By Robert C. Qiu, Zhen Hu, Husheng Li, Michael C. Wicks(auth.)
The writer offers a unified remedy of this hugely interdisciplinary subject to aid outline the proposal of cognitive radio. The publication starts off with addressing matters akin to the basic approach idea and simple mathematical instruments similar to spectrum sensing and computing device studying, earlier than relocating directly to extra complicated thoughts and discussions concerning the way forward for cognitive radio. From the basics in spectrum sensing to the purposes of cognitive algorithms to radio communications, and dialogue of radio structures and testbeds to teach the applicability of the speculation to perform, the writer goals to supply an advent to a quick relocating subject for college kids and researchers trying to increase a radical knowing of cognitive radio networks.
- Examines easy mathematical instruments prior to relocating directly to extra complicated options and discussions concerning the way forward for cognitive radio
- Describe the basics of cognitive radio, offering a step-by-step remedy of the subjects to allow innovative learning
- Includes questions, workouts and proposals for additional interpreting on the finish of every chapter
- Companion hosting MATLAB codes, and supplementary fabric together with exercises
Topics lined within the e-book contain: Spectrum Sensing: easy concepts; Cooperative Spectrum Sensing Wideband Spectrum Sensing; Agile Transmission ideas: Orthogonal Frequency department Multiplexing a number of enter a number of Output for Cognitive Radio; Convex Optimization for Cognitive Radio; Cognitive center (I): Algorithms for Reasoning and studying; Cognitive middle (II): video game concept; Cognitive Radio community IEEE 802.22: the 1st Cognitive Radio instant local quarter community average, and Radio structures and Testbeds.
Chapter 1 creation (pages 1–13):
Chapter 2 Spectrum Sensing: simple recommendations (pages 15–49):
Chapter three Classical Detection (pages 51–100):
Chapter four speculation Detection of Noncommutative Random Matrices (pages 101–118):
Chapter five huge Random Matrices (pages 119–234):
Chapter 6 Convex Optimization (pages 235–282):
Chapter 7 laptop studying (pages 283–321):
Chapter eight Agile Transmission strategies (I): a number of enter a number of Output (pages 323–337):
Chapter nine Agile Transmission suggestions (II): Orthogonal Frequency department Multiplexing (pages 339–350):
Chapter 10 video game conception (pages 351–380):
Chapter eleven Cognitive Radio community (pages 381–426):
Chapter 12 Cognitive Radio community as Sensors (pages 427–439):
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Extra resources for Cognitive Radio Communications and Networking: Principles and Practice
It does not involve complicated signal processing and has low complexity. In practice, energy detection is especially suitable for wideband spectrum sensing. The simultaneous sensing of multiple subbands can be realized by scanning the received wideband signal. Two stages of sensing are desirable. The ﬁrst stage uses the simplest energy detection. The second stage uses advanced techniques. We follow  and [39–42] for the development below. Although the process is for band-pass, in general, one can still deal with its low-pass equivalent form and eventually translate it back to its band-pass type .
The methods are nonparametric) . The PSD is deﬁned as the Fourier transform of the autocorrelation function. Since there are simple methods for estimating the correlation function—see [72, Chapter 6], it seems natural to estimate the PSD by using the estimated correlation function. Spectrum Sensing: Basic Techniques 31 Suppose that the total number of data samples is N. 73) n=0 where Rˆ x [l] = Rˆ x∗ [−l] for 0 ≤ l. This estimate is asymptotically unbiased and consistent [72, p. 586]. 74) and its variance decreases as 1/N, for small values of lag.
53). The normalized correlation coefﬁcient, fortunately, satisﬁes this condition. 54) g 2 (t)dt which is 0 ≤ |ρ| ≤ 1 and invariant to rotation and dilation of the functions f (t) and g(t). 55) if λi ∼ = 1 for 1 ≤ i ≤ 2T W + 1 where the third line of the equation is valid for some special cases. 54)) that is independent of the noise power term σn2 . This feature extraction has low computational cost since no eigenfunction is explicitly required. 40). Calculation of low-order eigenfunctions may be more accurate numerically.