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In this paper, we address the choice of the routing update period in Cognitive Radio Ad Hoc Networks with the objective of maximizing the capacity available at an arbitrary node acting as source. To this aim, first, the problem of the routing update period is reformulated to account for the slotted nature of the Cognitive Radio time induced by the...
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... PU activity affecting the routes in the first time slot composing the routing update period. The selected route will be used in all the subsequent time slots, until a new routing update packet will be received. Clearly, if the selected route is affected by PU activity in any of the subsequent time slots, then the route fails in such time slots. Fig. 2 shows an example of the update based strategy in the time domain. In the figure, we report the PU activity state for each time slot, along with the route chosen by the source according to the received routing update packet. More in detail, in the first routing update period the source selects route r 1 since: i) r 1 provides the ...
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... With reference to s 1 n+1 , at time slot n + 1 we have s 1 n+1 = i if the following three conditions hold simultaneously: i) S j (n+1) = 0; ∀ j > i, i.e., all the channels with a reward greater then r i are not available at time slot n + 1; ii) S i (n + 1) = 1, i.e., channel i is available at time slot n + 1; iii) i ≥ s 2 , i.e., the channel reported at time slot n as available at time slot n + 1 is still available. Hence, (23) follows by exploiting the channel independence assumption [16], [17], [18]. Similarly, with reference to s k n+1 , k > 1, we have s k n+1 = i if the following four conditions hold simultaneously: i) S j (n+1) = 0; ∀ j > i; ii) S i (n + 1) = 1; iii) i ≥ s k+1 n ; iv) i ≤ s k−1 n+1 , i.e., a channel reported at time slot n+1 as unavailable for time slot n+k−1 is not available at time slot n+k. ...
In TV White Space, the unlicensed users are required to periodically access a database to acquire information on the spectrum usage of the licensed users. In addition, the unlicensed users can access the database on-demand, whenever they believe convenient, to update the spectrum availability information. In this paper, we design the optimal database access strategy, i.e., the strategy allowing the unlicensed users to jointly: 1) maximize the expected overall communication opportunities through on-demand accesses; and 2) respect the regulatory specifications. To this aim, we develop a stochastic analytical framework that allows us to account for: 1) the PU activity dynamics; 2) the quality dynamics among the different channels; and 3) the overhead induced by the database access. Specifically, at first, we prove that the database access problem can be modeled as a Markov decision process, and we show that it cannot be solved through brute-force search. Then, we prove that the optimal strategy exhibits a threshold structure, and we exploit this threshold property to design an algorithm able to efficiently compute the optimal strategy. The analytical results are finally validated through simulations.
... In fact, whenever a CR user receives a routing update, it acquires some knowledge on the current PU activities over the different routes. Hence, the shorter are the update periods, the better the CR user can exploit such a knowledge to prioritize the routes [10]. On the other hand, the shorter are the periods, the higher is the overhead induced within the network. ...
... In this subsection, we validate the optimality of the route priority function stated in Theorems 1 and 3 for both the considered routing strategies. More in detail, we compare through Montecarlo simulations the average aggregate capacity C f R * (K) computed with Algorithms 1 and 2 with those obtained by exhaustive search of the priority function f * maximizing the average aggregate capacity as in (10). ...
... Specifically, Fig. 5 presents the difference between the results obtained with Algorithm 1 and the results obtained through exhaustive search for both the considered PU activity models. The (x) coordinate of the dot represents the aggregate route capacity C f R * (K) computed with Algorithm 1, whereas the (y) coordinate represents the average aggregate capacity C f * (K) computed with (10). Clearly, if y = x, then the two capacities are exactly the same, meaning that Algorithm 1 actually finds the optimal route priority function. ...
To fully unleash the potentials of the cognitive radio (CR) paradigm, new challenges must be addressed. Specifically, as regards the network layer, the problem of the route priority, i.e., the problem of prioritizing the routes for the CR packet transmission, is crucial, since the communication opportunities provided by a route are deeply affected by the primary-user (PU) activity. Furthermore, whenever the CR network layer exploits proactively acquired information on the PU activity, update packets need to be exchanged among the CR users, inducing so a route overhead independently of the adopted routing protocol. Hence, in this paper, we analytically derive the optimal route priority rule, i.e., the route priority rule maximizing the achievable capacity, by jointly accounting for the PU activity and the route overhead. To this aim, at first, we formulate the optimal route priority problem, and we prove that its computational complexity through exhaustive search is exponential. Then, we provide the closed-form expressions of the achievable capacity. Stemming from these expressions, we derive the optimal route priority, and we design a computational-efficient search algorithm. All the theoretical results are derived by adopting two routing strategies and two PU activity models.
... v l (t) denotes the position of the l-th PU at time instant t. The l-th PU traffic on the m-th channel is modeled as a two-state birth-death process (Cacciapuoti et al., 2013aCacciapuoti et al., 2014), with death rate α l;m and birth rate β l;m . In the on state, the l-th PU is active on channel m with probability P on l;m ¼ β l;m =ðα l;m þ β l;m Þ whereas in the off state it is inactive with probability P off l;m ¼ 1 À P on l;m . ...
Channel availability is defined as the probability of a licensed channel being available for the communications of unlicensed users. Channel availability is a key parameter for an effective design of channel selection strategies as well as routing metrics in cognitive radio networks. In static scenarios, the availability of a channel depends only on the primary user's activity. Differently, in mobile scenarios, the availability of a channel dynamically varies in time due to the changes of the users' relative positions. In this paper, we design a channel-availability estimation strategy by explicitly accounting for the features of mobile scenarios. The simulation results reveal the benefits of adopting the proposed strategy in cognitive radio networks.
... The channel-outage influence on the DRM control performance is also analysed in [1]. In order to lower the channel outage not only the temporal opportunities originated by the absence of PU activity in its protection range 110111213 but also the spatial opportunities caused also, for example, by the relative mobility between PU and SU [12] can be exploited. CU's can be out of the protection range and, therefore spatial spectrum opportunities can occur, allowing to lower the channel outage. ...
The paper deals with the reduction of the channel outage in a smart grid scenario since it plays a crucial role on the control performance of the Demand/Response Management. A study on a two-way cognitive-based switching procedure is carried out and tools for the optimum sensing-time evaluation are provided. Such evaluation considers a cost function that takes into account both the sensing-accuracy improvement gained by increasing the sensing time and the transmission-capacity degradation induced by sensing-time increasing.
... The time interval between these exchanges, referred to as routing update period, deeply affects the overall routing performance, independently of the adopted routing protocol. In fact, the shorter are the update periods, the more accurate are the the routing decisions [7]. However, the shorter are the periods, the higher is the overhead induced within the network. ...
... Such a formulation takes also into account the slotted nature of the CR time induced by the spectrum sensing functionality [8]. Then, we analytically derive the optimal route priority rule, and the theoretical analysis is carried out by adopting two different widely-adopted PU activity models [7] for conferring generality to the analysis: i) Bernoulli PU Activity Model, in which the PU activity is time independent; ii) Markov PU Activity Model, in which the PU activity exhibits a time correlation according to a Markov Chain. ...
The problem of choosing the route providing the best communication opportunities among the available routes is particularly challenging in self-organizing Cognitive Radio networks, since the communication opportunities are deeply affected by the primary-user (PU) activity. Furthermore, whenever the route selection exploits proactively acquired information on the PU activity, routing update packets need to be periodically exchanged among the nodes. The time interval between these exchanges, i.e., the routing update period, deeply affects the overall communication opportunities provided by a route, regardless of the adopted routing protocol. Hence, in this paper, we analytically derive the optimal route priority rule in the sense of maximizing the average capacity, by accounting for both the PU activity and the routing update period. The theoretical analysis is conducted by adopting two different widely-adopted PU activity models to confer generality to the analysis. Finally, the analytical results are validated through numerical evaluations.
One of the primary factors contributing to cognitive radio's reputation as a very promising strategy for 5G wireless networks is its ability to significantly increase spectrum consumption efficiency, which is really fairly considerable. Primary (or licensed) users (PUs) are aware of the necessity for range access to the groups for which they have licenses, and secondary users (SUs), sometimes known as cognitive users, are permitted to access the same range in an unobtrusive manner. These two different kinds of clients provide different cognitive radio networks (CRN) concepts a typical range.
Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in mobile cognitive radio networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.
