Linear prediction and entropy coding are widely used in speech, audio, video, and other coding systems. Rate-distortion optimization (RDO) is another relevant coding technique, which is especially useful when the encoder performance depends on tunable parameters from distinct coding subsystems. This work presents a RDO strategy for variable-rate encoders that use linear prediction and entropy coding. The method is suitable when the signal to be compressed is short-term stationary and the prediction order varies over signal segments. The second contribution of this paper is to customize the method to the compression of time-domain OFDM-based cellular radio signals. The method enables a 4G / 5G packet-based fronthaul to flexibly trade-off rate and distortion depending on the amount of information to be transmitted and, consequently, benefit from multiplexing gain. Simulation results with LTE signals show that the proposed method achieves improved performance with a relatively low computational cost, while operating with time-domain signals under different traffic scenarios.

This paper discusses the application of long short-term memory (LSTM) neural networks to maintain the synchronization of a real-time clock in holdover operation, that is, while the timing reference input of the clock is unavailable. The approach trains the LSTM network based on timestamps acquired while the slave clock is locked to its reference input coming from a master clock. When the slave clock loses its reference and enters holdover mode, the LSTM takes over and controls the clock. We evaluate the method on a testbed consisting of IEEE 1588 Precision Time Protocol (PTP) clocks based on field-programmable gate arrays (FPGA), where we collect nanosecond-accurate timestamps for offline analysis. We evaluate two oscillator stability scenarios: when the PTP clocks rely on oven-controlled crystal oscillators (OCXOs) and when they use crystal oscillators (XOs). In both cases, we demonstrate that the algorithm can sustain the clock synchronization accuracy within reasonable limits over intervals of 1000 seconds in two different temperature scenarios.

The 5G mobile communication systems introduce multiples functional splits between base station elements, new transmission bands and a large number of antennas supporting beamforming. In this scenario, a viable strategy to avoid building penetration losses is to deploy the antenna elements indoor and use a fronthaul link to establish the connection between them and the rest of the radio access network. This work explores a 5G fronthaul scheme based on analog radio over coaxial cables, leveraging this existing fixed access infrastructure to facilitate 5G deployments. Results indicate that the fronthaul solution discussed here is able to support the high capacity requirement of 5G, with the additional benefits of low transmit power, low latency and low cost. The main novelty of this paper is to investigate the potential of this kind of fronthaul architecture considering both the physical layer and economic aspects.

Massive Beamforniing systems in next-generation Centralized Radio Access Network architectures can substantially increase the requirements on fronthaul interfaces. This paper proposes a novel and efficient model to reduce the overhead generated by the transfer of beamforming coefficients between base station elements. This approach creates a compressed version for said coefficients, in the beamspace domain, using a clustering strategy over sets (windows) of most powerful beams. The method can be extended for any antenna configuration and also parameterized to adjust the trade-off between radio performance and the fronthaul load reduction. Simulations in typical mobile outdoor scenarios show that the proposed method can present Signal-to-Interference-plus-Noise-Ratio performance comparable to the usual dimensionality reduction approach, i.e., beam selection, while generating lower fronthaul overhead.

Provisioning of cost-effective Ethernet-based fronthaul by reusing the LAN infrastructure available in most commercial buildings is challenging predominantly in terms of the required bandwidth and synchronization. In contrast to a synchronous fronthaul, a PTP-based Ethernet network must cope with estimation noise introduced by packet delay variation (PDV) for synchronization recovery. The SYNC packet used for PTP on such networks is expected to suffer from significant PDV due to the fronthaul traffic and other background traffic. Further challenges are met when the switches adopted in the network do not support PTP and therefore synchronization can only be done by end-devices. Focusing on this scenario, this paper analyzes the problems that may affect the time offset estimation accuracy and presents schemes to mitigate these problems. The performance is evaluated through a self-developed FPGA-based testbed and the results suggest that the end-to-end PTP approach can fulfill the less strict time alignment requirements of 3GPP standards if PDV is handled properly.

The evolving broadband access systems using copper networks are currently deployed in a frequency band that goes up to 30 MHz, as specified in VDSL2. As hybrid fiber-copper architectures become more important in the industry and academia, using shorter loop lengths (i.e. up to 250 meters) from the last distribution point to users enables adopting even higher frequencies to achieve very high data rates of 500 Mbps and beyond, as is the case with the G.fast standard under development by ITU-T. In this work, a time-domain simulator has been developed to evaluate G.fast system performance. System capacity is evaluated with different cyclic extension lengths and different reference loop topologies specified by ITU-T. The simulation results show that G.fast systems are robust to bridgetaps and capable of providing very high data rates for all simulated loop topologies to support next generation ultra high speed broadband services.