objective of maximizing the sum rate, the three-dimensional joint design problem is often formulated as a mixed integer nonlinear programming (MINLP) which is an NP-hard problem and very complicated to solve. We resort to semidefinite relaxation (SDR) techniques to convert to the cumbersome optimization problem into a convex problem by introducing an interference-related auxiliary variable. An iterative algorithm based on semidefinite programming is proposed to achieve the optimal solution. The zero-forcing (ZF) beamforming and the singular value decomposition (SVD)-based beamforming with the best scheduling are simulated for performance comparisons, and our simulation shows that the proposed scheme is much superior to the ZF scheme and quite close to the SVD scheme with a slight performance gap of 1 bps/Hz.

Cyclic-delay diversity (CDD) based frequency-selective scheduling is an effective technique to increase system capacity with low complexity. By increasing the channels' frequency selectivity with cyclic delays, the merit of multi-user diversity can be exploited more effectively in the system. This paper aims to optimize the design of the CDD-aided frequency-selective scheduling for downlink OFDMA systems. First, a low-complexity search of CD-values is proposed that results in a higher system capacity than the existing methods. Second, a new feedback method is proposed based on the principle of proportional-fair, where users who currently enjoy more downlink source are allotted less uplink resource for feedback. The method is shown to provide a better tradeoff between system capacity and fairness than the traditional methods, under a fixed feedback overhead. The effectiveness of the proposed method is verified by extensive computer simulations.

Smart phones with touch screens have become very popular and have changed our behaviors of using handsets. However, using touch screens is not safe for mobile phone users especially when they are driving cars. Thus, many applications using smart phones cannot be initiated because users who are driving the cars cannot easily touch the small icons on the screens of smart phones. To overcome this issue, we propose a proximity sensors based "no-touch" mechanism for smart phones by applying proximity sensors to initiate mobile applications without the need of touching the screen. We will discuss how to implement the proximity-sensors driven "no-touch" mechanism in Android platform and investigate its performance issues regarding detection accuracy and power consumption. Speech-oriented applications using the proposed "no-touch" mechanism on Android is also demonstrated in this paper.

A cognitive radio (CR) system scans the wide spectrum to find available spectrum. One of key challenges in using these temporarily available spectrums is that the bandwidth of the available spectrum are not equally wide. The other challenge for spectrum decision scheme in CR systems is that many secondary users (SUs) may choose the same channel simultaneously, resulting in channel contention. In this paper, we develop a load balancing spectrum decision scheme for unequal-bandwidth CR networks. We apply the concept of the delay bandwidth (DB) product to select a suitable channel for each user among many unequal-width channels. Compared with other existing unequal bandwidth spectrum decision schemes, our simulation results show that the proposed DB-based spectrum decision can improve the overall system throughput up to 40\% in the considered case.

In this paper, we propose the group-based time control mechanism to improve the network overload and delay performance in the femtocell-based machine type communications (MTC) networks. The MTC network may be congested if numerous MTC devices concurrently deliver messages to the MTC server. Femtocells can solve the radio access network (RAN) congestion, however the core network (CN) congestion becomes more difficult to handle. The proposed group-based time control method can spread the traffic load of MTC devices over the time, and thereby mitigate the RAN overload and CN overload simultaneously. Numerical results show that the proposed approach can significantly improve the network congestion and message delay compared to the existing methods.

This paper studies the joint resource allocation problem of antenna, subchannel, transmission power, and phase duration for the relay-enhanced bidirectional multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) networks. The goal of resource allocation is to minimize the transmission energy in the networks with multiple relay stations (RSs) under the data rate constraints of user equipment (UE). The challenges of this resource allocation problem arise from the complication of multiple-phase assignments within a subchannel since the RS can provide an additional transmission path from the base station to the UEs. The green resource allocation (GRA) schemes with reduced computational complexity are proposed in this paper to develop the joint resource allocation algorithm for the UEs with the consideration of direct and two-hop communications. Both the separate downlink (DL) and uplink (UL) and mixed DL and UL relaying assignments are adopted to obtain the solutions for proposed GRA schemes. Simulation results show that the GRA schemes can provide comparably better energy conservation with the consideration of quality-of-service (QoS) support.

In this paper, we present an analytical framework to evaluate the effects of multiple spectrum handoffs on channel utilization and latency performances in cognitive radio (CR) networks. During the transmission period of a secondary connection, multiple interruptions from the primary users result in multiple spectrum handoffs. In order to decide the target channel for each spectrum handoff and resume the unfinished transmission, wideband sensing is performed in an on-demand reactive manner. Although spectrum handoff procedure can enhance channel utilization, transmission latency of the secondary users is prolonged due to multiple handoffs. Thus, two fundamental issues in CR networks with multiple spectrum handoffs arise: (1) to what extent the channel utilization can be improved; and (2) how long the transmission latency will be extended for the secondary users. To solve the first problem, we introduce the preemptive resume priority (PRP) M/G/1 queueing network to characterize the channel usage behaviors of CR networks. Based on this queueing network, channel utilization under various traffic arrival rates and service time distributions can be evaluated. Furthermore, on top of the proposed queueing network, a state diagram is developed to characterize the effects of multiple handoff delay on the transmission latency of the secondary users. The analytical results can provide a helpful insight to study the effects of traffic arrival rates and service time on channel utilization and transmission latency and then facilitate the designs of admission control rules for the secondary users subject to their performance requirements.

In this paper, we present a joint antenna beamforming and power allocation technique to maximize the multiuser sum rate in an underlying microcellular system which reuses the same spectrum of a macrocellular system. One challenge in this kind of hierarchical cognitive radio (HCR) systems is to manage the interference between the macrocell and the microcell. The key contribution of this paper is to develop an optimization technique for antenna beamforming that can maximize the achievable sum rate of the underlying cognitive radio (CR) microcellular system and control the interference between the macrocell and the microcell with a satisfaction level. The proposed technique optimizes the sum rate performance by maximizing its lower bound and transfers the original non-convex problem into a convex optimization problem by introducing auxiliary variables to confine the intra-user interference power among the secondary system. Next, an iterative sum rate maximization (ISM) algorithm is developed to find the beamforming weights and the allocated power for each secondary user to simultaneously maximize system sum rate, coverage, and concurrent multiuser transmission probability in the HCR system. The developed joint design methodology provides valuable insights into the design of an optimal HCR system for various numbers of users as well as cell coverage, and can quantitatively optimize the performance tradeoffs in the hierarchical multiuser CR systems for current and future wireless communication applications.

In this paper, we investigate how to determine an optimal target channel sequence with the minimum cumulative handoff delay in cognitive radio networks. When the secondary user encounters multiple spectrum handoffs during its transmission period, the effects of multiple interruptions from the high-priority primary users and the traffic statistics of both the primary and the secondary users should be incorporated in the design of the optimal target sequence. The optimal target channel sequence can guild the secondary user to change its operating channel when the primary user's interruptions occur. With M candidate channels and L elements in the target channel sequence for spectrum handoffs, the exhaustive search (ES) requires time complexity of O(ML). In this paper, we propose a dynamic programming (DP) algorithm with time complexity of O(LM2) to determine an optimal target channel sequence. Furthermore, we propose a greedy algorithm with time complexity of O(M) and prove that the greedy algorithm only requires comparing six target channel sequences. Numerical results show that the low-complexity greedy algorithm can yield similar cumulative handoff delay performance as the optimal DP-based or ES-based algorithms in most cases except when the primary users' service time distributions at their operating channels are different.

In this paper, we present an analytical framework to evaluate the latency performance of connection-based spectrum handoffs in cognitive radio (CR) networks. During the transmission period of a secondary connection, multiple interruptions from the primary users result in multiple spectrum handoffs and the need of predetermining a set of target channels for spectrum handoffs. To quantify the effects of channel obsolete issue on the target channel predetermination, we should consider the three key design features: 1) general service time distribution of the primary and secondary connections; 2) different operating channels in multiple handoffs; and 3) queuing delay due to channel contention from multiple secondary connections. To this end, we propose the preemptive resume priority (PRP) M/G/1 queuing network model to characterize the spectrum usage behaviors with all the three design features. This model aims to analyze the extended data delivery time of the secondary connections with proactively designed target channel sequences under various traffic arrival rates and service time distributions. These analytical results are applied to evaluate the latency performance of the connection-based spectrum handoff based on the target channel sequences mentioned in the IEEE 802.22 wireless regional area networks standard. Then, to reduce the extended data delivery time, a traffic-adaptive spectrum handoff is proposed, which changes the target channel sequence of spectrum handoffs based on traffic conditions. Compared to the existing target channel selection methods, this traffic-adaptive target channel selection approach can reduce the extended data transmission time by 35 percent, especially for the heavy traffic loads of the primary users.

In this paper, we propose a low-complexity distributed stable subchannel allocation algorithm combined with a low-cost reconfigurable switched multi-beam directional antenna to improve the capacity and the fairness performance for the orthogonal frequency division multiple access (OFDMA)-based femtocell systems. With the proposed deferred acceptance algorithm, the stable subchannel allocation scheme can achieve a better tradeoff between capacity and fairness. Numerical results show that the proposed stable subchannel allocation scheme with the switched multi-beam antenna can significantly improve femtocell capacity compared to the traditional user-oriented subchannel allocation scheme and ensure the fairness as compared to the traditional channel-oriented subchannel allocation scheme.

While the zero-forcing (ZF) transmit beamforming is a widely used technique for realizing multi-input multi-output (MIMO) broadcast transmissions, the ZF receiver combined with multiuser scheduling is an effective alternative that yields improved robustness against CSI mismatch caused by feedback link errors. In this paper we consider the practical scenario that channels are imperfectly estimated at the receiver. Our goal is to characterize the sum rate performance of the receive ZF MIMO broadcast systems under channel estimation errors. We derive analytic sum-rate expressions for both the uniform transmit power and transmit water-filling cases. Our analytic results characterize the sum-rate floor incurred by channel estimation errors. Numerical simulations are used to confirmed the analytic study.

Handoff is the process to transfer an ongoing call for a mobile station from the serving base station to another base station. Most conventional handoff initiation methods are based on a fixed hysteresis to decide when the handoff process is initiated. A suitable hysteresis for handoff initiation can simultaneously reduce handoff rate and dropping probability. However, conventional handoff initiation method is not suitable for emerging data-oriented communication systems. In this paper, a novel handoff initiation method is proposed in an attempt to increase data rates. The proposed handoff initiation method is not only based on signal quality measurement, but also handoff interruption time. According to our simulation results, the proposed method can yield higher data rates and has more robust performances than other handoff initiation methods.

In this paper the joint effects of the inter-cell macrodiversity schemes combined with intra-cell spatial multiplexing are investigated for MIMO-OFDM systems. We compare the SINR and throughput performances of cellular Alamouti, cellular cyclic delay diversity (CDD) schemes and pure macrodiversity at the cell edge. Our simulation results indicate that cellular Alamouti scheme outperforms cellular CDD scheme, and the CDD has similar performance as the pure macrodiversity scheme. This result is useful to determine the suitable macrodiversity scheme when spatial multiplexing are implemented at the user terminals.

In this paper, we investigate the impact of a receiver amplifier on orthogonal frequency division multiplexing (OFDM) and single-carrier block transmission (SCBT) systems which are two potential air interfaces for future short-range wireless communications at 60GHz radio spectrum. We derive a generalized baseband equivalent model for the receiver amplifier by using a power series to capture the nonlinear distortion behavior with several parameters. Based on this model, we develop a simulation flow in the baseband domain for evaluating the system performance deterioration with the non-ideal receiver amplifier. Computer simulations are used to compare the performance of OFDM and SCBT systems under several radio frequency (RF) parameters, such as 1dB compression point, the 3rd-order intercept point, etc. The developed methodology across the baseband and RF can help us jointly optimize the system performance and implementation cost with the nonlinear amplifier at the receiver side.

In this paper, we investigate the service coverage for interweaving cognitive radio networks with cooperative relays in shadowed areas within the metropolitan region. We highlight the influence of relays on the primary and secondary systems in severe shadow fading channels. The outage probability is utilized as a comprehensive performance metric to characterize the coverage quality for the cognitive radio system with relays. We show that cooperative spectrum sensing among the secondary transmitter and relays can improve the vacant spectrum detection probability and the false alarm probability, thereby making the additionally introduced interference by relays to be constrained within a tolerable limit. Overall, our analysis unveils that both the spectrum utilization rate and the spatial coverage can be significantly improved through the use of the relays, and there exists the spatial selectivity phenomena for both the primary and secondary systems.

In this paper, we present an analytical framework to design system parameters for load-balancing multiuser spectrum decision schemes in cognitive radio (CR) networks. Unlike the non-load-balancing methods that multiple secondary users may contend for the same channel, the considered load-balancing schemes can distribute the traffic loads of secondary users to multiple channels. Based on the preemptive resume priority (PRP) M/G/1 queueing theory, a spectrum decision analytical model is proposed to evaluate the effects of multiple interruptions from the primary user during each link connection, the sensing errors (i.e., missed detection and false alarm) of the secondary users, and the heterogeneous channel capacity. With the objective of minimizing the overall system time of the secondary users, we derive the optimal number of candidate channels and the optimal channel selection probability for the sensing-based and the probability-based spectrum decision schemes, respectively. We find that the probability-based scheme can yield a shorter overall system time compared to the sensing-based scheme when the traffic loads of the secondary users is light, whereas the sensing-based scheme performs better in the condition of heavy traffic loads. If the secondary users can intelligently adopt the best spectrum decision scheme according to sensing time and traffic conditions, the overall system time can be improved by 50% compared to the existing methods.

In this paper, we present a 3-cell network multiple-input multiple-output (MIMO) architecture with fractional frequency frequency (FFR) and a novel tri-sector frequency partition scheme. One fundamental question to apply the network MIMO technique in such a high interference environment is: how many base stations should be coordinated together to provide sufficient performance? We will demonstrate that the FFR-based 3-cell network MIMO architecture with the proposed tri-sector frequency partition can not only effectively overcome the inter-group interference, but can avoid executing the complex multi-base-station joint processing for a huge number of cluster of cells at all locations. It will be shown that the proposed 3-cell network MIMO with the rearranged tri-sector frequency partition strategy can outperform the 7-cell network MIMO with omni-directional antennas. Various sector antenna architectures and the method for determining the inner region of the FFR cell planning are also discussed and analyzed on top of the network MIMO system. We hope that this study can provide important insights into the design of the network MIMO systems from the perspectives of architecture and deployment.

This paper addresses the analysis of diversity-multiplexing tradeoff (DMT) for a decode-and-forward (DF)-based cooperative network coding (CNC) system. The exact outage probability is also provided. Our results show that network coding can assist the relay node to improve multiplexing and diversity gain.

This paper investigates how to characterize the effect of multiple spectrum handoff delay on the extended data delivery time of the secondary users' connections in cognitive radio (CR) networks. Whenever a primary user appears, the spectrum handoff procedures are initiated for the interrupted secondary users. Through spectrum sensing, the secondary user can reactively decide the target channel for spectrum handoff to resume its unfinished transmission. Clearly, many interruptions from the primary users will result in multiple handoffs, thereby increase the extended data delivery time of a secondary connection. In this paper, we develop a Markov transition model integrating with the preemptive resume priority (PRP) M/G/1 queueing network to characterize the multiple handoff delay, resulting from the sensing time, the handshaking time, the channel switching time, and the waiting time. The analytical results can facilitate the designs of admission control rules for the secondary users subject to their latency requirements and provide a framework to determine whether the spectrum sensing technology can effectively shorten the extended data delivery time under various traffic arrival rates and service time as well as sensing time.

In this paper, a new hybrid stacked cyclic delay diversity (HSCDD) scheme is proposed for the multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems to flexibly maximize the tradeoff between multiplexing and diversity gains. Unlike the existing double space time transmit diversity (DSTTD) and the stacked cyclic delay diversity (SCDD) schemes which are suitable for the integer multiplexing rate, the proposed HSCDD scheme can achieve arbitrary number of multiplexing rate. Another advantage of the proposed HSCDD scheme over the DSTTD and the stacked CDD schemes is the flexibility in selecting the required number of transmit antennas. With this provided flexibility, the HSCDD can optimize the tradeoff between diversity and multiplexing gains to match the user's requirements.

In this paper, we propose three-cell network multiple-input multiple-output (MIMO) schemes to mitigate the inter-cell interference in a multi-cell system with each cell using the same spectrum. One fundamental question to apply the network MIMO technique in such a high interference environment is: how many cell should coordinate together to provide sufficient signal-to-interference plus noise ratio (SINR)? Consider interferences from the other coordinated groups, we find that on top of the tri-sector directional antenna and fractional frequency reuse (FFR), the network MIMO based on the proposed three-cell coordination strategy can outperform seven-cell based network MIMO with omni-directional antenna. We also consider the effect of different cell sectorings by using 120° and 60° beamwidths directional antennas.

In this paper, we jointly design antenna beamforming and power allocation to maximize the sum rate for multiple users in a hierarchical cognitive radio (CR) system, where an underlying microcellular system adopts dynamic spectrum access techniques to reuse the same spectrum of a macrocellular system. One challenge in this kind of hierarchical CR systems is the interference management between the macrocell and the microcell. We suggest utilizing the antenna beamforming technique to overcome the interference between the macrocell and the microcell, while maximizing the achievable sum rate of the underlying CR microcellular system. We formulate an optimization problem to achieve the objective of maximizing sum rate subject to the constraints of the maximum allowable interference power to the primary system, the minimum required signal-to-interference plus noise power ratio (SINR) for the secondary users, and the maximum transmission power for the underlying CR base station (BS). An iterative rate maximization (IRM) algorithm is developed to find the optimal solution. This developed methodology provides an important insight into the design of an optimal hierarchical CR system for various numbers of users as well as cell coverage.

In this paper, we present an analytical approach to design the inner region size for the fractional frequency reused (FFR) time division duplex (TDD) orthogonal frequency division multiple access (OFDMA) systems when supporting asymmetrical traffic services. In TDD system, supporting asymmetrical traffic can be achieved by adjusting the ratio of downlink and uplink transmission period. Nevertheless, the intercell interference may be deteriorated by the cross-slot interference resulting from the different downlink and uplink ratios among neighboring cells. Although the FFR technique can overcome the inter-cell interference, the impact of the cross-slot interference of TDD systems due to various downlink to uplink traffic ratios in different cells on the inner region size of a FFR-based OFDMA system is an open issue to our best knowledge. Intuitively, the larger the inner region of a FFR system, the higher the system capacity. Thus, this paper can provide a systematic method to determine the size of the inner region of FFR-based OFDMA system for different ratios of the downlink to uplink traffic among two neighbor cells.

It goes without saying that the femtocells will be widely employed in the next generation wireless networks since the femtocells improve indoor capacity and coverage with low power and less cost. However, as the femtocells become popular, the femtocell users suffer from the complicated two-tier interference, including the macrocell-to-femtocell and femtocell-to-femtocell interference. Therefore, the femtocells pose a difficult challenge on managing the interference in a autonomous and distributed manner. In this paper, we investigate how to distributedly select the sub-channels for the OFDMA-based femtocell systems to reduce interference and to improve indoor capacity under a link reliability requirement. We develop the channel-gain oriented and interference-avoidance oriented distributed channel selection schemes. Simulation results show that the interference from the macrocell and other femtocells significantly degrades femtocell link reliability and capacity. However, by properly adjusting the number of used sub-channels, the developed channel selection schemes can improve capacity and ensure the link reliability.

Thanks to silence suppression, the VoIP capacity can be improved. When the voice users are listening or thinking, the VoIP system does not need to allocate resources to them, thereby being able to accommodate more users. The main issue is how to immediately allocate resources to the user, when the user transits from the silence state to the talkspurt state. The IEEE 802.16e/m system provides two typical bandwidth request methods: unicast polling and multicast polling. The unicast-polling method can ensure the bandwidth request delay, but needs more slots for polling. Contrarily, the multicast-polling method needs fewer slots for polling, but the bandwidth request delay cannot be guaranteed. Therefore, we propose the hybrid-polling method, which combines the multicast and unicast polling. Simulation results demonstrate that by properly designing system parameters, the hybrid-polling method can achieve better tradeoff between the bandwidth request failure rate and the resources needed for polling. Moreover, the proposed hybrid-polling method can achieve comparable VoIP capacity as the multicast-polling method, with a guaranteed bandwidth request delay.

Femtocells are low-power, low-cost and user-deployed home base stations that can improve poor indoor coverage and enhance system capacity. However, as femtocells are deployed extensively, the two-tier interference will become serious, including the interference from macrocells and that from femtocells. Thanks to the low side lobe, the E-plane horns based reconfigurable antenna can be used to reduce the interference and enhance the capacity. In this paper, we investigate the effects of applying E-plane horns based reconfigurable directional antenna on link reliability and capacity of the orthogonal frequency-division multiple access (OFDMA) based femtocell systems. We compare our newly designed E-plane horns based reconfigurable antenna,and the sector antenna provided in the IEEE 802.16 m, as well the omni-directional antenna. It is shown that E-plane horns based reconfigurable antenna system has higher femtocell capacity than the IEEE 802.16 m sector antenna system due to the advantages of low side lobe and high main-lobe gain.

Abstract—In this paper, we present a computable bit error rate (BER) expression for the binary signals in the IEEE 802.15.3a ultra-wideband (UWB) channel. In the literature, the impacts of the RAKE receiver’s finger numbers and lognormal shadowing on the BER performance have not been reported yet. We propose a characteristic function (CF) based BER formula to overcome the convergence problem of the existing moment generating function (MGF) approach when the BER calculation takes account of the shadowing effect. Simulation results demonstrate that the proposed CF-based computable BER formula can accurately estimate the complete effects of the cluster and ray arrival processes, the lognormal fading as well as shadowing, and the finger numbers at RAKE receivers. Index Terms—Bit error rate (BER), IEEE 802.15.3a channel model, RAKE receiver, shadowing, ultra-wideband (UWB). I.

In this paper, we present an approximation formula and the close-form expression for the sum rate of the transmit and the receive zero-forcing (ZF) multiple-input multiple-output (MIMO) broadcast systems with user selection, respectively. Instead of assuming a large number of users to obtain a scaling law as most current work, we derive the sum rate formulas of the ZF MIMO broadcast systems with a small number of scheduled users. By analysis and simulations, we find that when taking the variations of feedback channel into account, the receive ZF MIMO broadcast system is more robust to feedback errors and can deliver equal or even higher sum rate than the transmit ZF MIMO broadcast system. We discuss whether a feedback channel is suitable to send channel state information (CSI) for calculating transmit antenna beamforming weights, or suitable to send CSI for selecting users in the receive ZF MIMO broadcast system. Our results show that as the variation of feedback channel errors increases from 0.5 to 1.5, the receive ZF 3 × 3 MIMO broadcast system can provide 36% to 116% higher sum rate than the transmit ZF 3 × 3 MIMO broadcast system in the case of 20 users at signal to noise ratio (SNR) equal to 20 dB. Providing that more feedback bandwidth and an error-free feedback channel are available, the transmit ZF MIMO broadcast system can achieve higher sum rate than the receive ZF MIMO broadcast system.

The purpose of this paper is to reduce the complexity of scheduling scheme for OFDMA/SDMA system with multimedia traffic. To achieve this purpose, we propose a low-complexity beamforming-based scheduling (LCBS) scheme to solve the multi-dimensional (space, time, and frequency) radio resource allocation problem in OFDMA/SDMA systems. By utilizing a semi-orthogonal user selection (SUS) algorithm, the LCBS scheme has a good trade-off between the implementation complexity and system performance in throughput, QoS guarantee (for all traffics), and fairness guarantee (for non-real-time and best effort traffics). Simulation results show that the LCBS scheme can efficiently improve the fairness performance compared to most existing algorithms.

Spectrum handoff occurs when the primary users appear in the licensed band occupied by the secondary users. Spectrum handoff procedures aim to help the secondary users to vacate the occupied licensed spectrum and find suitable target channel to resume the unfinished transmission. In this paper, we discuss how to select the target channels to minimize the total service time with multiple spectrum handoffs. We propose a preemptive resume priority (PRP) M/G/1 queueing network model to evaluate total service time for various target channels selections. Then, we suggest a low-complexity greedy algorithm to select target channels. Numerical results show that a spectrum handoff scheme based on greedy selection strategy can reduce total service time compared to the randomly selection scheme.

In this paper, we investigate the impact of imperfect feedback on the multi-user multi-input multi-output (MU-MIMO) broadcast systems. We consider the MU-MIMO system with either zero-forcing (ZF)-based transmit beamforming or receive beamforming. By analysis and simulations, we find that if the effects of feedback channel variations are taken into account, the receive ZF MIMO broadcast system can have higher sum rate than the transmit ZF MIMO broadcast system. The transmit ZF scheduler outperforms the receive ZF scheduler at the cost of feedback bandwidth and perfect channel state information.

In this paper, we study the spectrum selection problem in cognitive radio network with emphasis on resolving the channel contention and the spectrum sharing issues of multiple secondary users. For the traditional channel selection methods, the secondary users select their operating channels based on various criteria. However, these methods neglect the effect that multiple secondary users may content for the same channel if they have the same consensus on one particular good channel. Compared to the existing spectrum selection methods, we consider the sensing-based and the probability-based spectrum selection schemes which can prevent too many secondary users from contending the same channel. An analytical model integrated with the preemptive resume priority M/G/1 queuing network theory is developed to evaluate the overall transmission time of the both schemes. Based on this model, we discuss how to find the optimal selection probability for the probability-based scheme. Furthermore, we also analyze in which condition dependent of sensing time and traffic parameters that the sensing- or the probability-based scheme should be used. Based on the analytical results, we provide a principle to guide system operators which scheme should be used in CR networks. Then, we conclude that channel selection scheme should be adaptive to the variations of the traffic characteristics.

In this paper, we propose a three-cell network multiple-input multiple-output (MIMO) scheme to mitigate the inter-cell interference in an orthogonal frequency division multiple access (OFDMA) system with each cell using the same spectrum. One fundamental question to apply the network MIMO technique in such a high interference environment is: how many base station should coordinate together to provide sufficient signal-to-interference plus noise ratio (SINR) performance? We find that on top of the tri-sector directional antenna and fractional frequency reuse (FFR), the network MIMO based on the proposed three-cell coordination strategy can already improve SINR performance significantly compared to the 19-cell coordination network MIMO without FFR and tri-sector cellular architecture.

Medium access control (MAC) protocol design for cooperative networks over multi-packet reception (MPR) channels is a challenging topic, but has not been addressed in the literature yet. In this paper, we propose a MAC protocol to exploit the cooperation diversity for throughput enhancement over MPR channels. The proposed approach can efficiently utilize the idle periods for packet relaying, and can thus effectively limit the throughput loss resulting from the relay phase. By means of a Markov chain model, the worst-case throughput analysis is conducted. Specifically, we derive (i) a closed-form upper bound for the throughput penalty of the direct link that is caused by the interference of concurrent packet relay transmission; (ii) a closed-form lower bound for the throughput gain that a user with packet transmission failure can benefit thanks to cooperative packet relaying. The results allow us to investigate the throughput performance of the proposed protocol directly in terms of the MPR channel coefficients. Simulation results confirm the system-wide throughput advantage achieved by the proposed scheme, and also validate the analytic results.

In this paper, we propose a novel target selection algorithm to reduce handoff frequency in wireless multi-hop relay networks. In a metropolitan environment, signals are reflected and shadowed by buildings. Thus the signal changes rapidly. To mend this problem, IEEE 802.16j defines a new network component-relay station (RS) to improve coverage. Since an RS looks like a base station (BS) for MS, handoff frequency increases proportionally to the number of deployed RSs. Signaling overhead and interruption in upper layer application service become serious problems. The proposed target selection algorithm combining signal strength and time-of-arrival information of frame can reduce handoff frequency significantly.

The IEEE 802.16e world interoperability for microwave access (WiMax) system plays an important role in the future wireless metropolitan area network (WMAN). Orthogonal frequency division multiple access (OFDMA), adopted in the IEEE 802.16 e WiMax system, has many advantages in the physical layer, but also poses many challenges for radio resource allocation. One of interesting radio resource allocation issue in the OFDMA system is to partition the overall radio resource (bandwidth and time duration) into two portions: one for random access and the other for connection-oriented access. In the IEEE 802.16 e WiMax system, a truncated binary backoff algorithm is adopted to resolve the contention in random access, while the time-division OFDMA is used for the connection-oriented access. The main contribution of this paper is to design an analytical approach to determine the optimal amount of reserved radio resource in both time and frequency domains for random access, with the objective of maximizing the overall efficiency of radio resource while satisfying the delay requirements for supporting real-time services. Furthermore, an analytical model for calculating the access latency and the efficiency of the reserved radio resources is developed.

Through wide-band spectrum sensing, cognitive radio (CR) can identify the opportunity of reusing the frequency spectrum of other wireless systems. However, wide-band spectrum sensing requires energy consumption processes. In this paper, we aim to relieve the burden of spectrum scanning in a CR system by means of location awareness. We investigate to what extent a CR system with location awareness capability can establish a scanning-free region where a peer-to-peer connection of the secondary CR users can coexist with an infrastructure-based connection of the primary user. We compute the concurrent transmission probability of a peer-to-peer connection and an infrastructure-based connection in a system based on the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol. It has been shown that the frequency band of the legacy system can be reused up to 45% by the overlaying cognitive ad hoc network if certain location techniques help CR users locate primary and other secondary users. In summary, a CR system equipped with location awareness techniques can dramatically reduce the need of spectrum sensing thanks to the capability of identifying the concurrent transmission region in a hybrid infrastructure-based and ad hoc overlaying systems. Hence, from another aspect, the issue of wide-band spectrum sensing in CR systems is resolved fundamentally.

The paper proposes a cellular neural network and utility (CNNU)-based radio resource scheduler for multimedia CDMA communication systems supporting differentiated quality-of-service (QoS). Here, we define a relevant utility function for each connection, which is its radio resource function weighted by a QoS requirement deviation function and a fairness compensation function. We also propose cellular neural networks (CNN) to design the utility-based radio resource scheduler according to the Lyapunov method to solve the constrained optimization problem. The CNN is powerful for complicated optimization problems and has been proved that it can rapidly converge to a desired equilibrium; the utility-based scheduling algorithm can efficiently utilize the radio resource for system, keep the QoS requirements of connections guaranteed, and provide the weighted fairness for connections. Therefore, the CNNU-based scheduler, which determines a radio resource assignment vector for all connections by maximizing an overall system utility, can achieve high system throughput and keep the performance measures of all connections to meet their QoS requirements. Simulation results show that the CNNU-based scheduler attains the average system throughput greater than the EXP and the HOLPRO scheduling schemes by an amount of 23% and 33%, respectively, in the QoS guaranteed region.

Medium access control (MAC) protocol design for cooperative networks over multi-packet reception (MPR) channels is a challenging topic, but has not been addressed in the literature yet. In this paper, we propose a cooperative multi-group priority (CMGP) based MAC protocol to exploit the cooperation diversity for throughput enhancement over MPR channels. The proposed approach can bypass the computationally-intensive active user identification process. Moreover, our method can efficiently utilize the idle periods for packet relaying, and can thus effectively limit the throughput loss resulting from the relay phase. By means of a Markov chain model, the worst-case throughput analysis is conducted. The results allow us to investigate the throughput performance of the proposed CMGP protocol directly in terms of the MPR channel coefficients. Simulation results confirm the system-wide throughput advantage achieved by the proposed scheme, and also validate the analytic results.

The wireless mesh network (WMN) is a promising solution to support wireless broadband applications. However, mesh networks face the power unfairness issue. Compared to the users far away from the gateway, the users near the gateway have to relay more traffic and consume more power. This paper proposes a scalable ring-based WMN that can ensure power fairness among users by adjusting its ring widths. On top of the ring-based cell structure, frequency planning is suggested to reduce the contending users, thereby making the system more scalable to accommodate more users. To investigate the overall tradeoffs among power fairness, capacity, and coverage, we develop an analytical model to evaluate the throughput and power consumption of the ring-based WMN using carrier sense multiple access (CSMA) medium access control (MAC) protocol in the unsaturated situation. Then, the optimization approach is applied to determine the best number of rings and the optimal ring widths, aiming to maximize cell capacity and coverage subject to the requirement of power fairness. Numerical results show that compared to the uniform ring-width strategy, the variable ring-width design criterion can improve cell capacity and coverage.

In this paper, we present bit error rate (BER)- minimized space-time-frequency (STF) block codes for multi- input multi-output (MIMO) highly frequency-selective block- fading channels. We consider the IEEE 802.15.3 a ultra-wide band (UWB) channel models (CM) 1-4. Based on a new STF block codes design criterion with the objective of minimizing BER, we develop an efficient searching algorithm for the design of the optimal STF block codes which maximize the coding gain. For 128 subcarriers with two subcarriers jointly encoding with 2-4 transmitting antennas, we find that the optimal STF block codes for all the IEEE 802.15.3 a UWB channel models CM 1-4 can be found. Furthermore, the designed STF block codes outperform the recently published high-rate full-diversity STF codes [1] by 1 dB. Last, the proposed STF codes can be decoded by maximum likelihood decoding approach, which is simpler than the sphere decoding principle used in [1].

In this paper, we will investigate the spectrum handoff schemes for the cognitive radio networks. Spectrum handoff occurs when the primary users appear and the secondary users are using this particular primary user's licensed channel. We compare two major types of spectrum handoff schemes. One is the reactive-sensing spectrum handoff, where the target channel for spectrum handoff is selected or sensed only after the spectrum handoff request is made. The other one is the proactive-sensing spectrum handoff, for which the target channel is predetermined. The advantage of the reactive spectrum handoff is the accuracy of the selected target channel, but pay the cost of sensing time. By contrast, the proactive spectrum handoff avoid the sensing time, but the pre-determined target channel may not be available. We will provide a preemptive resume priority M/G/1 queueing network model to analyze in which condition that the reactive- or proactive-sensing spectrum handoff should be used dependent of sensing time.

This brief considers blind signal source separation via algebraic known modulus algorithm. It is shown that through proper signal modulus design the estimation accuracy of the beamforming vector, as well as the performance of signal separation, can be improved. Specifically, based on a matrix perturbation analysis we propose a criterion, in the form of minimizing the maximal singular value of the modulus code matrix, for enhancing robustness of the beamforming vector against measurement noise. A closed-form solution is then derived and its performance is tested through numerical simulation.

In this paper, we investigate the resource allocation issue in spatial-multiplexing based downlink multiuser multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The key issue is the allocation of subchannels and power among users to share the system. Previous literature mainly focus on maximizing capacity but ignore the intrinsic coverage shrinkage problem in a MIMO system. Particularly, we formulate a system with larger coverage and link reliability under a proportional rate requirement among users. To alleviate the complexity of jointly optimal subchannel and power allocation, we at first propose two suboptimal subchannel allocation algorithms, namely, user-oriented and subchannel-oriented allocations. We next propose a low-complexity power allocation method.

Orthogonal frequency division multiple access (OFDMA), adopted in the IEEE 802.16e WiMax system, possesses many advantages in the physical layer, but also poses many challenges from the aspect of radio resource allocation. In the IEEE 802.16e WiMax system, a truncated binary backoff algorithm is adopted for resolving the contention in random access, while the time-division OFDMA is used for the connection-oriented access. One interesting radio resource allocation issue in such an OFDMA system is to partition the overall radio resource (bandwidth and time duration) into two portions: one for random access and the other for connection-oriented access. The main contribution of this paper is to design an analytical approach to determine the optimal amount of reserved radio resource in both time and frequency domains for random access with the objective of maximizing the overall efficiency of radio resource while satisfying the delay requirements for supporting real-time services. Furthermore, an analytical model for calculating the access latency and the efficiency of the reserved radio resources is developed.

We consider the downlink of a multiuser multi-input multi-output (MIMO) broadcast channel under a single cell structure. To study the achievable link coverage performance of zero-forcing beamforming (ZFB) and zero-forcing dirty-paper coding (ZF-DPC) when the channel state information (CSI) is available to the transmitter. First we develop analytical closed-form expressions for the link outage probability and coverage reliability of baseline ZFB and ZF-DPC when no multiuser scheduling involved. We find that the coverage performance of ZFB can only approach to that of the weakest link of ZF-DPC under predetermined required SNR and outage probability. Secondly, for exploring the achievable cell coverage, we discuss the strongest link performance of both broadcast beam- forming schemes under multiuser scheduling. Under a framework of analysis for ZF-DPC and simulation for ZFB, we show that a soft coverage enhancement can be easily done by using scheduling techniques without extra hardware power consumption.

Relay stations (RSs) are usually used to enhance the signal strength for the users close to the cell boundary. However, transmission through a relay station needs two transmission phases, i.e., one is from the base station to the relay station and the other is from relay station to mobile stations. Thus, relay may also decrease system capacity if two-phase transmission time is considered. As a result, whether or not data are transmitted by one-hop or two-hop transmission should be determined based on both signal strength and throughput. In this paper, we investigate the optimal relay location aiming to maximize system capacity. We consider two relay selection rules for determining whether a two-hop transmission is necessary: signal strength-oriented and throughput-oriented. We find that the signal strength-oriented two-hop transmission may yield even lower system capacity than the one-hop transmission. Based on the throughput-oriented rule, we find that the throughput in the two-hop transmission can be higher than that in the one-hop transmission at some locations. We also identify the optimal relay location that can achieve the highest system capacity.

Besides delivering high data rates in a point-to-point scenario, multi-input multi-output (MIMO) antenna techniques can broadcast personalized data to multiple users in the point-to-multipoint scenario. Zero-forcing beamforming (ZFB) is a suboptimal but simple MIMO broadcast technique, which basically decouple the MIMO channel into many parallel single- input single-output (SISO) channels. In this article, we first derive the closed-form expression for the sum rate of the ZFB MIMO broadcast system with random user selection. Secondly, under the condition with finite users, we develop a virtual user approach approximation method for estimating the sum rate of the ZFB MIMO broadcast system with exhaustive user selection. Our results indicate that the proposed analysis method can accurately estimate the optimal sum-rate throughput of ZFB.

Relying on a simple flag-assisted mechanism, a multi- group priority queueing (MGPQ) medium access control (MAC) protocol is proposed for the multipacket reception (MPR) channel. The proposed MGPQ scheme is capable of overcoming two major performance bottlenecks inherent in the existing MPR MAC protocols. First, the proposed solution can automatically produce the list of active users by observing the network traffic conditions, removes the need of active user estimation algorithm, and thus can largely reduce the algorithm complexity. Second, the packet blocking constraint imposed on the active users for keeping compliant with prediction is relaxed. As a result, the proposed MGPQ is not only applicable to both homogeneous and heterogeneous cases, but also outperforms the existing MPR MAC protocols. Simulation results show that the network throughput can be improved by 40% maximum and 14% average as compared with the well known dynamic queue (DQ) MAC protocol.

With the goal to provide ubiquitous wireless Internet access, large-scale deployment of outdoor public access networks will continue at a rapid pace in the next years. In outdoor environment, connecting access points (APs) via cables is costly and requires a lot of cabling engineering work. Therefore, the multihop-augmented network architecture has become an option of upsurging interest. This paper investigates how to deploy APs in an outdoor public access network to improve throughput and quality-of-service (QoS) while extending the coverage by multihop communications. Frequency planning is suggested to improve user throughput with QoS provisioning, and to make the multihop network more scalable to facilitate coverage extension. To investigate the overall tradeoffs among QoS, throughput, and coverage, we develop an analytical throughput and delay model for this multihop network using the carrier sense multiple access (CSMA) medium access control (MAC) protocol. This model also considers bidirectional asymmetric traffic for users operating in the unsaturated situation. Then, we apply an optimization approach to determine the optimal number of APs in a cluster and the best separation distances between APs, aiming to maximize the capacity and coverage of a cluster of APs subject to the delay requirement.

In this paper, we present an analytical method to evaluate the bit error rate (BER) of the ultra-wideband (UWB) system in the IEEE 802.15.4a standardized channel model. The IEEE 802.15.4a UWB channel model is more general and based on more measurements than the earlier IEEE 802.15.3a model. It also poses new challenge in analyzing UWB performance in such a channel. First, the power delay profile become a function of randomly arriving cluster and ray arrival time. Second, the signal amplitude in the IEEE 802.15.4a channel is modeled by a Nakagami random variable of which the Nakagami fading parameter is log-normally distributed. Thus, the signal amplitude is a nonlinear function of a log-normally distributed random variable. By means of counting integral of Lebesgue measure theory, the analytical expression for the BER performance in the IEEE 802.15.4a UWB channel is presented. We apply this analytical model to investigate the impacts of various UWB channel parameters on the system performance and provide some useful insights into the design of UWB transceiver.

Dynamic spectrum access (DSA) is the key feature of cognitive radio (CR) networks, but it also poses many new challenges on the medium access control (MAC) design. One of key challenges is the fact that the secondary CR users can only borrow the licensed spectrum from the primary users for a short period of time. Hence, unlike many available multi-channel MAC protocols for ad hoc networks where throughput is the main performance issue, the DSA protocols in CR networks shall place more emphases on the access latency. Hence, one fundamental issue arises: how can the spectrum be dimensioned for control channels in order to minimize the access delay of DSA protocol in CR networks? In this paper, we provide a comparative study in an analytical manner on the latency performance of two DSA protocols: 1) dedicated control channel, and 2) embedded dedicated control channel approaches. Our results show that an optimal ratio of the control channel bandwidth over the total channel bandwidth can be found to minimize the latency of DSA with dedicated control channels. However, the delay performance of DSA with dedicated control channels is more sensitive to the variations of the data lengths than that of DSA with embedded control channels. Hence, we conclude that the way of dimensioning the spectrum for control frames for DSA in CR networks should be adaptive to the variations of the traffic characteristics and the number of users.

The wireless mesh network (WMN) is an essential low-power solution to support ubiquitous broadband services. However, mesh networks face the power unfairness and throughput bottleneck issues. Compared to the users far away from the gateway, the users near the gateway need to relay more traffic and consume more power. This paper proposes a scalable ring-based WMN that can ensure power fairness among users by adjusting the ring widths. On top of the ring-based WMN, frequency planning is suggested to overcome the throughput bottleneck issue for the inner-ring users near the gateway, thereby making the system more scalable to accommodate more users and facilitating coverage extension. We also investigate the overall tradeoffs of the ring-based WMN in terms of power fairness, capacity, and coverage. An analytical model is developed to evaluate the throughput and power consumption of the ring- based WMN using carrier sense multiple access (CSMA) MAC protocol in the unsaturated situation. Then, a mixed-integer nonlinear optimization problem aiming to maximize cell capacity and coverage subject to the constraint of power fairness is formulated. Applying this optimization approach, we obtain the optimal number of rings and the associated ring widths of the ring-based WMN.

As the IEEE 802.11 wireless local area networks (WLANs) have become very popular and the emerging IEEE 802.16e wireless metropolitan area networks (WMANs) also attract a great deal of attentions, in the future we can expect the appearance of the hybrid IEEE 802.11 WLANs and IEEE 802.16e WMANs systems. Clearly, seamless handoffs in this hybrid network will become an increasingly important issue. However, the system which has shortest handoff latency may not always have the highest throughput. In this paper, we develop a dynamic network selection scheme to determine the connecting system in a new "WLAN to hybrid WLAN/WiMax" handoff scenario, in which we consider both the vertical and horizontal handoffs. The proposed scheme requests the station to wait an additional network selection time "tw" before the selection to maximize the amount of delivered bits during a dwelling time. We also formulate an optimization problem to find the optimal network selection time t*w, and show that the proposed scheme can improve the delivered information bits during handoff.

Combining multi-input multi-output (MIMO) antenna techniques with orthogonal frequency division multiplexing (OFDM) modulation (MIMO-OFDM) becomes an attractive air-interface solution for the next generation high speed wireless systems. Nevertheless, because the total available transmit power is split uniformly across transmit antennas in MIMO-OFDM systems, increasing the number of transmit antennas leads to a smaller signal-to-noise ratio (SNR) per degree of freedom. Thus the coverage performance of this kind of MIMO-OFDM system becomes an essential issue. In this paper by means of order statistics and Glivenko-Cantelli theorem, we develop an analytical expressions for the link outage probability and cell coverage reliability of OFDM-based spatial multiplexing systems in a frequency selective fading channel, respectively.

This paper proposes a low-complexity coverage-oriented subcarriers assignment (COSA) algorithm which can efficiently assign the subchannels to enhance the coverage reliability of the orthogonal frequency division multiplexing (OFDM) based spatial multiplexing systems in the multiuser environment. Motivated by the increasing popularity of OFDM and the multi-input multi-output (MIMO) technique, how to use the multiuser and the frequency diversity simultaneously to improve the coverage reliability of the diversity-deficient multiplexing system is an essential issue. Because the total available transmit power is split uniformly across transmit antennas in the OFDM-based spatial multiplexing systems, increasing the number of transmit antennas leads to a smaller signal-to-noise ratio (SNR) per degree of freedom. Thus we propose the COSA algorithm to enhance the coverage performance of this system. Besides, by means of order statistics and Glivenko- Cantelli Theorem, we develop an analytical expression for the link outage probability and cell coverage reliability of the OFDM-based spatial multiplexing systems when using COSA, respectively.

For the cognitive radio (CR) network, a fundamental issue is how to identify the spectrum opportunities. First, each CR node determines whether there exists transmission opportunities on unlicensed bands. If not, this node will find other opportunity on licensed bands. Thus, increasing the opportunities of concurrent transmissions in unlicensed bands can reduce the overhead of wide-band sensing. In this paper, based on the carrier sensing multiple access protocol, the paper proposed a novel concurrent transmissions MAC (CT-MAC) protocol to identify the possibility of establishing the second link in the presence of the first link in the unlicensed band. In addition to reducing the overhead of wide-band sensing, the proposed CT-MAC scheme can enhance overall throughput and is backward compatible with the IEEE 802.11 standard.

Despite its low-complexity, the zero-forcing receiver is known to suffer from noise enhancement to restore the spatially multiplexed data in a single-user MI MO system. Nevertheless, in the multiuser system, the poor-channel avoidance property of the scheduling technique provides a natural way to overcome the drawback of noise enhancement (Heath et al., 2001). In this paper, we present an analytical framework to evaluate the performance of the zero-forcing receiver operating in the multiuser MIMO system with user scheduling. Using the order statistics technique, we derive closed-form expressions for the sum-rate capacity of the multiuser MIMO system that employs the simple spatial multiplexing at the transmitter and zero-forcing processing at the receiver with a number of scheduling algorithms. These closed-form expressions hold for an arbitrary finite number of users and facilitate efficient numerical evaluations for cases of practical interest. In addition, the tractable analysis provides insight into how the scheduling technique affects the performance of the multiuser MIMO system under scalar feedback and vector feedback. The results are also extended to the case of heterogonous users with unequal average SNR.

Frequency selective fading may affect the orthogonality of the spreading codes in the multi-carrier direct sequence code division multiple access (MC-DS-CDMA) systems. In this paper, we define a new performance metric called the multiple access interference (MAI) coefficient for the MC-DS-CDMA system to quantitatively predict the impact of inter-code interference with the time- and frequency-domain spreading in a frequency selective fading channel. With the help of MAI coefficient, a novel interference avoidance code assignment strategy is proposed. By jointly considering the incurred MAI effect and the blocking probability in the code tree structure, the proposed interference avoidance code assignment method can effectively reduce the MAI for the multi-rate MC-DS-CDMA system, while maintaining very good call blocking rate performance.

In the multi-rate multi-carrier direct sequence code division multiple access (MC-DS-CDMA) system, how to allocate subcarrier power to improve the received signal quality, control the multiple access interference (MAI) at an acceptable level, and simultaneously maintain high call admission is an interesting and challenging issue. In this paper, we propose a joint subcarrier power allocation and code assignment scheme for the synchronous multi-rate MC-DS-CDMA system with time- and frequency-domain spreading. The proposed scheme consists of a subcarrier power allocation method to optimize the received signal power as well as an interference avoidance code assignment strategy to eliminate MAI. For this purpose, we define a performance metric called the MAI coefficient to quantitatively predict the incurred MAI before assigning a spreading code. The simulation results show that the proposed joint subcarrier power allocation and code assignment strategy not only can reach a better received signal quality but also achieve high call admission rate.

Wireless mesh network (WMN) is a viable solution to support ubiquitous broadband services with low transmission power. This paper investigates the tradeoffs among delay, throughput, and coverage in a multi-channel ring-based wireless mesh network. In the proposed WMN, a simple ring-based frequency planning is employed to effectively utilize available channels. To evaluate the delay and throughput in the proposed WMN, we develop a physical (PHY)/medium access control (MAC) cross-layer analytical framework, which incorporates the carrier sense multiple access (CSMA) MAC protocol and a physical-layer distance-based rate adaptation with multi-hop connections. On top of the analytical model, the mixed-integer nonlinear programming (MINLP) optimization approach is applied to analytically determine the optimal number of rings and the associated ring widths, aiming at maximizing the coverage of a mesh cell subject to the requirements of throughput and delay.

Multi-carrier direct sequence code division multiple access (MC-DS-CDMA) systems can support multi-rate services with a great deal of flexibility by dynamically changing time-and frequency-domain spreading gains. However, in a frequency-selective fading channel, the multi-rate users using the time spreading codes with an ancestor-to-child relation, even with orthogonal frequency spreading codes, may still be affected by different quantities of multiple access interference (MAI). How to design an effective radio resource management strategy for the MC-DS-CDMA systems subject to different levels of MAIs becomes a new and important issue. In this paper, based on the error rate analysis in the physical layer, we observe and define a new performance metric, called the MAI coefficient, which can quantitatively predict the MAI effect imposed on a particular code in a multi-rate MC-DS-CDMA system. Furthermore, from the radio resource management perspective, we develop an interference avoidance code assignment scheme based on the MAI coefficient. By simulations and analysis, we demonstrate that the proposed interference avoidance code assignment method can effectively reduce the MAI in a multi-rate MC-DS-CDMA system, while maintaining very good call blocking rate performance.

This paper proposes an efficient radio resource allocation (RRA) mechanism in multirate mixed-size WCDMA cellular systems. The proposed RRA mechanism deals with power and rate allocation for non-handoff and handoff users sepa-rately. For non-handoff users, RRA mechanism use a novel multi-qualify balancing power algorithm and mutirate re-moval algorithm to meet a common balanced signal quality for all cells. Also, for soft handoff users, RRA mechanism has a joint power and rate allocation (JPRA) algorithm to determine an appropriate allocation of transmission power and service rate for multirate soft handoffs. Via the JPRA algorithm for soft handoff, the system can achieve power bal-ancing among cells. Simulation results show that the JPRA algorithm has better cell’s service coverage and higher sys-tem capacity.

Adaptively adjusting transmission rate and power to concurrently enhance goodput and save energy is an important issue in the wireless local area network (WLAN). However, goodput enhancement and energy saving are two contradictory goals, since high data rate usually requires high transmission power to meet the bit-error-rate (BER) requirement. To achieve the tradeoff between energy efficiency and goodput, therefore, we suggest a fast channel-driven rate and power adaptation (CDRPA) algorithm. Furthermore, we develop a physical (PHY)/medium access control (MAC) cross-layer analytical method to evaluate goodput and energy efficiency, incorporating the impacts of Nakagami fading channel and the carrier sense multiple access (CSMA) MAC protocol. This paper compares two adaptation

The wireless mesh network (WMN) is an economical solution for disseminating broadband wireless information in the intelligent transportation systems (ITS). This paper investigates the issue of deploying access points (APs) in an ITS wireless mesh network, where several adjacent APs form a cluster. Each AP in a cluster operates as a wireless relay to forward neighboring AP's traffic to the central access point connected to the Internet through cables. In general, access points are placed for maximizing cell coverage. However, larger coverage of an AP leads to lower throughput and longer delay in the access link as well as in the relay link. To find the optimal tradeoffs among delay, capacity, and coverage, we develop a physical (PHY)/medium access control (MAC) cross-layer analytical model to evaluate the throughput and delay of the considered ITS wireless mesh network. We consider the carrier sense multiple access (CSMA) protocol and the impact of hop distance on the data rate in the physical layer. Then, we apply the mixed-integer nonlinear programming (MINLP) optimization approach to determine the optimal number of APs in a cluster and the best cell radius of each AP, aiming at maximizing the capacity and coverage of a cluster of APs subject to the delay and fairness requirements.

This paper provides the bit error rate (BER) analysis of the antipodal and orthogonal binary signals under the ultra-wideband (UWB) channel. We offer an analytical expression and its evaluation formula for the BER. The channel model we consider is the IEEE 802.15.4a UWB channel. We take into account of the impact of all the key parameters, including inter-cluster arrival rate, cluster decay constant, the inter-ray arrival rate, ray decay constant, parameters of the power delay profile (PDP), and the distribution of a Nakagami fading signal. For the IEEE 802.15.4a UWB channel, the effects of clustering are characterized by a Poisson process, and the inter-ray arrival time is modeled as the hyperexponential random variable. We propose a systematic analytical method to evaluate the BER performance of the UWB signal associated with such joint continuous Nakagami and discrete Poisson random variable. Thus, the developed analytical model is useful in evaluating the performance of an UWB signal in the IEEE 802.15.4a channel without time consuming simulations.

This paper presents an analytical expression for the bit error rate (BER) of the antipodal and orthogonal binary signals in the ultra-wideband (UWB) channel, of which the unique characteristics include the cluster property and highly dense multipath effect. Specifically, we consider the IEEE 802.15.3a UWB channel and take into account of the impact of all the key parameters, consisting of the cluster arrival rate, cluster decay factor, the number of rays per cluster, and the distribution of a non-Rayleigh fading signal. For the IEEE 802.15.3a UWB channel, the effects of clustering are characterized by a Poisson discrete random variable, and the magnitude of the signal is modelled by lognormal random variable. In this paper, we develop an analytical model to compute the signal with such joint continuous lognormal and discrete Poisson random variable. Hence, the developed analytical model can be useful in evaluating the performance of an UWB signal in the IEEE 802.15.3a channel without time consuming simulations.

In recent years, the IEEE 802.11 wireless local area networks (WLANs) have become a very popular access network technique. However, due to the large handoff latency, most application scenarios are limited to hot spot areas without handoff across different access points (APs). As the advance of many real-time or highly interactive applications such as voice over IP (VoIP), one of burning issues for the WLAN is to reduce the handoff latency. Current studies in the literature have indicated that the latency in searching available APs dominates the handoff latency. Thus, we propose a simple analytical model to calculate the search latency and success probability for the handoff in the IEEE 802.11 WLAN. A new performance metric, namely the "effective search time (EST)", is introduced to evaluate the latency that is required for a station to successfully search a channel from neighboring APs. Based on the developed model, the optimum numbers of the probe request and response transmission for minimizing the EST can be obtained. Our results demonstrate that, in the environment of 100 contending stations, the EST with optimum number of transmissions can be reduced by 30% compared to that in the legacy IEEE 802.11 WLAN.

In this paper, we propose a fast channel-driven rate and power adaptation (CDRPA) algorithm for wireless local area networks (WLAN). The proposed algorithm jointly adjusts the rate and power for data frame transmission in a generalized Nakagami fading channel. Different from the previous works, the CDRPA algorithm bases on the channel quality observed from the previous ACK frames and makes the selection only from the limited combinations of rate and power, instead of exhausted searching. Thus, the CDRPA algorithm greatly reduces the complexity compared to the previous approaches. Furthermore, to meat the different performance requirements, we propose several alternatives based on the CDRPA algorithm. Prom the viewpoint of energy saving, the CDRPA algorithm determines the transmit power first to maximize energy efficiency; while achieving the maximum goodput performance, the CDRPA first selects the appropriate transmit rate to ensure the successful transmissions. The numerical results show that the CDRPA algorithm can approach to the optimum performance in terms of energy efficiency and goodput while keeping the low complexity

The wireless mesh network (WMN) is an economical solution to support ubiquitous broadband services. This paper investigates the tradeoffs among quality-of-service (QoS), capacity, and coverage in a scalable multichannel ring-based WMN. We suggest a simple frequency planning in the proposed ring-based WMN to improve the capacity with QoS support, and to make the system more scalable in terms of coverage. We develop a physical (PHY)/medium access control (MAC) cross-layer analytical model to evaluate the delay, jitter, and throughput of the proposed WMN, by taking account of the carrier sense multiple-access (CSMA) MAC protocol, and the impact of hop distance on transmission rate in the physical layer. Furthermore, the mixed-integer nonlinear programming optimization approach is applied to determine the optimal number of rings and the associated ring widths, aiming at maximizing the capacity and coverage of a mesh cell subject to the delay requirement

Multicarrier direct-sequence code-division multiple access (MC-DS-CDMA) becomes an attractive technique for the future fourth-generation (4G) wireless system because it can flexibly adapt transmission rates by changing both time and frequency spreading factors and possesses many physical-layer advantages in dispersive fading channels. However, power control errors (PCE) and the complete multiple access interference (MAI) from all the intersubcarriers may significantly degrade the performance of the MC-DS-CDMA system. In this paper, we propose an analytical method to evaluate the joint effects of the PCE and the complete MAI on the multirate MC-DS-CDMA system. From analysis and simulation, we obtain some important insights into the performance issues of the MC-DS-CDMA system. First, the effect of PCE can exacerbate the impact of the complete MAI on the MC-DS-CDMA system, or vice versa . For BER=10/sup -3/ in a considered case, the joint effect of the complete MAI and PCE further degrades the performance by 2.1 dB compared with the sum of the degradation from the complete MAI and the PCE individually. Second, increasing frequency or time-domain spreading gain can improve the performance of the MC-DS-CDMA system, but the system also becomes more sensitive to power control errors. Third, a larger PCE can possibly make the frequency-domain diversity diminish faster than the gain obtained from the time-domain spreading although an MC-DS-CDMA system with a larger frequency-domain spreading gain (M) is usually better than that with a larger time-domain spreading gain (G/sub o/). In our example, for the standard deviation of PCE (/spl sigma//sub e/) equal to 0 dB, the BERs with (M,G/sub o/)= (4, 16) and (16, 4) are 9.3/spl times/10/sup -4/ and 3.7/spl times/10/sup -5/, respectively, while for /spl sigma//sub e/=4 dB, the BER performances of the two cases are all in the order of 10/sup -3/.

In this paper, we present a cross-layer analytical framework to jointly investigate antenna diversity and multiuser scheduling under the generalized Nakagami fading channels. We derive a unified capacity formula for the multiuser scheduling system with different multiple-input multiple-output antenna schemes, including: 1) selective transmission/selective combining (ST/SC); 2) maximum ratio transmission/maximum ratio combining (MRT/MRC); 3) ST/MRC; and 4) space-time block codes (STBC). Our analytical results lead to the following four observations regarding the interplay of multiuser scheduling and antenna diversity. First, the higher the Nakagami fading parameter, the lower the multiuser diversity gain for all the considered antenna schemes. Second, from the standpoint of multiuser scheduling, the multiple antennas with the ST/SC method can be viewed as virtual users to amplify multiuser diversity order. Third, the boosted array gain of the MRT/MRC scheme can compensate the detrimental impact of the reduced amount of fading gain on multiuser scheduling, thereby resulting in greater capacity than the ST/SC method. Last, employing the STBC scheme together with multiuser diversity may cause capacity loss due to the reduced amount of fading gain, but without the supplement of array gain.

The parallel multichannel stop-and-wait (SAW) hybrid automatic repeat request (HARQ) mechanism is one of key technologies for high-speed downlink packet access in the wideband code division multiple access system. However, this parallel HARQ mechanism may encounter a serious stall problem, resulting from the error of the negative acknowledgement (NACK) changing to the acknowledgement (ACK) in the control channel. In the stall situation, the receiver waits for a packet that will be no longer be sent by the transmitter and stops delivering the medium access control (MAC) layer packets to the upper layer. The stall issue seriously degrades the quality of service for the high-speed mobile terminal owing to the high probability of NACK-to-ACK errors. In this paper, we present an analytical approach to compare three stall avoidance schemes: the timer-based, the window-based, and the indicator-based schemes. To this end, we first propose a new performance metric-gap processing time, which is defined as the duration for a nonrecoverable gap appearing in the MAC layer reordering buffer until it is recognized. Second, we derive the probability mass functions and the closed-form expressions for the average gap processing time of these three stall avoidance schemes. It will be shown that our analytical results match the simulations well. Further, by analysis, we demonstrate that the indicator-based stall avoidance scheme outperforms the timer-based and the window-based schemes. The developed analytical approaches can help determine a proper number of processes for the parallel SAW HARQ mechanisms. We also show that the analytical formulas can be used to design the number of acceptable fully loaded users for an admission control policy subject to the gap processing time constraint. In the future, our analysis can facilitate the MAC/radio link control (RLC) cross-layer design because the gap processing time in the MAC layer is closely related to the RLC timeout mechanism and the window size in the RLC retransmission mechanism.

Recent studies have revealed that the remarkable capacity improvement resulting from an open-loop multiple-input-multiple-output (MIMO) spatial multiplexing system may come at the sacrifice of degrading link reliability. This tradeoff between antenna multiplexing gain against antenna diversity gain may translate into smaller coverage areas. In this paper, we suggest using the multiuser diversity to replenish the diversity-deficient spatial multiplexing MIMO system. Specifically, we propose a fair scheduling scheme, called the strongest-weakest-normalized-subchannel-first (SWNSF) scheduling, which requires only limited amount of feedback. Our analysis and results indicate that the SWNSF scheduling can significantly increase the coverage of the multiuser MIMO system while further improving the system capacity.

In this paper, the authors proposed an explicit rate change notification (ERCN) mechanism to improve the transmission control protocol (TCP) performance in the wideband code-division multiple-access (WCDMA) system. The base station in the WCDMA system dynamically adapts transmission data rates according to the wireless channel condition. Therefore, if the TCP sender can not be notified about the rate change in the wireless channel, congestion in the radio link between the base station and mobile terminal occurs quite often. In this work, the relation between the bandwidth in the physical layer and the maximum queue size in the TCP layer was first derived, and then obtain a closed-form expression for the average throughput in terms of average queue size, queue delay and channel capacity. The results by simulations were validated. The analytical formula could facilitate the design of a cross-layer TCP-physical congestion control mechanism for the rate adaptive wireless system.

Despite its simplicity, the zero-forcing receiver is known to suffer from noise enhancement to restore the spatially multiplexed data in a point-to-point MIMO system. Nevertheless, in the point-to-multipoint multiuser system, the poor channel avoidance property of the scheduling technique provides a natural way to overcome the drawback of noise enhancement. In this paper, we present an analytical framework to evaluate the performance of the zero-forcing receiver operating in the multiuser MIMO system with scalar feedback scheduling. Using the order statistics technique, we derive closed-form expressions for the achievable throughput of the zero-forcing receiver operating in the multiuser MIMO environment. Our analytical results indicate that the cross-layer cooperation of the simple zero-forcing receiver and scheduling technique can achieve the full capacity of the point-to-point MIMO system.

The MC-DS-CDMA possesses the advantages of the immunity against multipath dispersion of orthogonal frequency division multiple access (OFDM) and the flexibility of the time-domain and frequency-domain spreading technique. However, power control errors (PCE) may significantly degrade the performance of the MC-DS-CDMA system. In this paper, we propose an easily computed analytical method to evaluate the bit error rate performance of the multi-rate MC-DS-CDMA system with the time/frequency 2-domain spreading subject to PCE. Furthermore, the proposed analysis also considers the effect of multiple access interference (MAI) from all the subcarriers in the multi-rate MC-DS-CDMA system. We find that power control errors will offset the gain of frequency domain spreading. More importantly, the larger the value of frequency domain spreading, the more the sensitivity to PCE.

Packet scheduling in a WCDMA system poses a new challenge due to its nature of variable bit rates and location-dependent, time-varying channel conditions. In this work, three new downlink scheduling algorithms for a WCDMA base station are proposed to support multimedia transmissions. Using a credit management and a compensation mechanism, our algorithms provide rate guarantee and fair access to mobile terminals. In particular, we propose to allow a user to simultaneously use multiple OVSF codes in a time-sharing manner, which we call a multicode, shared model. Using multiple codes allows us to compensate those users suffering from bad communication quality or even errors. The proposed schemes can tolerate a multistate link condition (compared to the typically assumed two-state, or good-or-bad, link condition) by adjusting the number of OVSF codes and the spreading factor of each code. Simulation results show that the proposed schemes do achieve higher bandwidth utilization while keeping transmission delay low.

In the third-generation partnership project technical specification, orthogonal variable spreading factor (OVSF) codes are used as channelization codes. The use of OVSF codes can provide variable data rates to flexibly support applications with different bandwidth requirements. Most works in the literature assume that only one single OVSF code is used to support one connection. This may sometimes waste the scarce wireless bandwidth since the allocated bandwidth will increase exponentially as the spreading factor decreases, i.e., a user may be "overserved." In this paper, we consider the possibility of using multiple OVSF codes to support a connection. We show how using multiple codes can reduce the internal and external fragmentations of an OVSF code tree. The tradeoff between bandwidth utilization and hardware complexity of a multicode system is analyzed. The result shows that using two or three codes will be quite cost effective. Several multicode assignment and reassignment strategies, namely, random, leftmost, crowded-first-space, and crowded-first-code, are proposed based on such an environment. Our simulation results show significant increase in code tree utilization and significant reduction in code blocking probability by using the crowded-first-space and crowded-first-code schemes.

This paper presents an analytical expression for the signal-to-noise ratio (SNR) of the pulse position modulated (PPM) signal in an ultrawideband (UWB) channel with multiple transmit and receive antennas. A generalized fading channel model that can capture the cluster property and the highly dense multipath effect of the UWB channel is considered. Through simulations, it is demonstrated that the derived analytical model can accurately estimate the mean and variance properties of the pulse-based UWB signals in a frequency-selective fading channel. Furthermore, the authors investigate to what extent the performance of the PPM-based UWB system can be further enhanced by exploiting the advantage of multiple transmit antennas or receive antennas. Numerical results show that using multiple transmit antennas in the UWB channel can improve the system performance in the manner of reducing signal variations. However, because of already possessing rich diversity inherently in the UWB channel, using multiple transmit antennas does not provide diversity gain in the strict sense [i.e., improving the slope of bit error rate (BER) versus SNR] but can possibly reduce the required fingers of the RAKE receiver for the UWB channel. Furthermore, because multiple receive antennas can provide higher antenna array combining gain, the multiple receive antennas technique can be used to improve the coverage performance for the UWB system, which is crucial for a UWB system due to the low transmission power operation.

Abstract The growing demand of high speed multi‐class data transmissions poses new challenges on wireless networks. The major objectives of the next generation wireless networks include: (1) achieving high data throughput in the fast varying wireless channel; (2) transmitting multi‐class data by service multiplexing; and (3) controlling the delay for delay sensitive service. To achieve these goals, in the context of wideband code division multiple access (WCDMA) system, we propose a cost‐function based rate adaptation mechanism by taking account of both physical layer channel impacts and higher layer performance parameters, such as buffer occupancy and service priority. We implement this cross‐layer rate mechanism by exploiting the transport format (TF) selection procedure in the medium access control (MAC) layer of the WCDMA system. Through the proposed cross‐layer cost function, the TF selection procedure can dynamically adapt suitable spreading factors every transmission time interval (TTI), usually 10–80 ms. Through simulations in a flat Rayleigh fading channel, we show that the proposed cross‐layer cost‐function based rate adaptation mechanism can effectively improve throughput and reduce the buffer occupancy for multi‐class services for the WCDMA system at the cost of slightly higher power efficiency. Copyright © 2005 John Wiley & Sons, Ltd.

Recent studies have revealed that the capacity improvement resulting from an open-loop multiple-input-multiple-output (MIMO) spatial multiplexing system may come at the sacrifice of reducing link reliability. The tradeoff of antenna multiplexing gain against antenna diversity gain may lead to smaller cell coverage sizes. In This work, we propose a scheduling scheme, called the strongest-weakest-normalized-subchannel-first (SWNSF) scheduling, to replenish the diversity-deficient MIMO system with multiuser diversity. Our analysis and results indicate that the SWNSF scheduling can significantly increase both cell coverage and system capacity requiring only limited amount of feedback.

Multi-class services change the design paradigm and pose new challenges for future wireless networks. In the future, a voice-oriented macrocell and a data-oriented microcell will co-exist to support multi-class services. In this paper, we develop an analytical framework to determine the optimal pilot power ratio for the CDMA system considering the impact of hierarchical cellular structure, cochannel interference, shadowing, and multi-class service requirements. An analytical approach for determining the pilot power in the hierarchical cellular system is developed to maximize system capacity in supporting multi-class traffic. Based on this analytical approach, we find that to maximize the system capacity, it is necessary to increase the pilot power of the microcell, but lower the pilot power of the macrocell.

This paper presents a cross-layer analytical framework to jointly investigate antenna diversity and multiuser scheduling under the generalized Nakagami fading channels and derives a unified capacity formula for the multiuser scheduling system with different multiple-input multiple-output (MIMO) antenna schemes, including the selective combining (SC), maximum ratio combining (MRC) and space-time block codes (STBC). Our analytical results indicate that, in the presence of multiuser diversity, the multiple antennas with the SC method can be viewed as virtual users to amplify multiuser diversity order, while the MRC scheme can even yield greater capacity than the SC scheme due to the increased array gain. However, applying the STBC method on top of the multiuser scheduling system may lead to capacity loss due to the reduced amount of fading.

This paper aims to evaluate the goodput performance of the IEEE 802.11 a wireless local area network (WLAN) from both the media access control (MAC) layer and physical (PHY) layer perspectives. From the physical layer perspective, we analyze the packet error rate performance of the orthogonal frequency division multiplexing (OFDM) based WLANs under the generalized Nakagami fading channel. According to the Chernoff bound analysis in the Nakagami fading channel, we derive the approximate packet error rate for the IEEE 802.11a WLAN with different data rates and fading parameters. Furthermore, we propose an efficient channel driven rate adaptation (CDRA) scheme. Through a PHY/MAC cross-layer analysis, we demonstrate that the goodput of the CDRA scheme indeed approaches to that of the optimal dynamic programming based rate adaptation method, while avoiding the complex calculations in selecting transmission parameters as the optimal method.

This paper aims to determine the optimal number of clusters in an observation area for a wireless sensor network. We demonstrate that this goal can be achieved by a cross-layer approach from both perspectives of the power efficiency in the medium access control (MAC) layer and the coverage performance in the physical (PHY) layer.

According to the IEEE 802.11n, the next generation wireless local area networks (WLAN) aim to support data rates at least 100 Mbps, as measured at the medium access control (MAC) layer. Thus, it is important to develop a cross-layer analytical model to evaluate the MAC layer goodput performance with consideration of the physical layer effects. This paper suggests such an analytical model to calculate the MAC layer goodput for the IEEE 802.11a WLAN in the Nakagami fading channel, while incorporating the effects of channel estimation, delay spread and signal detection scheme in the physical layer. Furthermore, we develop a simple and efficient channel-driven rate adaptation (CDKA) scheme to dynamically adjust the transmission parameters to maximize the MAC layer goodput. From out numerical results, we find that some physical (PHY) modes in the IEEE 802.11a are unnecessary. We also find that as rms delay spread increases, the goodput in the MAC layer can be improved due to frequency diversity. For the case of rms delay spread changing from 50 to 200 nsec with 15 dB Eh/No, the goodput is improved from 12 Mbps to 15 Mbps.

In this paper we develop a cross-layer analytical approach from both the physical layer and the medium access control (MAC) layer to evaluate the performance of the IEEE 802.11 wireless local area network (WLAN). From the physical layer, this analytical approach incorporates the effects of both capture and directional antenna, while from the MAC layer, our model takes account of the carrier sense multiple access with collision avoidance (CSMA/CA) protocol. Through a cross-layer modeling technique, this analytical framework can provide valuable insights of the physical layer impact on the throughput performance of the CSMA/CA MAC protocol. These insights can be helpful in developing a MAC protocol to fully take advantage of directional antennas for enhancing the performance of the WLAN.

This paper presents an analytical framework to evaluate the downlink capacity and fairness performance for high speed wireless data networks from both the physical layer and the network layer perspectives. From the physical layer standpoint, we take into account of propagation loss, log-normal shadowing and Nakagami fading. From the network layer perspective, we model different scheduling policies and the impact of nonuniform traffic intensity of mobile users into our framework. We analyze the joint effects of radio channel impairments, nonuniform traffic intensity and scheduling algorithms on the downlink capacity and fairness performance of wireless data networks based on the developed framework. Our results indicate that it is crucial for wireless scheduling algorithms to consider not only the radio channel impairments, but also the traffic intensity distribution in the whole network.

The desired properties of a medium access control (MAC) protocol in mobile ad hoc network (MANET) include: (1) meet quality of service (QoS) requirements for real-time nodes, (2) be decentralized, (3) achieve fairness from viewpoint of throughput or energy consumption, and (4) be immune to the hidden node problem. Though there have been numerous proposed MAC protocols for the IEEE 802.11 WLAN, few of them possess all of the four properties mentioned above. Our protocol can support real-time traffic and satisfy QoS requirements, and can achieve fairness among non-real-time nodes. Also, without using any centralized control, it can be easily deployed in MANET. An analytic model of the protocol's throughput has also been developed. We compare the protocol's throughput obtained from its analytic model and simulation to validate each other.

Packet scheduling in a WCDMA system poses a new challenge due to its nature of variable bit rates and location-dependent, time-varying channel conditions. In this work, three new downlink scheduling algorithms for a WCDMA base station are proposed to support multimedia transmissions. Using a credit management and a compensation mechanism, our algorithms provide rate guarantee and fair access to mobile terminals. In particular, we propose to allow a user to simultaneously use multiple OVSF codes in a time-sharing manner, which we call a multicode, shared model. Using multiple codes allows us to compensate those users suffering from bad communication quality or even errors. The proposed schemes can tolerate a multi-state link condition (compared to the typically assumed twostate, or good-or-bad, link condition) by adjusting the number of OVSF codes

This paper investigates different power allocation algorithms for downlink handoff in the wideband code division multiple access system from both outage performance and power efficiency perspectives. Traditionally, the site selection diversity transmission (SSDT) scheme has been viewed as the optimal solution for downlink handoff from the standpoint of power efficiency, which selects the best base station to transmit downlink signals to the user. However, from the outage performance perspective, the user at the cell boundary may have poor quality if there is only one serving base station. Thus, we are motivated to develop an improved SSDT scheme that can take both power efficiency and outage performances in account. In this paper, we propose a joint link proportional power allocation (LPPA) and SSDT scheme. Through analysis and simulation, we demonstrate that the proposed joint LPPA and SSDT scheme is the optimal power allocation solution for downlink handoff in the WCDMA system, which not only can improve the outage performance over the SSDT scheme when the traffic intensity is low, but can be very power efficient as the SSDT scheme in the heavy traffic situation.

In this paper, we investigate stall avoidance mechanisms for the W-CDMA system with high speed downlink packet access (HSDPA). The stall avoidance mechanisms are aimed to reduce transmission delays and keep in-sequence delivery of MAC layer data to the upper layer. By analysis and simulation, we compare three stall avoidance mechanisms, including the timer-based, the window-based, and the indicator-based methods. For comparison, we introduce a new performance metric called the gap processing time. Our results show that for E/sub b//N/sub 0/ equal to 7 dB in the AWGN channel, the average gap processing time for the indicator-based method is 8.49 transmission time intervals (TTIs) and those for the window-based method and the timer-based method are 36.38 TTIs and 175.5 TTIs, respectively. In a Rayleigh fading channel, we find that the gap processing time of the window-based and the indicator-based methods are higher than in the AWGN channel, while the timer-based method has the same gap processing time in both the AWGN channel and the Rayleigh fading channel.

In the 3GPP technical specification, OVSF codes are used as channelization codes. The use of OVSF codes can provide variable data rates to flexibly support applications with different bandwidth requirements. Most works in the literature assume that only one signal OVSF code is used to support one connection. This may sometimes waste the scarce wireless bandwidth since the allocated bandwidth will increase exponentially as the spreading factor decreases, i.e., a user may be "over-served". In this paper, we consider the possibility of using multiple OVSF codes to support a connection. We show how using multiple codes can reduce internal fragmentation and external fragmentation of a OVSF code tree. The tradeoff between bandwidth utilization and hardware complexity of a multi-code system is analyzed. The result shows that using 2 or 3 codes will be quite cost-effective. Several multi-code assignment and reassignment strategies, namely random, left-most, crowded-first-space, and crowded-first-code, are proposed based on such environment. Our simulation results show significant increase in code tree utilization and significant reduction in code blocking probability by using the crowded-first-space and crowded-first-code schemes.

In this paper we present a simple analytical model to evaluate the impact of measurement errors on the closed-loop power control for code division multiple access (CDMA) systems. By introducing a new performance measurement, the probability of false command in power control, P/sub FC/, a closed form formula for calculating P/sub FC/ with consideration of measurement errors is presented. Furthermore, we derive a bit error rate (BER) performance bound in terms of P/sub FC/ for the CDMA system with closed-loop power control (CLPC). The proposed analytical approach can quantitatively evaluate the performance of the CLPC taking into account of the effects of measurement errors and Doppler frequency under a Rayleigh fading channel. Through simulation and analysis, we show that the proposed analytical BER bound can accurately estimate the BER performance of the CLPC under the impact of measurement errors. Interestingly, we find that the CLPC is less sensitive to measurement errors due to the non-linear operation in the one step up/down power control scheme compared with the variable-step size power control.

In this paper, we present a new hierarchical cell system with an underlaid TDD/CDMA microcell and overlaying FDD/CDMA macrocells. With an objective to exploit the underutilized FDD uplink capacity due to traffic asymmetry, the TDD/CDMA microcell is operated within the uplink frequency band of the overlaying FDD/CDMA macrocells. By jointly applying the proposed antenna arrays at the cell site and a new power ratio adjustment technique, we evaluate the outage probabilities of the macrocell and the microcell. From the simulation results, we demonstrate that the full capacity of the TDD/CDMA microcell can be obtained without degrading the performance of FDD/CDMA macrocells.

Handover in heterogeneous cellular networks is one of the hottest topics for the wireless networks beyond the third generation. This paper presents a new downlink soft handover algorithm to solve the serious "power exhausting" problem during the traditional handover process in heterogeneous cellular systems. The power exhausting problem occurs when in the traditional handover process, the serving base stations usually adopt the equal power allocation (EPA) strategy for different sizes of cells. Thus we propose a constrained unequal power allocation (CUPA) technique to serve handover users by taking account of the effects of different cell sizes and shadowing. Compared to the EPA method, our simulation results show that the proposed soft handover algorithm combining with the CUPA technique can improve total system capacity by 5% in a homogeneous cellular system, and 73% in a heterogeneous cellular system.

This paper presents a new cross-slot interference-resolving algorithm for supporting unbalanced traffic between downlink and uplink in TDD-CDMA systems. To avoid cross-slot interference between the downlink and uplink, traditional algorithms typically require a global control on the transmission direction in each time slot among all cells. Obviously, this requirement significantly limits one of the key advantages in the TDD system - enabling different rate asymmetry. We observe that the tri-sector cellular architecture possesses additional orthogonality inherently due to the directivity of directional antenna, which can provide an additional degree of freedom in allocating radio resource (i.e., time slots). Thus we propose a new distributed slot allocation algorithm based on the tri-sector cellular architecture. The advantage of the proposed algorithm is to enable a TDD-CDMA system to provide asymmetric services with different rates of asymmetry in all cells, thereby having more flexibility in handling uniform traffic patterns.

We investigate the error statistics of the close-loop power control scheme with variable spreading factors for the multi-rate services in the third generation wideband CDMA (WCDMA) system. We demonstrate that the long scrambling pseudo-noise code, besides its well known feature in differentiating users and base stations, can improve power control false command over a frequency-selective fading channel as well. Our computer simulation results indicate that the SIR measurement error can be reduced by 3 dB in a typical case, and the power control false command can be reduced from 27 % to 10 % in comparison to using pure short Walsh codes. It is shown that the close-loop power control error is a composite function of the spreading factor, target E/sub b/ / N/sub o/ and Doppler frequency. The statistics of closed loop power control false command can be easily characterized by a Bernoulli distribution with a parameter provided by our results.

In this paper, we prove that the problem of maximizing data throughput by adaptive modulation and power control while meeting packet error requirements is NP-complete. A heuristic algorithm for integrated link adaptation and power control is, thus, proposed to achieve specified error rates and to improve overall throughput for real-time applications in broadband wireless packet networks. The algorithm divides terminals into groups according to their signal path gains and periodically adapts transmissions based on the required error rates, actual error statistics, and average transmission power of each terminal group. Transmission power is adjusted by an enhanced Kalman-filter method to ensure successful reception. Extensive simulation results reveal that the algorithm consistently delivers the specified error performance and attempts to maximize network throughput for a wide range of parameter settings.

This paper presents a new concept of realtime SIR estimation for CDMA systems. Unlike conventional symbol-based SIR estimator according to the successively received symbols, we propose a new chip-based SIR estimation method. The key contribution of this paper is to propose a mechanism to exploit the spreading codes of CDMA signals to estimate SIR. Under the COST 259 W-CDMA channel model, the simulation results demonstrate that the proposed chip-based SIR estimation algorithm can accurately measure SIR in less than 10/sup -4/ second in several chips, which is about over 1,000 times faster than the traditional symbol-based SIR estimation algorithms.

In this paper, using the EDGE system as an example, we apply a Kalman filter power control method based on interference tracking and prediction to packet voice services in wireless networks. Our results reveal that power control significantly improves the spectral efficiency by enabling 1/3 frequency reuse while maintaining a stringent requirement of 2% packet loss probability for a voice service. More specifically, for allocated spectrum of 1.8, 3.6 and 5.4 MHz, the 1/3 reuse with the Kalman power control can yield 102.5%, 49.5% and 32.5% improvement in spectral efficiency, respectively, over 3/9 reuse (regardless of whether or not power control is used). We also find that the Kalman method provides 20% additional spectral efficiency when compared with a traditional SIR power control method, and the former method is more robust than the latter for increased power update period. The protocol requirements for the implementation of the Kalman method in the EDGE system are also discussed.

We propose an improved sectorization scheme, called narrow-beam quad-sector cell (NBQC) for cellular networks, in which each cell is divided into four sectors and each sector is covered by a 60/spl deg/ antenna. The NBQC structure allows easy implementation of the concept of interleaved channel assignment (ICA), which can take full advantage of antenna directivity. With ICA, the NBQC system can enhance the system performance from several perspectives. First, the NBQC has better coverage performance than the current three-sector cellular architecture. Second, we demonstrate that in a typical radio environment, the NBQC system ran achieve a reuse cluster size N=2 with the signal-to-interference ratio (SIR) as high as 11 dB in 90% of the cell area, which is a 3-5-dB improvement over the existing cellular architectures. Third, as compared to the most advanced three-sector clover-leaf cell architecture with reuse cluster size N=3, the NBQC system with ICA can achieve reuse cluster size N=2 with very slight degradation in SIR performance, thereby still improving the system capacity by about 10% over a wide range of ninetieth percentile SIR requirements.

One of the elements missing in most literature related to power control is mobility. Without it, the performance of a power control scheme and its convergence cannot be reliably predicted. In the paper we use random walk to model mobility. Combining the mobility model with a conventional CCI analysis, we study linear power control in a more dynamic environment.