Several new architectures are under investigation for cloud radio access networks, assuming distinct splits of functionality among the network elements. Consequently, the research on radio data compression for the fronthaul is based on assumptions that correspond to a wide variety of tradeoffs among data rate, signal distortion, latency and computational cost. This paper describes a method for LTE downlink point-topoint signal compression based on linear prediction and Huffman coding, which is suitable for low cost encoding and decoding units with stringent restrictions on power consumption. The proposed method can work at various compression factors, such as 3.3:1 at an average EVM of 0.9%, or 4:1 at an average EVM of 2.1%.
G.fast is a new standard from the International Telecommunication Union, which targets 1 Gb/s over short copper loops using frequencies up to 212 MHz. This new technology requires accurate parametric cable models for simulation, design, and performance evaluation tests. Some existing copper cable models were designed for the very high speed digital subscriber line spectra, i.e., frequencies up to 30 MHz, and adopt assumptions that are violated when the frequency range is extended to G.fast frequencies. This paper introduces a simple and causal cable model that is able to accurately characterize copper loops composed by single or multiple segments, in both frequency and time domains. Results using G.fast topologies show that, apart from being accurate, the new model is attractive due to its low computational cost and closed-form expressions for fitting its parameters to measurement data.
Abstract-This paper presents a fronthaul signal compression scheme based on linear prediction coding (LPC) adapted to orthogonal frequency division multiplexing (OFDM) signals. The proposed method is capable of providing fine tuning of the compression factor, which is an alternative to legacy compression methods that tune the compression factor by changing the discrete number of bits of the quantizer and, consequently, are only able to do so with coarse resolution.
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