When Cellular Meets WiFi in Wireless Small Cell Networks

The deployment of small cell base stations(SCBSs) overlaid on existing macro-cellular systems is seen as a key solution for offloading traffic, optimizing coverage, and boosting the capacity of future cellular wireless systems. The next-generation of SCBSs is envisioned to be multi-mode, i.e., capable of transmitting simultaneously on both licensed and unlicensed bands. This constitutes a cost-effective integration of both WiFi and cellular radio access technologies (RATs) that can efficiently cope with peak wireless data traffic and heterogeneous quality-of-service requirements. To leverage the advantage of such multi-mode SCBSs, we discuss the novel proposed paradigm of cross-system learning by means of which SCBSs self-organize and autonomously steer their traffic flows across different RATs. Cross-system learning allows the SCBSs to leverage the advantage of both the WiFi and cellular worlds. For example, the SCBSs can offload delay-tolerant data traffic to WiFi, while simultaneously learning the probability distribution function of their transmission strategy over the licensed cellular band. This article will first introduce the basic building blocks of cross-system learning and then provide preliminary performance evaluation in a Long-Term Evolution (LTE) simulator overlaid with WiFi hotspots. Remarkably, it is shown that the proposed cross-system learning approach significantly outperforms a number of benchmark traffic steering policies

Enhancing Coexistence in the Unlicensed Band with Massive MIMO

We consider cellular base stations (BSs) equipped with a large number of antennas and operating in the unlicensed band. We denote such system as massive MIMO unlicensed (mMIMO-U). We design the key procedures required to guarantee coexistence between a cellular BS and nearby Wi-Fi devices. These include: neighboring Wi-Fi channel covariance estimation, allocation of spatial degrees of freedom for interference suppression, and enhanced channel sensing and data transmission phases. We evaluate the performance of the so-designed mMIMO-U, showing that it allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. The same is not true for conventional listen-before-talk (LBT) operations. As a result, mMIMO-U boosts the aggregate cellular-plus-Wi-Fi data rate in the unlicensed band with respect to conventional LBT, exhibiting increasing gains as the number of BS antennas grows.Comment: To appear in Proc. IEEE ICC 201

Caching with Unknown Popularity Profiles in Small Cell Networks

A heterogenous network is considered where the base stations (BSs), small base stations (SBSs) and users are distributed according to independent Poisson point processes (PPPs). We let the SBS nodes to posses high storage capacity and are assumed to form a distributed caching network. Popular data files are stored in the local cache of SBS, so that users can download the desired files from one of the SBS in the vicinity subject to availability. The offloading-loss is captured via a cost function that depends on a random caching strategy proposed in this paper. The cost function depends on the popularity profile, which is, in general, unknown. In this work, the popularity profile is estimated at the BS using the available instantaneous demands from the users in a time interval $[0,\tau]$. This is then used to find an estimate of the cost function from which the optimal random caching strategy is devised. The main results of this work are the following: First it is shown that the waiting time $\tau$ to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite, provided the user density is greater than a predefined threshold. In this case, $\tau$ is shown to scale as $N^2$, where $N$ is the support of the popularity profile. Secondly, a transfer learning-based approach is proposed to obtain an estimate of the popularity profile used to compute the empirical cost function. A condition is derived under which the proposed transfer learning-based approach performs better than the random caching strategy.Comment: 6 pages, Proceedings of IEEE Global Communications Conference, 201

A Multi-Game Framework for Harmonized LTE-U and WiFi Coexistence over Unlicensed Bands

The introduction of LTE over unlicensed bands (LTE-U) will enable LTE base stations (BSs) to boost their capacity and offload their traffic by exploiting the underused unlicensed bands. However, to reap the benefits of LTE-U, it is necessary to address various new challenges associated with LTE-U and WiFi coexistence. In particular, new resource management techniques must be developed to optimize the usage of the network resources while handling the interdependence between WiFi and LTE users and ensuring that WiFi users are not jeopardized. To this end, in this paper, a new game theoretic tool, dubbed as \emph{multi-game} framework is proposed as a promising approach for modeling resource allocation problems in LTE-U. In such a framework, multiple, co-existing and coupled games across heterogeneous channels can be formulated to capture the specific characteristics of LTE-U. Such games can be of different properties and types but their outcomes are largely interdependent. After introducing the basics of the multi-game framework, two classes of algorithms are outlined to achieve the new solution concepts of multi-games. Simulation results are then conducted to show how such a multi-game can effectively capture the specific properties of LTE-U and make of it a "friendly" neighbor to WiFi.Comment: Accepted for publication at IEEE Wireless Communications Magazine, Special Issue on LTE in Unlicensed Spectru

A Threshold Based Handover Triggering Scheme in Heterogeneous Wireless Networks

The widespread popularity of Wireless Local Area Network (WLAN) is recognized as an effective approach to complementing cellular networks for the high data rate and cost effective connectivity delivered to mobile users. Efficient handover and offloading schemes for integrated WLAN and cellular networks, referred to as Heterogeneous Wireless Networks, have thus attracted lots of attentions from both academia and industry. This paper proposes a novel Multiple-Threshold based Triggering (MTT) scheme for Cellular-to-WLAN handover control. Aiming at minimizing the probability of handover failures and unnecessary handovers, three thresholds are calculated based on a variety of network parameters such as system performance requirements, radius of the WLAN coverage, user mobility and handover delays. The thresholds are then compared against the predicted user residence time and estimated channel holding time inside WLAN to make vertical handover decisions (VHDs). Simulations were carried out to evaluate the effectiveness of MTT and results show that MTT minimizes handover failures and avoids unnecessary handovers in integrated cellular and WLAN networks, thus providing satisfactory Quality of Service (QoS) to users and improving system resource utilization

Dynamic edge-caching for mobile users: minimising inter-AS traffic by moving cloud services and VMs

In recent years, Cloud technology has revolutionized the way services are delivered to end-users. The advent of truly mobile computing in the form of smart phones and tablets has also driven the demand for Cloud resources in order to compensate for the inherent lack of local resources on these devices. Furthermore, modern mobile devices are equipped with multiple network interfaces and in combination with the rapid deployment of wireless networks, it is expected that they will always have Internet connectivity and access to Cloud resources. In this paper we will focus on traffic management for interactive multimedia services accessed by a mobile user by means of dynamic migration of a Virtual Machine. Network performance measurements are taken from a network of virtualization-enabled hosts that perform live migrations of a Virtual Machine which hosts multimedia content. The data is used as input to an equation that determines whether a migration would be beneficial in terms of traffic localization based on a user's mobility characteristics and network usage patterns. The contribution of this paper lies in the proposed mechanism of managing traffic for interactive services in the context of mobile cloud computing. This helps alleviate the increased network costs introduced by dynamic migrations driven by Quality of Service parameters and may result in increased network traffic for the benefit of improved QoS