127 research outputs found

    Handshaking Protocols and Jamming Mechanisms for Blind Rendezvous in a Dynamic Spectrum Access Environment

    Get PDF
    Blind frequency rendezvous is an important process for bootstrapping communications between radios without the use of pre-existing infrastructure or common control channel in a Dynamic Spectrum Access (DSA) environment. In this process, radios attempt to arrive in the same frequency channel and recognize each other’s presence in changing, under-utilized spectrum. This paper refines existing blind rendezvous techniques by introducing a handshaking algorithm for setting up communications once two radios have arrived in the same frequency channel. It then investigates the effect of different jamming techniques on blind rendezvous algorithms that utilize this handshake. The handshake performance is measured by determining the probability of a handshake, the time to handshake, and the increase in time to rendezvous (TTR) with a handshake compared to that without. The handshake caused varying increases in TTR depending on the time spent in each channel. Four different jamming techniques are applied to the blind rendezvous process: noise, deceptive, sense, and Primary User Emulation (PUE). Each jammer type is analyzed to determine how they increase the TTR, how often they successfully jam over a period of time, and how long it takes to jam. The sense jammer was most effective, followed by PUE, deceptive, and noise, respectively

    A Reinforcement Learning Approach for the Multichannel Rendezvous Problem

    Full text link
    In this paper, we consider the multichannel rendezvous problem in cognitive radio networks (CRNs) where the probability that two users hopping on the same channel have a successful rendezvous is a function of channel states. The channel states are modelled by two-state Markov chains that have a good state and a bad state. These channel states are not observable by the users. For such a multichannel rendezvous problem, we are interested in finding the optimal policy to minimize the expected time-to-rendezvous (ETTR) among the class of {\em dynamic blind rendezvous policies}, i.e., at the ttht^{th} time slot each user selects channel ii independently with probability pi(t)p_i(t), i=1,2,…,Ni=1,2, \ldots, N. By formulating such a multichannel rendezvous problem as an adversarial bandit problem, we propose using a reinforcement learning approach to learn the channel selection probabilities pi(t)p_i(t), i=1,2,…,Ni=1,2, \ldots, N. Our experimental results show that the reinforcement learning approach is very effective and yields comparable ETTRs when comparing to various approximation policies in the literature.Comment: 5 pages, 9 figures. arXiv admin note: text overlap with arXiv:1906.1042

    Communication Primitives in Cognitive Radio Networks

    Full text link
    Cognitive radio networks are a new type of multi-channel wireless network in which different nodes can have access to different sets of channels. By providing multiple channels, they improve the efficiency and reliability of wireless communication. However, the heterogeneous nature of cognitive radio networks also brings new challenges to the design and analysis of distributed algorithms. In this paper, we focus on two fundamental problems in cognitive radio networks: neighbor discovery, and global broadcast. We consider a network containing nn nodes, each of which has access to cc channels. We assume the network has diameter DD, and each pair of neighbors have at least k≥1k\geq 1, and at most kmax≤ck_{max}\leq c, shared channels. We also assume each node has at most Δ\Delta neighbors. For the neighbor discovery problem, we design a randomized algorithm CSeek which has time complexity O~((c2/k)+(kmax/k)⋅Δ)\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta). CSeek is flexible and robust, which allows us to use it as a generic "filter" to find "well-connected" neighbors with an even shorter running time. We then move on to the global broadcast problem, and propose CGCast, a randomized algorithm which takes O~((c2/k)+(kmax/k)⋅Δ+D⋅Δ)\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta+D\cdot\Delta) time. CGCast uses CSeek to achieve communication among neighbors, and uses edge coloring to establish an efficient schedule for fast message dissemination. Towards the end of the paper, we give lower bounds for solving the two problems. These lower bounds demonstrate that in many situations, CSeek and CGCast are near optimal

    Fully Distributed Channel-Hopping Algorithms for Rendezvous Setup in Cognitive Multiradio Networks

    Full text link
    • …
    corecore