Rongfei Fan

Optimal selection of channel sensing order in cognitive radio

This paper investigates the optimal sensing order problem in multi-channel cognitive medium access control with opportunistic transmissions. The scenario in which the availability probability of each channel is known is considered first. In this case, when the potential channels are identical (except for the availability probabilities) and independent, it is shown that, although the intuitive sensing order (i.e., descending order of the channel availability probabilities) is optimal when adaptive modulation is not used, it does not lead to optimality in general with adaptive modulation. Thus, a dynamic programming approach to the search for an optimal sensing order with adaptive modulation is presented. For some special cases, it is proved that a simple optimal sensing order does exist. More complex scenarios are then considered, e.g., in which the availability probability of each channel is unknown. Optimal strategies are developed to address the challenges created by this additional uncertainty. Finally, a scheme is developed to address the issue of sensing errors.

Optimal multi-channel cooperative sensing in cognitive radio networks

In this paper, optimal multi-channel cooperative sensing strategies in cognitive radio networks are investigated. A cognitive radio network with multiple potential channels is considered. Secondary users cooperatively sense the channels and send the sensing results to a coordinator, in which energy detection with a soft decision rule is employed to estimate whether there are primary activities in the channels. An optimization problem is formulated, which maximizes the throughput of secondary users while keeping detection probability for each channel above a pre-defined threshold. In particular, two sensing modes are investigated: slotted-time sensing mode and continuous-time sensing mode. With a slotted-time sensing mode, the sensing time of each secondary user consists of a number of mini-slots, each of which can be used to sense one channel. The initial optimization problem is shown to be a nonconvex mixed-integer problem. A polynomial-complexity algorithm is proposed to solve the problem optimally. With a continuous-time sensing mode, the sensing time of each secondary user for a channel can be any arbitrary continuous value. The initial nonconvex problem is converted into a convex bilevel problem, which can be successfully solved by existing methods. Numerical results are presented to demonstrate the effectiveness of our proposed algorithms.

Channel Sensing-Order Setting in Cognitive Radio Networks: A Two-User Case

This paper investigates the sensing-order problem in two-user multichannel cognitive medium access control. When adaptive modulation is not adopted, although brute-force search can be used to find the optimal sensing-order setting of the two users, it has huge computational complexity. Accordingly, we propose two suboptimal algorithms, namely, the greedy search algorithm and the incremental algorithm, which have comparable performance with that of brute-force search and have much less computational complexity. It is shown that, with a high probability, either suboptimal algorithm can reach an optimal point if a backoff mechanism is used for contention resolution. When adaptive modulation is adopted, it is observed that the traditional stopping rule does not lead to an optimal point in the two-user case. Furthermore, we demonstrate that the adoption of adaptive modulation affects the optimal sensing-order setting of the two users, compared with the case without adaptive modulation. These findings imply that the stopping rule and the sensing-order setting should be jointly designed from a systematic point of view.

Cognitive Radio: How to Maximally Utilize Spectrum Opportunities in Sequential Sensing

This paper investigates the problem of maximally utilizing the spectrum opportunities in cognitive radio networks with multiple potential channels. In particular, the optimal sensing order problem in multi-channel cognitive medium access control with opportunistic transmissions is studied. It is first shown that, when the potential channels are identical (except for the availability probabilities) and independent, the intuitive sensing order (i.e., descending order of the channel availability probabilities) does not lead to optimality in general. A dynamic programming approach for the search of optimal sensing order is then presented. Finally, for some special cases, it is proved that a simple optimal sensing order does exist.

Network Lifetime Maximization With Node Admission in Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSNs) are expected to support multimedia services such as delivery of video and audio streams. However, due to the relatively stringent quality-of-service (QoS) requirements of multimedia services (e.g., high transmission rates and timely delivery) and the limited wireless resources, it is possible that not all the potential sensor nodes can be admitted into the network. Thus, node admission is essential for WMSNs, which is the target of this paper. Specifically, we aim at the node admission and its interaction with power allocation and link scheduling. A cross-layer design is presented as a two-stage optimization problem, where at the first stage the number of admitted sensor nodes is maximized, and at the second stage the network lifetime is maximized. Interestingly, it is proved that the two-stage optimization problem can be converted to a one-stage optimization problem with a more compact and concise mathematical form. Numerical results demonstrate the effectiveness of the two-stage and one-stage optimization frameworks.

Power Allocation Robust to Time-Varying Wireless Channels in Femtocell Networks

We investigate the power-allocation problem in a two-tier femtocell network including a macrocell and multiple femtocells. Due to shadowing and fading effects, at each cell (macrocell or femtocell), the power levels of desired signal and interference signals vary with time. Under this circumstance, one method to achieve transmission quality in the cells is to first get channel state information (CSI) of all desired links and interference links and then perform power allocation. This method has very large communication and computation overhead. In this paper, we focus on power allocation in which the transmit power levels of the users in the cells do not need to change when the wireless channels fluctuate. We formulate a power-allocation problem subject to bounded outage probability in any cell (macrocell or femtocell). The formulated problem has probabilistic constraints. It is hard to have closed-form expressions of the probabilistic constraints. To solve this, we propose novel transformations of the constraints, based on which we obtain constraints in the format of worst-case value-at-risk. Such constraints can be converted to convex constraints, and thus, the research problem is transformed to a convex problem. For the convex problem, we provide an iterative algorithm that converges quickly. We also investigate the case when the channel gain distributions are unknown. Our proposed schemes have the merits of very small communication and computation overhead and are particularly useful in fast-fading environments.

Joint medium access control, routing and energy distribution in multi-hop wireless networks

It is a challenging task for multi-hop wireless networks to support multimedia applications with quality-of service (QoS) requirements. This letter presents a joint cross layer optimization approach, i.e., joint medium access control, routing, and energy distribution. User satisfaction represented by user utility is maximized within the required network lifetime, given the constraints on the total available energy in the network and the minimum user rates. Although the resulting optimization problem is nonlinear and nonconvex, we prove that it is approximately equivalent to a two-step convex problem. Furthermore, we prove that the problem of maximizing network utility within achievable network lifetime is quasiconvex.

Match-Filtering Based TOA Estimation for IR-UWB Ranging Systems

Accurate geolocation techniques using IR-UWB (impulse radio ultra-wideband) signals have gained more and more interests recently. To exploit the fine time resolution of IR- UWB most, TOA (time of arrival) based ranging is the most appropriate technique. Considering the challenges for the design of TOA estimation algorithms in many aspects, a three-step match-filtering detection based algorithm is proposed. First, the search region for DP (direct path) detection is determined; then, a rough detection of DP is made by threshold comparison; last, a refined search process is carried out to obtain the precise location of DP. The threshold-factor in the second step is dynamically set using a joint-metric in terms of kurtosis and RMS (root mean square) delay spread of the match-filtering output. Performance comparisons with other algorithms prove that the proposed three-step algorithm can achieve a good tradeoff between computation efficiency and estimation accuracy. The reliability of TOA estimation result is also discussed. Three levels of reliability are defined and the probability density functions for TOA estimation errors in each level are modeled. It is expected to improve the final location estimation accuracy further by properly incorporating the reliability information into the positioning algorithms.

Robust Localization in Wireless Sensor Networks

Localization is essential for wireless sensor networks (WSNs). It is to determine the positions of sensor nodes based on incomplete mutual distance measurements. In this paper, to measure the accuracy of localization algorithms, a ranging error model for time of arrival (TOA) estimation is given, and the Cramer-Rao bound (CRB) for the model is derived. Then an algorithm is proposed to deal with the case where 1) ranging error accumulation exists, and 2) some anchor nodes broadcast inaccurate/wrong location information. Specifically, we first present a ranging error-tolerable topology reconstruction method without knowledge of anchor node locations. Then we propose a method to detect anchor nodes that have inaccurate/wrong position information. Simulations demonstrate the improvement of our algorithm compared to others.

Average Rate Maximization in Relay Networks Over Slow Fading Channels

In this paper, the average rate maximization problem given transmission power constraints in a decode-and-forward (DF) relay network over slow fading channels is studied. For the problem, either a long- or short-term power constraint is imposed on the source and relays. In each combination of the power constraints at the source and relays, the original optimization problem is decomposed into subproblems, each corresponding to a specific channel gain realization. For each subproblem, a fast algorithm with closed-form solutions is provided. The case with additional peak power spectrum density constraints is also investigated. Numerical results are presented to demonstrate the effectiveness of the algorithms.