Distributed Detection in Massive MIMO Wireless Sensor Networks Under Perfect and Imperfect CSI

Chawla, Apoorva; Patel, Adarsh; Jagannatham, Aditya K.; Varshney, Pramod K.

doi:10.1109/tsp.2019.2924588

Cited by 29 publications

(24 citation statements)

References 32 publications

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“…• The NP criterion and the GLRT frameworks are subsequently exploited for designing fusion rules for the detection of known and unknown spatially correlated parameters, respectively, with perfect CSI. This is different from [32], [33] that derive only the NP criterion based fusion rules using the local sensor decisions. • To enhance the detection performance at the FC, an optimization framework is also developed for deriving the optimal sensor gains, which is absent in [13], [14], [30], [32] that are based on equal sensor power allocation.…”

Section: B Main Contributionsmentioning

confidence: 93%

“…• This work considers a sensor network wherein multiple sensors transmit their amplified analog observations to the FC for the detection of a spatially correlated parameter. This is in contrast to [30]- [33], which consider the transmission of local sensor decisions to the FC. The proposed framework considers the effects of parameter correlation, which naturally exists due to the spatial proximity of the sensor nodes.…”

Section: B Main Contributionsmentioning

confidence: 99%

“…Furthermore, massive MIMOs facilitate a significant transmit power reduction for the devices, proportional to the number of antennas at the BS, without compromising the asymptotic rate of the users [24]- [26]. Hence, there has been a growing interest in employing massive MIMOs in WSNs [27]- [33]. In this context, Jiang et al [27] investigated the estimation and detection performance of a WSN using a massive antenna array, wherein the sensors communicate over a coherent MAC, followed by the performance characterization of the NP and energy detectors, and the associated linear minimum mean squared error (LMMSE) estimator.…”

Section: A State-of-the-art Reviewmentioning

confidence: 99%

“…Ciuonzo et al [31] have developed sub-optimal fusion rules for massive antenna based WSNs, wherein the channel is modeled by Rician fading. The authors of [32], [33] have analyzed the performance of the NP criterion based simplified fusion rules for low-SNR distributed detection in massive MIMO WSNs, incorporating also the reliabilities of the local sensor decisions. Furthermore, Dey et al [34] propose and analyze the performance of sub-optimum decision fusion techniques conceived for an orthogonal frequency division multiplexing (OFDM) based collaborative wideband spectrum sensing system, wherein the DFC is equipped with a massive antenna array.…”

Section: A State-of-the-art Reviewmentioning

confidence: 99%

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Distributed Parameter Detection in Massive MIMO Wireless Sensor Networks Relying on Imperfect CSI

Chawla

Sarode

Jagannatham

et al. 2021

IEEE Trans. Wireless Commun.

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Distributed parameter detection is conceived for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs), where multiple sensors collaborate to detect the presence/ absence of a spatially correlated parameter. Neyman-Pearson (NP) and generalized likelihood ratio test (GLRT)-based detectors are developed at the fusion center (FC) for known and unknown parameter detection scenarios, respectively. More explicitly, the GLRT detector also has to estimate the unknown parameter value. Closed-form expressions are derived for the probabilities of detection (PD) and false alarm (PF A) in order to characterize the performance of the proposed schemes. Furthermore, the optimal sensor transmit gains are determined for maximising the detection performance attained. An asymptotic performance analysis is carried out for determining the gain scaling laws for the massive MIMO WSN considered, when the number of antennas tends to infinity. The proposed framework is also extended to the realistic imperfect channel knowledge scenario at the FC, followed by the development of the associated fusion rules and analytical results to characterize the performance. Our simulation results closely tally the theoretical findings.Index Terms-Wireless sensor networks (WSNs), massive multiple-input multiple-output (MIMO), generalized likelihood ratio test (GLRT), Neyman-Pearson (NP) criterion.

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Section: B Main Contributionsmentioning

confidence: 93%

Section: B Main Contributionsmentioning

confidence: 99%

Section: A State-of-the-art Reviewmentioning

confidence: 99%

Section: A State-of-the-art Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Parameter Detection in Massive MIMO Wireless Sensor Networks Relying on Imperfect CSI

Chawla

Sarode

Jagannatham

et al. 2021

IEEE Trans. Wireless Commun.

Self Cite

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show abstract

“…Among the candidate techniques, the MMSE detector represents a linear option while lattice reduction-aided successive interference cancellation (LR-SIC) [23][24][25][26][27][28] provides nonlinear solutions with good tradeoff between performance and complexity for channels with large coherence blocks. The use of low-complexity, energy-efficient ADCs is also foreseen to be appealing in case of energy-constrained receive sides, such as cluster heads in massive MIMO-based wireless sensor networks (WSNs), as studied in [29][30][31][32].In general, more advanced detectors lead to improved bit error rate (BER) performance for MIMO systems (with either ideal or low-resolution ADCs) but also higher computational complexity and higher power consumption. It is challenging to compare the different schemes and choose the one that yields the best balance between them.…”

mentioning

confidence: 99%

On the Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Lattice Reduction Aided Detectors

et al. 2020

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As an effective technology for boosting the performance of wireless communications, massive multiple-input multiple-output (MIMO) systems based on symmetric antenna arrays have been extensively studied. Using low-resolution analog-to-digital converters (ADCs) at the receiver can greatly reduce hardware costs and circuit complexity to further improve the energy efficiency (EE) of the system. There are significant research on the design of MIMO detectors but there is limited study on their performance in terms of EE. This paper studies the effect of signal detection on the EE in practical systems, and proposes to apply several signal detectors based on lattice reduction successive interference cancellation (LR-SIC) to massive MIMO systems with low-precision ADCs. We report results on their achievable EE in fading environments with typical modeling of the path loss and detailed analysis of the power consumption of the transceiver circuits. It is shown that the EE-optimal solution depends highly on the application scenarios, e.g., the number of antennas employed, the cell size, and the signal processing efficiency. Consequently, the signal detector must be properly selected according to the application scenario to maximize the system EE. In addition, medium-resolution ADCs should be selected to balance their own power consumption and the associated nonlinear distortion to maximize the EE of system. Symmetry 2020, 12, 406 2 of 20 Significant work has been done on the symbol detection in massive MIMO systems with low-precision ADCs. A class of techniques recover the transmitted symbols directly from the nonlinear model. In [13], iterative decision feedback receiver is studied for quantized MIMO systems. In [14], message passing dequantization (MPDQ) is applied to achieve multiuser detection for systems employing single-bit quantization. In [15], generalized approximate message passing is employed for massive MIMO with low-resolution ADCs. On the other hand, the tremendous detection algorithms designed for MIMO systems with ideal ADCs [16][17][18][19][20][21][22] can also be directly applied to quantized systems by using a linearized additive quantization noise model (AQNM). This will result in certain loss in the effective signal-to-noise ratio (SNR) but may offer solutions with moderate complexity, which are feasible for practical applications. Among the candidate techniques, the MMSE detector represents a linear option while lattice reduction-aided successive interference cancellation (LR-SIC) [23][24][25][26][27][28] provides nonlinear solutions with good tradeoff between performance and complexity for channels with large coherence blocks. The use of low-complexity, energy-efficient ADCs is also foreseen to be appealing in case of energy-constrained receive sides, such as cluster heads in massive MIMO-based wireless sensor networks (WSNs), as studied in [29][30][31][32].In general, more advanced detectors lead to improved bit error rate (BER) performance for MIMO systems (with either ideal or low-resolution ADCs) bu...

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Achieving Maximum Sum Spectral Efficiency with Channel Estimation

Taneja

Rana

Saluja

2021

Advanced Computing and Intelligent Technologies

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The major challenges faced by the mobile users are the poor quality of communication and the fast battery drainage. The varying channel characteristics in wireless communication is the main reason for huge signal drops and poor signal quality. The channel estimation plays an important role and is crucial for determining the accuracy of the system. In this paper, the two channel estimation techniques, namely least square (LS) and minimum mean square error (MMSE) are analyzed for multi-antenna communication scenario. The different practical channel scenarios are considered to evaluate the performance of the proposed system in terms of average sum spectral efficiency (SE). It is observed that the maximum average sum SE is obtained with MMSE channel estimation for Rician fading channels. The impact of varying channel statistics with spatial correlation is also evaluated for different pilot reuse factors. Further, the comparison of different receive combiners based on the channel estimates for signal detection is performed in terms of computational complexity.

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Distributed Detection in Massive MIMO Wireless Sensor Networks Under Perfect and Imperfect CSI

Cited by 29 publications

References 32 publications

Distributed Parameter Detection in Massive MIMO Wireless Sensor Networks Relying on Imperfect CSI

Distributed Parameter Detection in Massive MIMO Wireless Sensor Networks Relying on Imperfect CSI

On the Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Lattice Reduction Aided Detectors

Achieving Maximum Sum Spectral Efficiency with Channel Estimation

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