Theoretical Limits on Time Delay Estimation for Ultra-Wideband Cognitive Radios

In this paper, theoretical limits on time delay estimation are studied for ultra-wideband (UWB) cognitive radio systems. For a generic UWB spectrum with dispersed bands, the Cramer-Rao lower bound (CRLB) is derived for unknown channel coefficients and carrier-frequency offsets (CFOs). Then, the effects of unknown channel coefficients and CFOs are investigated for linearly and non-linearly modulated training signals by obtaining specific CRLB expressions. It is shown that for linear modulations with a constant envelope, the effects of the unknown parameters can be mitigated. Finally, numerical results, which support the theoretical analysis, are presented.Comment: IEEE ICUWB 200

Cramer-Rao Bounds for Joint RSS/DoA-Based Primary-User Localization in Cognitive Radio Networks

Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.Comment: 20 pages, 11 figures, 1 table, submitted to IEEE Transactions on Wireless Communication

Simultaneous Routing and Power Allocation using Location Information

To guarantee optimal performance of wireless networks, simultaneous optimization of routing and resource allocation is needed. Optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communication resources to the links. Simultaneous routing and resource allocation (SRRA) problems have been studied under the assumption that (global) channel state information (CSI) is collected at a central node. This is a drawback as SRRA depends on channels between all pairs of nodes in the network, thus leading to poor scalability of the CSI-based approach. In this paper, we first investigate to what extent it is possible to rely solely on location information (i.e., position of nodes) when solving the SRRA problem. We also propose a distributed heuristic based on which nodes can locally adjust their rate based on the local CSI. Our numerical results show that the proposed heuristic achieves near-optimal flow in the network under different shadowing conditions

Prediction-Based Channel Selection Prediction in Mobile Cognitive Radio Network

The emerging 5G wireless communications enabled diverse multimedia applications and smart devices in the network. It promises very high mobile traffic data rates, quality of service as in very low latency and improvement in user’s perceived quality of experience compared to current 4G wireless network. This encourages the increasing demand of significant bandwidth which results a significant urge of efficient spectrum utilization. In this paper, modelling, performance analysis and optimization of future channel selection for cognitive radio network by jointly exploiting both CR mobility and primary user activity to provide efficient spectrum access is studied.  The modelling and prediction method is implemented by using Hidden Markov Model algorithm. The movement of CR in wireless network yields location-varying spectrum opportunities. The current approaches in most literatures which only depend on reactive selection spectrum opportunities result of inefficient channel usages. Moreover, conventional random selection method tends to observe a higher handoff and operation delays in network performance.  This inefficiency can cause continuous transmission interruptions leading to the degradation of advance wireless services. This work goal is to improve the performance of CR in terms number of handoffs and operation delays. We perform simulation on our prediction strategy with a commonly used random sensing method with and without location. Through simulations, it is shown that the proposed prediction and learning strategy can obtain significant improvements in number of handoffs and operation delays performance parameters. It is also shown that future CR location is beneficial in increasing mobile CR performance. This study also shows that the number of primary user in the network and the PU protection range affect the performance of mobile CR channel selection for all methods

Hybrid analog-digital processing system for amplitude-monopulse RSSI-based MiMo wifi direction-of-arrival estimation

We present a cost-effective hybrid analog digital system to estimate the Direction of Arrival (DoA) of WiFi signals. The processing in the analog domain is based on simple wellknown RADAR amplitude monopulse antenna techniques. Then, using the RSSI (Received Signal Strength Indicator) delivered by commercial MiMo WiFi cards, the DoA is estimated using the socalled digital monopulse function. Due to the hybrid analog digital architecture, the digital processing is extremely simple, so that DoA estimation is performed without using IQ data from specific hardware. The simplicity and robustness of the proposed hybrid analog digital MiMo architecture is demonstrated for the ISM 2.45GHz WiFi band. Also, the limitations with respect to multipath effects are studied in detail. As a proof of concept, an array of two MiMo WiFi DoA monopulse readers are distributed to localize the two-dimensional position of WiFi devices. This costeffective hybrid solution can be applied to all WiFi standards and other IoT narrowband radio protocols, such us Bluetooth Low Energy or Zigbee.This work was supported in part by the Spanish National Projects TEC2016-75934-C4-4-R, TEC2016-76465-C2-1-R and in part by Regional Seneca Project 19494/PI/14

Opportunistic Spectrum Access in LTE-Advanced networks

[ES] Esta tesina tiene como objetivo investigar, estudiar y desarrollar una solución para el acceso oportunista al espectro no licenciado en sistemas LTE. La solución propuesta apusta por la implementación de un coordinador que decida, dada la ubicación del usuario y el estado de sus interferencias, que recursos no licenciados tiene disponibles.[EN] Long Term Evolution Advanced (LTE-A) has emerged as a promising mobile broadband access technology to cope with the increasing demand of traffic in wireless networks. However, the higher spectral efficiency of LTE-A is not enough without a better management of the scarce and overcrowded electromagnetic spectrum. Cognitive Radio (CR) has been proposed as a feasible solution to the problem of spectrum scarcity. Among all the mechanisms provided by CR, the Opportunistic Spectrum Access (OSA) aims at making opportunistic use of certain licensed bands whenever the primary system is not affected. This operation requires spectral awareness in order to avoid interferences with licensed systems. In spite of having some spectrum sensing mechanisms, LTE-A technology lacks other tools that are needed in order to improve the knowledge of the radio environment. In this framework, this Master Thesis studies the implementation of a Geo-located Data Base (Geo-DB) that collects the location of free pieces of spectrum available for OSA, based on a cooperative channel-state declaration. Moreover, the potential benefit of this LTE-compliant OSA solution is evaluated using a calibrated simulation tool. The results allow us to optimally configure the system and show that the proposed opportunistic system is able to significantly improve its performance with the available bandwidth.Herranz Claveras, C. (2012). Opportunistic Spectrum Access in LTE-Advanced networks. http://hdl.handle.net/10251/27271.Archivo delegad

Time-delay estimation in cognitive radio and MIMO systems

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 87-95.In this thesis, the time-delay estimation problem is studied for cognitive radio systems, multiple-input single-output (MISO) systems, and cognitive single-input multiple-output (SIMO) systems. A two-step approach is proposed for cognitive radio and cognitive SIMO systems in order to perform time-delay estimation with significantly lower computational complexity than the optimal maximum likelihood (ML) estimator. In the first step of this two-step approach, an ML estimator is used for each receiver branch in order to estimate the unknown parameters of the signal received via that branch. Then, in the second step, the estimates from the first step are combined in various ways in order to obtain the final time-delay estimate. The combining techniques that are used in the second step are called optimal combining, signal-to-noise ratio (SNR) combining, selection combining, and equal combining. It is shown that the performance of the optimal combining technique gets very close to the Cramer-Rao lower bound (CRLB) at high SNRs. These combining techniques provide various mechanisms for diversity combining for time-delay estimation and extend the concept of diversity in communications systems to the time-delay estimation problem in cognitive radio and cognitive SIMO systems. Simulation results are presented to evaluate the performance of the proposed estimators and to verify the theoretical analysis. For the solution of the time-delay estimation problem in MISO systems, ML estimation based on a genetic global optimization algorithm, namely, differential evolution (DE), is proposed. This approach is proposed in order to decrease the computational complexity of the ML estimator, which results in a complex optimization problem in general. A theoretical analysis is carried out by deriving the CRLB. Simulation studies for Rayleigh and Rician fading scenarios are performed to investigate the performance of the proposed algorithm.Koçak, FatihM.S