Channel Estimation Algorithms for OFDM-IDMA: Complexity and Performance

Hammarberg, Peter; Rusek, Fredrik; Edfors, Ove

doi:10.1109/twc.2012.030512.110426

Cited by 34 publications

(17 citation statements)

References 21 publications

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“…In addition a Cyclic Prefix (CP) of length L B , assumed to be larger than the channel length, is inserted to prevent lSI and Inter Carrier Interference (ICI). As a result, the time domain data sequence, including CP, has the form [18]:…”

Section: Impulsive Noise Channel Modelmentioning

confidence: 99%

C7. Performance analysis of Single Carrier and multicarrier IDMA signals in non-Gaussian impulsive channel

Hagras¹

2015

2015 32nd National Radio Science Conference (NRSC)

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Although there is a rich literature on Interleave Division Multiple Access (IDMA) communication systems, current results are mainly restricted to the Additive White Gaussian Noise (A WGN) and ignored the impulsive noise effects (Non-Gaussian noise) which might be dominant in many practical applications. Aiming to fill this research gap, this paper investigates the performance of both Single Carrier IDMA (SC-IDMA) and Multi Carrier IDMA (MC-IDMA) systems over non-Gaussian impulsive noise channel. Gaussian Mixture Model (GMM) has been widely used in analyzing the performance of communication systems under impulsive noise. The resultsshow that the MC-IDMA is better than SC-IDMA by about 4 dB at bit error rate equal t010-4 under the same parameters. The performance analysis for both systems shows that the SC-IDMA is more sensitive than MC IDMA to the impulsive noise with increasing the impulsive noise parameters and the number of users in the system. I. INTRODUCTIONImpulsive noise is the common impairment in communication systems, which is caused by many different sources, such as car ignitions, high voltage cables, hair dryers, vacuums cleaner and microwave ovens, thus the impulsive noise models were analyzed depending on different several sources as literature in last twenty years [1]- [3]. It is complicated to accurately model impulsive noise interference, but a model of impulsive noise from any sources has two common properties, first, the time arrival of an impulsive noise is unpredictable, but it arrives with probability, second, the amplitude of impulsive noise is much higher than the background noise and it has short period. The Gaussian noise model is not appropriate due to in-frequent and high level noise spikes. Hence, the noise distribution for communication systems should be discussed as non-Gaussian noise. Many communication systems have been studied over impulsive noise channel as given in [4], [5]. There are many non Gaussian channel models such as Middleton class-A model [5], Laplace channel model [6], a-stable channel model [7], [8], and GMM [9]. Recently, SC-IDMA is regarded as a special form of SC-Code Division Multiple Access (SC-CDMA) by treating interleaving index sequence as multiple access codes. In general, SC-IDMA outperforms conventional SC-CDMA in terms of power and bandwidth. SC-IDMA inherits many advantages from SC-CDMA, in particular, diversity against fading and mitigation of the user interference problem [10]-[12]. The key principle of SC-IDMA system is that the chip level interleavers should be different for different users. The benefits of SC-IDMA are the following [13], [14]: l. Random coding where different users have unique random signatures. 2. Time diversity, where chip interleaving is capable of increasing the achievable time diversity in time selective channels. 3. Joint coding and spreading design, which is capable of improving the attainable bandwidth efficiency and error resilience.Recently, the MC version of IDMA was proposed in [15] by combining SC-IDMA with orthogonal ...

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Section: Impulsive Noise Channel Modelmentioning

confidence: 99%

C7. Performance analysis of Single Carrier and multicarrier IDMA signals in non-Gaussian impulsive channel

Hagras¹

2015

2015 32nd National Radio Science Conference (NRSC)

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“…Take reference signal (RS) collision as an example, if two UEs have selected the same RS, not only would the UE detection be affected but the channel estimation (CE) could be so deviated that the equalization in the context of high-overloading would be deteriorated even for the detected UE and therefore the detected UE would be highly unlikely to be successfully decoded whatever socalled advanced receiver such as MPA/EPA/ESE receiver is employed. The impact of channel estimation accuracy on the performance of the relevant receivers have been investigated in the literature [10][11][12]. To address the above critical issues, the paper [6] provided four potential discriminations among multiplexed UEs committing AGF transmissions and furthermore introduced a state-of-art blind MUD which can achieve AGF-HOL even at single receive antenna case.…”

Section: Introductionmentioning

confidence: 99%

Blind Multi-user Detection Based on Receive Beamforming for Autonomous Grant-Free High-Overloading Multiple Access

Zeng

Liu

et al. 2019

2019 IEEE 2nd 5G World Forum (5GWF)

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Massive number of internet of things (IoT) devices are expected to simultaneously connect to the mMTC and beyond future generations of wireless network, posing severe challenge to aspects such as RACH procedure, user equipment detection and channel estimation. Although spatial combining has provided significant gains in conventional grant-based transmission, this technique is stuck in dilemma when it comes to autonomous grant-free transmission tailored for IoT use cases. To address this, blind spatial combining and its incorporation in the dataonly MUD are elaborated in this paper answering to both the academic and industry's concern in the overloading potential of autonomous grant-free (AGF) transmission. Blind spatial combining could be interpreted as blind receive beamforming heuristically. Simulation results show that the blind spatial combining enhanced data-only MUD performance for AGF transmission is rather impressive.

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“…In the UWB literature, the channel estimation has mostly been studied for its effect on error rate performance and computational complexity [8][9][10][11][12][13][14][15][16][17][18]. For example, a minimum mean square error (MMSE) estimator has been proposed by Beek et al, and they have shown that it gives a performance gain over least square (LS) estimator [8].…”

Section: Introductionmentioning

confidence: 99%

Low-complexity WLMMSE channel estimator for MB-OFDM UWB systems

Hajjaj

Chainbi

Bouallègue

2015

J Wireless Com Network

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Widely linear (WL) minimum mean square error (MMSE) channel estimation scheme have been proposed for multiband orthogonal frequency division multiplexing ultra-wideband (MB-OFDM UWB) systems dealing with non-circular signals. This WLMMSE channel estimation scheme provides significant performance gain, but it presents a high computational complexity compared with the linear one. In this paper, we derive an adaptive WLMMSE channel estimation scheme that significantly reduces the computational complexity. The complexity reduction is done in two stages. The first stage consists of a real evaluation of the WLMMSE channel estimator and the second stage follows with a reduced-rank filtering based on the singular value decomposition (SVD). Computational complexity evaluation shows that the proposed low-rank real-valued WLMMSE channel estimator has computation cost comparable with the linear MMSE. Additionally, simulations of the bit error rate (BER) performance show comparable performance with the WLMMSE channel estimator especially at high signal-to-noise ratio (SNR).

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Channel Estimation Algorithms for OFDM-IDMA: Complexity and Performance

Cited by 34 publications

References 21 publications

C7. Performance analysis of Single Carrier and multicarrier IDMA signals in non-Gaussian impulsive channel

C7. Performance analysis of Single Carrier and multicarrier IDMA signals in non-Gaussian impulsive channel

Blind Multi-user Detection Based on Receive Beamforming for Autonomous Grant-Free High-Overloading Multiple Access

Low-complexity WLMMSE channel estimator for MB-OFDM UWB systems

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