Shimin Gong

Maximize Secondary User Throughput via Optimal Sensing in Multi-Channel Cognitive Radio Networks

In a cognitive radio network, the full-spectrum is usually divided into multiple channels. However, due to the hardware and energy constraints, a cognitive user (also called secondary user) may not be able to sense two or more channels simultaneously. As different channels may have different primary user activities and time-varying channel qualities, an important task is to select which channels to sense and access for a given time period so that the available spectrum left by the primary users can be fully utilized by the secondary user. In this paper, we propose an optimal sensing channel selection policy based on partially observable Markov decision process (POMDP). The proposed policy takes the time-varying channel state into consideration and intends to optimally exploit spectrum resources for the secondary user. In addition to selecting optimal channel to sense, we also derive the optimal sensing time which leads to maximized throughput of the secondary user.

Power Efficiency Maximization in Cognitive Radio Networks

Cognitive radio technology is used to improve spectrum efficiency by having the cognitive radios act as secondary users to access primary frequency bands when they are not currently being used. In general conditions, cognitive secondary users are mobile nodes powered by battery and consuming power is one of the most important problem that facing cognitive networks; therefore, the power consumption is considered as a main constraint. In this paper, we study the performance of cognitive radio networks considering the sensing parameters as well as power constraint. The power constraint is integrated into the objective function named power efficiency which is a combination of the main system parameters of the cognitive network. We prove the existence of optimal combination of parameters such that the power efficiency is maximized. Then we reformulate the objective function to incorporate the throughput. According to different constraints or degree of significance, we may put proper weight to each term so that we could obtain more preferable combination of parameters. Computer simulations have given the optimal solution curve for different weights. We can draw the conclusion that if we put more emphasis on power efficiency, the transmit power is a more critical parameter, however if throughput is more important, the effect of sensing time is significant.

Robust Performance of Spectrum Sensing in Cognitive Radio Networks

The successful coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to be spectrum aware and know which spectrum bands are occupied by PUs. Such awareness can be achieved in several ways, one of which is spectrum sensing. While existing spectrum sensing methods usually assume known distributions of the received primary signals, such an assumption is often too strong and unrealistic, and leads to unreliable detection performance in practical networks. In this paper, we design robust spectrum sensing algorithms under the distribution uncertainty of primary signals. After formulating the optimal sensing design as a robust optimization problem, we decompose it into a series of analytically tractable semi-definite programs, and propose an iterative algorithm to search the optimal decision threshold while maintaining the desirable false alarm probability during the iterations. Numerical results verify that our robust sensing algorithm improves the worst-case detection probability and reduces the system sensitivity on decision variables.

Threshold-Learning in Local Spectrum Sensing of Cognitive Radio

Spectrum sensing is important for cognitive radios to utilize the idle spectrum opportunities, and recently cooperation schemes have been introduced to enhance spectrum sensing in specific areas. However, when a mobile cognitive node roams among heterogenous wireless network, it will be difficult to catch the changes of primary users behavior, or to setup the cooperation relationship with local network nodes in a short time. In this paper, an self-learning spectrum sensing framework is proposed, which can enable the single mobile cognitive node to work in unknown wireless environment. When the wireless environment changes, the main sensing parameters (such as decision threshold, sampling frequency) could be adapted to optimum in the self- earning process. One adaptive algorithm is proposed to find the optimal decision threshold in energy detection sensing method. Simulation results show that, the proposed scheme could converge to optimal sensing parameters in spatial and temporal varying environment.

Optimal power control in interference-limited cognitive radio networks

Power control for multiple cognitive radios is investigated for broadcast service under the coverage of primary base station. Multiple secondary broadcasting base stations are assumed to operate in the same frequency band as that of primary system. However, the performance of the primary users can be degraded due to the interference from secondary base stations. Therefore, to cope with this problem, a new interference metric is considered to account for the impact on the broadcast service of the primary system. This new interference metric focuses on the percentage of interfered primary receivers. Furthermore, we consider the broadcast service of secondary receivers to be optimized for the utility function. To maximize the total utility of coexisting secondary receivers, an iterative method is proposed for the optimal power allocation. Simulation results show that the proposed algorithm converges to the optimal power level while guaranteeing interference level to the primary system.

Spectrum sensing under distribution uncertainty in cognitive radio networks

The successful coexistence of cognitive radio systems with licensed system requires the secondary users the capability of interference-awareness, i.e., knowing which spectrum bands are occupied by primary users, i.e., the legacy users. Spectrum sensing thus is a key enabling module, which usually models the sensing process as a binary hypothesis testing assuming known signal distribution. However, an unrealistic assumption regarding the signal distribution easily leads to unreliable detection probability. In this paper, we study the sensing performance considering the distribution uncertainty in hypothesis testing, i.e., the actual distribution function of the received signal strength is not known. According to different signal characteristics, we define appropriate uncertainty sets respectively for different hypotheses. Then we present an approximate approach to determine the robust decision threshold, and investigate the performance bounds for the detection probability under distribution uncertainty. Moreover, we provide an analytical expression for the lower bound of detection probability. Numerical results are given to validate our conclusions.

Two-Phase Indoor Positioning Technique in Wireless Networking Environment

Positioning of real world objects (e.g., people) in indoor environment will facilitate location dependent or context-aware applications. Due to severe multi-path fading effect in indoor wireless environment, received signal strength indicator (RSSI) based indoor positioning systems usually require a great amount of human intervention for data measurement during the system initiation. This paper proposes a novel two-phase positioning technique that has been implemented and tested in real environment. Experiment results show that our method can significantly cut down the requirements on data acquisition and achieve satisfactory performance in terms of error distance.

Deferrable load scheduling of smart meter with intermittent communication connection

The demand response program in smart grid implements real-time pricing to reduce power consumption during peak hours. However, sometimes a smart meter cannot receive the price information due to the failure happened in the smart meters or the neighborhood area network gateways communication interface. In this paper, we study the deferrable load scheduling problem in a scenario where the data connection used to transmit the price information can be intermittent. We formulate a partially observable Markov decision process (POMDP) model to obtain the optimal price inquiry policy for the smart meter when it cannot receive the real-time price information. Then given the power price information, a Markov decision process (MDP) is formulated and solved for the deferrable load scheduling problem. The numerical results obtained in this paper can be used to set parameters properly according to different scheduling requirement.

Robust threshold design for cooperative sensing in cognitive radio networks

The successful coexistence of cognitive radio systems and licensed systems requires the secondary users to have the capability of sensing and keeping track of primary transmissions. While existing spectrum sensing methods usually assume known distributions of the primary signals, such an assumption is often not true in practice. As a result, applying existing sensing methods directly will often lead to unreliable detection performance in practical networks. In this paper, we try to improve the sensing performance under the distribution uncertainty of primary signals. We formulate the optimal sensing design as a robust optimization problem, and propose an iterative algorithm to determine the optimal decision threshold for each user. Extensive simulations demonstrate the effectiveness of our proposed algorithm.

Secure and Efficient Content Distribution in Crowdsourced Vehicular Content-Centric Networking

In future intelligent transportation systems, a large amount of content needs to be efficiently and securely exchanged between vehicles and roadside units via vehicular networks to improve the driving and traveling experience. To solve the challenges caused by poor-quality wireless links and the mobility of vehicles, vehicular content-centric networking (VCCN) emerges as a promising paradigm, which has a better content distribution efficiency, mobility, and security via named data and in-networking caching compared with an IP-based network. However, providing a high-quality experience for content distribution in VCCN is challenging due to the dynamic network topologies, varying wireless channel conditions, and vehicle user privacy. In this paper, we propose a novel crowdsourced VCCN framework for secure and efficient content distribution. This framework enables the nearby vehicles to crowdsource their caching resources and radio links for cooperative content distribution. We formulate the problem as the maximization of all users’ payoff and propose an online scheduling method to solve this solution. Furthermore, we adopt identity-based proxy reencryption and named function networking to secure the process of content distribution. The simulation results show that our proposals improve the performance of VCCN in terms of average requester utility compared with original CCN forwarding strategies.