Deploying femtocells has been taken as an effective solution for removing coverage holes and improving wireless service performance in 3G‐beyond wireless networks such as WiMAX and Long Term Evolution (LTE). This article investigates a novel framework of dynamic spectrum management for femtocell networks, called cognitive‐empowered femtocells (CEF), aiming at mitigating both cross‐tier and intratier interferences with minimum modifications required on the corresponding macrocell network. With the proposed framework, each CEF base station (BS) and the femtocell users can utilize spatiotemporally available radio resources for the access traffic. We conclude that the proposed CEF framework can effectively complement the existing femtocell design and serve as a value‐added feature to the state‐of‐the‐art femtocell technologies, while achieving high scalability and interoperability by minimizing the required modifications on the macrocell protocol design.

This paper provides an extensive analysis on interferences due to different interfering sources within cognitive-empowered femtocell (CEF) networks. Based on the interference analysis and formulations, a stochastic dual control (SDC) approach is introduced for dynamic sensing coordination, aiming to achieving efficient interference mitigation without involving global and centralized control efforts. Simulation results show that the proposed SDC approach can effectively reduce interferences under highly dynamic environments within CEF networks when compared with other spectrum sensing strategies.

In ad hoc cognitive radio (CR) networks, medium access control (MAC) design has been raised as a major challenge due to its highly dynamic nature and strong user diversity, particularly in situations where a dedicated control channel is not reserved among the distributed CR nodes. In this paper, we propose a novel Stochastic Medium Access (SMA) scheme that takes interference constraints into account to improve spectrum sharing efficiency. Specifically, the proposed SMA scheme is developed to serve in a CR network without dedicated control channels, such that the probability of successful channel accesses can be maximized. The formulated optimization problem is then solved by using a dynamic Markov-Chain Monte-Carlo scheme. Moreover, the paper introduces a suite of mechanisms for implementation of the proposed SMA scheme, including segmentation of long packets and contention resolution, which is working on top of power controlled Request-to-Send (RTS) and Clear-to-Send (CTS) exchanges in a multichannel environment. An analytical model is developed on the proposed SMA scheme using an absorbing Markov chain model to evaluate throughput of the secondary user network. Extensive simulation is conducted to study the impact of some important factors on the proposed SMA scheme, such as channel conditions and secondary traffic loads.

The presence of a predefined control channel in ad hoc wireless networks is a common assumption widely accepted by the research community. However, it may not always be the case in some future networking scenarios with high-network dynamics and strong user diversity, such as cognitive radio (CR) ad hoc networks. This paper investigates channel negotiation in CR ad hoc networks without a predefined control channel by introducing a novel gossip-enabled stochastic channel negotiation (GES-CN) framework. The channel negotiation process is first formulated as an optimization problem, aiming to improve the probability of successful channel negotiation in the CR network while achieving sufficient suppression on the interferences to the primary networks. With the GES-CN framework, we develop an analytical model on the probability of successful channel negotiation as well as the resultant overhead in terms of the number of channel negotiation attempts made before achieving a successful channel negotiation process via an absorbing Markov chain. Numerical results demonstrate the merits of the proposed GES-CN framework and validate the developed analytical model. We conclude that the proposed GES-CN framework is an excellent candidate for the future distributed CR ad hoc networks with high dynamics and heterogeneity.

Due to the heterogeneity and versatility of emerging services and applications in wireless networks, it has been a great challenge on improving the network utility by taking advantage of the spatial and temporal diversity of radio resource consumption. This paper is committed to solving this problem by introducing a service-aware spectrum sharing algorithm (SSA) in a joint radio resource management (JRRM) architecture, where a spectrum pool is adopted for leisure spectrum resource management in heterogeneous wireless networks. Based on an objective utility function, the JRRM unit could optimize the spectrum scheduling decisions for the composing networks with awareness of the related supporting services. Moreover, to facilitate a precise decision process, we illustrate an transmission rate requirement prediction model (TRPM) that is adaptive to the system condition variants to forecast service requests. Experiment results show that the proposed SSA can solidly enhance the system performance in terms of radio resource usage ratio, system throughput, user service access ratio, and eventually achieve better network utility.

In this paper, a novel scheme for cognitive radio (CR) spectrum sensing in medium access control (MAC) layer, called as extended knowledge-based reasoning (EKBR), is proposed. The target of EKBR is to improve the fine sensing efficiency by jointly considering a number of network states and environmental statistics, including fast sensing results, short-term statistical information, channel quality, data transmission rate, and channel contention characteristics. This is for a better estimation on the optimal range of spectrum for fine sensing so as to adaptively reduce the overall channel sensing time. Performance analysis is conducted on the proposed EKBR scheme using a multidimensional absorbing Markov chain to evaluate various performance metrics of interest, such as average sensing delay (or referred to as sensing overhead in the study), average data transmission rate, and percentage of missed spectrum opportunities. Numerical results show that the proposed EKBR scheme achieves better performance than that by the state-or-the-art techniques while yielding less computation complexity and sensing overhead.

In this paper, a novel cooperative stochastic channel prioritization algorithm is presented for the purpose of improving spectrum sensing efficiency in cooperative cognitive radio systems. The proposed algorithm achieves the goal by prioritizing the channels for fine sensing based on both local statistics obtained by the cognitive radio as well as long-term spatiotemporal statistics obtained from other cognitive radios. Channel priority is determined in a stochastic manner by performing statistical fusion on the local statistics and statistics from neighboring cognitive radios to obtain a biasing density from which stochastic sampling can be used to identify the likelihood of channel availability. Therefore, the individual cognitive radios collaborate to improve the likelihood of each cognitive radio in obtaining available channels. Simulation results show that the proposed cooperative stochastic channel prioritization algorithm can be used to reduce both sensing overhead and percentage of missed opportunities when implemented in a complimentary manner with existing cooperative cognitive radio systems.

In this paper, a novel statistics-driven spectrum sensing algorithm is developed for improving spectrum sensing efficiency in the media access control (MAC) layer of cognitive radio (CR) systems. The proposed algorithm aims to achieve higher spectrum sensing efficiency and spectrum access opportunity by prioritizing channels for fine sensing based on the statistical likelihood of channel availability. The sensing priority is obtained by jointly exploiting the long-term spatiotemporal statistics recorded from the historical result of fine sensing, the short-term statistical information of channel condition obtained from a small-scale observation window, and the instantaneous statistical information obtained from fast sensing. Simulation results show that the proposed prioritization algorithm can achieve improved data transmission rates and reduced missed spectrum access opportunities when compared to the conventional non- prioritization spectrum sensing approach for situations where cooperative spectrum sensing is not suitable.

In this paper, we propose a Timed Efficient and Secure Vehicular Communication (TSVC) scheme with privacy preservation, which aims at minimizing the packet overhead in terms of signature overhead and signature verification latency without compromising the security and privacy requirements. Compared with currently existing public key based packet authentication schemes for security and privacy, the communication and computation overhead of TSVC can be significantly reduced due to the short message authentication code (MAC) tag attached in each packet for the packet authentication, by which only a fast hash operation is required to verify each packet. Simulation results demonstrate that TSVC maintains acceptable packet latency with much less packet overhead, while significantly reducing the packet loss ratio compared with that of the existing public key infrastructure (PKI) based schemes, especially when the road traffic is heavy.