Dimensiones de la Dependencia Emocional

With reference to the analysis on state-of-the-art prediction methods, as pre- sented in Chapter 2, HMM-based spectrum occupancy prediction is considered the most appropriate scheme for real-time channel occupancy state prediction owing to its statistical properties and characteristics. To this end, this chapter describes how the spectrum sensing problem can be formulated as an HMM and how HMMs can be further used to model and predict spectrum occupancy. More specifically, in Section 4.2 the related work on spectrum occupancy mod- elling and estimation using Markov and Hidden Markov processes (HMPs) is reviewed, whereas in Section 4.3 the theoretical background of HMMs is de- scribed. The spectrum occupancy model in the context of CR including the PU activity model, the channel model and the spectrum sensing model are described in Section 4.4. Based on this model, the framework for modelling the perceived spectrum occupancy as an HMM is presented in terms of the model’s structure and parameters. In Section 4.5 the algorithms for HMM state estimation and prediction are presented. These algorithms include the forward-backward algorithm, the Viterbi algorithm, the Baum-Welch Algo- rithm (BWA) and the state prediction algorithm. Note that in the literature, HMPs are more commonly referred to as HMMs, as the term HMP emphasises the process itself rather than its use as a model. Therefore, the term HMM will be used throughout this thesis.

4.2

Related Work

The existence of a Markovian pattern in spectrum occupancy has been exper- imentally validated in [104], through spectrum activity measurements in the 928-948 MHz paging band. The main objective of that work was to estimate the hidden sequence of channel occupancy base on the observation sequence of past spectrum sensing results with the use of the Viterbi algorithm. How- ever, this approach is restricted only to parameter estimation for a narrow spectrum of 20 MHz. Furthermore, its applicability to real-world scenarios is significantly limited as perfect spectrum sensing has been assumed.

An HMM-based dynamic spectrum allocation algorithm for CR has been proposed in [105]. The proposed algorithm is based on a Markov chain with a finite-state observable process, whose parameters are estimated online using the Baum-Welch training algorithm. The CR accesses the spectrum based on the estimated parameters and the PU activity is inferred based on the joint probability of the observation sequence and the hidden state. However, the proposed access method has only been based on the occupancy state estimation rather than spectrum occupancy prediction. This approach has been found to outperform the traditional Carrier Sense Multiple Access (CSMA) method in terms of SNR. However, due to the assumption of error-free spectrum sens- ing the applicability of the proposed algorithm is limited only to high SNR scenarios.

In [106], an HMM-based channel status predictor has been proposed as a means of minimising the negative impact of response delays caused by SDR platforms. The model’s parameters are estimated by a simple statistical pro- cess over a training sequence of spectrum sensing outputs instead of using a

ally, although the proposed model has been clearly described, its prediction performance has not been compared with other prediction methods.

In [107] a discrete-time Markov chain has been used to model the spec- trum sensing problem in the time domain. By using the Viterbi algorithm, a sequence detection algorithm is proposed to decode the PU state given the observation sequence. Unlike the previous studies, spectrum sensing errors have been considered in terms of the probabilities of missed detection and false alarm. More specifically, the proposed algorithm has been based on the assumption that spectrum sensing can be described by an HMM and has ex- ploited the forward-backward algorithm to estimate the actual channel state through a noisy channel to improve the sensing accuracy of ED-based spectrum sensing in terms of Pf a and Pmd. The proposed sequence detection algorithm

improves the overall sensing performance with up to 10 dB SNR gains com- pared to classical ED. However, the Baum-Welch algorithm has not been used to estimate the model’s parameters and the problem of predicting future PU occupancy states has not been considered.

Existing research has shown that HMMs can effectively describe tempo- ral spectrum occupancy. However, the majority of literature is focused on estimating the actual occupancy state for a current time instant rather than predicting it in future time instants. Furthermore, the majority of the afore- mentioned studies applied HMMs for decision-theoretic access protocols at the MAC layer. In this work, HMM is used to model the PHY-layer spectrum sens- ing for CR by integrating the PU occupancy statistics as a means of improving spectrum sensing efficiency. Given that HMM provides a simple yet effective framework for modelling PHY-layer spectrum sensing this approach results in reduced complexity compared to MAC-layer approaches that require more sophisticated statistical tools such as queuing theory models or Partial Ob- servable Markov Decision Processes (POMDP). In addition, the HMM-based PHY-layer approach can be further exploited for cross-layer implementation of efficient spectrum sensing and access mechanisms.

using an HMM as a means of improving the sensing efficiency of autonomous SUs in terms of energy and time consumption. On-line training of the HMM- based prediction model is considered to facilitate real-time parameter estima- tion, and thus, prediction. In addition, spectrum sensing errors are considered in the model formulation to account for realistic CR scenarios.