To capture the small signal of smart sensor system, the high-precision sigma delta analog to digital converter (ΣΔ ADC) is the essential components. Based on the reinforcement learning (RL) algorithm, a high-efficiency design method for 16-bit ΣΔ ADC is proposed in this research. The key circuit modules in the ΣΔ ADC are extracted to be separately optimized to decrease the design complexity. The prior knowledge of circuits can be introduced into the search process of RL algorithm to accelerate the search speed. In addition, the deep neural network (DNN) models are trained to replace the circuit simulator to reduce the cost of simulation resource. Two key circuits in the ΣΔ ADC are optimized. Compared with the advanced method, the proposed optimization method has the fastest convergency and best performance, which demonstrates the effectiveness of the proposed method. In addition, the simulation time can be decreased by 98.3 %, which can greatly speed up the optimization process. The optimized ΣΔ ADC is taped out. The test results show that the effective resolution is 16.16 bit, and the voltage noise is 8.7e-05 V. Compared with the original chip, the effective resolution can be improved by 6.3%, and the noise voltage can be decreased by 47%. Therefore, the proposed optimization design method for ΣΔ ADC can greatly decrease the design cycle and improve the performance of ΣΔ ADC, which presents the enormous potential application in the internet of things.

We consider an underlay wiretap cognitive two-way relay network (CTWRN), where two secondary sources exchange their messages via multiple secondary decode-and-forward digital network coding relays in the presence of an eavesdropper by using a three-phase time division broadcast protocol and sharing the licensed spectrum of primary users. To mitigate eavesdropping attacks, an artificial noise (AN)-aided opportunistic relay selection scheme, called generalized max-min (GMM) relay selection is proposed to enhance physical layer security for the wiretap CTWRNs. The performance of the GMM scheme is analyzed, and evaluated by the exact closed-form outage probability and intercept probability. Additionally, we also provide asymptotic approximations for the outage probability and intercept probability at high signal-to-noise ratio. For comparison, we analyze the performance of the conventional max-min (MM) relay selection scheme as well. It is shown that the GMM scheme outperforms the MM scheme in terms of the security-reliability tradeoff (SRT), where the security and reliability are quantified by the intercept probability and outage probability, respectively. Moreover, the SRTs of the MM and GMM schemes can be substantially improved by increasing the number of secondary relays, while the improvement of the GMM scheme is more evident than that of the MM scheme.

Relay transmissions play an important role in many types of communication systems. In this paper, we consider packet-level coded transmissions over lossy relay links with the finite-buffer and coding coefficients delivery cost constraints. We propose a low-complexity coding scheme where packets are encoded from sequentially formed random subsets of source packets called batches. The relay recodes only from the buffered packets belonging to the same batch to maintain the code sparsity, lowering the packet header overhead and the decoding complexity compared to the random linear network coding (RLNC). To analyze the performance, we first propose an absorbing Markov chain model to analyze the RLNC transmission over finite-buffer relay links. The finite-length analysis not only provides a lower bound on the completion time using any sparser random codes but also characterizes each individual batch's transmission of the proposed code. Based on the analysis, another Markov chain is proposed to determine the decoding failure probability and the expected completion time of the batch coding scheme. As shown through analysis and simulations, the proposed scheme achieves higher effective end-to-end rates than RLNC when the coding coefficients delivery cost is considered and is also with much lowered decoding complexity thanks to its sparseness.

Along with the development of information technology, the traditional electrical grid is moving to smart grid technology. By using the smart grid, the users and utility providers can more efficiently manage and generate power. Along with the advantages, the smart grid is also faced with new security concerns. In the smart grid, the user's citizen identity information should be preserved and the offensive user should be traced. For some low‐capacity devices, it is indispensable to perform complicated computation by using outsourcing computation. The authors provide the outsourcing computation through public cloud. Anonymity and traceability are two important security properties in the smart grid. They are the unity of opposites. On the basis of the security requirements, they propose the balanced anonymity and traceability for outsourcing small‐scale data linear aggregation (BAT‐LA) in the smart grid. The formal definition, system model and security model are presented. Then, a concrete BAT‐LA protocol is designed by using the elliptic curve cryptography and proxy re‐encryption. Through security analysis and performance analysis, the designed BAT‐LA protocol is provably secure and efficient.

In the cooperative spectrum sensing, the bad channel environment where there are a few of malicious nodes, often results in deterioration of detection performance. This paper focuses on the spectrum sensing in the case malicious nodes and proposes an accumulated credibility-based weighted cooperative spectrum sensing algorithm. In the algorithm, the global fusion center weights the local detection result of each detection node according to its accumulated credibility. The higher the reliability of the node is, the larger the weight of the node is. It minimizes the harmful effects of accidental local detection errors. The simulation results show that the proposed algorithm can avoid the interference of malicious nodes and improve the spectrum sensing performance effective.

Spectrum sensing is an essential technique for cognitive radios. This paper addresses the spectrum sensing under uncertain noise power and proposes an adaptive spectrum sensing algorithm. The adaptive decision threshold of energy detection is derived, which is based on the weighted probability of detection and probability of false alarm. By means of estimating noise power and signal power, the decision threshold is able to adapt to the noise fluctuation. Simulation results show that the proposed algorithm has robustness to the noise uncertainty. It can be used for detecting various kinds of signal without any knowledge of noise power or signal power.