Factores que influyen en el aprendizaje de la lectura y escritura.

This thesis is organized as follows: Chapter 2 studies cooperative spectrum sensing for a multi- channel CRN. The user scheduling and spectrum sharing are devised. Chapter 3 and Chapter 4 focus on cooperation between SUs and PUs for access. In Chapter 3, the SUs cooperate with the PUs to enhance the security of the PUs and gain spectrum access opportunities. Chapter 4 studies risk-aware cooperation in a multi-channel CRN for access, taking into consideration the trustiness of SUs. Chapter 5 investigates cooperation with PUs for credits. The partner selection and payment determination are studied. Finally, Chapter 6 concludes this research and outlines some further research topics.

Cooperative Spectrum Sensing in

Multi-Channel CRNs

In this chapter, we study dynamic spectrum sensing in a multi-channel environment, which integrates cooperative spectrum sensing and spectrum sharing [59]. Due to hardware limitation, each SU can only choose one channel in spectrum sensing and access one channel at a time for spectrum sharing. The objective of the CRN is to maximize the expected available time of all the channels, under the constraint that the PUs are sufficiently protected. To this end, SUs decide which channels to be sensed. Different from the existing works, a more general scenario is considered in this chapter, where the main differences are: i) the detection performance of individual SU depends on the channel condition, which may differ from user to user; and ii) the channels are considered to present different usage characteristics, such as average sojourn idle time and the probability of being idle. Due to those factors, the channel selection problem becomes more challenging. We formulate the channel selection problem as a nonlinear integer programming problem. Depending on the problem formulation, we first define an associated stochastic optimization problem of the original deterministic optimization problem. Then, we apply the cross-entropy (CE) method of stochastic optimization to find the channel selection

Literature Review

solution efficiently. After spectrum sensing, we study spectrum sharing, which is modeled using a more general game based on the framework of weighted congestion game. SUs with different channel conditions are assigned different weights, with the purpose of favoring SUs with better channel conditions. In the proposed game, each SU chooses a channel from the available channel set to maximize their own interests. An algorithm that can help SUs to achieve Nash Equilibrium (NE) is proposed. It is proved that the algorithm can achieve NE. Simulation results are provided to show the performance of the proposed algorithms.

2.1

Literature Review

In the literature, many works on cooperative spectrum sensing for the single channel case have been reported [37, 39, 51, 52, 60]. The authors in [37] propose a cooperative spectrum sens- ing scheme to improve the spectrum sensing in the presence of shadowing or fading effects. In [52], the authors propose a relay-based cooperation mechanism, which is a two-user cooper- ative spectrum sensing scheme. This cooperation scheme shows that the detection time can be reduced. The authors in [51] propose a selective-relay based cooperative sensing scheme with no dedicated reporting channel. In [60], they also study the sensing and transmission trade-off and show that the performance in terms of the spectrum hole utilization can be significantly improved using cooperative relaying. An optimal sensing scheme for the multiuser coopera- tion is proposed in [39]. Since there usually exist multiple channels in the system, DSA in multi-channel CRNs has drawn increasing attentions recently.

For spectrum sensing in multi-channel scenarios, from the single user’s perspective, the quickest detection is studied with the objective of finding an idle period from multiple channels as fast as possible using the theory of partially observable Markov decision process (POMD- P) in [61] and dynamic programming in [62], respectively. Besides that, from the system’s perspective, the issue regarding how to assign SUs to different channels for maximizing the

system performance are studied in [63–67]. In [63], heuristic channel selection algorithms are designed for cooperative spectrum sensing to maximize the number of available channels. In [65], the authors study this issue to maximize the throughput of SUs. However, a common assumption is made that all the SUs have the same sensing performance for all channels. In practice, the sensing performance of SUs depends on the channel conditions from the PUs to the SUs, which usually differs from user to user. Moreover, the channel usage characteristics of PUs are not taken into consideration in the existing literature.

For spectrum sharing, diverse approaches have been proposed in the literature. In [68], the auction game is utilized, where SUs, PUs, and spectrum bands, are modeled as auctioneers, bidders and bidding articles, respectively. In [69], SUs share the available channels by access- ing the channel with equal probability. In [70], the spectrum access based on multi-channel ALOHA protocol is studied using the theory of potential games, without considering avail- able duration of channels. In [71], channel allocation is studied using a stable marriage game, which aims to find the most stable pairings between the users and channels. Recently, conges- tion game has gained much attentions, which is a prominent approach to model the scenario where multiple rational users share a set of common resource. It has been utilized to solve the issue of spectrum sharing in [19,72,73], where congestion game is utilized for SUs to share the channels and each SU chooses one channel for accessing to maximize its own utility. However, all SUs are treated equally, ignoring their channel conditions.

To simulate cooperative spectrum sensing in multi-channel CRNs, spectrum sensing and spectrum sharing needs to be considered jointly. By carefully designing spectrum sensing and sharing strategies, individual SU can be motivated to participate into cooperative spectrum sensing. In this chapter, we propose a cooperative framework to improve the performance of each individual SU so that selfish SUs are interested in the cooperation, which integrates user scheduling for spectrum sensing and spectrum sharing, considering i) various detection capa- bilities of individual SUs due to different the channel conditions and ii) dynamic of channel

System Model

Table 2.1: Summary of important symbols.

Symbol Definition

N The number of SUs

K The number of channels

αj Transition rate from state ON to OFF for channel j

βj Transition rate from state OFF to ON for channel j

T_ONj Sojourn time for channel j being in ON state

T_{OF F}j Sojourn time for channel j being in OFF state

PP U The transmission power of the PU

M The number of samples in observation period

δ The detection threshold

Q Qfunction

hi,j Average channel gain from the P Ujto SUi

σ2 _{Variance of the Gaussian noise}

pd(i, j) Detection probability of SUion channel j

pf(i, j) False alarm probability of SUion channel j

γ_i,j Average received SNR at SUifrom P Uj

Sj Set of SUs selecting channel j

Fd(j) Cooperative detection probability for channel j

Ff(j) Cooperative false alarm probability for channel j

Fm(j) Cooperative misdetection probability for channel j

usage characteristics in terms of average sojourn idle time and the probability of being occu- pied.