Nowadays, with the increasing demand for wireless communication systems, basically Wireless Local Area Networks (WLANs) and Mobile communication systems, higher data rates with better Quality of Service (QoS) are required. While Heterogeneous Networks (Het-Nets) are under study toward 5G technology in mobile communication, WiFi Access Points (APs) are considered a potential layer within those multiple Radio Access Technologies (RATs). Significant network capacity gain can be achieved not only through aggressive reuse of spectrum across the multiple tiers in the network, but also through harnessing an additional spectrum in un-licensed bands by integrating WiFi in the network [1]. Different criteria should be investigated in order to allow both the WiFi APs and the end user to operate on the best suitable channel, where the basic one of those criteria is the "load" of the operating channels. We propose in this paper a novel and accurate algorithm for the estimation of WiFi 802.11n physical channels load through the observation of the non-overlapped channels and estimating as a result the load of the entire physical channels. Once the channels load is estimated using the proposed algorithm, the channel assignment based on the minimal load value is facilitated, thus providing faster response of an AP channel selection and faster end user connection for better Quality of Experience (QoE).

On the road towards 5G, a proliferation of Heterogeneous Networks (HetNets) is expected. Long Term Evolution (LTE) and Wireless Fidelity (WiFi) cooperation is needed in order to ensure a balanced traffic load based on different criteria so that the end user will benefit from the maximum throughput with no disturbance or deterioration in the service quality. Thus, alternative plans for exploiting already existing under-utilized WiFi infrastructure become more attractive than expanding the LTE spectrum or increasing the capacity by deployment of additional LTE Base Stations (BSs). To find a more effective spectrum utilization method, alleviate the spectrum scarcity problem of cellular networks and ensure additional capacity, we propose in this paper a solution to calculate the minimum needed number of WiFi Access Points (APs) that will be able to handle the transferred heavy users from LTE advanced (LTE-A) to WiFi. The dimensioning method that we propose in this paper is based on the remaining available capacity of WiFi channels taking into consideration the overlapping characteristics of the physical channels to estimate the percentage of busy time or occupation of the AP channels. Based on this approach, we can investigate first the remaining available capacity in terms of available throughput of WiFi that could be distributed over the transferred LTE users, then the minimum required number of WiFi APs that will be supporting the LTE network for efficient traffic offloading.

With the increasing demand for wireless communication systems, while Heterogeneous Networks (Het-Nets) are under study toward 5G technology in mobile communication systems, WiFi Access Points (APs) are considered a potential layer within those multiple Radio Access Technologies (RATs). Significant network capacity gain can be achieved not only through aggressive reuse of spectrum across the multiple tiers in the network, but also by integrating WiFi in the mobile network through additional spectrum in unlicensed bands. Different criteria should be investigated in order to allow both the WiFi APs and the end user to operate on the best suitable channel, where the basic one of those criteria is the "load" of the operating channels. We propose in this paper an accurate algorithm for the estimation of WiFi 802.11n physical channels load through 3 channels observations only. Once the channel load is estimated using the proposed algorithm, the channel assignment based on the minimal load value is acquired, thus providing faster response of an AP channel selection and faster end user connection for better Quality of Experience (QoE).

With the increasing demand for data traffic and with the massive foreseen deployment of the Internet of Things (IoT), higher data rates and capacity are required in mobile networks. While Heterogeneous Networks (HetNets) are under study toward 5G technology, Wireless Fidelity (WiFi) Access Points (APs) are considered a potential layer within those multiple Radio Access Technologies (RATs). For this purpose, we have proposed in this paper a novel WiFi dimensioning method, to offload data traffic from Long Term Evolution (LTE) to WiFi, by transferring the LTE energy consuming heavy users, to the WiFi network. First, we have calculated the remaining available capacity of the WiFi network based on the estimated load of each WiFi physical channel using the overlapping characteristic of the channels. Then, we were able through this dimensioning method, to calculate the minimum needed number of WiFi APs that ensure the same or better throughput for the LTE transferred users. By this method, we have ensured additional capacity in the LTE network with minimum investment cost in the WiFi network. Finally, we have estimated the profit sharing between LTE and WiFi by considering data bundles subscription revenues and the infrastructure capital and operational costs. We have calculated for each network the profit share using a coalition game theory Shapley value that pinpoints the benefit of the cooperation using the proposed dimensioning method.

Nowadays, with the increasing demand for data traffic and with the massive foreseen deployment of the Internet of Things (IOT), higher data rates and capacity are required in mobile networks. While Heterogenous Networks (HetNets) are under study toward 5G technology, Wireless Fidelity (WiFi) Access Points (APs) are considered a potential layer within those multiple Radio Access Technologies (RATs). For this purpose, we propose in this paper a novel WiFi dimensioning method, to offload data traffic from Long Term Evolution (LTE) to WiFi, to ensure a balanced traffic between both networks. This dimensioning method, calculates the remaining available capacity of the WiFi network based on the estimated load of each WiFi physical layer channel, by considering the channels overlapping characteristic, thus calculating the minimum needed number of WiFi APs that ensure same or better throughput for the transferred LTE heavy users. Having the minimum needed WiFi APs that will support LTE, we estimate then the profit sharing between LTE and WiFi by considering data bundles subscription revenues and the infrastructure capital and operational costs. We calculate for each network the profit share using a coalition game theory Shapley value that pinpoints the benefit of the cooperation using the proposed dimensioning method.Moreover, most of the smart devices are equipped with WiFi capabilities, and based on different studies, more than 80% of mobile traffic came from indoor environment. Thus, WiFi could have an advantage of establishing a communication infrastructure over other wireless communication networks [2]. However, most of current WiFi networks consist of randomly deployed WiFi cells since there is no limitations or policies on WiFi AP deployment [4]. The unplanned installation of APs may cause the WiFi networks to be implemented inefficiently.There have been several studies on WiFi cell deployment problems. In [4], the minimum required number of WiFi APs was investigated based on the active users' density, the coverage of the WiFi AP and the transmission probability of a user, without taking into consideration the WiFi network available capacity. In [5], the authors propose WiFi deployment algorithms based on realistic mobility characteristics of users to deploy WiFi APs for continuous service for mobile users, based on maximum continuous coverage where WiFi network capacity was not considered. In [6], the number of APs required for WiFi offloading with different quality of service for data delivery was quantified, however, authors just provided a feasibility study on such offloading solution through real mobility traces and did not perform any mathematical analysis for this problem.