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Register a new userZOS: A Fast Rendezvous Algorithm Based on Set of Available Channels for Cognitive Radios
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Added by
Hai Liu
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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Most of existing rendezvous algorithms generate channel-hopping sequences based on the whole channel set. They are inefficient when the set of available channels is a small subset of the whole channel set. We propose a new algorithm called ZOS which uses three types of elementary sequences (namely, Zero-type, One-type, and S-type) to generate channel-hopping sequences based on the set of available channels. ZOS provides guaranteed rendezvous without any additional requirements. The maximum time-to-rendezvous of ZOS is upper-bounded by O(m1*m2*log2M) where M is the number of all channels and m1 and m2 are the numbers of available channels of two users.
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In cognitive radio networks, rendezvous is a fundamental operation by which two cognitive users establish a communication link on a commonly-available channel for communications. Some existing rendezvous algorithms can guarantee that rendezvous can be completed within finite time and they generate channel-hopping (CH) sequences based on the whole channel set. However, some channels may not be available (e.g., they are being used by the licensed users) and these existing algorithms would randomly replace the unavailable channels in the CH sequence. This random replacement is not effective, especially when the number of unavailable channels is large. In this paper, we design a new rendezvous algorithm that attempts rendezvous on the available channels only for faster rendezvous. This new algorithm, called Interleaved Sequences based on Available Channel set (ISAC), constructs an odd sub-sequence and an even sub-sequence and interleaves these two sub-sequences to compose a CH sequence. We prove that ISAC provides guaranteed rendezvous (i.e., rendezvous can be achieved within finite time). We derive the upper bound on the maximum time-to-rendezvous (MTTR) to be O(m) (m is not greater than Q) under the symmetric model and O(mn) (n is not greater than Q) under the asymmetric model, where m and n are the number of available channels of two users and Q is the total number of channels (i.e., all potentially available channels). We conduct extensive computer simulation to demonstrate that ISAC gives significantly smaller MTTR than the existing algorithms.
Blind rendezvous is a fundamental problem in cognitive radio networks. The problem involves a collection of agents (radios) that wish to discover each other in the blind setting where there is no shared infrastructure and they initially have no knowledge of each other. Time is divided into discrete slots; spectrum is divided into discrete channels, ${1,2,..., n}$. Each agent may access a single channel in a single time slot and we say that two agents rendezvous when they access the same channel in the same time slot. The model is asymmetric: each agent $A_i$ may only use a particular subset $S_i$ of the channels and different agents may have access to different subsets of channels. The goal is to design deterministic channel hopping schedules for each agent so as to guarantee rendezvous between any pair of agents with overlapping channel sets. Two independent sets of authors, Shin et al. and Lin et al., gave the first constructions guaranteeing asynchronous blind rendezvous in $O(n^2)$ and $O(n^3)$ time, respectively. We present a substantially improved construction guaranteeing that any two agents, $A_i$, $A_j$, will rendezvous in $O(|S_i| |S_j| loglog n)$ time. Our results are the first that achieve nontrivial dependence on $|S_i|$, the size of the set of available channels. This allows us, for example, to save roughly a quadratic factor over the best previous results in the important case when channel subsets have constant size. We also achieve the best possible bound of $O(1)$ time for the symmetric situation; previous works could do no better than $O(n)$. Using the probabilistic method and Ramsey theory we provide evidence in support of our suspicion that our construction is asymptotically optimal for small size channel subsets: we show both a $c |S_i||S_j|$ lower bound and a $c loglog n$ lower bound when $|S_i|, |S_j| leq n/2$.
In Cognitive Radio Networks (CRNs), the secondary users (SUs) are allowed to access the licensed channels opportunistically. A fundamental and essential operation for SUs is to establish communication through choosing a common channel at the same time slot, which is referred to as rendezvous problem. In this paper, we study strategies to achieve fast rendezvous for two secondary users. The channel availability for secondary nodes is subject to temporal and spatial variation. Moreover, in a distributed system, one user is oblivious of the other users channel status. Therefore, a fast rendezvous is not trivial. Recently, a number of rendezvous strategies have been proposed for different system settings, but rarely have they taken the temporal variation of the channels into account. In this work, we first derive a time-adaptive strategy with optimal expected time-to-rendezvous (TTR) for synchronous systems in stable environments, where channel availability is assumed to be static over time. Next, in dynamic environments, which better represent temporally dynamic channel availability in CRNs, we first derive optimal strategies for two special cases, and then prove that our strategy is still asymptotically optimal in general dynamic cases. Numerous simulations are conducted to demonstrate the performance of our strategies, and validate the theoretical analysis. The impacts of different parameters on the TTR are also investigated, such as the number of channels, the channel open possibilities, the extent of the environment being dynamic, and the existence of an intruder.
Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method.
Cognitive radios sense the radio spectrum in order to find unused frequency bands and use them in an agile manner. Transmission by the primary user must be detected reliably even in the low signal-to-noise ratio (SNR) regime and in the face of shadowing and fading. Communication signals are typically cyclostationary, and have many periodic statistical properties related to the symbol rate, the coding and modulation schemes as well as the guard periods, for example. These properties can be exploited in designing a detector, and for distinguishing between the primary and secondary users signals. In this paper, a generalized likelihood ratio test (GLRT) for detecting the presence of cyclostationarity using multiple cyclic frequencies is proposed. Distributed decision making is employed by combining the quantized local test statistics from many secondary users. User cooperation allows for mitigating the effects of shadowing and provides a larger footprint for the cognitive radio system. Simulation examples demonstrate the resulting performance gains in the low SNR regime and the benefits of cooperative detection.
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