A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> Software radios are emerging as platforms for multiband multimode personal communications systems. Radio etiquette is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that moderate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based reasoning about such etiquettes. Cognitive radio enhances the flexibility of personal services through a radio knowledge representation language. This language represents knowledge of radio etiquette, devices, software modules, propagation, networks, user needs, and application scenarios in a way that supports automated reasoning about the needs of the user. This empowers software radios to conduct expressive negotiations among peers about the use of radio spectrum across fluents of space, time, and user context. With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them. Software radio provides an ideal platform for the realization of cognitive radio. <s> BIB001 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio. <s> BIB002 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> We investigate the dynamic spectrum sharing problem among primary and secondary users in a cognitive radio network subject to QoS constraints for secondary users and interference constraints for primary users. For a scenario where only mean channel gains from secondary users to primary receiving points, which are averaged over short-term fading, are available, we derive outage probabilities for secondary users and interference constraint violation probabilities for primary users. Based on the analysis, we develop a framework to perform joint admission control and rate/power allocation for secondary users such that statistical guarantees on the violation probabilities of both the QoS and the interference constraints are achieved. Spectrum access by the secondary users can exploit the time-varying nature of the activity of the primary users, and thereby much higher throughput can be achieved compared to the case where primary users are assumed to be active at all time. Also, via extensive numerical analysis, throughput performances of primary and secondary users are investigated considering different levels of implementation complexity due to channel estimation. <s> BIB003 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> Localization of primary users (PUs) is a feature that can be very helpful for all the functional tasks of a cognitive radio (CR) network. Among the possible algorithms, weighted centroid localization (WCL) appears as the best candidate being a simple and robust range free localization technique that does not require any knowledge on the PU signal and on the radio environment parameters. We analyze the adoption of different weighting strategies considering in particular the dependence of the root mean square error (RMSE) on the position of the PU within the area considered. Numerical results show that weighting strategies that emphasize the difference between higher and lower weights are more robust to situations in which the PU moves toward the side of the area. Two secondary user (SU) selection strategies are also proposed to alleviate the border and noisy measurements effects in harsh propagation environments. <s> BIB004 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> Cognitive radio is a smart wireless communication concept that is able to promote the efficiency of the spectrum usage by exploiting its free frequency bands, namely spectrum holes. Detection of spectrum holes is one of the first steps of implementing a cognitive radio system. Another step towards the feasibility and a real implementation of a cognitive radio network is the problem of location awareness. This problem arises when we do consider a realistic scenario in hybrid overlay/underlay systems, when these spectrum opportunities permit cognitive radios to transmit below the primary users tolerance threshold. In this case, the cognitive radio, have to estimate robustly the primary users locations in the network in order to adjust its transmission power function of the estimated location in the network. Adding to this the fact that in wideband radio one may not be able to acquire signals at the Nyquist sampling rate due to the current limitations in Analog-to-Digital Converter (ADC) technology, we end up with a system that should, at a sub-Nyquist rate, properly recover the bands over which the primary users transmit and estimate their location in the network. In this paper 1, we proposed to analyze all these arisen problems. During the problem formulation and when analyzing more deeply the equations related to each question apart, we will make the link between the formulation of spectrum sensing, location awareness and the hardware limitation by describing those problems in a unique compressed sensing formalism. Via the proposed framework, we made it possible to overcame a challenging postulate of fixed frequency spectrum allocation by also estimating the spectrum usage boundaries in a blind way. <s> BIB005 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> We propose to fuse two main enabling features in cognitive radio systems (CRS): spectrum sensing and location awareness in a single compressed sensing based formalism. In this way, we exploit sparse characteristics of primary units to be detected, both in terms of spectrum used and location occupied. The compressed sensing approach also allows to overcome hardware limitations, in terms of the incapacity to acquire measurements and signals at the Nyquist rate when the spectrum to be scanned is large. Simulation results for realistic network topologies and different compressed sensing reconstruction algorithms testify to the performance and the feasibility of the proposed technique to enable in a single formalism the two main features of cognitive sensor networks. <s> BIB006 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> Abstract This article introduces the classic locating method based on the receiving signal strength in the cognitive radio and puts forward a cognitive radio-receiving signal strength (CR-RSS) localization algorithm which solves the problem of secondary users locating the primary user and succeeds in estimating the primary user's location and transmission power. Through the establishment of cognitive radio network, evaluating the number of secondary users, sampling and the environmental factors to the results in CR-RSS approach. The consequence shows that this approach can effectively locate the primary user and the technology of localization in cognitive radio can assist network optimization. <s> BIB007 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> In this paper, we proposed two hybrid RSS/TOA localization techniques in cognitive radio system including serial and parallel schemes. The proposed methods joint two location estimation techniques such as received signal strength (RSS) and time of arrival (TOA). First, we divide the consideration area into two cases: inner area and outer area using convex hull algorithm. We can conclude that the RSS is suitable for using in inner area and TOA should be chosen to use in outer area. Second, we enhance accuracy of RSS technique by applying the improved best linear unbiased estimation (iBLUE) method. In simulation results, we compare the performance and time duration of the RSS, TOA estimation and the two proposed methods: serial scheme and parallel scheme with iBLUE. Our simulation shows that the proposed method in serial scheme with iBLUE has better performance than RSS and TOA. Moreover, it is faster than TOA about 60%. <s> BIB008 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> In cognitive radio networks, secondary users (SUs) can share spectrum with primary users (PUs) under the condition that no interference is caused to the PUs. To evaluate the interference imposed to the PUs, the cognitive systems discussed in the literature usually assume that the channel state information (CSI) of the link from a secondary transmitter to a primary receiver (interference link) is known at the secondary transmitter. However, this assumption may often be impractical in cognitive radio systems, since the PUs need to be oblivious to the presence of the SUs. The authors first discuss PU localisation and then introduce an uplink resource allocation algorithm for orthogonal frequency division multiple access-based cognitive radio systems, where relative location information between primary and SUs is used instead of CSI of the interference link to estimate the interference. Numerical and simulation results show that it is indeed effective to use location information as a part of resource allocation and thus a near-optimal capacity is achieved. <s> BIB009 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> Localization of primary users (PUs) and secondary users (SUs) is one of the essential features of cognitive radio networks (CRNs). Given that there is no communication between PUs and SUs, localization of the whole network is a challenging task. In this paper, we propose a two-stage localization algorithm that combines multidimensional scaling (MDS) and Procrustes analysis for a CRN with proximity information. In the proposed algorithm, a hybrid-connectivity-and-estimated-distance-based strategy is introduced to get maximum benefit from the information available in the network. Simulations are included to compare the proposed algorithm with weighted centroid localization (WCL) in terms of the root-mean-square-error (RMSE) performance, as well as the Cramer–Rao lower bound (CRLB) for CRN localization. It is proved that the proposed algorithm outperforms the WCL solutions for the CRN localization problem. <s> BIB010 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> In cognitive radio networks (CRNs), localization of primary users (PUs) and secondary users (SUs) can enable several key capabilities such as location aware routing and power control mechanisms for SUs. Therefore, SUs in a network must accurately locate PUs in order to efficiently use spectrum holes without interfering to the PUs. Accurate localization of PUs in CRN is an important but challenging task due to the unique constraint of CRNs, i.e., the non cooperative nature of PUs making the localization algorithm rely solely on sensing results. In this paper we propose cluster based CRN localization using multidimensional scaling (MDS) that improves accuracy, especially for irregular CRNs. Using the traditional MDS approach leads to low localization accuracy and higher computational complexity. Based on this fact, this paper proposes a novel cluster based multidimensional scaling algorithm for CRN localization (CB-MDS). Furthermore Cramer–Rao lower bound (CRLB) is derived to analyze the performance of the proposed algorithm. Moreover, extensive simulations are performed to confirm that the proposed CB-MDS algorithm is robust to noise and performs better than existing algorithms in attaining the CRLB. <s> BIB011 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> In this paper a local geometry alignment algorithm is presented for locating the primary users (PUs) and Secondary users (SUs) in cognitive radio network. Based on the estimated distance between PUs and SUs for the neighbors within certain communication range, the relative configuration of all the users in the network is obtained initially and is refined finally to get the global position of every user in the network. The localization performance of the proposed approach is compared to multidimensional scaling and principal component analysis. Furthermore the lower bound on error i.e., the Cramer Rao lower bound (CRLB) is also derived to check the performance of the proposed algorithm. <s> BIB012 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s> B. MDS Based Localization for Cognitive Radio Networks <s> An energy efficient localization algorithm with high accuracy for the cognitive radio networks (CRNs) is proposed in this letter. The key idea underlying this proposed algorithm is to provide the optimal transmission range for secondary users (SUs) to minimize the power consumption of the overall CRNs. Since energy efficiency is a key factor to consider in designing a node in the CRNs, location information of primary users (PUs) and SUs is valuable to calculate the optimal transmission range so that a spectrally efficient CRN can opportunistically take advantage of the spectrum with no or little interference to the PUs. In addition, the optimal positions of the SUs with known location are determined to improve the final localization accuracy for the whole CRN, which enables the optimal transmission range to be obtained. Thereby, the users in the CRN are localized with minimum energy consumption for a given target root mean square error. Simulation results validate the effectiveness of the proposed algorithm by showing the optimal transmission range for various number of users in the network. <s> BIB013
One of the all-time regulated resources for wireless communication is RF spectrum. From smart-phone users to scanners, from digital TV receivers to door-openers, every wireless device requires an RF spectrum. These ever-increasing demand for RF spectrum leads to deploy the new concept of dynamic spectrum access BIB002 , BIB003 . One of the promising technologies to overcome the problem of spectrum scarcity is cognitive radio networks (CRNs) BIB001 . Many spectrum sensing techniques have been extensively studied in the past decade. Spatial spectrum sensing is one of the spectrum sensing techniques for unlicensed users to not interfere the licensed users in the spatial domain. In CRNs, the localization of primary users (PUs) and secondary users (SUs) is beneficial in order to create an efficient CRN. Since PUs are not cooperative with SUs in nature, localization of all the users, including PUs, for the whole CRN is a challenging task. In CRNs, localization of PUs and SUs can enhance the system optimization in following aspects BIB009 : • Measurements of the spectrum occupancy are precisely performed, • Localization will also determine the reliability of links between SUs, • It will help in determining the angle of arrival/departure of the signal toward PUs, which allows to use beamforming to reduce the interference to the PUs, • Localization will optimize the CRNs, thus maximizing the frequency reuse in the space domain, • An optimal SUs network can be modeled based on the location information of PUs. In BIB004 BIB005 BIB006 , authors deployed different localization algorithms for CRNs, where they assume that the distances between PUs and SUs are available. As in sensor networks, it is possible to estimate the distances between SUs since they can communicate with each other. But the distance between a PU and a SU cannot be estimated in practice due to the fact that PUs and SUs do not communicate with each other in CRNs. In BIB007 , BIB008 the authors propose the use of directional antennas for locating the PUs in CRNs. Since the PUs and SUs do not interact, the distances between PUs and SUs is a challenging task BIB010 . In BIB010 , BIB011 , BIB012 , MDS based localization methods for CRNs are proposed where the distances are estimated using RSS measurements between the SUs while proximity only (binary) information is considered between PUs and SUs. In BIB010 a centralized MDS based method is proposed to determine the location of PUs and SUs in CRNs. Since the localization accuracy of centralized MDS based method suffers when the network topology is irregular, cluster-based semi-centralized MDS is introduced in BIB011 to determine the location of PUs and SUs. The localization accuracy also depends on the geometry of anchors in the network. An analysis shows that the location of anchors has a large impact on the localization accuracy BIB013 .