Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond

Rappaport, Theodore S.; Xing, Yunchou; Kanhere, Ojas; Ju, Shihao; Madanayake, Arjuna; Mandal, Soumyajit; Alkhateeb, Ahmed; Trichopoulos, Georgios C.

doi:10.1109/access.2019.2921522

Cited by 1,702 publications

(1,142 citation statements)

References 147 publications

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“…3(b), the encoder parameters are trained according to the trained and frozen decoder parameters by using the stored values of l 0 and l 1 layers in the first step in a supervised learning setup. Here, the precoder in the transmitter is determined by the parameters W (1) e . Then, the coded bits are transmitted using a pulse shaping filter p(t) over an AWGN channel.…”

Section: A Autoencoder-based Code Designmentioning

confidence: 99%

“…The autoencoder architecture, which is composed of 6 layers as illustrated in Fig. 3(a), can be expressed layer-by-layer as l 0 :z (0) = s, x (1) = φ 0 (z (0) ) = s l 1 :z (1) = θ (1) x (1) + b (1) , x (2) = Q(φ 1 (z (1) ) + n (1) ) l 2 :z (2) = θ (2)…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

“…n , b (1) , · · · , b (5) }, and L(·) is the loss function. In parallel, the output z (5) is updated as z (5) n = z (5) n−1 + ∇ Θ n−1 (z (5) n−1 )(Θ n − Θ n−1 ).…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

“…Wireless communication systems are trending towards ever higher carrier frequencies, due to the large bandwidths available [1]. These high frequencies are made operational by the use of large co-phased antenna arrays to enable directional beamforming.…”

Section: Introductionmentioning

confidence: 99%

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Autoencoder-Based Error Correction Coding for One-Bit Quantization

Balevi

Andrews

2020

IEEE Trans. Commun.

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This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in the receivers. Specifically, it is first shown that the optimum error correction code that minimizes the probability of bit error can be obtained by perfectly training a special autoencoder, in which "perfectly" refers to converging the global minima. However, perfect training is not possible in most cases.To approach the performance of a perfectly trained autoencoder with a suboptimum training, we propose utilizing turbo codes as an implicit regularization, i.e., using a concatenation of a turbo code and an autoencoder. It is empirically shown that this design gives nearly the same performance as to the hypothetically perfectly trained autoencoder, and we also provide a theoretical proof of why that is so. The proposed coding method is as bandwidth efficient as the integrated (outer) turbo code, since the autoencoder exploits the excess bandwidth from pulse shaping and packs signals more intelligently thanks to sparsity in neural networks. Our results show that the proposed coding scheme at finite block lengths outperforms conventional turbo codes even for QPSK modulation. Furthermore, the proposed coding method can make one-bit quantization operational even for 16-QAM.

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Section: A Autoencoder-based Code Designmentioning

confidence: 99%

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

“…n , b (1) , · · · , b (5) }, and L(·) is the loss function. In parallel, the output z (5) is updated as z (5) n = z (5) n−1 + ∇ Θ n−1 (z (5) n−1 )(Θ n − Θ n−1 ).…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autoencoder-Based Error Correction Coding for One-Bit Quantization

Balevi

Andrews

2020

IEEE Trans. Commun.

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“…A map of spatially correlated SF with the BS and UT locations. The map of spatially correlated SF is generated by filtering a map of independent SF using an exponential function in(3). SF [dB]∼N(0,4) in a UMi LOS scenario.…”

mentioning

confidence: 99%

A Millimeter-Wave Channel Simulator NYUSIM with Spatial Consistency and Human Blockage

Ju¹,

Kanhere²,

Xing³

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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112

105

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Accurate channel modeling and simulation are indispensable for millimeter-wave wideband communication systems that employ electrically-steerable and narrow beam antenna arrays. Three important channel modeling components, spatial consistency, human blockage, and outdoor-to-indoor penetration loss, were proposed in the 3rd Generation Partnership Project Release 14 for mmWave communication system design. This paper presents NYUSIM 2.0, an improved channel simulator which can simulate spatially consistent channel realizations based on the existing drop-based channel simulator NYUSIM 1.6.1. A geometry-based approach using multiple reflection surfaces is proposed to generate spatially correlated and time-variant channel coefficients. Using results from 73 GHz pedestrian measurements for human blockage, a four-state Markov model has been implemented in NYUSIM to simulate dynamic human blockage shadowing loss. To model the excess path loss due to penetration into buildings, a parabolic model for outdoorto-indoor penetration loss has been adopted from the 5G Channel Modeling special interest group and implemented in NYUSIM 2.0. This paper demonstrates how these new modeling capabilities reproduce realistic data when implemented in Monte Carlo fashion using NYUSIM 2.0, making it a valuable measurement-based channel simulator for fifth-generation and beyond mmWave communication system design and evaluation.

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Beamforming and Precoding Techniques

2020

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Beamforming and precoding/combining are techniques aimed at processing multiantenna signals at the transmitter and/or at the receiver of a wireless communication system. While they have been routinely used to improve performance in current and previous generations of mobile communications systems, they are expected to play a more fundamental role in 5th Generation (5G) New Radio (NR) cellular systems, whose functionalities have been defined in the first phase of 3GPP 5G standardization process. Besides operating in traditional cellular sub‐6 GHz frequency band, 5G NR has been natively designed to work in the higher millimeter‐wave (MMW) band. At lower frequencies, multiantenna techniques for 5G NR are mainly refinements of those originally designed for 4G Long‐Term Evolution (LTE). On the contrary, to cope with the peculiarities of MMW scenarios, such as the larger number of antenna elements, the more directional transmission, and the higher path loss values, new dynamic, user‐specific, and computationally efficient multiantenna solutions and procedures have been incorporated in 5G NR specifications. In particular, since multiantenna techniques for 5G NR generally need detailed channel state information (CSI), a complete redesign of the set of reference signals and procedures used for CSI acquisition and reporting was carried out. 5G NR is continuously evolving and new features will be added, while the existing ones will be enhanced in the second phase of 5G standardization, with particular emphasis on beam management operations, reduction of CSI overhead, unconventional transmission methods, robustness against spatial correlation among channels, and software‐based reconfigurable antennas.

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Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond

Cited by 1,702 publications

References 147 publications

Autoencoder-Based Error Correction Coding for One-Bit Quantization

Autoencoder-Based Error Correction Coding for One-Bit Quantization

A Millimeter-Wave Channel Simulator NYUSIM with Spatial Consistency and Human Blockage

Beamforming and Precoding Techniques

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