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Register a new userMulti-Gigabit Wireline Systems over Copper: An Interference Cancellation Perspective
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Added by
S.M. Zafaruddin
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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Interference cancellation is the main driving technology in enhancing the transmission rates over telephone lines above 100 Mbps. Still, crosstalk interference in multi-pair digital subscriber line (DSL) systems at higher frequencies has not been dealt with sufficiently. The upcoming G.(mg) fast DSL system envisions the use of extremely high bandwidth and full-duplex transmissions generating significantly higher crosstalk and self-interference signals. More powerful interference cancellation techniques are required to enable multi-gigabit per second data rate transmission over copper lines. In this article, we analyze the performance of interference cancellation techniques, with a focus on novel research approaches and design considerations for efficient interference mitigation for multi-gigabit transmission over standard copper lines. We also detail novel approaches for interference cancellation in the upcoming technologies.
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The Large Intelligent Surface (LIS) concept is a promising technology aiming to revolutionize wireless communication by exploiting spatial multiplexing at its fullest. Despite of its potential, due to the size of the LIS and the large number of antenna elements involved there is a need of decentralized architectures together with distributed algorithms which can reduce the inter-connection data-rate and computational requirement in the Central Processing Unit (CPU). In this article we address the uplink detection problem in the LIS system and propose a decentralize architecture based on panels, which perform local linear processing. We also provide the sum-rate capacity for such architecture and derive an algorithm to obtain the equalizer, which aims to maximize the sum-rate capacity. A performance analysis is also presented, including a comparison to a naive approach based on a reduced form of the matched filter (MF) method. The results shows the superiority of the proposed algorithm.
A photonic approach for radio-frequency (RF) self-interference cancellation (SIC) incorporated in an in-band full-duplex radio-over-fiber system is proposed. A dual-polarization binary phase-shift keying modulator is used for dual-polarization multiplexing at the central office (CO). A local oscillator signal and an intermediate-frequency signal carrying the downlink data are single-sideband modulated on the two polarization directions of the modulator, respectively. The optical signal is then transmitted to the remote unit, where the optical signals in the two polarization directions are split into two parts. One part is detected to generate the up-converted downlink RF signal, and the other part is re-modulated by the uplink RF signal and the self-interference, which is then transmitted back to the CO for the signal down-conversion and SIC via the optical domain signal adjustment and balanced detection. The functions of SIC, frequency up-conversion, down-conversion, and fiber transmission with dispersion immunity are all incorporated in the system. An experiment is performed. Cancellation depths of more than 39 dB for the single-tone signal and more than 20 dB for the 20-MBaud 16 quadrature amplitude modulation signal are achieved in the back-to-back case. The performance of the system does not have a significant decline when a section of 4.1-km optical fiber is incorporated.
Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. The resulting symbol detector is based on integrating dedicated machine-learning (ML) methods into the iterative SIC algorithm. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed ML-based MIMO receivers. Furthermore, in the presence of CSI uncertainty, DeepSIC significantly outperforms model-based approaches. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.
We propose and experimentally demonstrate an interference management system that removes wideband wireless interference by using photonic signal processing and free space optical communication. The receiver separates radio frequency interferences by upconverting the mixed signals to optical frequencies and processing the signals with the photonic circuits. Signals with GHz bandwidth are processed and separated in real-time. The reference signals for interference cancellation are transmitted in a free space optical communication link, which provides large bandwidth for multi-band operation and accelerates the mixed signal separation process by reducing the dimensions of the un-known mixing matrix. Experimental results show that the system achieves 30dB real-time cancellation depth with over 6GHz bandwidth. Multiple radio frequency bands can be processed at the same time with a single system. In addition, multiple radio frequency bands can be processed at the same time with a single system.
In this paper, we propose the joint interference cancellation, fast fading channel estimation, and data symbol detection for a general interference setting where the interfering source and the interfered receiver are unsynchronized and occupy overlapping channels of different bandwidths. The interference must be canceled before the channel estimation and data symbol detection of the desired communication are performed. To this end, we have to estimate the Effective Interference Coefficients (EICs) and then the desired fast fading channel coefficients. We construct a two-phase framework where the EICs and desired channel coefficients are estimated using the joint maximum likelihood-maximum a posteriori probability (JML-MAP) criteria in the first phase; and the MAP based data symbol detection is performed in the second phase. Based on this two-phase framework, we also propose an iterative algorithm for interference cancellation, channel estimation and data detection. We analyze the channel estimation error, residual interference, symbol error rate (SER) achieved by the proposed framework. We then discuss how to optimize the pilot density to achieve the maximum throughput. Via numerical studies, we show that our design can effectively mitigate the interference for a wide range of SNR values, our proposed channel estimation and symbol detection design can achieve better performances compared to the existing method. Moreover, we demonstrate the improved performance of the iterative algorithm with respect to the non-iterative counterpart.
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