No / Eigenvalue based spectrum sensing schemes such as Maximum Minimum Eigenvalue (MME), Maximum Energy Detection (MED) and Energy with Minimum Eigenvalue (EME) have higher spectrum sensing performance without requiring any prior knowledge of Primary User (PU) signal but the decision hypothesis used in these eigenvalue based sensing schemes depends on the calculation of maximum eigenvalue from covariance matrix of measured signal. Calculation of the covariance matrix followed by eigenspace analysis of the covariance matrix is a resource intensive operation and takes overhead time during critical process of spectrum sensing. In this paper we propose a new blind spectrum sensing scheme based on the approximation of the maximum eigenvalue using state of the art results from Random Matrix Theory (RMT). The proposed sensing scheme has been evaluated through extensive simulations on wireless microphone signals and the proposed scheme shows higher probability of detection (Pd) performance. The proposed spectrum sensing also shows higher detection performance as compared to energy detection scheme and RMT based sensing schemes such as MME and EME.

One of the major challenges of Cognitive Radio (CRNs) is the spectrum handoff issue. Spectrum handoff happens when a Primary Users (PUs) appears in a spectrum band that is occupied by a Secondary User (SU). In such a case, SU should empty this spectrum band and perform a handoff procedure and search for an available free one. This process will be continued until the SU completes its data transmission. To avoid multiple spectrum handoffs, the spectrum handoff procedure should be performed in the unlicensed channels rather than the licensed channels. Thus, the number of handoffs can be reduced as no more spectrum handoffs will occur since all users have priority in this type of spectrum channel. This technique will help secondary users' QoS from degradation. This paper proposes a prioritized spectrum handoff decision scheme in a mixture spectrum environment of unlicensed and licensed channels, in order to reduce the handoff delay. The licensed channels in the proposed scheme have been modelled using a pre-emptive resume priority (PRP) M/M/C queue. In contrast, the unlicensed channels have been modelled using an M/M/C retrial priority queue. In order to examine the performance of the implemented model, the handoff and new SUs are considered with equal and different priorities. Experimental results show that the prioritized handoff scheme outperforms the other scheme in terms of average handoff delay under various traffic arrival rates as well as the number of licensed and unlicensed channels used.

We develop efficient coordination techniques that support inelastic traffic in large-scale distributed dynamic spectrum access DSA networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). Basically, learning algorithms allow DSA users to learn by interacting with the environment, and use their acquired knowledge to select the proper actions that maximize their own objectives, thereby "hopefully" maximizing their long-term cumulative received reward/throughput. However, when DSA users' objectives are not carefully coordinated, learning algorithms can lead to poor overall system performance, resulting in lesser per-user average achieved rewards. In this thesis, we derive efficient objective functions that DSA users an aim to maximize, and that by doing so, users' collective behavior also leads to good overall system performance, thus maximizing each user's long-term cumulative received rewards. We show that the proposed techniques are: (i) efficient by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributive by being implementable in a decentralized manner. / Graduation date: 2013

The objective of the research is to develop analog spectrum processing techniques for cognitive radio (CR) applications in CMOS technology. CR systems aim to use the unoccupied spectrum allocations without any license when the primary users are not present. Therefore, the successful deployment of CR systems relies on their ability to accurately sense the spectrum usage status over a wide frequency range serving various wireless communication standards. Meanwhile, to maximize the utilization of the available spectrum segments, the bandwidth of the signal has to be highly flexible, so that even a small fraction of spectrum resources can be fully utilized by CR users. One of the key enabling technologies of variable bandwidth communication is a tunable baseband filter. In this research, a reconfigurable CR testbed system is presented as groundwork for the researches related with CR systems. With the feasibility study on the multi-resolution spectrum sensing (MRSS) functionality, a method for determining sensing threshold for MRSS functionality is presented, and a fully integrated MRSS receiver in CMOS technology is demonstrated. On the other hand, a reconfigurable CMOS analog baseband filter which can change its bandwidth, type and order with high resolution for CR applications is presented. In sum, an analog spectrum sensing method as well as a highly flexible analog baseband filter architecture is established and implemented in CMOS technology. Both designs are targeting the utilization of the analog signal processing capability with the aid of the digital circuits.

Cognitive radio (CR) technology has recently been introduced to opportunistically exploit the spectrum. We present a robust and cost-effective design to ensure the improvement of spectrum efficiency with CR. We first propose probability-based spectrum sensing by utilizing the statistical characteristics of licensed channel occupancy, which achieves nearly optimal performance with relatively low complexity. Based on the statistical model, we then propose periodic spectrum sensing scheduling to determine the optimal inter-sensing duration and vary the transmit power at each data sample to enhance throughput and reduce interference. We further develop a probability-based scheme for combination of local sensing information collected from cooperative CR users, which enables combination of both synchronous and asynchronous sensing information. To satisfy the stringent bandwidth constraint for reporting, we also propose to simultaneously send local sensing data to a combining node through the same narrowband channel. With proper preprocessing at individual users, such a design maintains reasonable detection performance while the bandwidth required for reporting does not change with the number of cooperative users. To better utilize the spectrum and avoid possible interference, we propose spectrum shaping schemes based on spectral precoding, which enable efficient spectrum sharing between CR and licensed users and exhibit the advantages of both simplicity and flexibility. We also propose a novel resource allocation approach based on the probabilities of licensed channel availability obtained from spectrum sensing. Different from conventional approaches, the probabilistic approach exploits the flexibility of CR to ensure efficient spectrum usage and protect licensed users from unacceptable interference.

Thesis (M.Sc.) -- University of Limpopo, 2021 / Cognitive Radio (CR) is a promising technology designed to solve many issues, especially spectrum underutilisation and scarcity. The requirement for spectrum effectiveness was essential, and consequently, the possibility of CR arrived along and introduced the unlicensed Secondary Users (SU). SU can operate on the unlicensed and licensed spectrum bands on a condition that they avoid interference with the licensed Primary Users (PU). This approach is called the Dynamic Spectrum Allocation (DSA) and has solved the underutilisation of spectrum using the spectrum holes. The United States of America’s telecommunication regulator Federal Communication Commission (FCC) introduces spectrum bands by unlicensed users looking at the rapid growth of wireless applications and devices; therefore, the Fixed Spectrum Allocation (FSA) become inadequate because of the spectrum crowded issues. Accomplishing this design requirement while meeting the Quality of Service (QoS) of SU is a challenge; thus, the cross-layer design (CLD) was introduced to enhance the efficiency and effectiveness of Cognitive Network (CN). CLD arrangements in Cognitive Radio Network (CRN) are empowering; however, there are yet numerous issues and difficulties that must be addressed, such as resource allocation and others that may negatively impact network performance. Routing in CRN also necessitates the cross-layering approach. Therefore, in this work, designing a protocol that will solve routing issues and channel selection will also maximise spectrum opportunistically. In this study, we propose the Optimised Cognitive Cross-layer Multipath Probabilistic Routing (OCCMPR) protocol, which is the optimised version of Cognitive Cross-layer Multipath Probabilistic Routing (CCMPR). We used MATLAB simulation installed in the Windows 10 operating system (OS) tool to generate comparison results. We compared the OCCMPR protocol with the existing protocols, the Cognitive Ad-hoc On-demand Distance Vector (CAODV) and the CCMPR protocols. / Council for Scientific and Industrial Research (CSIR)

Thesis ( M.Sc. (Computer Science)) -- University of Limpopo, 2019 / Cognitive radio network, which enables dynamic spectrum access, addresses the shortage of radio spectrum caused by ever-increasing wireless technology. This allows efficient utilisation of underutilised licenced spectrum by allowing cognitive radios to opportunistically make use of available licenced spectrum. Cognitive radios (CR), also known as secondary users, must constantly sense the spectrum band to avoid interfering with the transmission of the licenced users, known as primary users. Cognitive radios must cooperate in sensing the spectrum environment to avoid environmental issues that can affect the spectrum sensing. However, cooperative spectrum sensing is vulnerable to Byzantine attacks where selfish CR falsify the spectrum reports. Hence, there is a need to design and implement a defence mechanism that will thwart the Byzantine attacks and guarantee correct available spectrum access decisions. The use of extreme studentized deviate (ESD) test together with consensus algorithms are proposed in this study to combat the results of the availability of Byzantine attack in a cognitive radio network. The ESD test was used to detect and isolate falsified reports from selfish cognitive radios during the information sharing phase. The consensus algorithm was used to combine sensing reports at each time k to arrive at a consensus value which will be used to decide the spectrum availability. The proposed scheme, known extreme studentized cooperative consensus spectrum sensing (ESCCSS), was implemented in an ad hoc cognitive radio networks environment where the use of a data fusion centre (DFC) is not required. Cognitive radios make their own data fusion and make the final decision about the availability of the spectrum on their sensed reports and reports from their neighbouring nodes without any assistance from the fusion centre. MATLAB was used to implement and simulate the proposed scheme. We compared our scheme with Attack-Proof Cooperative Spectrum Sensing to check its effectiveness in combating the effect of byzantine attack.

To explore spectral opportunities in wideband regime for cognitive radio we need a wideband spectrum sensing receiver. Current wideband receiver architectures need wideband analog to digital converter (ADC) to sample wideband signal. As current state-of-art ADC has limitation in terms of power and sampling rate, we need to explore some alternative solutions. Compressive sampling (CS) data acquisition method is one of the solutions. Cognitive Radio signal, which is sparse in frequency domain can be sampled at Sub-Nyquist rate using low rate ADC. To relax the receiver complexity in terms of performance requirement we can use Modulated Wideband Converter (MWC) architecture, a Sub-Nyquist sampling method. In this thesis circuit design of this architecture covers signal within a frequency range of 500 MHz to 2.1 GHz, with a channel bandwidth of 1600 MHz. By using 8 parallel lines with channel trading factor of 11, effective sampling rate of 550 MHz is achieved for successful support recovery of multi-band input signal of size N=12.

Cognitive Radio (CR) has received tremendous research attention over the past decade, both in the academia and industry, as it is envisioned as a promising solution to the problem of spectrum scarcity. ACR is a device that senses the spectrum for occupancy by licensed users(also called as primary users), and transmits its data only when the spectrum is sensed to be available. For the efficient utilization of the spectrum while also guaranteeing adequate protection to the licensed user from harmful interference, the CR should be able to sense the spectrum for primary occupancy quickly as well as accurately. This makes Spectrum Sensing(SS) one of the where the goal is to test whether the primary user is inactive(the null or noise-only hypothesis), or not (the alternate or signal-present hypothesis). Computational simplicity, robustness to uncertainties in the knowledge of various noise, signal, and fading parameters, and ability to handle interference or other source of non-Gaussian noise are some of the desirable features of a SS unit in a CR. In many practical applications, CR devices can exploit known structure in the primary signal. IntheIEEE802.22CR standard, the primary signal is a wideband signal, but with a strong narrowband pilot component. In other applications, such as military communications, and blue tooth, the primary signal uses a Frequency Hopping (FH)transmission. These applications can significantly benefit from detection schemes that are tailored for detecting the corresponding primary signals. This thesis develops novel detection schemes and rigorous performance analysis of these primary signals in the presence of fading. For example, in the case of wideband primary signals with a strong narrowband pilot, this thesis answers the further question of whether to use the entire wideband for signal detection, or whether to filter out the pilot signal and use narrowband signal detection. The question is interesting because the fading characteristics of wideband and narrowband signals are fundamentally different. Due to this, it is not obvious which detection scheme will perform better in practical fading environments. At another end of the gamut of SS algorithms, when the CR has no knowledge of the structure or statistics of the primary signal, and when the noise variance is known, Energy Detection (ED) is known to be optimal for SS. However, the performance of the ED is not robust to uncertainties in the noise statistics or under different possible primary signal models. In this case, a natural way to pose the SS problem is as a Goodness-of-Fit Test (GoFT), where the idea is to either accept or reject the noise-only hypothesis. This thesis designs and studies the performance of GoFTs when the noise statistics can even be non-Gaussian, and with heavy tails. Also, the techniques are extended to the cooperative SS scenario where multiple CR nodes record observations using multiple antennas and perform decentralized detection. In this thesis, we study all the issues listed above by considering both single and multiple CR nodes, and evaluating their performance in terms of(a)probability of detection error,(b) sensing-throughput trade off, and(c)probability of rejecting the null-hypothesis. We propose various SS strategies, compare their performance against existing techniques, and discuss their relative advantages and performance tradeoffs. The main contributions of this thesis are as follows: The question of whether to use pilot-based narrowband sensing or wideband sensing is answered using a novel, analytically tractable metric proposed in this thesis called the error exponent with a confidence level. Under a Bayesian framework, obtaining closed form expressions for the optimal detection threshold is difficult. Near-optimal detection thresholds are obtained for most of the commonly encountered fading models. Foran FH primary, using the Fast Fourier Transform (FFT) Averaging Ratio(FAR) algorithm, the sensing-through put trade off are derived in closed form. A GoFT technique based on the statistics of the number of zero-crossings in the observations is proposed, which is robust to uncertainties in the noise statistics, and outperforms existing GoFT-based SS techniques. A multi-dimensional GoFT based on stochastic distances is studied, which pro¬vides better performance compared to some of the existing techniques. A special case, i.e., a test based on the Kullback-Leibler distance is shown to be robust to some uncertainties in the noise process. All of the theoretical results are validated using Monte Carlo simulations. In the case of FH-SS, an implementation of SS using the FAR algorithm on a commercially off-the ¬shelf platform is presented, and the performance recorded using the hardware is shown to corroborate well with the theoretical and simulation-based results. The results in this thesis thus provide a bouquet of SS algorithms that could be useful under different CRSS scenarios.

Relay-assisted cooperative communication exploits spatial diversity to combat wireless fading, and is an appealing technology for next generation wireless systems. Several relay cooperation protocols have been proposed in the literature. In amplify-and-forward (AF)relaying, which is the focus of this thesis, the relay amplifies the signal it receives from the source and forwards it to the destination. AF has been extensively studied in the literature on account of its simplicity since the relay does not need to decode the received signal. We propose a novel optimal relaying policy for two-hop AF cooperative relay systems. In this, an average power-constrained relay adapts its gain and transmit power to minimize the fading-averaged symbol error probability (SEP) at the destination. Next, we consider a generalization of the above policy in which the relay operates as an underlay cognitive radio (CR). This mode of communication is relevant because it promises to address the spectrum shortage constraint. Here, the relay adapts its gain as a function of its local channel gain to the source and destination and also the primary such that the average interference it causes to the primary receiver is also constrained. For both the above policies, we also present near-optimal, simpler relay gain adaptation policies that are easy to implement and that provide insights about the optimal policies. The SEPs and diversity order of the policies are analyzed to quantify their performance. These policies generalize the conventional fixed-power and fixed-gain AF relaying policies considered in cooperative and CR literature, and outperform them by 2.0-7.7 dB. This translates into significant energy savings at the source and relay, and motivates their use in next generation wireless systems.