Cognitive Radio offers non-interfering use of spectrum which requires three main tasks: Spectrum Sensing, Spectrum Analysis and Spectrum Allocation. The aim of this study is to focus on spectrum sensing in cognitive radio which is a recently introduced technology in order to increase the spectrum efficiency.
Increasing efficiency of the spectrum usage is an urgent need as the number of wireless users is increasing rapidly. Cognitive radio arises to be a good solution to spectral crowding problem by introducing the opportunistic usage of frequency bands that are not heavily occupied by licensed users (Primary user) since they cannot be utilized by users other (Secondary user) than the license owners at the moment. Cognitive radio can sense the spectrum and detect the idle frequency bands, thus secondary users can be allocated in those bands when primary users do not use those in order to avoid any interference to primary user by secondary users. Several Spectrum Sensing Methods proposed in the literature are theoretically analyzed and interpreted in the sense of advantages and drawbacks.
Table of Contents
1. Introduction
1.1 Introduction and Background
1.2 Outline of the Book
2. Introduction to cognitive radio
2.1 Brief History
2.2 Dynamic Spectrum Access (DSA)
2.3 Cognitive Radio (CR)
2.3.1 Cognitive Radio’s Key Benefits
2.3.2 Cognitive Radio Features
3. Spectrum sensing in cognitive radio
3.1 Issues and Challenges in Spectrum Sensing
3.1.1Channel Uncertainty
3.1.2Noise Uncertainty
3.1.3Aggregate Interference Uncertainty
3.1.4Sensing Interference Limit
3.2 Different Spectrum Sensing Techniques
4. Spectral overlap based energy sensing
4.1 Centralized framework for time-domain sensing
4.2 Analytical interference model
5. Compressed sensing based spectrum sensing
5.1 Overview of Compressed Sensing
5.1.1 Sparsity
5.1.2 Sensing Matrix
5.1.3 Sparse Signal Recovery
5.1.4 Uniqueness Conditions for Minimization Problems
5.1.5 Mutual Coherence
5.1.6 Restricted Isometry Property
5.1.7 Measurement bounds
5.1.8 Recovery Algorithms
5.2 Compressed Wideband Sensing in Cooperative Cognitive Radio Networks
5.2.1 Compressed Spectrum Sensing at Individual CRs
5.2.2 Results
6. Subcarrier and power allocation in OFDMA
6.1 Introduction
6.2 OFDM
6.2.1 OFDM Transmitter
6.2.2 OFDM Receiver
6.3 OFDMA
6.2.1 OFDMA Transmitter
6.2.2 OFDMA Receiver
6.4 Subcarrier Allocation
6.4.1 system model and problem formulation
6.2.2 sensible greedy approach
Objectives & Core Topics
This work aims to enhance spectrum efficiency in wireless communication by exploring advanced spectrum sensing techniques and resource allocation strategies within cognitive radio networks. The research addresses the challenges of physical spectrum scarcity through dynamic spectrum access, utilizing energy detection, compressed sensing, and efficient subcarrier allocation algorithms in OFDMA systems.
- Spectrum sensing methodologies for identifying underutilized frequency bands.
- Application of Compressed Sensing (CS) for reduced-rate wideband spectrum sensing.
- Development of analytical interference models for wireless mesh networks.
- Optimization of subcarrier and power allocation in OFDMA systems to satisfy user requirements.
- Comparative analysis of low-complexity algorithms for resource management.
Excerpt from the Book
4.1 Centralized framework for time-domain sensing
The mesh client tunes to a single pre-decided primary channel and senses the received power for the entire duration available. This is essentially a superposition of the received power due to several transmitters. These transmitters may be on different channels, and only a small proportion of their transmit power leaks into the channel in which the measurement is done. Thus, this leakage power is a function of the separation between the channels used for transmission and measurement. If the channel for measurement is fixed, and the leakage power for each transmitter is isolated from the aggregate received power, then the individual transmitter channels can be estimated.
We assume a simple free space path loss model and that all primary stations use the same transmit power. From Figure 4, the average normalized power received on channel fx at node x due to primary station 1 on channel f1 only, when separated by a distance D1,x is given by, P1,x=I1,x α1D1,x- β , Here, α1 = GtGrc2 /(4πf1) 2, where Gt and Gr are the transmit and receiving antenna gains, and c is the speed of light. I1,x is the spectral overlap factor between the channels of transmitter and receiver, and is either made available as standard data or can be calculated through power mask requirements [4]. It is the proportion of the original transmit power that gets leaked into the channel used for measurement.
Summary of Chapters
Introduction: Provides the background for efficient spectrum usage and outlines the book's focus on spectrum sensing in cognitive radio.
Introduction to cognitive radio: Details the history, the evolution of Dynamic Spectrum Access (DSA), and the key functional components of Cognitive Radio (CR).
Spectrum sensing in cognitive radio: Discusses the primary challenges like channel and noise uncertainty and introduces various sensing techniques.
Spectral overlap based energy sensing: Explores energy detection methods in wireless mesh networks and proposes a centralized framework for time-domain sensing.
Compressed sensing based spectrum sensing: Examines the theoretical foundations of compressed sensing and its application for wideband spectrum sensing in cognitive networks.
Subcarrier and power allocation in OFDMA: Investigates optimal resource management in OFDMA systems, proposing greedy algorithms for subcarrier and power allocation to meet user demands.
Keywords
Cognitive Radio, Spectrum Sensing, Compressed Sensing, Dynamic Spectrum Access, Energy Detection, OFDMA, Subcarrier Allocation, Wireless Mesh Networks, Signal Recovery, Sparsity, Interference Management, Spectrum Efficiency, Resource Allocation, Orthogonal Frequency Division Multiplexing, Wideband Sensing
Frequently Asked Questions
What is the primary focus of this work?
The book focuses on improving spectrum utilization in wireless networks through spectrum sensing and efficient resource allocation, specifically within the framework of cognitive radio.
What are the central themes discussed?
The core themes include spectrum sensing techniques, compressed sensing, interference modeling, and optimization strategies for OFDMA-based communication systems.
What is the primary research goal?
The main goal is to identify idle frequency bands using cognitive radio technology and to optimize power and subcarrier allocation for secondary users without interfering with primary licensed users.
Which scientific methods are employed?
The research uses analytical interference modeling, linear programming, compressed sensing algorithms (such as Basis Pursuit), and greedy optimization algorithms for resource allocation.
What is covered in the main section?
The main section covers energy-based spectrum sensing, compressed sensing for sub-Nyquist sampling, and the implementation of greedy algorithms for subcarrier assignment in OFDMA.
Which keywords best characterize this research?
Key terms include Cognitive Radio, Compressed Sensing, OFDMA, Spectrum Sensing, and Resource Allocation.
How does the proposed ACG algorithm function?
The ACG algorithm is a suboptimal method designed for subcarrier assignment that achieves high performance while reducing computational complexity compared to iterative algorithms.
What is the role of sparsity in this research?
Sparsity is the fundamental principle behind compressed sensing, allowing the recovery of wideband signals from fewer samples than required by the Nyquist rate.
- Citar trabajo
- Joydeep Dutta (Autor), 2022, Spectrum sensing techniques in cognitive radio, Múnich, GRIN Verlag, https://www.grin.com/document/1169156
