Ultra Reliable Low Latency Communications in Massive Multi-Antenna Systems

Bana, Alexandru-Sabin; Xu, Guixian; Carvalho, Elisabeth de; Popovski, Petar

doi:10.1109/acssc.2018.8645402

Cited by 21 publications

(21 citation statements)

References 19 publications

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“…Simulation results show that the BER performance of the LBL receiver is very close to that of maximum likelihood receiver while maintaining a linear latency processing time in contrast to other existing schemes in the literature. BER PIC [9] MMSE [8] AMP [15] EP [14] LBL EP [11] ML [7] (a) N = 24, K = 8, M = 4.…”

Section: Discussionmentioning

confidence: 99%

“…obs , and λ (t) , respectively. Now, we estimate the soft symbols of x based on the Gaussian likelihood function in (7). The soft symbol estimates and its variance are given as…”

Section: Expectation Propagation Receivermentioning

confidence: 99%

“…Massive multiple-input-multiple-output (M-MIMO) technology has been proposed to support ultra reliability and low latency (URLLC) data transmissions. The reduction of minimum transmission time intervals (TTI) and bit-error-rate (before coding) requirements from 15 ms and 10 −3 to 1 ms and 10 −5 in 4G and 5G NR URLLC [2]- [7], respectively, pose a challenging problem in developing an M-MIMO receiver. Two types of M-MIMO receivers have been investigated in the literature; classical and Bayesian learning receivers.…”

Section: Introductionmentioning

confidence: 99%

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A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

Kosasih

Hardjawana

Vucetic

et al. 2020

2020 IEEE Wireless Communications and Networking Conference (WCNC)

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Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multipleinput-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum 2 dB and 19 times for various M-MIMO system configurations.

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Section: Discussionmentioning

confidence: 99%

“…obs , and λ (t) , respectively. Now, we estimate the soft symbols of x based on the Gaussian likelihood function in (7). The soft symbol estimates and its variance are given as…”

Section: Expectation Propagation Receivermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

Kosasih

Hardjawana

Vucetic

et al. 2020

2020 IEEE Wireless Communications and Networking Conference (WCNC)

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“…It will be considered that the rank of R l , r l , is equal to the number of users, similarly to [57]. Therefore, Λ l is only going to have r l non-zero diagonal elements.…”

Section: System Modelmentioning

confidence: 99%

Massive Wireless Energy Transfer With Statistical CSI Beamforming

Monteiro

López

Alves

2021

IEEE J. Sel. Top. Signal Process.

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Wireless energy transfer (WET) is a promising solution to enable massive machine-type communications (mMTC) with low-complexity and low-powered wireless devices. Given the energy restrictions of the devices, instant channel state information at the transmitter (CSIT) is not expected to be available in practical WET-enabled mMTC. However, because it is common that the terminals appear spatially clustered, some degree of spatial correlation between their channels to the base station (BS) is expected to occur. The paper considers a massive antenna array at the BS for WET that only has access to i) the first and second order statistics of the Rician channel component of the multiple-input multiple-output (MIMO) channel and also to ii) the line-of-sight MIMO component. The optimal precoding scheme that maximizes the total energy available to the singleantenna devices is derived considering a continuous alphabet for the precoders, permitting any modulated or deterministic waveform. This may lead to some devices in the clusters being assigned a low fraction of the total available power in the cluster, creating a rather uneven situation among them. Consequently, a fairness criterion is introduced, imposing a minimum amount of power allocated to the terminals. A piece-wise linear harvesting circuit is considered at the terminals, with both saturation and a minimum sensitivity, and a constrained version of the precoder is also proposed by solving a non-linear programming problem. A paramount benefit of the constrained precoder is the encompassment of fairness in the power allocation to the different clusters. Moreover, given the polynomial complexity increase of the proposed unconstrained precoder, and the observed linear gain of the system's available sum-power with an increasing number of antennas at the ULA, the use of massive antenna arrays is desirable.

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“…Essentially, there is a tradeoff between the detection reliability and the processing delay. Improving the fundamental trade-off between reliability and processing delay is crucial for the fifth generation (5G) cellular networks [4], [5], where the requirement on the bit error rate (BER) lies in the range of 10 −7 − 10 −5 and the processing delay should be lower than the duration of each transmission time interval (TTI) ranging from 0.125 ms to 1 ms in 5G New Radio [6], [7].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

Kosasih,

Miloslavskaya,

Hardjawana

et al. 2021

Preprint

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The stringent requirements on reliability and processing delay in the fifth-generation (5G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-meansquare-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference cancellation scheme, and a decision statistics combining concept. We then develop a high performance M-MIMO receiver, integrating the proposed detector with a low complexity sequential decoding for polar codes. Simulation results of the proposed detector show a significant performance gain compared to other low complexity detectors. Furthermore, the proposed M-MIMO receiver with sequential decoding ensures one order magnitude lower complexity compared to a receiver with stack successive cancellation decoding for polar codes from the 5G New Radio standard.

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Ultra Reliable Low Latency Communications in Massive Multi-Antenna Systems

Cited by 21 publications

References 19 publications

A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

Massive Wireless Energy Transfer With Statistical CSI Beamforming

A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

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