Introduction to spread-spectrum antimultipath techniques and their application to urban digital radio

Turin, G.

doi:10.1109/proc.1980.11645

Cited by 566 publications

(118 citation statements)

References 24 publications

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“…From our perspective, the metrics of interest are the phase and energy of the paths to be re-injected in the taps of the RAKE filter [8]. The decision making consists in the computation (and the choice) of the combining method, such as the maximum ratio or equal gain combining, the single or largest path selection methods [9]. The implementation of the RAKE is reconfigured accordingly.…”

Section: Cognitive Radio In the Short Termmentioning

confidence: 99%

High-Level Design Approach for the Specification of Cognitive Radio Equipments Management APIs

Moy

2009

J Netw Syst Manage

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Cognitive Radio (CR) equipments are radio devices that support the smart facilities offered by future cognitive networks. Even if several categories of equipments exist (terminal, base station, smart PDA, etc.), each requiring different processing capabilities (and associated cost or power consumption), these equipments have to integrate also a set of new capabilities as regards CR support, in addition to the usual radio signal processing elements. This implies real-time radio adaptation and sensing capabilities, but not only. We assert that it is necessary to add inside the radio equipments some management facilities for that purpose, and we propose in this paper a high-level design approach for the specification of a management framework. This includes a set of designing rules, based on hierarchical units that are distributed over three levels, and the associated APIs necessary to efficiently manage CR features inside a CR equipment. The proposed architecture is called HDCRAM (Hierarchical and Distributed Cognitive Architecture Management). HDCRAM is an extension of a former hierarchical and distributed reconfiguration management (HDReM) architecture, which is derived from our previous research on Software Defined Radio (SDR). The HDCRAM adds to the HDReM's reconfiguration management facilities the necessary new management features, which enable the support of sensing and decision making facilities. It consists in the combination of one Cognitive Radio Management Unit (CRMU) with eachReconfiguration Management Unit (ReMU) IntroductionThe design of cognitive radio (CR) equipments will not be done efficiently if one uses the same tools as those used for the design of conventional radio equipments. One reason is that designing such a radio device is not solely a radio signal processing issue anymore. Of course, as in many other industrial domains, such as aeronautics, it becomes a hardware/software mixed issue with a plurality of capabilities and running modes on the one hand. But on the other hand, designing cognitive radio equipments also consists in designing the necessary management needed to support the real-time sensing of the environment and the real-time adaptation of the signal processing. Signal processing in our approach is not restricted to the physical layer and also includes all layers' signal processing, up to the application layer. Another reason is that the range of ways a CR equipment will have to run, will be very wide compared to that of the current radio equipment operation. Abstraction is necessary during the design phase of a CR equipment. In this respect, this paper proposes a high-level design approach, 2 integrating the design rules needed for the management of the signal processing operators, as well as the exploration means as regards the operation modes.Even if designing the signal processing part of a radio chain is not an easy task, it is however usual. The new design aspect lies in the management part and the associated impact on the signal processing, which has to be fl...

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Section: Cognitive Radio In the Short Termmentioning

confidence: 99%

High-Level Design Approach for the Specification of Cognitive Radio Equipments Management APIs

Moy

2009

J Netw Syst Manage

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“…For all our detectors, we will assume perfect knowledge of the channel coefficients. 2 Due to ISI, optimal detection requires that all symbols be detected jointly, since at the very least, each symbol overlaps with the preceding and succeeding symbols. However, as we will see, the effects of ISI are relatively negligible due to the excellent autocorrelation properties of pseudorandom codes.…”

Section: Reception Techniquesmentioning

confidence: 99%

Time-selective signaling and reception for communication over multipath fading channels

Bhashyam

Sayeed

Aazhang

2000

IEEE Trans. Commun.

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Abstract-The mobile wireless channel affords inherent diversity to combat the effects of fading. Existing code-division multiple-access systems, by virtue of spread-spectrum signaling and RAKE reception, exploit only part of the channel diversity via multipath combination. Moreover, their performance degrades under fast fading commonly encountered in mobile scenarios. In this paper, we develop new signaling and reception techniques that maximally exploit channel diversity via joint multipath-Doppler processing. Our approach is based on a canonical representation of the wireless channel, which leads to a time-frequency generalization of the RAKE receiver for diversity processing. Our signaling scheme facilitates joint multipath-Doppler diversity by spreading the symbol waveform beyond the intersymbol duration to make the channel time-selective. A variety of detection schemes are developed to account for the intersymbol interference (ISI) due to overlapping symbols. However, our results indicate that the effects of ISI are virtually negligible due to the excellent correlation properties of the pseudorandom codes. Performance analysis also shows that relatively small Doppler spreads can yield significant diversity gains. The inherently higher level of diversity achieved by time-selective signaling brings the fading channel closer to an additive white Gaussian noise channel, thereby facilitating the use of powerful existing coding techniques for Gaussian channels.

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“…The first receiver with which we compare is the correlator receiver (CR); see [2] and [16] for details. With the correlator receiver, since the transmitted signal is random, we correlate the received signal with the most significant eigenfunction of and use the peaks in the correlator output to detect the signal.…”

Section: Focused Detectormentioning

confidence: 99%

Focused detection via multiresolution analysis

Moura

1998

IEEE Trans. Signal Process.

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Abstract-In many applications, there are strong discrepancies between the signal models assumed in the design phase and the actual signals encountered in the field. These discrepancies penalize significantly the performance of the matched filter that is fine tuned to the preassumed conditions.We propose a geometric framework that designs, via wavelet multiresolution-based techniques, a receiver whose performance is to a large degree insensitive to these mismatches. We say that the receiver is a focused detector. The approach defines a signal set S that identifies the class of diverse conditions that are expected to arise. We illustrate the method in the context of multipath problems. The matched filter, which is a simple receiver, assumes that S is a singleton. When this is not the case, the matched filter experiences strong degradation. On the other hand, the optimal receiver for the signal set S is practically infeasible since it requires a multidimensional nonlinear optimization.The paper designs the focused receiver as a good compromise between these two extremes. We replace the signal set S by a linear subspace G-the representation subspace-that minimizes a measure of similarity with S. We choose G to be a multiresolution subspace. This choice resolves to satisfaction several issues: The subspace design is reduced to the design of a single shiftable scaling function, the similarity between S and G can be computed explicitly, and the focused receiver that computes the energy of the orthogonal projection on G is implemented by a bank of correlators matched to scaled/delayed versions of the reshaped scaling function followed by an energy detector. We assume that the transmitted signal is a sample of a random process. The signal set S becomes an ensemble of linear spaces.We introduce the modified deflection as the appropriate similarity measure. The paper details our algorithm, describes how to compute the modified deflection, and illustrates the performance results that can be obtained.

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Introduction to spread-spectrum antimultipath techniques and their application to urban digital radio

Cited by 566 publications

References 24 publications

High-Level Design Approach for the Specification of Cognitive Radio Equipments Management APIs

High-Level Design Approach for the Specification of Cognitive Radio Equipments Management APIs

Time-selective signaling and reception for communication over multipath fading channels

Focused detection via multiresolution analysis

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