JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

Research on SLM Algorithm for PAPR reduction in MB-OFDM UWB Systems

AbstractMultiband orthogonal frequency division multiplexing (MB-OFDM) is one of the key techniques of ultra wideband (UWB) systems. A major drawback of MB-OFDM technique is the high peak-to-average power ratio (PAPR) of the transmit signal. In this paper, a novel phase sequence of selected mapping algorithm which makes the side information not needed is designed to lower the PAPR of MB-OFDM UWB signals. It is also shown that comparable PAPR reduction performance with the original SLM algorithm can be achieved with a small increase in signal power. Simulation results show that there must be equilibriums between SLM computational complexity and PAPR performance. The objective of the new algorithm is to lower PAPR close to ordinary SLM technique with reduced computational complexity with little performance degradation and achieves better system resource utilization

The Error Performance and Fairness of CUWB Correlated Channels

AbstractThe symbol period becomes smaller compared to the channel delay in multiband orthogonal frequency division multiplexing (MB-OFDM) cognitive ultra wideband (CUWB) wireless communications, the transmitted signals experiences frequency-selective fading and leads to performance degradation. In this paper, a new design method for space-time trellis codes in MB-OFDM systems with correlated Rayleigh fading channels is introduced. This method converts the single output code symbol into several STTC code symbols, which are to be transmitted simultaneously from multiple transmitter-antennas. By using Viterbi optimal soft decision decoding algorithm, we investigate both quasi-static and interleaved channels and demonstrate how the spatial fading correlation affects the performance of space–time codes over these two different MB-OFDM wireless channel models. Simulation results show that the performance of space–time code is to be robust to spatial correlation. When the system bandwidth increases, the long term fairness quality will gradually become better and finally converges to 1