Data Offload Research Articles

Proposal and Simulation with NS3 of a New offloading Algorithm between LTE/LTE-Advanced and Wi-Fi

The field of mobile telecommunications is an integral part of our society. In the wake of IT, it is implemented and used in all areas of society. This deep anchoring, which mobile networks use today, poses significant problems for operators in terms of speed, capacity demands and customer satisfaction, particularly for the quality of services, but also in terms of profitability and cost of services investments. Reports from Ericsson and Cisco which are representative bases for calculating world total data traffic in cellular mobile networks predict that the number of devices connected to IP networks will be more than three times the global population by 2023. There will be 3.6 networked devices per capita by 2023, up from 2.4 networked devices per capita in 2018. There will be 29.3 billion networked devices by 2023, up from 18.4 billion in 2018. To answer these challenges, two choices are offered to the operators the evolution of the standards or the interconnection of several networks. In this order of idea, we carried out an 4G/4G+ -Wi-Fi offloading simulation algorithm. It was up to us to design an offloading algorithm and then to simulate through a dedicated environment in a scenario allowing to implement it. To achieve this, it was first necessary for us to understand the concept of mobile data offload, to know the types of mobile data offload and to choose one on which the present work will be based. Later we carried out an analysis of the existing data offload algorithms to be able to bring out the limits. From these limits we have designed and proposed a new traffic offloading algorithm with a different approach compared to the existing one studied in the literature. After all this, we have developed and simulated a scenario comprising a 3GPP network in this case the 4G + network, a non 3GPP network in this case the WiFi network, and we have implemented our algorithm via NS3 network simulation software. With this work, we have proposed a solution to overcome an investment deemed expensive to deploy 5G in Africa.

Enabling Large-Scale Simulations of Quantum Transport with Manycore Computing

The non-equilibrium Green’s function (NEGF) is being utilized in the field of nanoscience to predict transport behaviors of electronic devices. This work explores how much performance improvement can be driven for quantum transport simulations with the aid of manycore computing, where the core numerical operation involves a recursive process of matrix multiplication. Major techniques adopted for performance enhancement are data restructuring, matrix tiling, thread scheduling, and offload computing, and we present technical details on how they are applied to optimize the performance of simulations in computing hardware, including Intel Xeon Phi Knights Landing (KNL) systems and NVIDIA general purpose graphic processing unit (GPU) devices. With a target structure of a silicon nanowire that consists of 100,000 atoms and is described with an atomistic tight-binding model, the effects of optimization techniques on the performance of simulations are rigorously tested in a KNL node equipped with two Quadro GV100 GPU devices, and we observe that computation is accelerated by a factor of up to ∼20 against the unoptimized case. The feasibility of handling large-scale workloads in a huge computing environment is also examined with nanowire simulations in a wide energy range, where good scalability is procured up to 2048 KNL nodes.

Real-World Spatio–Temporal Behavior Aware D2D Multicast Networks

Studies show that real-world user mobility has certain spatio-temporal correlations. However, most of the existing works on device-to-device multicast (D2MD) networks use randomly generated user locations, thus fail to capture the real-world spatio-temporal behavior of the users. In this work, we propose a realistic performance evaluation of the D2MD network that takes into account the joint spatio-temporal correlations of the users in the network. The proposed performance evaluation will be beneficial in assessing the viability of the D2MD networks before their actual deployment. Further, in order to acquire the required joint spatio-temporal behavior, we propose two novel methods namely observed mobility exploitation (OME) and expected mobility exploitation (EME). In OME, the joint spatio-temporal behavior is extracted from the past location information of the users. On the other hand, in EME, the past location information of a set (i.e., the training set) of users, following an average spatial-temporal behavior similar to the intended users, is utilized to estimate the intended users’ joint spatio-temporal behavior. The realistic performance of two D2MD networks is evaluated for a tractable campus set-up in terms of content delivery time and data offload ratio. It has been shown that for the D2MD networks the data offloading varies spatio-temporally and goes up to 35%. Further, a comparison is drawn between the conventional cellular unicast and multicast networks, and the two D2MD networks.

WiFi Helps Define the Relevant Market for Wireless Services

The Federal Communications Commission (“FCC”) has recognized that WiFi plays an increasingly important role as a competitor to the services offered by the mobile wireless industry, including when evaluating the state of competition in the mobile wireless marketplace. When reviewing proposed mergers, however, the FCC relies on a product market definition, “mobile telephony/broadband services,” that has been unchanged for approximately a decade and that does not account for the changed role of WiFi in the competitive marketplace during this time. Notably, the FCC continues to rely on this market definition without undertaking any particular analysis and without the support of any empirical evidence. Since the FCC adopted its market definition: (1) the choice of WiFi has become a clear reflection of how Americans use wireless devices; (2) market conditions for wireless services have changed rapidly; (3) more data has migrated to WiFi offload — transmitting wireless data to a WiFi network — than has remained on the cellular networks, and most wireless services are used in both nomadic and fixed environments; (4) WiFi capabilities in wireless devices have evolved and become ubiquitous; (6) new technologies have not appeared to lessen demand for WiFi; (7) cable providers are challenging wireless carriers with WiFi offload and wireless services; and (8) consumers have become sensitive to price and quality in choosing between cellular services or WiFi. When the use of WiFi for consumer data offload is examined, a hypothetical monopolist may reveal that “mobile telephony/broadband services” are not a separate market. The FCC’s market definition was first adopted in 2008, when 3G technologies were first being deployed and 4G was only on the planning boards. Today, the technologies of 2008, and the associated market definitions, are outdated. Instead, new 5G technologies are being deployed. In this context, the FCC’s reliance on an outdated market definition results in an inaccurate understanding of competition in the market. Before conducting competitive analyses of the mobile marketplace, the FCC should carefully examine consumer choices of communications services and develop market definitions that reflect current technologies and consumer practices. WiFi, and the businesses that provide it, likely discipline prices of wireless services and are part of the same economic market.

Performance maximization of network assisted mobile data offloading with opportunistic Device-to-Device communications

Mobile data traffic inside mobile operator’s infrastructure is increasing exponentially every year. This increasing demand forces mobile network operators (MNOs) to seek for alternative communication methods in order to relieve the traffic load in base stations, especially in highly populated and crowded environments. Network assisted data offload and Device-to-Device(D2D) communications are two prominent methods to help MNOs solve this problem. In this study, a data offload framework is developed that incorporates network assisted multiple attribute decision making (MADM) for best network selection and D2D communications for exploiting user proximity in crowded environments. The performance of the framework is evaluated with simulation results as well as analytic solutions and performance bounds. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions and demonstrates the significant benefits of D2D communications when the density of D2D users is properly adjusted. The simulation results depict that up to 168% and 200% increase in user satisfaction and throughput can be achieved under high network load scenarios at optimal D2D density.

Cooperative Data Offload in Opportunistic Networks: From Mobile Devices to Infrastructure

Opportunistic mobile networks consisting of intermittently connected mobile devices have been exploited for various applications, such as computational offloading and mitigating cellular traffic load. In contrast to existing work, in this paper, we focus on cooperatively offloading data among mobile devices to maximally improve the probability of data delivery from a mobile device to intermittently connected infrastructure within a given time constraint, which is referred to as the \textit{cooperative offloading} problem. Unfortunately, the estimation of data delivery probability over an opportunistic path is difficult and cooperative offloading is NP-hard. To this end, we first propose a probabilistic framework that provides the estimation of such probability. Based on the proposed probabilistic framework, we design a heuristic algorithm to solve cooperative offloading at a low computation cost. Due to the lack of global information, a distributed algorithm is further proposed. The performance of the proposed approaches is evaluated based on both synthetic networks and real traces. Experimental results show that the probabilistic framework can accurately estimate the data delivery probability, cooperative offloading greatly improves the delivery probability, the heuristic algorithm approximates the optimum, and the performance of both the heuristic algorithm and distributed algorithm outperforms other approaches.

Macro and small cell/Wi-Fi networks: An analysis of deployment options as the solution for the mobile data explosion

With the tremendous growth in mobile data traffic triggered by increases in data subscriptions, powerful devices, and richer applications, operators are seeing Wi-Fi as not only a complementary technology to offload cellular data traffic but also as a seamless extension of their mobile networks. The advent of Hotspot 2.0, enhanced Wi-Fi quality of service (QoS) techniques, and transparent authentication mechanisms between mobile and Wi-Fi networks will pave the way for seamless mobility and roaming between these two technologies and provide a better quality of experience to the end user. With web-based applications like Skype, Google Talk, and WhatsApp becoming mainstream media for communication, operators are looking at the combination of licensed band and Wi-Fi technologies to meet capacity demands. This paper discusses the evolution of basic network Wi-Fi -based hotspots to become the trusted partner of mobile operators. We present architectures and required features for data offload and seamless mobility and provide an in-depth techno-economic analysis comparing various deployment alternatives for data offload in a heterogeneous network.