Intelligent applications rely significantly on the live machine learning pipeline, a couple of deep neural network (DNN) inference services, executed on mobile devices to meet functional requirements while ensuring user data privacy. However, executing these DNN services on resource-constrained mobile devices presents a considerable challenge: low throughput and high energy consumption of inference tasks. To address this issue, we proposeNiagara+, a novel system designed to enhance throughput by jointly scheduling DNN inference services across heterogeneous processors on mobile devices and offloading services to powerful edge servers. To achieve this,Niagara+encounters two critical challenges: unpredictable workload dynamics and high scheduling complexity. To effectively tackle these challenges,Niagara+employs a predictive model to forecast incoming workload patterns and orchestrates service allocation across device heterogeneous processors and edge servers through a combination of two-step offline scheduling optimization and online service dispatching strategies. We implementedNiagara+and conducted comprehensive experiments, demonstrating its superiority over state-of-the-art approaches, reducing DNN service latency by up to 2.6× under high-bandwidth networks and 9.1× under low-bandwidth networks, while consistently meeting stringent inference latency requirements.

A Code Division Multiple Access (CDMA) downlink receiver based on Adaptive Overlap-Cut (AOC) Method Frequency Domain Equalization (FDE) and non-linear feedback Parallel Interference Cancellation (PIC) is proposed. Firstly, the analysis for the generation and the distribution of the inherent error of Overlap-Cut (OC) Method FDE carries out the improved version of it called AOC Method FDE. Secondly, one PIC scheme with non-linear feedback is designed. After the nonlinear processing, the users' feedback information during the PIC iteration will be adjusted according to its reliability. As a result of both theoretical analysis and simulation, AOC Method FDE outperforms the original OC Method with lower complexity, and the non-linear feedback PIC improves the performance of the receiver further.

In this paper, we propose a modification of overlap-cut method (OC method) frequency domain equalization (FDE) to reduce complexity in code division multiple access (CDMA) downlink systems. FDE based on fast Fourier transform/inverse fast Fourier transform (FFT/IFFT) carries out single-tap equalization on received signal, moreover, its complexity is much lower than its time-domain counterpart. The analysis for the generation and the distribution of the inherent error of OC method FDE indicates some improvement to the equalizer. As a result of both theoretical analysis and simulation, we obtain a better performance with lower computation burden than conventional algorithms.

Based on the m-sequence, a time-domain channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) is designed. The m-sequence with cyclic prefix (CP) is inserted between OFDM symbols in time domain as training sequence. The cross-correlation is calculated between the received sequence with CP removed and the appointed circular shifted m-sequence, which takes reliability of the estimation of channel impulse response on account of the two-valued auto-correlation property of the m-sequence, thereby the channel frequency response can be advisably decided. As a result of both theoretical analysis and simulation, we obtain an impressive performance with less system overhead and lower computation burden than conventional algorithms.