In a typical installation of an 802.11 based WLAN (Wireless Local Area Network), mobile hosts would access the network through APs (Access Points), even when two mobile stations communicate within the same WLAN. Effectively, all the packets in a WLAN are required to forward through the AP according to the MAC (Medium Access Control) layer protocol. Since the AP has the same priority as the other mobile stations to access the channel, the AP usually becomes a bottleneck in WLANs and the network performance degrades significantly. In this paper, we propose a new MAC layer protocol for WLANs in order to improve the throughput performance. Theoretical analysis and simulation results show that our new protocol works much better in WLAN than the standard DCF.

With the rising popularity of delay-sensitive real-time multimedia applications (video, voice, and data) in IEEE 802.11 wireless local area networks (WLANs), it is becoming important to study the medium access control (MAC) layer delay performance of WLANs. The MAC layer delay can be classified into two categories: 1) medium access delay, and 2) delay at interface queue (IFQ). In this paper, based on a two-dimensional chain model, we analyze the medium access delay and give a method to calculate the IFQ delay. The proposed analysis is applicable to both the basic access and the RTS/CTS access mechanisms. Through extensive simulations, we evaluate our model. The simulation results show that our analysis is extremely accurate for both basic access and RTS/CTS access mechanism of the 802.11 DCF protocol.

Cellular heterogeneous networks (HetNets) with densely deployed small cells can effectively boost network capacity. The co-channel interference and the prominent energy consumption are two crucial issues in HetNets which need to be addressed. Taking the traffic variations into account, this paper proposes a theoretical framework to analyze spectral efficiency (SE) and energy efficiency (EE) considering jointly further-enhanced inter-cell interference coordination (FeICIC) and spectrum allocation (SA) via a stochastic geometric approach for a two-tier downlink HetNet. SE and EE are respectively derived and validated by Monte Carlo simulations. To create spectrum and energy efficient HetNets that can adapt to traffic demands, a non-convex optimization problem with the power control factor, resource partitioning fraction and number of subchannels for the SE and EE tradeoff is formulated, based on which, an iterative algorithm with low complexity is proposed to achieve the sub-optimal solution. Numerical results confirm the effectiveness of the joint FeICIC and SA scheme in HetNets. Meanwhile, a system design insight on resource allocation for the SE and EE tradeoff is provided.

Point cloud video contains not only color information but also spatial position information and usually has large volume of data. Typical rate distortion optimization algorithms based on Human Visual System only consider the color information, which limit the coding performance. In this paper, a Coding Tree Unit (CTU) level quantization parameter (QP) adjustment algorithm based on JND and spatial complexity is proposed to improve the subjective and objective quality of Video-Based Point Cloud Compression (V-PCC). Firstly, it is found that the JND model is degraded at CTU level for attribute video due to the pixel filling strategy of V-PCC, and an improved JND model is designed using the occupancy map. Secondly, a spatial complexity detection metric is designed to measure the visual importance of each CTU. Finally, a CTU-level QP adjustment scheme based on both JND levels and visual importance is proposed for geometry and attribute video. The experimental results show that, compared with the latest V-PCC (TMC2-18.0) anchors, the BD-rate is reduced by -2.8% and -3.2% for D1 and D2 metrics, respectively, and the subjective quality is improved significantly.

Memristor is drawing more and more attraction nowadays after HP Laboratory announced its invention. Since then many researchers are taking efforts to find its applications in various areas of the information technology. Among the important applications, one of the interesting issues is the research on memristor circuits. To put forward such research, there is an urgent demand to establish a memristor SPICE model, such that people could conduct SPICE simulation to obtain the performance of the memristor circuits under their investigation. This paper reports our efforts to meet the urgent demand. Based on the memristor device fabrication technology parameters, as well as the theoretical description on memristor, we first propose memristor SPICE models, then verify the effectiveness of the proposed models by applying it to some memristor circuits. Simulation results are satisfactory.

In this letter, we improve NF'07 (Nakanishi and Funabiki) VLR group signature scheme such that it satisfies exculpability and has lower computation costs. In the proposed scheme, a group member generates his own private key together with the group manager in order to realize exculpability while the signature size is not made longer. Also, a new revocation check method is proposed at the step of verifying, and the computation costs of verifying are independent of the number of the revoked members, while they are linear with the number of the revoked members in the original scheme. Thus, the proposed scheme is more efficient than the original scheme and can be applicable to mobile environments such as IEEE 802.1x.

In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.

As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.

The commonly used Deep Q Networks is known to overestimate action values under certain conditions. It's also proved that overestimations do harm to performance, which might cause instability and divergence of learning. In this paper, we present the Deep Sarsa and Q Networks (DSQN) algorithm, which can considered as an enhancement to the Deep Q Networks algorithm. First, DSQN algorithm takes advantage of the experience replay and target network techniques in Deep Q Networks to improve the stability of neural networks. Second, double estimator is utilized for Q-learning to reduce overestimations. Especially, we introduce Sarsa learning to Deep Q Networks for removing overestimations further. Finally, DSQN algorithm is evaluated on cart-pole balancing, mountain car and lunarlander control task from the OpenAI Gym. The empirical evaluation results show that the proposed method leads to reduced overestimations, more stable learning process and improved performance.