In this paper, we investigate the sum secrecy rate of cloud and edge processing in multi-cell downlink system by adopting the artificial noise precoding (ANP). To elaborate, in cloud processing, we consider the outdated channel state information (CSI) inevitably induced by fronthaul link which connects the cloud and radio units in return for centralized processing. In contrast, the radio unit (RU) in edge processing only manages its cell using timely but local CSI. Furthermore, under per-antenna power constraint, we obtain a sub-optimal precoder to maximize the sum-secrecy rate for both processing. With simulation results, we compare the secrecy rate of the cloud and edge processing, and show that the sub-optimal precoder performs well.

With increasing demand for wireless connectivity, IEEE 802.11 WLANs have become ubiquitous and continue to grow in number. This leads to high density of basic service sets with the significant co-channel interference (CCI) among them. This paper sheds light on the CCI caused by 802.11 MAC ACK frames, which has been less studied than the CCI caused by data frames. Based on stochastic geometry analysis, we propose Quiet ACK (QACK), a dynamic transmit power control algorithm for ACK frames. Fine-grained transmit power adjustment is enabled by CCI detection and CCI power estimation in the middle of a data frame reception. Our prototype using software-defined radio shows the feasibility and performance gain of QACK, i.e., 1.5x higher throughput than the legacy 802.11 WLANs. The performance of QACK is further evaluated in more general WLAN environments via extensive simulations using ns-3.

Antenna selection, a cost-effective way to enhance network performance, has been employed in a limited manner in wireless local area networks (WLANs) due to the lack of channel information at the transmitter. In this paper, a practical antenna selection system without using channel information is proposed. We first describe the practical issues of antenna selection system for infrastructure-based WLANs, and then, analyze its characteristics through an extensive measurement study. Based on that, we propose antenna selection algorithms of access point (AP) for both (1) unicast transmission and (2) multicast transmission and reception. The proposed algorithms are comparatively evaluated using prototype implementation in a commercial AP. It is demonstrated that the proposed algorithms achieve up to 34% throughput gain and 54% frame error rate reduction for unicast and multicast transmissions, respectively.

The demand for wireless communication in enterprise network has been increasing with the advent of "smart working environment." In enterprise wireless network, high density of access points (APs) and limited frequency resources raise the significance of radio resource management. In this paper, we propose a centralized algorithm, named JACPoT, for jointly executable transmission power control (TPC) and dynamic channel assignment (DCA). The proposed algorithm first guarantees that the links between AP and client stations are robust to the inter-cell interference via TPC. Next, it minimizes contention between neighboring APs via DCA utilizing a novel metric and annealed Gibbs sampler. Through ns-3 simulations based on measurement in real office environments, we demonstrate that JACPoT reduces uplink frame collisions by 36% and significantly enhances both data and voice traffic delivery.

Various packet-based simulation tools (e.g., NS-3) have been employed for design, validation, and evaluation of new protocols for WiFi networks since they offer cost efficiency, scalability, and reproducibility. These benefits come, however, at the expense of lack of realism compared to live testbed experiments. This is attributed in a major part to the difficulty of capturing detailed characteristics of channel dynamics, bit-level protocol specification (PHY layer), and application/user behaviors in a high-fidelity manner. The performance gap predicted by simulation and live testbed becomes even more pronounced when one considers a wide diversity of device characteristics and the way each device is used by end users. For example, smartphones generally show worse WiFi performance than other WiFi devices (e.g., laptops and tablets) because smartphones suffer from additional signal loss due to hand-grips and the low antenna gains of their embedded antennas. The goal of this study is to significantly close the gap by incorporating survey- and measurement-based smartphone WiFi characteristics and realistic hand-grip models into traditional WiFi network simulators (NS-3 in this study). The enhanced WiFi simulation tool's performance prediction capability is validated through an comparative study between testbed experiments and simulations.

Home-based monitoring of the changes on patients' state using non-invasive sensing device such as actigraph is important in the aspect of treatment, management and prevention of many chronic diseases. Although the characteristics of the internal structures (i.e., the fluctuation or the trend) inherent in the actigraphic signal can significantly represent the disease state, there has been no method to extract and analyze such information. In this paper, we have proposed a novel approach to determine the disease state based on features extracted from the structural information of patient's actigraphic data by utilizing a mathematical time-series modeling method, autoregressive (AR)-generalized conditional heteroskedascity (GARCH) models. Our approach consists of three steps: finding structural breaks as the disease state transitions, building a time-series model for each local segment, and analyzing the features from each model to classify the severity of the disease. We have applied the proposed method to the actigraphic data of the patients with depressive disorder, and the experimental results showed that features extracted by our modeling method have played an important role to discriminate disease severities.