Modeling and Analysis of Content Caching in Wireless Small Cell Networks

Network densification with small cell base stations is a promising solution to satisfy future data traffic demands. However, increasing small cell base station density alone does not ensure better users quality-of-experience and incurs high operational expenditures. Therefore, content caching on different network elements has been proposed as a mean of offloading he backhaul by caching strategic contents at the network edge, thereby reducing latency. In this paper, we investigate cache-enabled small cells in which we model and characterize the outage probability, defined as the probability of not satisfying users requests over a given coverage area. We analytically derive a closed form expression of the outage probability as a function of signal-to-interference ratio, cache size, small cell base station density and threshold distance. By assuming the distribution of base stations as a Poisson point process, we derive the probability of finding a specific content within a threshold distance and the optimal small cell base station density that achieves a given target cache hit probability. Furthermore, simulation results are performed to validate the analytical model.Comment: accepted for publication, IEEE ISWCS 201

Fast Packet Retransmissions in LTE

The cellular networks are evolving to meet the future requirements of data rate,coverage and capacity. The fourth generation mobile communication system, LTEhas been developed to meet these goals. LTE uses multiple antenna features andlarger bandwidths in order to accomplish this task. These features will furtherextend the requirements of data rate, coverage, latency and flexibility. LTE also utilizes the varying quality of the radio channel and the interferencefrom other transmitters by adapting the data rate to the instantaneous channelquality at all the time. This is typically referred to as Link Adaptation. Thelink adaptation fails from time to time due to the varying channel quality as wellas the interference from other transmitters. In order to counteract these failures,retransmission methods are employed. These methods detect the errors on thereceiver side and signals the transmitter for the retransmission of the erroneousdata. The efficiency of link adaptation increases if combined with a properly designedretransmission scheme at the expense of delays due to retransmissions. This master thesis focuses on the study of the retransmission schemes with fasterfeedback, resulting in a reduction in delay. The feedback is generated by makingan early estimate of the decoding outcome and sending it early to the transmitterresulting in faster retransmission. This is important in certain applications wherethe data transmission is intolerant to delays.The thesis work shows by system performance simulations that fast packet retransmission,precisely called Early HARQ Feedback, significantly affects the systemperformance together with the utilization of the link adaptation. The study alsoshows that the link adaptation, in certain scenarios, can be optimized to improvethe system performance. In that respect, it is also possible to increase the numberof retransmissions within the same resource utilization. That optimization is basicallycalled aggressive link adaptation. Consequently, Early HARQ Feedback incombination with aggressive link adaptation provides a large improvement in thedownlink performance of the studied cases

Techno-economic analysis of visible light communications

This paper investigates the main actors, emerging markets and applications of visible light communication (VLC). VLC is a relatively new optical wireless communication technology where light emitting diodes (LEDs) are used to transmit data. Investigating the emerging trends such as technical, political, social, economic, environmental, and regulatory, scenario planning tool is used to develop multiple plausible scenarios using the key identified uncertainties. Scenario planning tool is used for long range business planning and decision making under conditions of substantial uncertainty. In this paper, we apply Schoemaker's scenario planning method to identify and analyze the key uncertainties and to construct four different and plausible future scenarios for VLC. From the value point of view, the developed four scenarios have shown different implications to the actors (operator, device manufacturer, content provider ) of VLC technology and their business models

Device-to-device assisted mobile cloud framework for 5G networks

Abstract Due to the upsurge of context-aware and proximity aware applications, device-to-device (D2D) enabled mobile cloud (MC) emerges as next step towards future 5G system. There are many applications for such MC based architecture but mobile data offloading is one of the most prominent one especially for ultra dense wireless networks. The proposed system exploits the short range links to establish a cluster based network between the nearby devices, adapts according to environment and uses various cooperation strategies to obtain efficient utilization of resources. We proposed a novel architecture of MC in which the total coverage area of a eNB is divided into several logical regions (clusters). Furthermore, UEs in the cluster are classified into Primary Cluster Head (PCH), Secondary Cluster Head (SCH) and Standard UEs (UEs). Each cluster is managed by selected PCH and SCH. An algorithm is proposed for the selection of PCH and SCH which is based on signal-to-interference-plus-noise (SINR) and residual energy of UEs. Finally each PCH and SCH distributes data in their respective regions by efficiently utilizing D2D links. Simulation results demonstrate that the proposed D2D-enabled MC based approach yields significantly better gains in terms of data rate and energy efficiency as compared to the classical cellular approach

Learning-based caching in cloud-aided wireless networks

Abstract This letter studies content caching in cloud-aided wireless networks, where small cell base stations with limited storage are connected to the cloud via limited capacity fronthaul links. By formulating a utility (inverse of service delay) maximization problem, we propose a cache update algorithm based on spatio-temporal traffic demands. To account for the large number of contents, we propose a content clustering algorithm to group similar contents. Subsequently, with the aid of regret learning at small cell base stations and the cloud, each base station caches contents based on the learned content popularity subject to its storage constraints. The performance of the proposed caching algorithm is evaluated for sparse and dense environments, while investigating the tradeoff between global and local class popularity. Simulation results show 15% and 40% gains in the proposed method compared to various baselines