Joint Channel Estimation, Activity Detection and Data Decoding Based on Dynamic Message-Scheduling Strategies for mMTC

Renna, Roberto B. Di; Lamare, Rodrigo C. de

doi:10.1109/tcomm.2022.3151775

Cited by 16 publications

(5 citation statements)

References 55 publications

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“…The received signal at the output of the filter, contains the desired symbol, residual co-user interference and noise. We use similar assumptions given in [4], [7], [14], [19], [20], [21], [22] to approximate the ŝk as an AWGN channel given by…”

Section: Iterative Detection and Decodingmentioning

confidence: 99%

Iterative Detection and Decoding for Cell-Free Massive MIMO Using LDPC Codes

Ssettumba¹,

Renna²,

Landau³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This paper proposes an iterative detection and decoding (IDD) scheme for a cell free massive multiple input multiple output (CF-mMIMO) system. Users send coded data to the access points (APs), which is jointly detected at central processing unit (CPU). The symbols are exchanged iteratively in the form of log likelihood ratios (LLRs) between the detector and the lowdensity parity check codes (LPDC) decoder, increasing the coded system's performance. We propose a list-based multi-feedback diversity with successive interference cancellation (MF-SIC) to improve the performance of the CF-mMIMO. Furthermore, the proposed detector is compared with the parallel interference cancellation (PIC) and MF-PIC schemes. Finally, the bit error rate (BER) performance of CF-mMIMO is compared with the co-located mMIMO (Col-mMIMO).

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Section: Iterative Detection and Decodingmentioning

confidence: 99%

Iterative Detection and Decoding for Cell-Free Massive MIMO Using LDPC Codes

Ssettumba¹,

Renna²,

Landau³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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show abstract

“…Including imperfect CSIT to the recursion of power allocation coefficients, increases robustness against CSIT uncertainties. The detection procedure can employ several strategies that differ in the way the interference is mitigated [29], [30], [31], [32], [33], [34], [36], [35], [37], [38].…”

Section: B Robust Adaptive Power Allocation (R-apa)mentioning

confidence: 99%

Robust Power Allocation and Precoding for Cell-Free and Multi-Cell Massive MIMO Systems

Almeida¹,

Lamare²

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Multi-Cell (MC) systems are present in mobile network operations from the first generation to the fifth generation of wireless networks, and considers the signals of all users to a base station (BS) centered in a cell. Cell-Free (CF) systems works with a large number of distributed antennas serving users at the same time. In this context, Multiple-input multipleoutput (MIMO) techniques are used in both topologies and result in performance gains and interference reduction. In order to achieve the benefits mentioned, proper precoder design and power allocation techniques are required in the downlink (DL). In general, DL schemes assume perfect channel state information at the transmitter (CSIT), which is not realistic. This paper studies MC and CF with MIMO systems equipped with linear precoders in the DL and proposes an adaptive algorithm to allocate power in the presence of imperfect CSIT. The proposed robust adaptive power allocation outperforms standard adaptive and uniform power allocation. Simulations also compare the performance of both systems frameworks using minimum mean-square error (MMSE) precoders with robust adaptive power allocation and adaptive power allocation.

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“…[58], [59], [60], [61] is performed to subtract the common information from the received signal. We assume that perfect CSI is available at the receiver for simplicity [14].…”

Section: Rs For Cell-free Architecturesmentioning

confidence: 99%

Cluster Precoders for Cell-Free MU-MIMO Systems

Flores

Lamare

Mishra

2022

2022 International Symposium on Wireless Communication Systems (ISWCS)

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In this paper, we address two crucial challenges in the design of cell-free (CF) systems: degradation in the performance of CF systems by imperfect channel state information at the transmitter (CSIT) and high computational/signaling loads arising from the increasing number of distributed antennas and parameters to be exchanged. To mitigate the effects of imperfect CSIT, we employ rate-splitting (RS) multiple-access, which separates the messages into common and private streams. Unlike prior works, we present a clustered CF multi-user multiple-antenna framework with RS, which groups the transmit antennas in several clusters to reduce the computational and signaling loads. The proposed RS-CF system employs one common stream per cluster to exploit the network diversity. Furthermore, we propose new cluster-based linear precoders for this framework. We then devise a power allocation strategy for the common and private streams within clusters and derive closed-form expressions for the sum-rate performance of the proposed cluster-based RS-CF system. Numerical results show that the proposed clustered RS-CF system and algorithms outperform existing approaches.

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Joint Channel Estimation, Activity Detection and Data Decoding Based on Dynamic Message-Scheduling Strategies for mMTC

Cited by 16 publications

References 55 publications

Iterative Detection and Decoding for Cell-Free Massive MIMO Using LDPC Codes

Iterative Detection and Decoding for Cell-Free Massive MIMO Using LDPC Codes

Robust Power Allocation and Precoding for Cell-Free and Multi-Cell Massive MIMO Systems

Cluster Precoders for Cell-Free MU-MIMO Systems

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