The ultra-dense edge networks with mmWave and beamforming are envisioned as a potential solution to satisfy the high rate and capacity requirements in 5G networks. In IEEE 802.11 ad, which is the first beamforming-enabled WLAN standard, all stations (STs) contend for beamforming (BF) training opportunities in associated beamforming training (A-BFT) slots. However, due to limited number of A-BFT slots, BF training suffers from a severe collision issue, especially in dense networks, which results in a low channel utilization in the A-BFT stage. To achieve the maximum channel utilization, it is of significance to allocate A-BFT slots efficiently. Therefore, in this paper, we propose an analytical model to analyze IEEE 802.11 ad medium access control (MAC) protocol in BF training stage. In particular, we analyze the successful transmission probability and channel utilization of IEEE 802.11 ad MAC protocol in the dense network. Based on theoretical analysis, we provide the optimal number of A-BFT slots. In addition, theoretical analysis indicates that the maximum channel utilization in the A-BFT stage is barely e−1which is the same as that of slotted ALOHA protocol. Simulation results are provided to validate the accuracy of the analytical model and theoretical analysis.

Hybrid beamforming (BF) is a widely considered strategy to enable downlink multiuser transmission for mmWave communication systems. However, current mmWave WiFi standard, the IEEE 802.11 ad, does not support hybrid BF because it could only serve one user at a time. Thus, it is important to implement hybrid BF based on IEEE 802.11.ad such that it could be applied in future mmWave WiFi. In this paper, we propose a hybrid BF scheme compatible with the IEEE 802.11 ad, and then analyze its BF overhead and throughput gain. Theoretical analysis and simulation results show that with the increasing number of users, BF overhead increases linearly, whereas the throughput gain increases and then decreases. Specifically, we analyze the tradeoff between the hybrid BF overhead and the throughput gain of multiuser transmission enabled by hybrid BF based on an IEEE 802.11 ad setting. Our finding suggests that there is an optimal number of users that a hybrid BF enabled mmWave communication systems should support.

Hyper-dense deployment of small cells and reuse of available spectrum resources within a wireless cellular network are two promising techniques that can enhance network capacity to satisfy the ever increasing demand for high volumes of mobile data traffic. With the use of these techniques in a network, the network is prone to severe co-channel interference (CCI), due to short distances between the densely deployed small cells. Therefore, to avoid degradation of the network capacity, it is necessary to manage CCI by jointly allocating resources of the small cells. Further, the resource allocation scheme should minimize the computational burden for low-cost small cell base stations (BSs), be able to adapt to time-varying network load conditions, and reduce signaling overhead in the small cell backhauls with limited capacity. To this end, in this paper we present a resource allocation scheme which operates on two time-scales and utilizes cloud computing to determine resource allocation decisions. The resource allocation decisions are made at the cloud in a slow time-scale, and are further optimized at the BSs in a fast time-scale in order to adapt the decisions to fast varying wireless channel conditions. Achievable network capacity enhancements using the proposed scheme are demonstrated through simulation results.

Drive-thru Internet has become a popular solution vehicular Internet access. However, the quality-of-service provisioning for high-data-rate drive-thru Internet services poses significant challenges upon medium access control (MAC) in a large-scale and highly dynamic vehicular environment. In this paper, to achieve high throughput in drive-thru Internet, we exploit the mobility interdependency between neighboring vehicles and propose a density-adaptive MAC protocol by predicting the vehicle-density dynamics. Specifically, to predict the vehicle-density fluctuations in the drive-thru Internet scenario, we leverage Navier-Stokes equations to characterize the mobility interdependence and the dynamically waving vehicle-density which can be observed from the simulated vehicle traces in VISSIM. In tune with the dynamic vehicle-density, we propose an enhanced MAC protocol to dynamically adjust the contention window (CW) setting in order to improve the throughput. Extensive simulations validate the effectiveness of the utilized mobility model, and demonstrate the efficiency of the proposed MAC protocol in improving the overall resultant system throughput.

In this paper, we propose an e-health monitoring system with minimum service delay and privacy preservation by exploiting geo-distributed clouds. In the system, the resource allocation scheme enables the distributed cloud servers to cooperatively assign the servers to the requested users under the load balance condition. Thus, the service delay for users is minimized. In addition, a traffic-shaping algorithm is proposed. The traffic-shaping algorithm converts the user health data traffic to the nonhealth data traffic such that the capability of traffic analysis attacks is largely reduced. Through the numerical analysis, we show the efficiency of the proposed traffic-shaping algorithm in terms of service delay and privacy preservation. Furthermore, through the simulations, we demonstrate that the proposed resource allocation scheme significantly reduces the service delay compared to two other alternatives using jointly the short queue and distributed control law.

In this paper, we study the resource allocation in a device-to-device (D2D) communication underlaying green cellular network, where the base station (BS) is powered by sustainable energy. Our objective is to enhance the network sustainability and efficiency by introducing power control and cooperative communication. Specifically, we propose optimal power adaptation schemes to maximize the network efficiency under two practical power constraints. We then take the dynamics of the charging and discharging processes of the energy buffer into consideration to ensure the network sustainability. To this end, the energy buffer is modeled as a G/D/1 queue where the input energy has a general distribution. Power allocation schemes are proposed based on the statistics of the energy buffer to enhance the network efficiency and sustainability. Both theoretical analysis and numerical results demonstrate that our proposed power allocation schemes can improve the network throughput drastically while maintaining the network sustainability at a certain level.

ABSTRACT In amplify‐and‐forward (AF)‐based cooperative spectrum sensing system, the bit‐error‐rate (BER) performance and detection probability will decrease because of the existence of channel estimation error. In this paper, the influence of channel estimation error on system performance is firstly deduced, and then, linear minimum mean‐square error (LMMSE) channel estimation algorithm with filtering delay time‐domain windowing (LMMSE‐filtering‐DTW) technique and modified singular value decomposition‐based LMMSE algorithm are proposed to improve the channel estimation performance for code division multiple access system and orthogonal frequency division multiplexing system in AF cooperative scenario, respectively. Simulation results verify the effectiveness of the two proposed channel estimation algorithms in cooperative spectrum sensing, and whenEb/N0is bigger than 20 dB, given the required false alarm probability smaller than 15%, the difference of detection probability between the channel obtained using the proposed channel estimation algorithms and the ideal channel is less than 2.5%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

E-healthcare is an emerging and promising healthcare system to meet the increasing medical demand from aging population. It requires extensive data storage, pervasive data access, and reliable computing resources to support the real-time communication of critical health information and the real-time diagnosis. Recently, cloud computing, including public cloud and private cloud, with both scalability and accessibility is proposed to be integrated in the e-healthcare system. However, in meeting stringent medical requirements, private clouds lack necessary reliability, whereas public clouds suffer from communication delay. In this paper, we first introduce a cooperation framework to address the distinct challenger facing different clouds. It is inspired by the fact that private clouds are geographically deployed and public clouds can be regarded to possess infinite computing resources. In our framework, private clouds are designed to serve parts of local requests to public clouds, and rewarded by receiving help with excess requests. We adopt stochastic control theory to address the failure minimization issues for private clouds under random demand process. We prove the optimality of a policy constructed through recursion. Numerical and simulation results are presented to demonstrate that our proposed scheme can improve the reliability of private clouds, as well as reduce average delay of public clouds.

Delay constraint and sensor energy consumption requirements are two core issues for e-healthcare applications in wireless body area networks. In this paper, we investigate the data transmission scheduling problem to utilize the sleep and opportunistic transmission for energy efficiency, while guaran- teeing the worst-case delay for medical data transmission. To achieve sensor energy saving by exploiting propagation channel quality with a deterministic delay requirement poses challenges in developing a scheduling policy. We address this problem using a Lyapunov optimization formulation and propose a two-step scheduling algorithm. We prove that the algorithm can provide worst-case delay guarantee under certain conditions. Theoretical analysis and simulation results are presented to demonstrate the tradeoff between the transmission delay and energy consumption.

In this paper, we propose a distributed Prediction-based Secure and Reliable routing framework (PSR) for emerging Wireless Body Area Networks (WBANs). It can be integrated with a specific routing protocol to improve the latter's reliability and prevent data injection attacks during data communication. In PSR, using past link quality measurements, each node predicts the quality of every incidental link, and thus any change in the neighbor set as well, for the immediate future. When there are multiple possible next hops for packet forwarding (according to the routing protocol used), PSR selects the one with the highest predicted link quality among them. Specially-tailored lightweight source and data authentication methods are employed by nodes to secure data communication. Further, each node adaptively enables or disables source authentication according to predicted neighbor set change and prediction accuracy so as to quickly filter false source authentication requests. We demonstrate that PSR significantly increases routing reliability and effectively resists data injection attacks through in-depth security analysis and extensive simulation study.