4.1 Centralized framework for time-domain sensing
4.2ANALYTICAL INTERFERENCE MODEL
COMPRESSED SENSING BASED SPECTRUM SENSING
5.1Overview of Compressed Sensing:
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 (RIP):
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
CHAPTER 6: SUBCARRIER AND POWER ALLOCATION IN OFDMA
6.2.1. OFDM Transmitter
6.2.2. OFDM Receiver
6.3.1. OFDMA Transmitter
6.3.2. OFDMA Receiver
6.4. Subcarrier Allocation
6.4.1. SYSTEM MODEL AND PROBLEM FORMULATION
6.4.2.SENSIBLE GREEDY APPROACH
22.214.171.124. Resource Allocation Algorithm
126.96.36.199.2.4.2. Subcarrier Assignment Algorithms
Efficient usage of the limited natural resources is one of society’s greatest challenges. Just like petroleum and coal, the natural frequency spectrum is limited and needed to be used more efficiently in order not to use up all. According to survey  of Federal Communications Commission (FCC) in 2002, it has been found that spectrum access is more significant problem than physical scarcity of spectrum. Static allocation of the frequency spectrum does not meet the needs of current wireless technology. So, dynamic spectrum usage is required for wireless networks which will increase the availability of frequency spectrum. . For this purpose, cognitive radio is proposed as a new technology that provides optimum satisfaction of user requirements like effective spectrum usage.
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 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. There is generally five signal detection methods that are proposed are
1. Energy Detection, 2.Compress Sensing based Detection, 3.Covariance based Detection, 4. Matched Filtering based Detection, 5.Cyclostationary Feature Detection.
A brief overview on cognitive radio is discussed in chapter 2. Chapter 3 presents an introduction about spectrum sensing techniques in cognitive radio. In this report, energy detection, Compress Sensing based Detection for cognitive radios are examined in detail. and comparative performance results are obtained in wireless communication channels. Spectral overlap based energy detection is discussed in chapter 4 and compressed sensing based detection is presented in chapter 5.
The concept of cognitive radio was first proposed by Joseph Mitola III in a seminar at KTH (the Royal Institute of Technology in Stockholm) in 1998. It was a novel approach in wireless communications that Mitola later described as :
“Cognitive Radio is a radio for wireless communications in which either a network or a wireless node changes its transmission or reception parameters based on the interaction with the environment to communicate effectively without interfering with the licensed users.”
The following steps highlights genesis of the cognitive radio to its evolution till the present
› In 1998, Joseph Mitola III coined the term ‘Cognitive Radio’ for the first time in his doctoral thesis .
› In 2002, the Defense Advanced Research Projects Agency (DARPA) funded the NeXt Generation (DARPA-XG) program whose purpose was to define a policy based spectrum management framework so that the radios can make use of the spectrum holes existing in time and space.
› This drew the attention of the Federal Communications Commission (FCC) which then confirmed the underutilization of the bands based on the research conducted by it. Later the commission issued a Notice for Proposed Rule Making (NPRM) whose main aim was to explore the cognitive radio technology to improve efficiency of spectrum utilization.
› In 2004, the Institute of Electrical and Electronic Engineers (IEEE) formed the IEEE 802.22 working group for defining the Wireless Regional Area Network (WRAN), Physical (PHY) and Medium Access Control (MAC) layer specifications.
› By end 2005, IEEE launched the Project 1900 standard task group for next generation radio and spectrum management. It was related to giving standard terms and formal
› definitions for spectrum management, interference and co-existence analysis and policy architecture, dynamic spectrum access radio systems.
› In 2006, IEEE organized the first conference on cognitive radio CROWNCOM so as to bring together new ideas regarding the cognitive radio from a diverse set of researchers around the world.
› It was followed by FCC’s TV band unlicensed service project launch with cognitive radio technology.
› By 2008 end, the FCC established rules to allow cognitive devices to operate in TV White Spaces on a secondary basis.
› In 2010, FCC released a Memorandum Opinion and Order that determined the final rules for the use of white space by unlicensed wireless users.
› In July, 2011, the IEEE published IEEE 802.22 (WRAN) as an official standard.
› Currently, IEEE is working on the standard for recommended practice for installation and deployment of 802.22 systems.
The policy of spectrum licensing and its utilization lead to static and inefficient usage of spectrum. The requirement of different new technologies and market demand leads to spectrum scarcity and unbalanced utilization of frequencies. It has become essential to introduce new licensing policies and co-ordination infrastructure to enable dynamic and open way of utilizing the available spectrum. The basic idea of DSA is to allow secondary users or unlicensed users to access licensed spectrum bands as far as they do not cause any harmful interference with the primary users or licensed users of the bands. What this means is that Secondary Users could be allowed to use whatever unused spectrum or white space they find free. The opening up of spectrum in this manner results into availability of more spectrum and hence entry for new technologies. But there are few problems associated with DSA. They are:
› To detect the white spaces, Secondary Users need to sense the environment (channel) efficiently,
› Secondary Users should access channel in such a way that there will be no interference to Primary User transmission.
This new framework of spectrum access (i.e. DSA) is implemented using Cognitive Radios. Cognitive Radio (CR) is a system/model for wireless communication. It is built on software
defined radio which is an emerging technology providing a platform for flexible radio systems, multiservice, multi-standard, multiband, reconfigurable and reprogrammable by software .It has an intelligent layer that performs the learning of channel parameters in order to achieve optimal performance under dynamic and unknown situations. Most fundamental roles of a Cognitive Radio is to discover spectrum opportunities and detecting existence/return of Primary Users in the channel. Cognitive radio in very simple terms is - very smart radio that can:
› Observe the environment,
› Learn from environment, and
› Adjust to changing environment conditions.
Cognitive Radio offers optimal diversity (in frequency, power, modulation, coding, space, time, polarization and so on) which leads to:
□ Spectrum Efficiency- This will allow future demand for spectrum to be met and is the basic purpose of implementing Cognitive Radio.
□ Higher bandwidth services- Demand of Multimedia Broadcast Multicast Service (MBMS) is constantly on the rise which will be facilitated by the implementation of CR.
□ Graceful Degradation of Services - When conditions are not ideal, a graceful degradation of service is provided Cognitive Radio is very important in providing services to the users especially when they are mobile and the base stations in contact are constantly changing.
□ Improved Quality of Service (QoS) (latency, data rate, cost etc) – Quality of Service i.e. Suitability, availability and reliability of wireless services will improve from the user’s perspective.
□ Commercial Exploitation- CR promotes spectrum liberalization (makes it much easier to trade spectrum between users). Indeed, a business case may exist for becoming a spectrum broker, whereby a third party manages the trade between supplier and demander and receives a commission.
□ Benefits to the Service Provider- More customers in the market and/or increased information transfer rates to existing customers. More players can come in the market.
□ Future-proofed product- A CR is able to change to services, protocols, modulation, spectrum etc. without the need for a user and/or manufacturer to upgrade to a new device.
□ Common hardware platform- Manufacturers will gain from economies of scale because they no longer need to build numerous hardware variants, instead using a single common platform to run a wide range of software. This also assists in rapid service deployment.
□ Emergency service communications- Joint operations during major incidents would benefit greatly as police, fire, ambulance and coastguard could be linked together in one radio with each radio user sensing the spectrum being used by the other parties and reconfiguring itself.
□ Benefits to the Licensee- CR can pave the way for spectrum trading, where licensees would be allowed to lease a portion of their spectrum rights to third parties on a temporal, spatial or other appropriate basis to recoup some of the expense of its 24hour-a-day license and even make money.
If the frequency range from 40 MHz to 1000 MHz is carefully observed in figure 1 then this range can be classified into 3 sub-categories (i) Empty bands most of the time, (ii) Partially occupied bands, and (iii) Congested Bands. The main category of interest for the cognitive radio users is the first category in which the hardly used or empty bands are classified. In layman terms cognitive radio is nothing but a methodology wherein the first category of the frequency range is brought to the use for unlicensed users in such a way that interference to the licensed users is minimized.
Fig.2.1. Spectrum Utilization
For legal reasons the figure was deleted (note of the editor).
In order for the unlicensed or secondary users to use the licensed spectrum there are many things that should be taken care of in advance like
□ Scanning of the frequency spectrum for the discovery of different empty bands.
□ Selecting the best available band. The selection can be done on the basis of the secondary user’s application frequency requirement.
□ Before transmitting on the selected band the power level should be maintained such that it provides minimal interference to other users. Also the power level can be so adjusted as to have maximum number of secondary users in the frequency band of interest.
□ Spectrum sharing should be allowed so that other secondary users can also access the empty bands.
□ Even after the beginning of the transmission, the bands must be continuously checked for any primary user entering to transmit in this range. If so, then the secondary users should vacate the bands as quickly as possible and go on to some other empty frequency spectrum.
A basic cognitive cycle comprises of following three basic tasks:
› Spectrum Sensing
› Spectrum Analysis
› Spectrum Decision Making
Fig.2.2. Cycle describing the various tasks of the Cognitive Radio
For legal reasons the figure was deleted (note of the editor).
188.8.131.52 Spectrum Sensing:
The first and one of the most important requirement of a Cognitive Radio network is the spectrum sensing. Because while performing the Cognitive radio communication the first step should be the sensing of spectrum holes in the environment. Spectrum sensing is the ability to measure, sense and be aware of the parameters related to the radio channel characteristics. It is done across Frequency, Time, Geographical Space, Code and Phase.
184.108.40.206 Spectrum Analysis:
Spectrum analysis includes the channel state information estimation and the determination of the channel capacity. It is essentially determining which of the available unused bands are best for transmission in order to meet the quality of service (QoS) requirements of the particular cognitive radio. This requires analysis of the spectrum along with deciding which band is the best to transmit the signal. Spectrum Analysis includes estimating various channel parameters like the interference, path-loss, error rates and channel capacity. All these parameters determine the quality of a transmission from the cognitive users. After these parameters are estimated, the cognitive radio has to decide whether to transmit in a particular band or not. While spectrum sensing is a PHY layer functionality, spectrum analysis and decision are the functions of the higher layers (link- layer).
220.127.116.11 Spectrum Decision Making:
This is the final stage of cognitive radio cycle. It takes information about the spectrum holes from spectrum sensing device and also finds the channel capacity of those spectrum holes from the spectrum analysis part of the cycle. Then it takes decision whether the secondary user can transmit in that channel without interfering the primary user. It constantly adapting to the mobile changing environment and adjust the transmission power or even alteration of transmission parameters (such as modulation formats (e.g. low to high order QAM), variable symbol rates, different channel coding schemes) and characteristics by the Cognitive radio devices. And if the spectrum sensing device find that the primary user start transmission in that ‘spectrum hole’ then the decision making device make the secondary user to instantaneously stop transmission in that band and find another spectrum hole for transmission.
In this report, we focus attention on the particular task on which the very essence of cognitive radio rests: spectrum sensing, defined as the task of finding spectrum holes by sensing the radio spectrum in the local neighborhood of the cognitive radio receiver in an unsupervised manner. The term spectrum hole stands for those sub bands of the radio spectrum that are underutilized (in part or in full) at a particular instant of time and specific geographic location. Spectrum sensing involves the following subtasks:
1) Detection of spectrum holes;
2) Spectral resolution of each spectrum hole;
3) Estimation of the spatial directions of incoming interference;
4) Signal classification.
The subtask of spectrum-hole detection is, at its simplest form when the focus is on a white space (i.e., a subband is only occupied by white noise)
In terms of occupancy, subbands of the radio spectrum may be categorized as follows.
1) White spaces, which are free of RF interferers, except for noise due to natural and/or artificial sources.
2) Gray spaces, which are partially occupied by interferers as well as noise.
3) Black spaces, the contents of which are completely full due to the combined presence of communication and (possibly) interfering signals plus noise.
Several sources of uncertainty such as channel uncertainty, noise uncertainty, sensing interference limit etc. need to be addressed while solving the issue of spectrum sensing in cognitive radio networks. These issues are discussed in details as follows:
In wireless communication networks, uncertainties in received signal strength arise due to channel fading or shadowing. Sometimes, the primary signal may be experiencing a deep fade or being heavily shadowed by obstacles and no interference produces to the secondary. And as the secondary is not aware of the presence of the primary, it starts transmission in that frequency band of the primary. So it hampers the transmission of the primary. Therefore, cognitive radios have to be more sensitive to distinguish a faded or shadowed primary signal from a white space. Any uncertainty in the received power of the primary signal translates into a higher detection sensitivity requirement. Under severe fading, a single cognitive radio relying on local sensing may be unable
to achieve this increased sensitivity since the required sensing time may exceed the sensing period. This issue may be handled by having a group of cognitive radios (cooperative Sensing), which share their local measurements and collectively decide on the occupancy state of a licensed band.
In order to calculate the required detection sensitivity, the noise power has to be known, which is not available in practice, and needs to be estimated by the receiver. However the noise power (N) estimation is limited by calibration errors as well as changes in thermal noise caused by temperature variations. Since a cognitive radio may not satisfy the sensitivity requirement due to an underestimate of N, detection sensitivity should be calculated with the worst case noise assumption, thereby necessitating a more sensitive detector.
In future, due to the widespread deployment of secondary systems, there will be increased possibility of multiple cognitive radio networks operating over the same licensed band. As a result, spectrum sensing will be affected by uncertainty in aggregate interference (e.g. due to the unknown number of secondary systems and their locations). Though, a primary system is out of interference range of a secondary system, the aggregate interference may lead to wrong detection. This uncertainty creates a need for more sensitive detector, as a secondary system may harmfully interfere with primary system located beyond its interference range, and hence it should be able to detect them.
Primary goal of spectrum sensing is to detect the spectrum status i.e. whether it is idle or occupied so that it can be accessed by an unlicensed user. The challenge lies in the interference measurement at the licensed receiver caused by transmissions from unlicensed users. First, an unlicensed user may not know exactly the location of the licensed receiver which is required to compute interference caused due to its transmission. Second, if a licensed receiver is a passive device, the transmitter may not be aware of the receiver. So these factors need attention while calculating the sensing interference limit.
There are different spectrum sensing technique present in the literature. In this report, energy based detection and Compressed Sensing based spectrum sensing techniques for cognitive radio are examined in detail and comparative performance results are obtained in wireless communication channels.
Here the energy based spectrum sensing we have considered is based on overlapping of power of the spectral components. We are considering here the wireless mesh network (WMN) with dynamic spectrum access. A typical wireless mesh network consists of mesh routers (MRs) forming the backbone of the network, interconnected in an ad-hoc fashion. Each MR can be considered as an access point serving a number of users or mesh clients (MCs). The MCs could be mobile users, stationary workstations or laptops that exchange data over the Internet. They direct their traffic to their respective MRs, which then forwards it over the backbone, in a multi-hop manner, to reach the gateway that links to the Internet as shown in the figure below.
Fig 4.1. Mesh architecture with Mesh routers and Mesh Clients under them
- Quote paper
- Joydeep Dutta (Author), 2022, Spectrum sensing techniques in cognitive radio, Munich, GRIN Verlag, https://www.grin.com/document/1169156