Spectrum-aware cross-layered routing protocol for cognitive radio ad hoc networks
Introduction
Existing wireless devices operate on static spectrum access policy in which the licensed user is authorized to use pre-assigned radio frequency spectrum. Also, frequency bands called Industrial, Scientific and Medical (ISM) [1] are reserved by the spectrum regulatory bodies as unlicensed to motivate innovations and research in the field of wireless technology. Federal Communications Commission (FCC) conducted a spectrum utilization measurement and reported inefficient utilization of licensed spectrum bands at a given time and location [2]. In addition to this, existing spectrum policies are unable to meet the requirements of increasing wireless users. As a consequence, Dynamic Spectrum Access (DSA) has been proposed for efficient spectrum utilization, and thereby meet the increasing bandwidth requirements of unlicensed users [3], [4].
In DSA, Primary User (PU) is the exclusive user of the spectrum, and Secondary User (SU) is an unlicensed user allowed to use the PU spectrum as long as PU is not using the channel [5]. Hence, the foremost requirement of DSA is to equip the SU with Cognitive Radio (CR) at the physical layer and CR has to sense the licensed spectrum for the presence of PU before occupying the channel [6]. There onwards, sensing needs to be carried out periodically in order to protect the PU from SU transmission [5]. SU is also known as Cognitive User (CU). The temporarily available PU spectrum is termed as Spectrum Opportunity (SOP), Spectrum hole or White space. Dynamic spectrum availability necessitates potential spectrum management algorithms to control dynamic access of the spectrum. The central base station manages the spectrum access in a centralized CR Network (CRN) and is managed locally in a CR Ad Hoc Network (CRAHN) [7].
Application of CRN is expanding into various newly emerging fields, and routing is an important research problem that provides connectivity between the devices. Routing is very much required for the sharing of information in any wireless network, and is more challenging in the case of CRN. The stochastic nature of PU, the heterogeneous requirements from SU, heterogeneity in communication protocols and hardware resources, inconsistency in the availability of hardware resources, complicates the design of routing protocols in CRN. CRAHN can be represented as a graph with each edge consisting of multiple links of different weights. The number of links per edge is the number of common channels available between the two nodes. At a given time, the number of common channels depends on the number of PUs present in the transmission range of the two neighboring nodes, and idle or busy state of PUs. Unlike the traditional routing protocols, CRAHN based routing must choose the channel for every hop along with next node selection during the route discovery process. The chosen channel must meet the Quality Of Service (QoS) requirement of the SU and at the same time should not inhibit the working of PU.
The channel used by SU for data transmission may become unavailable during the routing process if the PU becomes active on that channel. As a consequence, the link failures are more frequent in CRAHN and route stability is a major concern with the channel availability being intermittent. Re-routing will account for the transmission of more control packets. Hence, channel selection is a critical step that needs to take into account PU activity on the channel as well as propagation characteristics of the channel i.e., channel quality. Therefore, to sufficiently meet the route requirements, a multi-parameter based routing solution is necessary. The existing research works focus on various influencing parameters such as: PU activity models [8], spectrum sensing requirements [6], [9], varying channel capacity [10], [11], cross-layering [12]. Though there are few initial attempts, to the best of our knowledge, the combined effect of all these parameters on SU route performance has not been studied yet. Accordingly, the contributions of the paper are summarized as follows:
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A novel channel selection metric signifying the probability of successful transmission for a channel is developed based on the time-varying channel capacity, channel availability pattern, sensing periodicity and presence of PU within the operating region of the one-hop SUs.
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The metric is then used for channel characterization and next-hop selection during the route discovery phase.
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A spectrum-aware routing algorithm is proposed using the probabilistic metric and its performance is evaluated.
The remaining part of the paper is organized as follows: Section 2 discusses the related work. Section 3-A presents the system model, Section 3-B presents through the derivation of probabilistic metrics used for the channel selection and next-hop selection, and Section 3-C explains the proposed spectrum-aware routing algorithm, Section 3-D takes through the route maintenance procedure. In Section 4, the simulation and evaluation results of the proposed spectrum-aware routing protocol is presented. Section 5 concludes the research work.
Section snippets
Related work
Research in the field of routing in CRAHN has started a decade ago and researchers are aiming at throughput improvement, delay minimization, energy consumption minimization, route stability, and minimization of PU interference.
Badarneh et al. [13] have used the probabilistic-based approach by accounting transmission time of a channel and channel availability time. Authors have proposed to select a channel that is available for a duration longer than the packet transmission time. In Cacciapuoti
System model, probabilistic metric design and routing algorithm
In this section, the first part presents system model, the second part explains the derivation of channel-based probabilistic metric and routing metric, and the third section discusses the proposed spectrum-aware routing algorithm, and finally, the fourth section discusses the route maintenance. Table 1 decsribes the important symbols used in the metric design.
Simulation and performance evaluation
In this section, the proposed algorithm is evaluated via ns-2 simulations. The proposed algorithm is also compared with: (i) Maximum Capacity (MaxCapacity) (ii) Maximum OFF time (MaxToff) (iii) Maximum Probability of Off time (MaxPoff) [14], [20], [27], [31], (iv) Maximum Probability Of Success (MaxPoS) [13] and (v) EMM-TRP [32] routing protocols. Even though authors [13], [20], [27], [31], [32] have used different number of interfaces in their work, we have simulated all the above protocols
Conclusion
In the proposed research, the probabilistic method is used to select the most available and preferable channel between any two secondary users. Also, the channel selection algorithm is executed periodically after every sensing phase to decide if the chosen channel is still available or not. In addition, a stable route is constructed by selecting a node that has the best available channel for transmission, and has minimum number of licensed users in its transmission range. The routing protocol
CRediT authorship contribution statement
Rashmi Naveen Raj: Conceptualization, Methodology, Software, Validation, Writing - original draft preparation. Ashalatha Nayak: Writing - review & editing, Visualization, Supervision. M. Sathish Kumar: Visualization, Formal analysis, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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