Showing papers by "Ali H. Sayed published in 2009"
TL;DR: Simulation results show that the proposed spectrum sensing schemes can considerably improve system performance, and useful principles for the design of distributed wideband spectrum sensing algorithms in cognitive radio networks are established.
Abstract: Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to legacy (primary) networks. In this paper, a novel wideband spectrum sensing technique referred to as multiband joint detection is introduced, which jointly detects the primary signals over multiple frequency bands rather than over one band at a time. Specifically, the spectrum sensing problem is formulated as a class of optimization problems, which maximize the aggregated opportunistic throughput of a cognitive radio system under some constraints on the interference to the primary users. By exploiting the hidden convexity in the seemingly nonconvex problems, optimal solutions can be obtained for multiband joint detection under practical conditions. The situation in which individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing is also considered. To address this issue by exploiting the spatial diversity, a cooperative wideband spectrum sensing scheme refereed to as spatial-spectral joint detection is proposed, which is based on a linear combination of the local statistics from multiple spatially distributed cognitive radios. The cooperative sensing problem is also mapped into an optimization problem, for which suboptimal solutions can be obtained through mathematical transformation under conditions of practical interest. Simulation results show that the proposed spectrum sensing schemes can considerably improve system performance. This paper establishes useful principles for the design of distributed wideband spectrum sensing algorithms in cognitive radio networks.
742 citations
01 Nov 2009
TL;DR: This work shows how to optimally select the weights, and proposes an adaptive algorithm to adapt them using local information at every node, and shows performance improvement in comparison to the case where fixed, non-adaptive weights are used.
Abstract: We study the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their measurements. In diffusion Kalman filtering strategies, neighboring state estimates are linearly combined using a set of scalar weights. In this work we show how to optimally select the weights, and propose an adaptive algorithm to adapt them using local information at every node. The algorithm is fully distributed and runs in real time, with low processing complexity. Our simulation results show performance improvement in comparison to the case where fixed, non-adaptive weights are used.
53 citations
TL;DR: It is shown that the proposed compensation scheme can effectively improve the signal-to-noise ratio at the receiver, simplifying the RF and analog circuitry design in terms of implementation cost, power consumption, and silicon fabrication yield.
Abstract: Physical impairments like IQ imbalance and phase noise can cause significant degradation in the performance of wireless communication systems. In this paper, the joint effects of IQ imbalance and phase noise on OFDM systems are analyzed, and a compensation scheme is proposed to improve the system performance in the presence of IQ imbalance and phase noise. The scheme consists of a joint estimation of channel and impairment parameters and a joint data symbol estimation algorithm. It is shown both by theory and computer simulations that the proposed scheme can effectively improve the signal-to-noise ratio at the receiver. As a result, the sensitivity of OFDM receivers to the physical impairments can be significantly lowered, simplifying the RF and analog circuitry design in terms of implementation cost, power consumption, and silicon fabrication yield.
50 citations
TL;DR: This paper investigates how the cross-modulation distortion can be compensated for by using digital signal processing techniques, and demonstrates how mixed-signal, i.e., joint analog and digital, processing techniques play a critical role in the emerging SDR and cognitive radio technologies.
Abstract: The wideband RF receiver in a software-defined radio (SDR) system suffers from the nonlinear effects caused by the front-end analog processing. In the presence of strong blocker (interference) signals, such nonlinearities introduce severe cross modulation over the desired signals. This paper investigates how the cross-modulation distortion can be compensated for by using digital signal processing techniques. In the proposed solution, the SDR scans the wide spectrum and locates the desired signal and strong blocker signals. After down-converting these signals separately to the baseband, the baseband processor processes them jointly to mitigate the cross-modulation interferences. As a result, the sensitivity of the wideband RF receiver to the nonlinearity impairment can be significantly lowered, simplifying the RF and analog circuitry design in terms of implementation cost and power consumption. The proposed approach also demonstrates how mixed-signal, i.e., joint analog and digital, processing techniques play a critical role in the emerging SDR and cognitive radio technologies.
50 citations
01 Dec 2009
TL;DR: It is shown that a flock of birds can self-organize into a V-formation if every bird were to employ a distributed LMS algorithm, known as diffusion LMS, which is fully distributed and runs in real time.
Abstract: Flocks of birds self-organize into V-formations when they need to travel long distances. It has been shown that this formation allows the birds to save energy, by taking advantage of the upwash generated by the neighboring birds. In this work we use a simple model for the upwash generated by a flying bird, and show that a flock of birds can self-organize into a V-formation if every bird were to employ a distributed LMS algorithm, known as diffusion LMS. The algorithm requires the birds to obtain measurements of the upwash, and also to communicate with neighboring birds. The result has interesting implications. First, a simple diffusion LMS-based algorithm can account for the self-organization of birds. The algorithm is fully distributed and runs in real time. Second, that birds can self-organize based on the air pressures generated by the other birds. Third, that some form of communication among birds is crucial to achieve the flight formation.
49 citations
19 Apr 2009
TL;DR: This paper presents an efficient adaptive combination strategy for diffusion algorithms over adaptive networks in order to improve the robustness against the spatial variation of SNR over the network.
Abstract: This paper presents an efficient adaptive combination strategy for diffusion algorithms over adaptive networks in order to improve the robustness against the spatial variation of SNR over the network. The diffusion least-mean square (LMS) algorithm with the proposed combination rule and its mean transient analysis are included. Simulation results show that the diffusion LMS algorithm with our combiners outperforms those with existing static combiners and the incremental LMS algorithm.
44 citations
TL;DR: This paper presents a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals and shows that according to the Neyman-Pearson criterion, this spectral correlation-based sensing technique is asymptotically optimal at very low SNR and with a large sensing time.
Abstract: Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at very low signal-to-noise ratio (SNR) In this paper, we present a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal We show that according to the Neyman-Pearson criterion, this spectral correlation-based sensing technique is asymptotically optimal at very low SNR and with a large sensing time From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR as low as -20 dB
43 citations
21 Jun 2009
TL;DR: Numerical results indicate that the beamforming method is more attractive than the spatial multiplexing method for the two-way communications.
Abstract: Relay transceiver processing for multiuser two-way communications is optimized based on zero-forcing (ZF) and minimum mean-square-error (MMSE) formulations under relay power constraints. Cochannel interference (CCI) frommultiusers and self-interference (SI) from two-way communications are efficiently mitigated by the proposed relay processing with multiple antennas. The system performance is examined in relation to the system BER by computer simulation. Numerical results indicate that the beamforming method is more attractive than the spatial multiplexing method for the two-way communications.
38 citations
01 Jan 2009
TL;DR: In this paper, the authors discuss motivation for incremental adaptive solutions and diffusion adaptive solutions, and conclude with acknowledgments of references to references from the authors and references from their own work.
Abstract: This chapter contains sections titled: Introduction Motivation Incremental Adaptive Solutions Diffusion Adaptive Solutions Concluding Remarks Acknowledgments References
37 citations
30 Nov 2009
TL;DR: Under uncertain channel conditions, local and global power control factors for amplify-and-forward relay processing and source-destination beamforming are jointly and iteratively designed based on a minimum mean-square-error criterion.
Abstract: Under uncertain channel conditions, local and global power control factors for amplify-and-forward relay processing and source-destination beamforming are jointly and iteratively designed based on a minimum mean-square-error (MMSE) criterion. The influence of imperfect channel information on system performance is examined by computer simulation. As a result, it is verified that the proposed power control methods can relieve the performance degradation arising from channel uncertainties.
35 citations
30 Nov 2009
TL;DR: In this article, a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals is presented. But the spectral features of the received signal are not correlated with the a priori known spectral features.
Abstract: Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we present a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that according to the Neyman-Pearson criterion, this spectra correlation-based sensing technique is asymptotically optimal at very low SNR and with a large sensing time. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR as low as -20 dB.
21 Jun 2009
TL;DR: In this paper, a cooperative sequential detection scheme is proposed to minimize the average sensing time required to reach a detection decision in cognitive radio networks, where the average number of required samples depends on the Kullback-Leibler distance between the distributions of the two hypotheses under test.
Abstract: Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper presents a cooperative sequential detection scheme tominimize the average sensing time that is required to reach a detection decision. In the scheme, each cognitive radio computes the Log-Likelihood ratio for its every measurement, and the base station sequentially accumulates these Log-Likelihood statistics and determines whether to stop making measurement. The average number of required samples depends on the Kullback-Leibler distance between the distributions of the two hypotheses under test. This suggests a criterion for selecting the most efficient radios to facilitate spectrum sensing. The paper also studies how to implement the scheme in a robust manner when the assumed statistical models have uncertainties. These ideas are illustrated through an example that assumes both the signal and noise are Gaussian distributed.
19 Apr 2009
TL;DR: This work focuses on multi-level diffusion algorithms, where a network running a diffusion algorithm is enhanced by adding special nodes that can perform different processing.
Abstract: We study the problem of distributed estimation, where a set of nodes are required to collectively estimate some parameter of interest from their measurements. Diffusion algorithms have been shown to achieve good performance, increased robustness and are amenable for real-time implementations. In this work we focus on multi-level diffusion algorithms, where a network running a diffusion algorithm is enhanced by adding special nodes that can perform different processing. These special nodes form a second network where a second diffusion algorithm is implemented. We illustrate the concept using diffusion LMS, provide performance analysis for multi-level collaboration and present simulation results showing improved performance over conventional diffusion.
21 Jun 2009
TL;DR: This work establishes the connection between the detection and estimation problems, proposes a distributed detection algorithm, and analyzes the performance of the algorithm in terms of its probabilities of detection and false alarm.
Abstract: We study the problem of distributed detection, where a set of nodes are required to decide between two hypotheses based on their measurements. We seek fully distributed implementations, where all nodes make individual decisions by communicating with their immediate neighbors, and no fusion center is necessary. This scheme provides the network with more flexibility, saves energy for communication and networking resources. Our distributed detection algorithm is based on a previously proposed distributed estimation algorithm. We establish the connection between the detection and estimation problems, propose a distributed detection algorithm, and analyze the performance of the algorithm in terms of its probabilities of detection and false alarm. We also provide simulation results comparing with other cooperation schemes.
01 Nov 2009
TL;DR: This work considers the problem of distributed detection, where a set of nodes are required to decide between two hypotheses based on their measurements, and proposes a distributed detection scheme based on diffusion least-squares techniques.
Abstract: We consider the problem of distributed detection, where a set of nodes are required to decide between two hypotheses based on their measurements. In diffusion implementations, nodes communicate with their neighbors and no fusion center is needed. In previous work we proposed a distributed detection scheme which was based on diffusion least-squares techniques. In this work we consider the case where nodes utilize diffusion LMS techniques instead. The proposed detector is capable of tracking changes in the active hypothesis. We analyze the performance of the detector, and provide simulation results comparing with other cooperation schemes.
TL;DR: A distributed sampling scheme based on the concept of innovations diffusion to select the sensor nodes in a wireless network with distributed processing capabilities for estimation or detection applications is presented.
Abstract: We consider a wireless network with distributed processing capabilities for estimation or detection applications. Due to limited communication resources, the network selects only a subset of sensor measurements for estimation or detection as long as the resulting fidelity is tolerable. We present a distributed sampling scheme based on the concept of innovations diffusion to select the sensor nodes. In the proposed scheme, sensor selection is accomplished through local communication and signal processing. In order to conserve energy and prolong system lifetime, the proposed algorithm selects a nearly minimum number of active sensors to ensure a desired fidelity for each working period. Extensive simulations illustrate the effectiveness of the proposed sampling scheme.
06 Oct 2009
TL;DR: Through BER simulations, it is illustrated that the full-duplex method performs better than the half-duple method in various signalto-noise ratio (SNR) scenarios, and it can be a promising candidate for future relay networks.
Abstract: In this paper, half- and full-duplex relay processing matrices and source-destination beamforming vectors are jointly optimized based on a minimum mean-square-error (MMSE) formulation under inequality constraints on the transmit power of the source and the relays. Through BER simulations, we illustrate that the full-duplex method performs better than the half-duplex method in various signalto-noise ratio (SNR) scenarios, and it can be a promising candidate for future relay networks.
01 Jan 2009
TL;DR: A modified Projection Onto Convex Sets (POCS) algorithm is introduced that is optimized for both the new L1 Open Service and Coarse/Acquisition signals employed by the future European Galileo and the Global Position System (GPS), respectively and the performance of the algorithm is compared with other state-of-art deconvolution algorithms.
Abstract: An important task of a Global Navigation Satellite System (GNSS) receiver is to achieve fine synchronization between the received Line-of-Sight (LOS) signal and the reference code, which would allow the computation of the satellite-receiver distance. This synchronization process, known also as tracking stage, requires the Doppler shift to be successfully removed from the received signal (or that the residual error is kept within allowable limits) and typically involves the estimation of signal parameters such as the code delay, the carrier frequency and/or carrier phase. A challenging issue in the estimation of the synchronization parameters is the mitigation of multipath effects that appear due to the wireless propagation channel characteristics. In this paper, we deal with the problem of joint LOS code delay and carrier phase estimation of GNSS signals in a multipath environment. The problem is formulated into a linear system of equations in which the unknowns are the channel complex coefficients corresponding to each observed signal sample. We introduce a modified Projection Onto Convex Sets (POCS) algorithm that we optimize for both the new L1 Open Service and Coarse/Acquisition (C/A) signals employed by the future European Galileo and the Global Position System (GPS), respectively. We compare the performance of the algorithm with other state-of-art deconvolution algorithms. The simulation results indicate that our modified POCS algorithm is the most resistant in closely-spaced multipath static channels both when LOS code delay and carrier phase estimation are concerned.
19 Apr 2009
TL;DR: This work proposes a linear fusion scheme for distributed spectrum sensing to combine the sensing results from multiple spatially distributed cognitive radios and shows that the optimal solution of such a nonconvex problem can be solved via semi-definite programming reformulation.
Abstract: As an enabling functionality of overlay cognitive radio networks, spectrum sensing needs to reliably detect licensed signal in the band of interest. To achieve reliable sensing, we propose a linear fusion scheme for distributed spectrum sensing to combine the sensing results from multiple spatially distributed cognitive radios. The optimal linear fusion design is formulated into a nonconvex optimization problem. We show that the optimal solution of such a nonconvex problem can be solved via semi-definite programming reformulation.
06 Oct 2009
TL;DR: This work focuses on hierarchical diffusion algorithms, where different nodes are allowed to have different responsibilities, as opposed to the previous work where every node performed exactly the same type of operations.
Abstract: We study the problem of distributed estimation, where a set of nodes are required to collectively estimate some parameter of interest from their measurements. Distributed implementations avoid the use of a fusion center and distribute the processing and communication across the entire network. Among distributed solutions, diffusion algorithms have been shown to achieve good performance, increased robustness and are amenable for ad-hoc implementation. In this work we focus on hierarchical diffusion algorithms, where we allow different nodes to have different responsibilities, as opposed to our previous work where every node performed exactly the same type of operations. Our results are general in the sense that they apply to any diffusion algorithm. We illustrate the concept using diffusion LMS, provide performance analysis for hierarchical collaboration and present simulation results showing improved performance over non-hierarchical methods.
06 Oct 2009
TL;DR: In this article, the authors proposed a cooperative sensing scheme that detects the existence of a common signal component in the signals received by multiple geographically distributed radios in a cognitive radio network, assuming that signals receive by different radios display strong coherence if they have a common source.
Abstract: Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper proposes a cooperative sensing scheme that detects the existence of a common signal component in the signals received by multiple geographically distributed radios. The scheme assumes that signals received by different radios display strong coherence if they have a common source. Detection of this coherence in a wireless environment is studied, especially when the transmitted signal is distorted by multipath channels.
Patent•
AT&T1
TL;DR: In this paper, a decision feedback equalizer (DFE) was proposed to improve the operation of a receiver by canceling the spatio-temporal interference effects caused by the MIMO channel memory with a set of Finite Impulse Response (FIR) filters.
Abstract: A MIMO Decision Feedback Equalizer improves operation of a receiver by canceling the spatio-temporal interference effects caused by the Multiple-Input-Multiple-Output (MIMO) channel memory with a set of Finite Impulse Response (FIR) filters in both the feed-forward and the feedback MIMO filters. The coefficients of these FIR filters can be fashioned to provide a variety of controls by the designer.
01 Dec 2009
TL;DR: Through system bit-error-rate simulations, it is verified that the performance of the leakage-based selection scheme is comparable to a signal-to-interference-plus-noise ratio (SINR)-based optimal beamforming method albeit at much lower complexity.
Abstract: In this paper, a distributed grouped-relay network with multiple source and destination nodes is introduced, and its distributed beamforming based on an average signal-to-leakage-plus-noise ratio (SLNR) is proposed to mitigate interference and colored noise from the multiple relay groups. Through system bit-error-rate simulations, it is verified that the performance of the leakage-based selection scheme is comparable to a signal-to-interference-plus-noise ratio (SINR)-based optimal beamforming method albeit at much lower complexity.
TL;DR: The proposed preprocessing performs a superposition of the first and second (second and first) STBC symbols and a selection of two transmit antennas and results show that the proposed method provides 1.7 dB SNR improvement in uncorrelated (correlated) channel environment at 10-3 bit error rate over the conventional antenna shuffling method.
Abstract: From 1820 4 times 4 binary matrices, 253 binary preprocessing matrices are designed for double space-time transmit diversity (DSTTD) systems with two 2 times 2 space-time block code (STBC) encoders. Among them, six matrices yielding the highest average minimum-post-processing SNR are proposed from numerical experimentation under uncorrelated channel conditions. The proposed preprocessing performs a superposition of the first and second (second and first) STBC symbols and a selection of two transmit antennas. Simulation results show that the proposed method provides 1.7 dB (1.8 dB) SNR improvement in uncorrelated (correlated) channel environment at 10-3 bit error rate over the conventional antenna shuffling method.
Journal Article•
06 Oct 2009
TL;DR: Previous work is extended by developing order-adaptive schemes that enforce frequency continuity and improve tracking performance and, as a result, the overall frequency mean-square error as well.
Abstract: During the entry, descent and landing phase (EDL) of the missions to Mars, the spacecraft's high dynamics imprints severe Doppler swings on the signals transmitted via the direct-to-Earth (DTE) channel. In order to recover the data that record the mission status from the received signal, a reliable estimate of the Doppler profile is required. We extend previous work by developing order-adaptive schemes that enforce frequency continuity and improve tracking performance and, as a result, the overall frequency mean-square error as well.
TL;DR: The objective of this interdisciplinary special issue is to highlight the important role of digital signal processing techniques in understanding and mitigating RF/analog circuit impairments.
Abstract: The objective of this interdisciplinary special issue is to highlight the important role of digital signal processing techniques in understanding and mitigating RF/analog circuit impairments. The issue presents 15 papers .
01 Apr 2009
TL;DR: Reference EPFL-CHAPTER-233386 URL: http://iracema.icsl.ucla.edu/publications/book_chapters/kalman_2009.pdf
Abstract: Reference EPFL-CHAPTER-233386 URL: http://iracema.icsl.ucla.edu/publications/book_chapters/kalman_2009.pdf Record created on 2017-12-19, modified on 2018-01-16