Massive Access for 5G and Beyond

Chen, Xiaoming; Ng, Derrick Wing Kwan; Yu, Wei; Larsson, Erik G.; Al-Dhahir, Naofal; Schober, Robert

doi:10.48550/arxiv.2002.03491

Cited by 15 publications

(18 citation statements)

References 209 publications

(210 reference statements)

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“…In addition, the SNRs of the simulation 10 Considering the requirement of the coverage area and the adopted carrier frequency of the RFID reader [8], [28], we assume that M = 8 antennas are equipped at the reader. results are defined in (4), and the relative coefficient is set as ζ = −20 dB, as defined in (5).…”

Section: Numerical Resultsmentioning

confidence: 99%

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

Liu,

Wei,

et al. 2020

Preprint

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Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems.However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

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Section: Numerical Resultsmentioning

confidence: 99%

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

Liu,

Wei,

et al. 2020

Preprint

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“…D UE to the growing interest in Internet-of-Things (IoT) applications, there has been an intense research effort on massive machine-type communications (mMTC) for 5G networks and beyond [1]- [3]. In these networks, standard medium access control protocols that recover the individual messages of participating devices require spectral resources that scale at least linearly with the number of active users [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks

Dommel

Utkovski

Simeone

et al. 2021

IEEE Signal Process. Lett.

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A fog-radio access network (F-RAN) architecture is studied for an Internet-of-Things (IoT) system in which wireless sensors monitor a number of multi-valued events and transmit in the uplink using grant-free random access to multiple edge nodes (ENs). Each EN is connected to a central processor (CP) via a finite-capacity fronthaul link. In contrast to conventional information-agnostic protocols based on separate source-channel (SSC) coding, where each device uses a separate codebook, this paper considers an information-centric approach based on joint source-channel (JSC) coding via a non-orthogonal generalization of type-based multiple access (TBMA). By leveraging the semantics of the observed signals, all sensors measuring the same event share the same codebook (with non-orthogonal codewords), and all such sensors making the same local estimate of the event transmit the same codeword. The F-RAN architecture directly detects the events' values without first performing individual decoding for each device. Cloud and edge detection schemes based on Bayesian message passing are designed and trade-offs between cloud and edge processing are assessed.

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“…The fifth-generation wireless communication is a major breakthrough for realizing pervasive wireless communication systems to meet the demands for ultra-high data rate [1]- [3]. In particular, fulfilling such requirement needs more dedicated spectral resources for mobile communication services.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, fulfilling such requirement needs more dedicated spectral resources for mobile communication services. On the other hand, the paucity of spectrum resources has turned resource allocation to one of the most challenging problems in wireless communication design [3]. To address this issue, in-band full duplex (IBFD) is considered as a promising technology providing a viable solution to tackle this challenge.…”

Section: Introductionmentioning

confidence: 99%

“…where U * n can be obtained from each objective, respectively. Note that Ûn (p, q) is the convexified objective function 3 for data rate in downlink and uplink. Since the optimization problem in ( 22) is convex and satisfies the Slater's constraint qualification [24], it can be effectively solved using optimization packages incorporating interior-point methods such as CVX [25] with polynomial time computational complexity.…”

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confidence: 99%

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Performance Trade-off Between Uplink and Downlink in Full-Duplex Communications

Khalili,

Mili,

2020

Preprint

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In this paper, we formulate two multi-objective optimization problems (MOOPs) in orthogonal frequency-division multiple access (OFDMA)-based in-band full-duplex (IBFD) wireless communications. The aim of this study is to exploit the performance trade-off between uplink and downlink where a wireless radio simultaneously transmits and receives in the same frequency. We consider maximizing the system throughput as the first MOOP and minimizing the system aggregate power consumption as the second MOOP between uplink and downlink, while taking into account the impact of selfinterference (SI) and quality of service provisioning. We study the throughput and the transmit power trade-off between uplink and downlink via solving these two problems. Each MOOP is a nonconvex mixed integer non-linear programming (MINLP) which is generally intractable. In order to circumvent this difficulty, a penalty function is introduced to reformulate the problem into a mathematically tractable form. Subsequently, each MOOP is transformed into a single-objective optimization problem (SOOP) via the weighted Tchebycheff method which is addressed by majorization-minimization (MM) approach. Simulation results demonstrate an interesting trade-off between the considered competing objectives.

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Massive Access for 5G and Beyond

Cited by 15 publications

References 209 publications

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks

Performance Trade-off Between Uplink and Downlink in Full-Duplex Communications

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