GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

Nawaz, Syed Junaid; Tiwana, Moazzam Islam; Patwary, Mohammad; Khan, Noor M.; Tiwana, Mohsin I.; Haseeb, Abdul

doi:10.5755/j01.eie.22.1.14114

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…This makes the estimation and tracking of such channels a challenging task, where any error in channel estimate can significantly affect the symbol detection performance. The scope of GA VOLUME 7, 2019 for estimation of such channels has been investigated in the literature, see e.g., [84], [86] for GA based estimation of sparse channels (UWAC), etc. The scope of GA for intelligent cognitive radio has been encouraged in [87].…”

Section: ) Genetic Programmingmentioning

confidence: 99%

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

et al. 2019

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The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users' service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum-and QML-assisted B5G networks are identified and discussed.

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Section: ) Genetic Programmingmentioning

confidence: 99%

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

et al. 2019

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“…For the estimation of sparse underwater acoustic channels, a SiT based channel estimation technique is proposed in [9]. In [21], a genetic algorithms (GA) based sparse multipath channels estimation technique with SiT sequence has been presented. In [4], authors have proposed a compressed sampling based technique for sensing of sparse multipath channels with SiT for single-input single-output (SISO) systems.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Amin

Mansoor

Nawaz

et al. 2016

Wireless Pers Commun

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Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing (CS) theory and prior available knowledge of channel's sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error (NCMSE) are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2 dB, 3.5 dB, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for BER = 10 −1.9 , the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1 dB, 2.5 dB, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity.

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“…By exploiting the sparsity of wireless multipath channels, SiT sequence based compressive channel sensing methods have been studied in various contexts such as single-input single-output (SISO) systems [14,46], sparse MIMO channels [47,48], and underwater acoustic channels [49]. In [46], a genetic algorithm (GA) based channel estimation method is proposed using an SiT sequence for SISO systems.…”

Section: Introductionmentioning

confidence: 99%

“…In [46], a genetic algorithm (GA) based channel estimation method is proposed using an SiT sequence for SISO systems. In [14], a Dantzig selector (DS) algorithm based method is proposed for estimation of SISO sparse multipath channels using a known SiT sequence.…”

Section: Introductionmentioning

confidence: 99%

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

Mansoor¹,

Nawaz²,

Gulfam³

2017

Applied Sciences

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Massive multiple-input multiple-output (massive-MIMO) is foreseen as a potential technology for future 5G cellular communication networks due to its substantial benefits in terms of increased spectral and energy efficiency. These advantages of massive-MIMO are a consequence of equipping the base station (BS) with quite a large number of antenna elements, thus resulting in an aggressive spatial multiplexing. In order to effectively reap the benefits of massive-MIMO, an adequate estimate of the channel impulse response (CIR) between each transmit-receive link is of utmost importance. It has been established in the literature that certain specific multipath propagation environments lead to a sparse structured CIR in spatial and/or delay domains. In this paper, implicit training and compressed sensing based CIR estimation techniques are proposed for the case of massive-MIMO sparse uplink channels. In the proposed superimposed training (SiT) based techniques, a periodic and low power training sequence is superimposed (arithmetically added) over the information sequence, thus avoiding any dedicated time/frequency slots for the training sequence. For the estimation of such massive-MIMO sparse uplink channels, two greedy pursuits based compressed sensing approaches are proposed, viz: SiT based stage-wise orthogonal matching pursuit (SiT-StOMP) and gradient pursuit (SiT-GP). In order to demonstrate the validity of proposed techniques, a performance comparison in terms of normalized mean square error (NCMSE) and bit error rate (BER) is performed with a notable SiT based least squares (SiT-LS) channel estimation technique. The effect of channels' sparsity, training-to-information power ratio (TIR) and signal-to-noise ratio (SNR) on BER and NCMSE performance of proposed schemes is thoroughly studied. For a simulation scenario of: 4 × 64 massive-MIMO with a channel sparsity level of 80% and signal-to-noise ratio (SNR) of 10 dB, a performance gain of 18 dB and 13 dB in terms of NCMSE over SiT-LS is observed for the proposed SiT-StOMP and SiT-GP techniques, respectively. Moreover, a performance gain of about 3 dB and 2.5 dB in SNR is achieved by the proposed SiT-StOMP and SiT-GP, respectively, for a BER of 10 −2 , as compared to SiT-LS. This performance gain NCME and BER is observed to further increase with an increase in channels' sparsity.

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GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

Cited by 3 publications

References 6 publications

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

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