In this paper, the linear complexity and minimal polynomials of Legendre sequences over Fq have been calculated, where q = pm and p is a prime number. Our results show that Legendre sequences have high linear complexity over Fq for a large part of prime power number q so that they can resist the linear attack method.

CMOS SoCs can reduce power consumption while maintaining performance by adopting voltage scaling (VS) technologies. The operating speed of the level converter (LC) strongly affects the effectiveness of VS technologies. However, PVT variations can cause serious problems to the LC, because the state-of-the-art LC designs do not give enough attention to this issue. In this work, we proposed to analyze the impact of PVT variations on the performance of the LC using a previously developed heuristic sizing methodology. Based on the evaluation results from different operating corners with different offset voltages and temperatures, we proposed a variation-tolerant LC that achieves both high performance and low energy with a high tolerability for PVT variations.

In this letter, we study the impact of feedback error on transmit beamforming systems with finite rate feedback. The feedback channel is modeled as a uniform symmetric channel. The outage probability, bit error rate (BER), diversity gain, and array gain formulas are given. Both analytical and simulation results show that feedback error with small probability will make the system behave badly at high signal-to-noise ratios (SNR).

Attenuated and delayed versions of the pulse signal overlap in multipath propagation. Previous algorithms can resolve them only if signal sampling is ideal, but fail to resolve two counterparts with non-ideal sampling. In this paper, we propose a novel method which can resolve the general types of non-ideally sampled pulse signals in the time domain via Taylor Series Expansion (TSE) and estimate multipath signals' precise time delays and amplitudes. In combination with the CLEAN algorithm, the overlapped pulse signal parameters are estimated one by one through an iteration method. Simulation results verify the effectiveness of the proposed method.

In order to realize an Internet-of-Things (IoT) with tiny sensors integrated in our buildings, our clothing, and the public spaces, battery lifetime and battery size remain major challenges. Power reduction in IoT sensor nodes is determined by both sleep mode as well as active mode contributions. A power state machine, at the system level, is the key to achieve ultra-low average power consumption by alternating the system between active and sleep modes efficiently. While, power consumption in the active mode remains dominant, other power contributions like for timekeeping in standby and sleep conditions are becoming important as well. For example, non-conventional critical blocks, such as crystal oscillator (XO) and resistor-capacitor oscillator (RCO) become more crucial during the design phase. Apart from power reduction, low-voltage operation will further extend the battery life. A 2.4GHz multi-standard radio is presented, as a test case, with an average power consumption in the µW range, and state-of-the-art performance across a voltage supply range from 1.2V to 0.9V.

We regard a De Bruijn sequence of order n as a bijection on $mathbb{F}_2^n$ and consider the transition mappings between them. It is shown that there are only two conjugate transformations that always transfer De Bruijn sequences to De Bruijn sequences.

Call admission control (CAC) is becoming vital for multimedia services in the ability of wireless/mobile networks to guarantee Quality of Service (QoS) partially due to the network's limited capacity. In this paper, we propose an optimal call admission control scheme with bandwidth reallocation algorithm (multi-class-CAC-BRA) for multi-classes of adaptive multimedia services in wireless/mobile networks. The multi-class-CAC-BRA approach optimizes revenue for service providers and satisfies QoS requirements for service users. The proposed approach adopts semi-Markov Decision Process to model both call admission control and bandwidth reallocation algorithm. In other words, whenever decisions are made, decisions are made for both call admission control and bandwidth reallocation. Since the non-adaptive multimedia traffic is a special case of the adaptive multimedia traffic, the non-adaptive optimal CAC scheme is a special case of our optimal multi-class-CAC-BRA scheme. Furthermore, the Interior-point Method in linear programming is used to solve the optimal decision problem. Simulation results reveal that the proposed multi-class-CAC-BRA scheme adapts itself well to adaptive multi-class multimedia traffic, achieves optimal revenue, and satisfies QoS requirements that are the upper bounds of handoff dropping probabilities. Our approach solves the optimal adaptive multimedia CAC problem. We believe that this work has both theoretical and practical significance.

In recent years, Graph Neural Networks has received enormous attention from academia for its huge potential of modeling the network traits such as macrostructure and single node attributes. However, prior mainstream works mainly focus on homogeneous network and lack the capacity to characterize the network heterogeneous property. Besides, most previous literature cannot the model latent influence link under microscope vision, making it infeasible to model the joint relation between the heterogeneity and mutual interaction within multiple relation type. In this letter, we propose a latent influence based self-attention framework to address the difficulties mentioned above. To model the heterogeneity and mutual interactions, we redesign the attention mechanism with latent influence factor on single-type relation level, which learns the importance coefficient from its adjacent neighbors under the same meta-path based patterns. To incorporate the heterogeneous meta-path in a unified dimension, we developed a novel self-attention based framework for meta-path relation fusion according to the learned meta-path coefficient. Our experimental results demonstrate that our framework not only achieves higher results than current state-of-the-art baselines, but also shows promising vision on depicting heterogeneous interactive relations under complicated network structure.

Wireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.