Combined Soft Hard Cooperative Spectrum Sensing in Cognitive Radio Networks

Providing some techniques to enhance the performance of spectrum sensing in cognitive radio systems while accounting for the cost and bandwidth limitations in practical scenarios is the main objective of this thesis. We focus on an essential element of cooperative spectrum sensing (CSS) which is the data fusion that combines the sensing results to make the final decision. Exploiting the advantage of the superior performance of the soft schemes and the low bandwidth of the hard schemes by incorporating them in cluster based CSS networks is achieved in two different ways. First, a soft-hard combination is employed to propose a hierarchical cluster based spectrum sensing algorithm. The proposed algorithm maximizes the detection performances while satisfying the probability of false alarm constraint. Simulation results of the proposed algorithm are presented and compared with existing algorithms over the Nakagami fading channel. Moreover, the results show that the proposed algorithm outperforms the existing algorithms. In the second part, a low complexity soft-hard combination scheme is suggested by utilizing both one-bit and two-bit schemes to balance between the required bandwidth and the detection performance by taking into account that different clusters undergo different conditions. The scheme allocates a reliability factor proportional to the detection rate to each cluster to combine the results at the Fusion center (FC) by extracting the results of the reliable clusters. Numerical results obtained have shown that a superior detection performance and a minimum overhead can be achieved simultaneously by combining one bit and two schemes at the intra-cluster level while assigning a reliability factor at the inter-cluster level

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Cooperative Spectrum Sensing based on 1-bit Quantization in Cognitive Radio Networks

The wireless frequency spectrum is a very valuable resource in the field of communications. Over the years, different bands of the spectrum were licensed to various communications systems and standards. As a result, most of the easily accessible parts of it ended up being theoretically occupied. This made it somewhat difficult to accommodate new wireless technologies, especially with the rise of communications concepts such as the Machine to Machine (M2M) communications and the Internet of Things (IoT). It was necessary to find ways to make better use of wireless spectrum. Cognitive Radio is one concept that came into the light to tackle the problem of spectrum utilization. Various technical reports stated that the spectrum is in fact under-utilized. Many frequency bands are not heavily used over time, and some bands have low activity. Cognitive Radio (CR) Networks aim to exploit and opportunistically share the already licensed spectrum. The objective is to enable various kinds of communications while preserving the licensed parties' right to access the spectrum without interference. Cognitive radio networks have more than one approach to spectrum sharing. In interweave spectrum sharing scheme, cognitive radio devices look for opportunities in the spectrum, in frequency and over time. Therefore, and to find these opportunities, they employ what is known as spectrum sensing. In a spectrum sensing phase, the CR device scans certain parts of the spectrum to find the voids or white spaces in it. After that it exploits these voids to perform its data transmission, thus avoiding any interference with the licensed users. Spectrum sensing has various classifications and approaches. In this thesis, we will present a general review of the main spectrum sensing categories. Furthermore, we will discuss some of the techniques employed in each category including their respective advantages and disadvantages, in addition to some of the research work associated with them. Our focus will be on cooperative spectrum sensing, which is a popular research topic. In cooperative spectrum sensing, multiple CR devices collaborate in the spectrum sensing operation to enhance the performance in terms of detection accuracy. We will investigate the soft-information decision fusion approach in cooperative sensing. In this approach, the CR devices forward their spectrum sensing data to a central node, commonly known as a Fusion Center. At the fusion center, this data is combined to achieve a higher level of accuracy in determining the occupied parts and the empty parts of the spectrum while considering Rayleigh fading channels. Furthermore, we will address the issue of high power consumption due to the sampling process of a wide-band of frequencies at the Nyquist rate. We will apply the 1-bit Quantization technique in our work to tackle this issue. The simulation results show that the detection accuracy of a 1-bit quantized system is equivalent to a non-quantized system with only 2 dB less in Signal-to-Noise Ratio (SNR). Finally, we will shed some light on multiple antenna spectrum sensing, and compare its performance to the cooperative sensing

COGNITIVE RADIO ENGINE MODEL UTILIZING SOFT FUSION BASED GENETIC ALGORITHM FOR COOPERATIVE SPECTRUM OPTIMIZATION

ABSTRACT Cognitive radio (CR) is to detect the presence of primary users (PUs) reliably in order to reduce the interference to licensed communications. Genetic algorithms (GAs) are well suited for CR optimization problems to increase efficiency of bandwidth utilization by manipulating its unused portions of the apparent spectrum. In this paper, a binary genetic algorithm (BGA)-based soft fusion (SF) scheme for cooperative spectrum sensing in cognitive radio network is proposed to improve detection performance and bandwidth utilization. The BGA-based optimization method is implemented at the fusion centre of a linear SF scheme to optimize the weighting coefficients vector to maximize global probability of detection performance. Simulation results and analyses confirm that the proposed scheme meets real time requirements of cognitive radio spectrum sensing and it outperforms conventional natural deflection coefficient-(NDC-), modified deflection coefficient-(MDC-), maximal ratio combining-(MRC-) and equal gain combining-(EGC-) based SDF schemes as well as the OR-rule based hard decision fusion (HDF). The propose BGA scheme also converges fast and achieves the optimum performance, which means that BGAbased method is efficient and quite stable also

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

MULTI USER COOPERATION SPECTRUM SENSING IN WIRELESS COGNITIVE RADIO NETWORKS

With the rapid proliferation of new wireless communication devices and services, the demand for the radio spectrum is increasing at a rapid rate, which leads to making the spectrum more and more crowded. The limited available spectrum and the inefficiency in the spectrum usage have led to the emergence of cognitive radio (CR) and dynamic spectrum access (DSA) technologies, which enable future wireless communication systems to exploit the empty spectrum in an opportunistic manner. To do so, future wireless devices should be aware of their surrounding radio environment in order to adapt their operating parameters according to the real-time conditions of the radio environment. From this viewpoint, spectrum sensing is becoming increasingly important to new and future wireless communication systems, which is designed to monitor the usage of the radio spectrum and reliably identify the unused bands to enable wireless devices to switch from one vacant band to another, thereby achieving flexible, reliable, and efficient spectrum utilisation. This thesis focuses on issues related to local and cooperative spectrum sensing for CR networks, which need to be resolved. These include the problems of noise uncertainty and detection in low signal to noise ratio (SNR) environments in individual spectrum sensing. In addition to issues of energy consumption, sensing delay and reporting error in cooperative spectrum sensing. In this thesis, we investigate how to improve spectrum sensing algorithms to increase their detection performance and achieving energy efficiency. To this end, first, we propose a new spectrum sensing algorithm based on energy detection that increases the reliability of individual spectrum sensing. In spite of the fact that the energy detection is still the most common detection mechanism for spectrum sensing due to its simplicity. Energy detection does not require any prior knowledge of primary signals, but has the drawbacks of threshold selection, and poor performance due to noise uncertainty especially at low SNR. Therefore, a new adaptive optimal energy detection algorithm (AOED) is presented in this thesis. In comparison with the existing energy detection schemes the detection performance achieved through AOED algorithm is higher. Secondly, as cooperative spectrum sensing (CSS) can give further improvement in the detection reliability, the AOED algorithm is extended to cooperative sensing; in which multiple cognitive users collaborate to detect the primary transmission. The new combined approach (AOED and CSS) is shown to be more reliable detection than the individual detection scheme, where the hidden terminal problem can be mitigated. Furthermore, an optimal fusion strategy for hard-fusion based cognitive radio networks is presented, which optimises sensing performance. Thirdly, the need for denser deployment of base stations to satisfy the estimated high traffic demand in future wireless networks leads to a significant increase in energy consumption. Moreover, in large-scale cognitive radio networks some of cooperative devices may be located far away from the fusion centre, which causes an increase in the error rate of reporting channel, and thus deteriorating the performance of cooperative spectrum sensing. To overcome these problems, a new multi-hop cluster based cooperative spectrum sensing (MHCCSS) scheme is proposed, where only cluster heads are allowed to send their cluster results to the fusion centre via successive cluster heads, based on higher SNR of communication channel between cluster heads. Furthermore, in decentralised CSS as in cognitive radio Ad Hoc networks (CRAHNs), where there is no fusion centre, each cognitive user performs the local spectrum sensing and shares the sensing information with its neighbours and then makes its decision on the spectrum availability based on its own sensing information and the neighbours’ information. However, cooperation between cognitive users consumes significant energy due to heavy communications. In addition to this, each CR user has asynchronous sensing and transmission schedules which add new challenges in implementing CSS in CRAHNs. In this thesis, a new multi-hop cluster based CSS scheme has been proposed for CRAHNs, which can enhance the cooperative sensing performance and reduce the energy consumption compared with other conventional decentralised cooperative spectrum sensing modes

Low Complexity Optimal Hard Decision Fusion under Neyman-Pearson Criterion

Decision fusion is a fundamental operation in many signal processing systems where multiple sensors collaborate to improve the accuracy and robustness of the decision being made. The decision of each individual binary decision maker (or sensor) is often error-prone due to various environment challenges. These challenges are mitigated to certain extent using the spatial diversity obtained by deploying the sensors over a geographically distributed area. Subsequently, the decisions from the individual sensors are collected and fused at a fusion center to obtain a global decision. One such recent application of decision fusion is cooperative spectrum sensing in cognitive radio networks (CRN). The secondary users (SUs) of the CRN are tasked to garner the much needed unutilized spectrum allocated to the primary users (PUs). It is important for the SUs to precisely detect the spectrum usage opportunities inorder to improve the spectral efficiency and also to restrict the interference caused to PUs in this process. However, these are two conflicting objectives. Tuning the system to low levels of interference to the primary network will result in higher missed spectrum utilization oppurtunities. Similarly, increasing the detection of spectral usage opportunities will lead to increased interference to the primary users. The fusion centers require optimal fusion rules that improve the spectral efficiency of the CRN and minimize the interference caused to the primary network. The spectrum sensing in this case is generally modeled as a binary hypothesis problem: ‘PU signal present’ and ‘PU signal absent’. The fusion rules are broadly classified into two categories, namely (i) non-randomized (ii) randomized. In a ‘non-randomized’ rule, the global decision generated is deterministic for all the combinations of the local observations received. And in a ‘randomized’ rule the global decision generated is random (0 or 1) with a certain probability distribution for some local observations. The design of the optimal randomized decision fusion is generally simple, however introduce randomness in the decision equations and are difficult to implement. Whereas vii the design of the optimal non-randomized hard decision fusion rule is difficult, and under the Neyman-Pearson (NP) criterion is known to be exponential in complexity. In this thesis, we develop low-complexity (i) optimal and (ii) near-optimal algorithms for two variants of non-randomized hard decision fusion problems under NP crierion (i) clairvoyant1 decision fusion and (ii) novel (semi-)blind decision fusion. In all the sub-categories considered therein, we present low-complexity algorithms and obtain receiver operating characteristics (ROCs) for different number of participating sensors (N) which was intractable with the existing approaches. We formulate a more generalized version of this problem called “Generalized Decision Fusion Problem (GDFP)” and relate it to the classical 0−1 Knapsack problem. Consequently we show that the GDFP has a worst case pseudo-polynomial time solution using dynamic programming approach. Additionaly, we show that the decision fusion problem exhibits semi-monotonic property in most practical cases. We propose to exploit this property to reduce the dimension of the feasible solution space. Subsequently, we apply dynamic programming to efficiently solve the problem with further reduction in complexity. Further, we show that though the non-randomized single-threshold likelihood ratio based test (non-rand-st LRT) is sub-optimal, its performance approaches the upper bound obtained by randomized LRT (rand LRT) with increase in N. This alleviates the need for employing the exponentially complex non-randomized optimal solution for N larger than a specific value. As a variant of GDFP, we propose novel (semi-)blind hard decision fusion rules that use the mean of the secondary user characteristics instead of their actual values. We show that these rules with slight (or no) additional system knowledge achieve better ROC than existing (semi-)blind alternatives. Finally, we present a branch and bound algorithm with novel termination to obtain 1A rule that has complete knowledge of the system viii a near-optimal solution as the proposed dynamic programming approach exhibits limitations for the GDFP that require high-precision computations. We validate the performance of the proposed branch and bound algorithm for a wide range of {high, low} precision and {monotonic, semi, non-monotonic} GDFPs. All the algorithms have been rigorously verified by simulations in Matla

Joint University Program for Air Transportation Research, 1991-1992

This report summarizes the research conducted during the academic year 1991-1992 under the FAA/NASA sponsored Joint University Program for Air Transportation Research. The year end review was held at Ohio University, Athens, Ohio, June 18-19, 1992. The Joint University Program is a coordinated set of three grants sponsored by the Federal Aviation Administration and NASA Langley Research Center, one each with the Massachusetts Institute of Technology (NGL-22-009-640), Ohio University (NGR-36-009-017), and Princeton University (NGL-31-001-252). Completed works, status reports, and annotated bibliographies are presented for research topics, which include navigation, guidance and control theory and practice, intelligent flight control, flight dynamics, human factors, and air traffic control processes. An overview of the year's activities for each university is also presented