Beyond Gbps Turbo Decoder on Multi-Core CPUs

International audienceThis paper presents a high-throughput implementation of a portable software turbo decoder. The code is optimized for traditional multi-core CPUs (like x86) and it is based on the Enhanced max-log-MAP turbo decoding variant. The code follows the LTE-Advanced specification. The key of the high performance comes from an inter-frame SIMD strategy combined with a fixed-point representation. Our results show that proposed multi-core CPU implementation of turbo-decoders is a challenging alternative to GPU implementation in terms of throughput and energy efficiency. On a high-end processor, our software turbo-decoder exceeds 1 Gbps information throughput for all rate-1/3 LTE codes with K < 4096

GRACE: Loss-Resilient Real-Time Video through Neural Codecs

In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal redundancy level in advance proves challenging. The latter reconstructs video from partially received frames, but dividing a frame into independently coded partitions inherently compromises compression efficiency, and the lost information cannot be effectively recovered by the decoder without adapting the encoder. We present a loss-resilient real-time video system called GRACE, which preserves the user's quality of experience (QoE) across a wide range of packet losses through a new neural video codec. Central to GRACE's enhanced loss resilience is its joint training of the neural encoder and decoder under a spectrum of simulated packet losses. In lossless scenarios, GRACE achieves video quality on par with conventional codecs (e.g., H.265). As the loss rate escalates, GRACE exhibits a more graceful, less pronounced decline in quality, consistently outperforming other loss-resilient schemes. Through extensive evaluation on various videos and real network traces, we demonstrate that GRACE reduces undecodable frames by 95% and stall duration by 90% compared with FEC, while markedly boosting video quality over error concealment methods. In a user study with 240 crowdsourced participants and 960 subjective ratings, GRACE registers a 38% higher mean opinion score (MOS) than other baselines

Potential deep learning approaches for the physical layer

Abstract. Deep learning based end-to-end learning of a communications system tries to optimize both transmitter and receiver blocks in a single process in an end-to-end manner, eliminating the need for artificial block structure of the conventional communications systems. Recently proposed concept of autoencoder based end-to-end communications is investigated in this thesis to validate its potential as an alternative to conventional block structured communications systems. A single user scenario in the additive white Gaussian noise (AWGN) channel is considered in this thesis. Autoencoder based systems are implemented equivalent to conventional communications systems and bit error rate (BER) performances of both systems are compared in different system settings. Simulations show that the autoencoder outperforms equivalent uncoded binary phase shift keying (BPSK) system with a 2 dB margin to BPSK for a BER of 10⁻⁵, and has comparable performance to uncoded quadrature phase shift keying (QPSK) system. Autoencoder implementations equivalent to coded BPSK have shown comparable BER performance to hard decision convolutional coding (CC) with less than 1 dB gap over the 0–10 dB Eb/N0 range. Autoencoder is observed to have close performance to the conventional systems for higher code rates. Newly proposed autoencoder model as an alternative to coded systems with higher order modulations has shown that autoencoder is capable of learning better transmission mechanisms compared to the conventional systems adhering to the system parameters and resource constraints provided. Autoencoder equivalent of half-rate 16-quadrature amplitude modulation (16-QAM) system achieves a better performance with respect to hard decision CC over the 0–10 dB Eb/N0 range, and a comparable performance to soft decision CC with a better BER in 0–4 dB Eb/N0. Comparable BER performance, lower processing complexity and low latency processing due to inherent parallel processing architecture, flexible structure and higher learning capacity are identified as advantages of the autoencoder based systems which show their potential and feasibility as an alternative to conventional communications systems

Dynamic task scheduling and binding for many-core systems through stream rewriting

This thesis proposes a novel model of computation, called stream rewriting, for the specification and implementation of highly concurrent applications. Basically, the active tasks of an application and their dependencies are encoded as a token stream, which is iteratively modified by a set of rewriting rules at runtime. In order to estimate the performance and scalability of stream rewriting, a large number of experiments have been evaluated on many-core systems and the task management has been implemented in software and hardware.In dieser Dissertation wurde Stream Rewriting als eine neue Methode entwickelt, um Anwendungen mit einer großen Anzahl von dynamischen Tasks zu beschreiben und effizient zur Laufzeit verwalten zu können. Dabei werden die aktiven Tasks in einem Datenstrom verpackt, der zur Laufzeit durch wiederholtes Suchen und Ersetzen umgeschrieben wird. Um die Performance und Skalierbarkeit zu bestimmen, wurde eine Vielzahl von Experimenten mit Many-Core-Systemen durchgeführt und die Verwaltung von Tasks über Stream Rewriting in Software und Hardware implementiert

Protocol stacks for power-aware wireless microsensor networks

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 71-72).In a distributed wireless sensor system, a need to prolong the lifetime of the network is crucial and limited by battery capacity. As communication traffic among sensor nodes is triggered by sensing events, the network can exploit these time-varying scenarios to obtain power savings by adjusting its operating conditions accordingly. A coherent design of application-specific network protocol stacks is the key. Specifically, embedding power aware features in the link layer and media access control (MAC) layer promises to extend the lifetime of the sensor network. The power-aware design will be illustrated on [mu]AMPS sensor node prototypes. With the integrated design framework, lower layers of the network stack provides configurable power-aware features to be controlled by higher network layers that maintain broaderview knowledge of the environment. TDMA has been chosen as a MAC Layer protocol for its inherited power-aware mechanism of radio shutdowns outside its TDMA slot and in absence of sensing events. Another level of power-aware features can be deployed in MAC ID and TDMA slot assignments. In a field of scattered sensor nodes, not all the nodes are in radio range of one another or of the base station. Hence, assigning N TDMA slots for the network of N sensor nodes that are not all in radio range will waste the receiver energy and link bandwidth. An algorithm for a re-use of MAC ID and MAC time slot is proposed based on the number of neighboring nodes. Hence, varying the number of neighboring nodes by varying the transmit power can optimize the system lifetime and bandwidth. An implementation of the Link and MAC infrastructure is completed. Power scalability is illustrated on [mu]AMPS node prototypes, with TDMA Media Access and a vehicle tracking application demonstration.by Phanaphat Piyada.M.Eng