arXiv subjects

Seung-Woo Ko

Publications and source records attributed to Seung-Woo Ko.

41 records · Page 3Linked to original sources

arXiv2017

Cognitive Random Access for Internet-of-Things Networks

This paper focuses on cognitive radio (CR) internet- of-things (IoT) networks where spectrum sensors are deployed for IoT CR devices, which do not have enough hardware capability to identify an unoccupied spectrum by themselves. In this sensor- enabled IoT CR network, the CR devices and the sensors are separated. It induces that spectrum occupancies at locations of CR devices and sensors could be different. To handle this difference, we investigate a conditional interference distribution (CID) at the CR device for a given measured interference at the sensor. We can observe a spatial correlation of the aggregate interference distribution through the CID. Reflecting the CID, we devise a cognitive random access scheme which adaptively adjusts transmission probability with respect to the interference measurement of the sensor. Our scheme improves area spectral efficiency (ASE) compared to a conventional ALOHA and an adaptive transmission scheme which attempts to send data when the sensor measurement is lower than an interference threshold.

cs.NI↗

arXiv2017

Live Prefetching for Mobile Computation Offloading

The conventional designs of mobile computation offloading fetch user-specific data to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching is proposed in this paper that seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.

cs.IT↗

arXiv2017

Sense-and-Predict: Opportunistic MAC Based on Spatial Interference Correlation for Cognitive Radio Networks

Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at secondary receivers (RXs), especially when their association distance is long. To cope with the issue, this paper proposes a novel MAC called sense-and-predict (SaP), where each secondary TX decides whether to access or not based on the prediction of the interference level at RX. Firstly, we provide the spatial interference correlation in a probabilistic form using stochastic geometry, and utilize it to maximize the area spectral efficiency (ASE) for secondary networks while guaranteeing the service quality of primary networks. Through simulations and testbed experiments using USRP, SaP is shown to always achieve ASE improvement compared with the conventional TX based sensing.

cs.IT↗

arXiv2016

User Attraction via Wireless Charging in Cellular Networks

A strong motivation of charging depleted battery can be an enabler for network capacity increase. In this light we propose a spatial attraction cellular network (SAN) consisting of macro cells overlaid with small cell base stations that wirelessly charge user batteries. Such a network makes battery depleting users move toward the vicinity of small cell base stations. With a fine adjustment of charging power, this user spatial attraction (SA) improves in spectral efficiency as well as load balancing. We jointly optimize both enhancements thanks to SA, and derive the corresponding optimal charging power in a closed form by using a stochastic geometric approach.

cs.IT↗

arXiv2015

Traffic Convexity Aware Cellular Networks: A Vehicular Heavy User Perspective

Rampant mobile traffic increase in modern cellular networks is mostly caused by large-sized multimedia contents. Recent advancements in smart devices as well as radio access technologies promote the consumption of bulky content, even for people in moving vehicles, referred to as vehicular heavy users. In this article the emergence of vehicular heavy user traffic is observed by field experiments conducted in 2012 and 2015 in Seoul, Korea. The experiments reveal that such traffic is becoming dominant, captured by the 8.62 times increase in vehicular heavy user traffic while the total traffic increased 3.04 times. To resolve this so-called vehicular heavy user problem (VHP), we propose a cell association algorithm that exploits user demand diversity for different velocities. This user traffic pattern is discovered first by our field trials, which is convex-shaped over velocity, i.e. walking user traffic is less than stationary or vehicular user traffic. As the VHP becomes severe, numerical evaluation verifies the proposed user convexity aware association outperforms a well-known load balancing association in practice, cell range expansion (CRE). In addition to the cell association, several complementary techniques are suggested in line with the technical trend toward 5G.

cs.NI↗