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Register a new userMulticasting in Cognitive Radio Networks: Algorithms, Techniques and Protocols
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Added by
Junaid Qadir
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Multicasting is a fundamental networking primitive utilized by numerous applications. This also holds true for cognitive radio networks (CRNs) which have been proposed as a solution to the problems that emanate from the static non-adaptive features of classical wireless networks. A prime application of CRNs is dynamic spectrum access (DSA), which improves the efficiency of spectrum allocation by allowing a secondary network, comprising of secondary users (SUs), to share spectrum licensed to a primary licensed networks comprising of primary users (PUs). Multicasting in CRNs is a challenging problem due to the dynamic nature of spectrum opportunities available to the SUs. Various approaches, including those based in optimization theory, network coding, algorithms, have been proposed for performing efficient multicast in CRNs. In this paper, we provide a self-contained tutorial on algorithms and techniques useful for solving the multicast problem, and then provide a comprehensive survey of protocols that have been proposed for multicasting in CRNs. We conclude this paper by identifying open research questions and future research directions.
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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 this paper, a novel spectrum association approach for cognitive radio networks (CRNs) is proposed. Based on a measure of both inference and confidence as well as on a measure of quality-of-service, the association between secondary users (SUs) in the network and frequency bands licensed to primary users (PUs) is investigated. The problem is formulated as a matching game between SUs and PUs. In this game, SUs employ a soft-decision Bayesian framework to detect PUs signals and, eventually, rank them based on the logarithm of the a posteriori ratio. A performance measure that captures both the ranking metric and rate is further computed by the SUs. Using this performance measure, a PU evaluates its own utility function that it uses to build its own association preferences. A distributed algorithm that allows both SUs and PUs to interact and self-organize into a stable match is proposed. Simulation results show that the proposed algorithm can improve the sum of SUs rates by up to 20 % and 60 % relative to the deferred acceptance algorithm and random channel allocation approach, respectively. The results also show an improved convergence time.
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.
The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.
Several organizations have built multiple datacenters connected via dedicated wide area networks over which large inter-datacenter transfers take place. This includes tremendous volumes of bulk multicast traffic generated as a result of data and content replication. Although one can perform these transfers using a single multicast forwarding tree, that can lead to poor performance as the slowest receiver on each tree dictates the completion time for all receivers. Using multiple trees per transfer each connected to a subset of receivers alleviates this concern. The choice of multicast trees also determines the total bandwidth usage. To further improve the performance, bandwidth over dedicated inter-datacenter networks can be carved for different multicast trees over specific time periods to avoid congestion and minimize the average receiver completion times. In this paper, we break this problem into the three sub-problems of partitioning, tree selection, and rate allocation. We present an algorithm called QuickCast which is computationally fast and allows us to significantly speed up multiple receivers per bulk multicast transfer with control over extra bandwidth consumption. We evaluate QuickCast against a variety of synthetic and real traffic patterns as well as real WAN topologies. Compared to performing bulk multicast transfers as separate unicast transfers, QuickCast achieves up to $3.64times$ reduction in mean completion times while at the same time using $0.71times$ the bandwidth. Also, QuickCast allows the top $50%$ of receivers to complete between $3times$ to $35times$ faster on average compared with when a single forwarding multicast tree is used for data delivery.
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