Connectivity Of Cognitive Radio Networks Research Articles

On k-channel connectivity in cognitive radio networks through channel assignment

Cognitive radio network (CRN), forces the secondary users (SUs) to vacate the channel and switch to other unused channel when a primary user (PU) starts using its channel. Thus, the channels available for an SU vary over time depending on PU’s activities. This temporal variation in channel availability can cause network partitioning, if there is no other available channel is present in the network which poses challenge among researchers “how to ensure robust connectivity in CRN?” A topology generated by a channel assignment is called k-channel connected if the nodes in the remaining topology can be connected when any set of (k-1) channels from channel set have been reclaimed by PUs and become unavailable to SUs. To the best of our knowledge, this is the first work on k-channel connectivity in CRN. We proved that this problem is NP-hard using c-colorability problem. To address this problem, we first present a polynomial time centralized algorithm, called Centralized k-channel Connected Spanning Subgraph (CCSSk), which use O(k2logn) approximation algorithm to find minimum cost subset k-connected subgraph problem as a subroutine. Based on CCSSk, we propose a distributed algorithm, called Distributed k-channel Connected Spanning Subgraph (DCSSk) with message complexity O(2n+n(1+Δ)+nlog2n), where n denotes the number of nodes and Δ is the maximum node degree in topology under maximum transmission power. Through simulations it has be observed that the proposed algorithms are able to preserve minimum energy paths and reduce the number of required channels efficiently to address the k-channel connected topology control problem. Simulation results also show that the CCSSk and DCSSk reduce the number of links by 55% and 45% respectively.

κ‐channel connectivity in cognitive radio networks for bounded tree‐width graphs

SummaryChannel connectivity problem in cognitive radio network (CCP in CRN) is to find a channel assignment for secondary users (SUs) such that underlying graph induced by SUs (potential graph) is connected. Channel connectivity problem in CRNs has been proved to be NP‐complete for general graph and remains NP‐complete even if the underlying potential graph is the tree. Fixed parameter tractability of CCP in CRN has been of research interest because of its practicability. In this work, we have proposed novel problem of fixed parameter tractability for κ‐CCP of CRN (κ‐CCP in CRN), ie, whether a given CRN remains connected when any of κ−1 channels are reclaimed by primary users, called κ‐CCP. This is very crucial to check and design an effective channel assignment algorithm that provides connectivity to SUs on channels reclamation by primary users. To our knowledge, fixed parameter tractability of κ‐CCP in CRNs has not been studied and becoming useful because of development of 5G if the underlying potential graph has bounded tree‐width and number of channels is parameterized. We address κ‐CCP in CRN and propose an O(α)O(α)nO(1) time algorithm if the underlying potential graph is bounded by tree‐width and generates a feasible channel assignment under which given CRN is κ‐channel connected, where α is the number of channels and n is the number of SUs in CRN. We show that the κ‐CCP is fixed parameter tractable, when underlying potential graph has bounded tree‐width and number of channels is parameterized. To count the different possible potential solution of κ‐CCP, we propose a polynomial time algorithm for the corresponding enumeration problem (the number of different feasible channel assignments) Enum‐κ‐CCP on the potential graph with bounded tree‐width when the number of channels is bounded by a constant. Through simulation, we have observed that the feasibility of κ‐CCP highly depends on the available channels at each SU and number of radio present at each SU.

Topology Control with Successive Interference Cancellation in Cognitive Radio Networks

Topology control is an important approach to maintain the connectivity of cognitive radio networks (CRNs). Most existing works assumed that secondary users (SUs) must vacate the spectrum reclaimed by primary users (PUs), resulting the inefficient spectrum utilization. In this paper, we consider the simultaneous transmissions of SUs with PUs; meanwhile, SUs are equipped with successive interference cancellation (SIC) to mitigate the interference from PUs, thereby enabling SUs to access the spectrum more aggressively than previous works. Although SIC has been studied in the information theory and signal processing, it is not well investigated in guaranteeing the connectivity of wireless networks, especially the CRNs. On this account, we propose both centralized and distributed SIC-based topology control algorithm to alleviate the impact of the unpredictable activities of PUs and the potential interference between SUs. In particular, we integrate power control with channel assignment to construct a bi-channel-connected and conflict-free CRN with the fewest required channels. Theoretical analysis reveals that the bi-channel-connectivity and conflict-free properties can be ensured by our proposed algorithms. Then, simulation results demonstrate the effectiveness of proposed algorithms in terms of reducing the number of required channels and improving the robustness of topologies, as compared with the prevailing topology control algorithms.

Bounds on Secondary User Connectivity in Cognitive Radio Networks

We consider a large-scale cognitive radio network (CRN) in a Rayleigh fading environment. Errors of spectrum sensing by secondary users (SUs) are considered in the letter. Cumulative inter-network interference (between primary and secondary networks) is calculated and outage probability of primary users (PUs) is derived in the closed-form. The upper and lower bounds on the percolation area are obtained and analyzed, within which the secondary network percolates while the PU's outage probability is kept below a tolerable threshold. The bounds depend on SU's correct detection and false alarm probabilities, and PU's transmit power and protection radius. Further we find that the percolation area that percolates SUs and meanwhile provides protection for PUs does not always exist.

Achieving Bi-Channel-Connectivity with Topology Control in Cognitive Radio Networks

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs transmitting on the same channel will be affected when the channel is requested by the PUs, thereby resulting in a possible network partition of CRNs. Therefore, how to maintain the connectivity of CRNs considering the activity of PUs is a critical problem. In this paper, we propose a centralized and a distributed topology control algorithm respectively to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using the minimum number of channels. In the power control phase, we tailor the topology for the channel assignment in the second phase. In the channel assignment phase, we utilize the graph coloring algorithm to achieve conflict-free transmission by assigning a channel to each SU. Theoretical analysis and simulation study show that the derived topology can maintain connectivity in the event of any single channel interruption by PUs. Simulation results also demonstrate that the proposed algorithms can efficiently reduce the average number of required channels for achieving bi-channel-connectivity and conflict-free transmission and ensure that the minimum power paths in the original network preserved in the final topology.

The asymptotic throughput and connectivity of cognitive radio networks with directional transmission

Throughput scaling laws for two coexisting ad hoc networks with m primary users (PUs) and n secondary users (SUs) randomly distributed in an unit area have been widely studied. Early work showed that the secondary network performs as well as stand-alone networks, namely, the per-node throughput of the secondary networks is $\Theta (1/\sqrt {n \log n})$. In this paper, we show that by exploiting directional spectrum opportunities in secondary network, the throughput of secondary network can be improved. If the beamwidth of secondary transmitter (TX)'s main lobe is $\delta = o(1/ \log n)$, SUs can achieve a per-node throughput of $\Theta (1/\sqrt {n \log n})$ for directional transmission and omni reception (DTOR), which is $\Theta (\log n)$ times higher than the throughput without directional transmission. On the contrary, if $\delta = \omega (1/ \log n)$, the throughput gain of SUs is 2π/δ for DTOR compared with the throughput without directional antennas. Similarly, we have derived the throughput for other cases of directional transmission. The connectivity is another critical metric to evaluate the performance of random ad hoc networks. The relation between the number of SUs n and the number of PUs m is assumed to be n = mβ. We show that with the HDP-VDP routing scheme, which is widely employed in the analysis of throughput scaling laws of ad hoc networks, the connectivity of a single SU can be guaranteed when β > 1, and the connectivity of a single secondary path can be guaranteed when β > 2. While circumventing routing can improve the connectivity of cognitive radio ad hoc network, we verify that the connectivity of a single SU as well as a single secondary path can be guaranteed when β > 1. Thus, to achieve the connectivity of secondary networks, the density of SUs should be (asymptotically) bigger than that of PUs.