Blind channel identification using the constant modulus adaptive algorithm

Gooch, R.P.; Harp, J.C.

doi:10.1109/icc.1988.13538

Cited by 16 publications

(9 citation statements)

References 5 publications

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“…In the sequel, we use the CMA as the preferred method of MF training [2]. Hence, the filter update relations (17) for the MFR-LE, and (18) for the MFR-DFE are used where is a constant used in the CMA to adjust the gain of the output for multilevel modulation schemes and as in Fig. 4.…”

Section: A Example Usage: Cma In Mfrmentioning

confidence: 99%

“…Blind DFEs are technically more involved than blind linear equalizers (LEs) and among the few techniques are [13]- [16]. Virtually few research exists for blind maximum-likelihood sequence estimation (MLSE), excluding joint channel and sequence estimation techniques, as its performance is directly related to the channel estimation [17], [18]. However, from a matched filtering perspective presented in this paper, alternative implementations of the blind MLSE can be seen.…”

Section: Introductionmentioning

confidence: 97%

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Blind Matched Filter Receiver

Baykal

2004

IEEE Trans. Circuits Syst. I

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Matched filter receiver (MFR) of Forney in the presence of intersymbol interference (ISI) and additive white Gaussian noise is investigated from a blind perspective, which includes blind maximum likelihood sequence detection, blind decision feedback equalization and linear blind equalization. Various realizations of the blind MFR are discussed. In a blind MFR, the channel can be easily estimated using a simple algorithm such as the constantmodulus algorithm (CMA). In this sense, the CMA can be used to estimate the channel directly without complicated matrix decomposition techniques. Zero forcing and minimum mean-square estimation based designs of the blind MFR are explored. Channel estimation using the CMA deteriorates in the presence of noise. A new method to tackle this problem is developed and shown to have satisfactory behavior in low signal-to-noise ratios. Tracking performance of the blind MFR is found to be acceptable in representative Rayleigh fading channels of mobile communication.Index Terms-Blind channel estimation, blind decision feedback equalization, constant-modulus algorithm (CMA), matched filter receiver.

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Section: A Example Usage: Cma In Mfrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Blind Matched Filter Receiver

Baykal

2004

IEEE Trans. Circuits Syst. I

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“…The hard decisions (estimated symbols) with interleaved zeros comprise the "input" sequence and the complex basebanded data comprises the "desired output" sequence. (See [2] for further discussion of this channel estimation procedure.) The two sequences are complex correlated to determine an appropriate system delay and then the LMS algorithm is run over successive sections (most of the experiments use 4 sections, each which are 1/4 of the .5 megasample snapshot) of the data snapshot.…”

Section: Figure 2 Demodulation Software Flowmentioning

confidence: 98%

Characterization of an empirically-derived database of time-varying microwave channel responses

Behm

Endres²,

Schniter³

et al.

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)

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This paper reports on the gathering, processing, and categorization of empirically derived time-varying channel responses. The passband data and datu collection information is provided courtesy of Applied Signal Technology (Sunnyvale, CA). It is the intent of this paper to provide the signal processing community with a database of timevarying fractionally-spaced channel responses and received sequences based on empirical measurements which can be used to test and refine existing time-varying channel models and also propose new ones.

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“…Most work involved in a spectrum sharing scenario overlook this issue, with assumptions of full or partial cross-channel knowledge, in ensuring its cross-interference respects the ITC as set by the LR. Though substantial work on blind channel estimation [31]- [33] has been carried out and been around long before the concept of CR was birthed, estimation techniques particular to the CR system setup have not been touched upon. These techniques implement techniques ranging from the most common constant modulus adaptive algorithm, to using blind trellis search and maximization of the average likelihood function, in gaining a blind estimate to cross-channel knowledge.…”

Section: Spectrum Sharingmentioning

confidence: 99%

Proactive spectrum sensing and spectrum sharing for cognitive radio.

Bajaj¹

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Cognitive radio (CR) is emerging as a field essential to the progress of wireless communications. With spectral efficiency at the forefront in protocol design, CR aims in combating the supposed spectrum crisis due to inefficiency in spectral utilization. Thus a lot more spectrum-hungry services can be supported by the limited frequency bands through opportunistic access or sharing of licensed spectrum. As spectrum sensing precedes any possible access or sharing of spectrum, it is crucial that the sensing result is accurate when the initial sensing decision is made. Most of the current sensing techniques are passive in nature (i.e. they do not involve any participation of the CR) and hence can incur sensing errors when eavesdropping in the midst of high-noise or fading environments. Thus we consider a novel proactive spectrum sensing scheme that increases the certainty in the spectrum decision while combating the hidden-node problem. This is achieved when the CR sends out a probing signal prior to the sensing process. We study the results of applying power constraints to the power available for probing, and see how this scheme proves beneficial to the licensed user (LU) in terms of greater protection while still maintaining the throughput needs of the CR. After proactive sensing we focus our attention on the issues raised with spectrum sharing in the field of CR communication. As outlined by most work in literature, cross-channel estimation still remains an issue. Assumptions regarding cross-channel information are common amongst work that deals with a spectrum sharing protocol. In actuality this is impractical, and there is no way for the CR to ascertain the actual interference it deals to the LU. Thus we propose a supervised probing and sensing model that enables a CR to estimate the magnitude of the cross-channel link between itself and the LU, thus allowing for interference-shielding. With probing power playing an essential role in cross-channel estimation, we optimize the incremental probing step-size to allow for the highest estimation success and study its effect on cross-channel estimation error. Finally we take the conventional spectrum sharing protocols one step further to design proactive spectrum sharing. With a protocol established with bandwidth efficiency in mind, we see how cooperation x Abstract between a proactive CR and a willing LU can prove doubly beneficial to both parties. Typically the LU would have to deal with the dynamic fading channel on its own, in meeting a target outage probability. But with a proactive scenario, the CR can take the initiative by sending out a cooperation request signal, to help relay the LU signal thus lowering the outage probability. For the CR, this opportunity means spectrum access, through superimposition of its own signal atop the forwarded LU signal. We also study the optimum power allocation strategy used by the CR for maximum LU power saving in maintaining the target outage probability at the licensed receiver (LR). Our results show that through innovations ...

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Blind channel identification using the constant modulus adaptive algorithm

Cited by 16 publications

References 5 publications

Blind Matched Filter Receiver

Blind Matched Filter Receiver

Characterization of an empirically-derived database of time-varying microwave channel responses

Proactive spectrum sensing and spectrum sharing for cognitive radio.

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