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Register a new userDigital Interference Mitigation in Space Division Multiplexing Self-Homodyne Coherent Detection
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Added by
Hanzi Huang
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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We propose a digital interference mitigation scheme to reduce the impact of mode coupling in space division multiplexing self-homodyne coherent detection and experimentally verify its effectiveness in 240-Gbps mode-multiplexed transmission over 3-mode multimode fiber.
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We experimentally demonstrate a 28-Gbaud 16-QAM self-homodyne digital radio-over- 33.6km-7-core-fiber system with entropy coding for mobile fronthaul, achieving error-free carrier aggregation of 330 100-MHz 4096-QAM 5G-new-radio channels and 921 100-MHz QPSK 5G-new-radio channels with CPRI-equivalent data rate up to 3.73-Tbit/s.
In this paper, a new modulation method defined as Ramanujan Periodic Subspace Division Multiplexing (RPSDM) is proposed using Ramanujan subspaces. Each subspace contains an integer valued Ramanujan Sum (RS) and its circular downshifts as a basis. The proposed RPSDM decomposes the linear time-invariant wireless channels into a Toeplitz stair block diagonal matrices, whereas Orthogonal Frequency Division Multiplexing (OFDM) decompose the same into diagonal. Advantages of such structured subspaces representation are studied and compared with an OFDM representation in terms of Peak-Average Power Ratio (PAPR) and Bit-Error-Rate (BER). Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) detectors are applied to evaluate the performance of OFDM and RPSDM techniques. Finally, the simulation results show that the proposed design (with an additional receiver complexity) outperforms OFDM under both detectors.
The ultimate performance of any wireless communication system is limited by electromagnetic principles and mechanisms. Motivated by this, we start from the first principles of wave propagation and consider a multiple-input multiple-output (MIMO) representation of a communication system between two spatially-continuous volumes of arbitrary shape and position. This is the concept of holographic MIMO communications. The analysis takes into account the electromagnetic noise field, generated by external sources, and the constraint on the physical radiated power. The electromagnetic MIMO model is particularized for a system with parallel linear sources and receivers in line-of-sight conditions. Inspired by orthogonal-frequency division-multiplexing, we assume that the spatially-continuous transmit currents and received fields are represented using the Fourier basis functions. In doing so, a wavenumber-division multiplexing (WDM) scheme is obtained whose properties are studied with the conventional tools of linear systems theory. Particularly, the interplay among the different system parameters (e.g., transmission range, wavelength, and sizes of source and receiver) in terms of number of communication modes and level of interference is studied. Due to the non-finite support of the electromagnetic channel, we prove that the interference-free condition can only be achieved when the receiver size grows to infinity. The spectral efficiency of WDM is evaluated via the singular-value decomposition architecture with water-filling and compared to that of a simplified architecture, which uses linear processing at the receiver and suboptimal power allocation.
The optical fibre is an essential tool for our communication infrastructure since it is the main transmission channel for optical communications. The latest major advance in optical fibre technology is spatial division multiplexing (SDM), where new fibre designs and components establish multiple co-existing data channels based on light propagation over distinct transverse optical modes. Simultaneously, there have been many recent developments in the field of quantum information processing (QIP), with novel protocols and devices in areas such as computing, communication and metrology. Here, we review recent works implementing QIP protocols with SDM optical fibres, and discuss new possibilities for manipulating quantum systems based on this technology.
A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. In this paper, the interference mitigation problem is tackled as a regression problem. Considering the complex-valued nature of radar signals, the complex-valued convolutional neural network is utilized as an architecture for implementation, which is different from the conventional real-valued counterparts. Meanwhile, as the useful beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared to the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.
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