Xiaoying Gan

Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach

In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.

Cooperative Spectrum Sharing in Cognitive Radio Networks: A Game-Theoretic Approach

We consider the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs), where the PU selects a proper set of secondary users to serve as the cooperative relays for its transmission. In return, the PU leases portion of channel access time to the selected SUs for their own transmission. The PU decides the portion of channel access time it will leave for the selected SUs (i.e., the cooperative relays), and the cooperative relays decide their respective power level used to help PUs transmission in order to achieve proportional access time to the channel. We assume that the PU and SUs are rational and selfish, i.e., they only aim at maximizing their own utility. As SUs utility is in term of their own transmission rate and the power cost for PUs transmission, so they will choose a proper power level to meet the tradeoff between transmission rate and power cost. PU will choose a proper portion of channel access time for the cooperative relays to attract them to employ higher power level. We formulate the problem as a non-cooperative game between PU and SUs, and prove that the proposed game converges to a unique Stackelberg equilibrium. By employing an iterative updating algorithm, we can achieve the unique equilibrium point.

Multicast Capacity for VANETs with Directional Antenna and Delay Constraint

Vehicular Ad Hoc Networks (VANETs) with base stations are called hybrid VANET, where base stations are deployed to improve the throughput capacity. In this paper, we study the multicast throughput capacity for hybrid wireless VANET with a directional antenna on each vehicle and the end-to-end delay is constrained. In the hybrid VANET, there are n mobile vehicles (or nodes) distributed in a unit area with m strategically deployed base stations connected using high-bandwidth wire links. There are n_s multicast sessions and each multicast session has one source which transmits identical data to its associated p destinations. We investigate the multicast throughput capacity for two mobility models with two mobility scales, respectively, while each vehicular node is equipped with a directional antenna and with a tolerant delay D. That is, a source node transmits to its p destinations only with the help of normal nodes within D consecutive time slots. Otherwise, the transmission will be performed with in the infrastructure mode, i.e., with the help of base stations. We demonstrate that the one dimensional i.i.d. slow mobility pattern catch the main feature of VANETs. And we find that the multicast throughput capacity of the hybrid wireless VANET greatly depends on the delay constraint D, the number of base stations m, and the beamwidth of directional antenna θ. In the order of magnitude, we obtain the closed form of the multicast throughput capacity of the hybrid directional VANET, where the impact of D, m and θ on the multicast throughput capacity is analyzed. Moreover, we derive the lower bound of the muticast throughput using a similar raptor coding approach.

An Estimation Algorithm of Channel State Transition Probabilities for Cognitive Radio Systems

In this paper, an estimation algorithm of channel state transition probabilities in Markov channel model for cognitive radio systems is proposed. The framework of POMDP is adopted to solve the problem of channel selection. Maximum likelihood method is used to estimate the channel state transition probabilities, which is crucial to POMDP. Central Limit Theorem is introduced to get the relationship between the precision, sample times and the channel state transition probability values. The simulation results show the reliability of the estimation results.

Coalitional Double Auction for Spatial Spectrum Allocation in Cognitive Radio Networks

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve spectrum utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and the spatial reusability simultaneously, which leaves room to further enhance the spectrum efficiency. In this paper, we introduce the coalition double auction for efficient spectrum allocation in CRNs, where secondary users (SUs) are partitioned into several coalitions and the spectrum reusability can be executed within each coalition. The partition formation process is not only related to the interference condition between SUs, but also the expected economic goals. Therefore, we propose a fully-economic spatial spectrum allocation mechanism by incorporating the coalition formation approach with auction theory. With the proposed scheme, the primary operator acts as an auctioneer, who performs multiple virtual auctions to form a stable partition of SUs and conducts a final auction to decide the winning SUs. Moreover, we propose a possible operation rules for the primary operator to iteratively change the partition, and prove that the virtual auctions could converge in finite time. Comprehensive theoretical analysis and simulation results are presented to show that our scheme can satisfy the crucial economic robustness properties of double auction, and outperform existing mechanisms.

Two Dimension Spectrum Allocation for Cognitive Radio Networks

In this paper, we develop a truthful and efficient combinatorial auction scheme under a novel spectrum allocation model that can achieve a worst-case approximation ratio \sqrt{m} in social welfare. We propose to tackle the dynamic spectrum access problem in cognitive radio (CR) networks with time-frequency flexibility requirements. We model the spectrum opportunity in a time-frequency division manner and the spectrum allocation as a combinatorial auction. Then we design an auction mechanism to reach the upper bound in polynomial time and propose a combined approach to improve the bound in the cost of increasing computational complexity. A truthful payment that gives incentive to the SUs for revealing the truthful valuation of the desirable bundle of slots is presented. In order to reduce the complexity, we simplify the general model to a modified model that only allows frequency flexibility, and then present a truthful, optimal and computationally efficient auction mechanism. Extensive simulation results of the social welfare and spectrum ratio show that the performance of the combined approximation algorithm is better than the sorting based greedy algorithm.

Joint Estimation of Clock Skew and Offset in Pairwise Broadcast Synchronization Mechanism

The problem of jointly estimating clock skew and offset for wireless sensor networks (WSNs) in a pairwise broadcast synchronization (PBS) protocol is considered. The random part of the delay is supposed to be an exponential random variable. We consider two estimators, i.e., joint maximum-likelihood estimator (JMLE) and generalized ML-like estimator (GMLLE) proposed by Leng and Wu . For both estimators, the corresponding algorithms are explicitly derived and presented. For the GMLLE, the corresponding performance bound based on the reduced set of observations is derived and the optimal value of a user-defined parameter is identified accordingly. At last, analytical results are corroborated by numerical experiments. We observe that: (i) JMLE usually outperforms GMLLE at the cost of larger computational complexity; (ii) JMLE, while achieving the same estimation accuracy as that of the LP method presented in , enjoys significantly lower computational complexity than that of the latter.

Converge Cast: On the Capacity and Delay Tradeoffs

In this paper, we define an ad hoc network where multiple sources transmit packets to one destination as Converge-Cast network. We will study the capacity delay tradeoffs assuming that n wireless nodes are deployed in a unit square. For each session (the session is a dataflow from k different source nodes to 1 destination node), k nodes are randomly selected as active sources and each transmits one packet to a particular destination node, which is also randomly selected. We first consider the stationary case, where capacity is mainly discussed and delay is entirely dependent on the average number of hops. We find that the per-node capacity is Θ (1/√(n log n)) (given nonnegative functions f(n) and g(n): f(n) = O(g(n)) means there exist positive constants c and m such that f(n) ≤ cg(n) for all n ≥ m; f(n)= Ω (g(n)) means there exist positive constants c and m such that f(n) ≥ cg(n) for all n ≥ m; f(n) = Θ (g(n)) means that both f(n) = Ω (g(n)) and f(n) = O(g(n)) hold), which is the same as that of unicast, presented in (Gupta and Kumar, 2000). Then, node mobility is introduced to increase network capacity, for which our study is performed in two steps. The first step is to establish the delay in single-session transmission. We find that the delay is Θ (n log k) under 1-hop strategy, and Θ (n log k/m) under 2-hop redundant strategy, where m denotes the number of replicas for each packet. The second step is to find delay and capacity in multisession transmission. We reveal that the per-node capacity and delay for 2-hop nonredundancy strategy are Θ (1) and Θ (n log k), respectively. The optimal delay is Θ (√(n log k)+k) with redundancy, corresponding to a capacity of Θ (√((1/n log k) + (k/n log k)). Therefore, we obtain that the capacity delay tradeoff satisfies delay/rate ≥ Θ (n log k) for both strategies.

Energy-Constrained Cooperative Spectrum Sensing in Cognitive Radio Networks

How to set spectrum sensing duration is an important issue in Cognitive Radio (CR) networks, which could greatly affect energy efficiency and system throughput. Over sensing would result in insufficient transmission period, while inadequate sensing would incur false alarm and miss detection. This paper studies how to choose an optimal sensing duration to strike a balance between energy consumption and system throughput. We focus on a cooperative sensing scenario, where several secondary users form a group to guarantee more accurate sensing results. By formulating the transmission cost in terms of the energy consumption of sensing process and transmission process, we propose a comprehensive utility function. The maximization of the utility function is obtained with the constraints of sufficient protect for primary users. The existence of the optimal sensing duration is proved accordingly. Numerical results show that secondary users can achieve almost the maximum throughput with significant energy saving when utilizing optimal sensing duration.

Joint Compressive Sensing in Wideband Cognitive Networks

In this paper, a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is discussed. An AIC RF front-end sampling structure is proposed requiring only low rate ADCs and few storage units for spectrum sampling. Multiple CRs collect compressed samples through AICs and recover spectrum jointly. A novel joint sparsity model is defined in this scenario, along with a universal recovery algorithm based on S-OMP. Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.