First Survey Results of Quantified User Behavior in User-in-the-Loop Scenarios for Sustainable Wireless Networks

Schoenen, Rainer; Bulu, Gurhan; Mirtaheri, Amir; Beitelmal, Tamer; Yanıkömeroğlu, Halim

doi:10.1109/vtcfall.2012.6399067

Cited by 9 publications

(21 citation statements)

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“…The target value is set to, e.g., ρ (t) = 90% here, in order to leave a margin of 10%. Recent survey results [18], [19] provide quantitative data on how users react to different levels of incentives, under different applications (QoS classes), and various scenarios. The individual user behavior cannot be known, but only the aggregate behavior of all users in a coverage region is required.…”

Section: User In the Loopmentioning

confidence: 99%

Erlang analysis of cellular networks using stochastic Petri nets and user-in-the-loop extension for demand control

Schoenen

Yanıkömeroğlu

2013

2013 IEEE Globecom Workshops (GC Wkshps)

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Abstract-Cellular networks face severe challenges due to the expected growth of application data rate demand with an increase rate of 100% per year. Over-provisioning capacity has been the standard approach to reduce the risk of overload situations. Traditionally in telephony networks, call blocking and overload probability have been analyzed using the Erlang-B and Erlang-C formulas, which model limited capacity communication systems without or with session request buffers, respectively. While a closed-form expression exists for the blocking probability for constant load and service, a steady-state Markov chain (MC) analysis can always provide more detailed data, as long as the Markov property of the arrival and service processes hold. However, there is a significant modeling advantage by using the stochastic Petri net (SPN) paradigm to model the details of such a system. In addition, software tool support allows getting numeric analysis results quickly by solving the state probabilities in the background and without the need to run any simulation. Because of this efficiency, the equivalent SPN model of the Engset, Erlang-B and Erlang-C situation is introduced as novelty in this paper. Going beyond the original Erlang scenario, the user-in-the-loop (UIL) approach of demand shaping by closed-loop control is studied as an extension. In UIL, demand control is implemented by a dynamic usage-based tariff which motivates users to reduce or postpone the use of applications on their smart phone in times of light to severe congestion. In this paper, the effect of load on the price and demand reduction is modeled with an SPN based on the classical Erlang Markov chain structure. Numeric results are easily obtained and presented in this paper, including probability density functions (PDF) of the load situation, and a parameter analysis showing the effectiveness of UIL to reduce the overload probability.

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Section: User In the Loopmentioning

confidence: 99%

Erlang analysis of cellular networks using stochastic Petri nets and user-in-the-loop extension for demand control

Schoenen

Yanıkömeroğlu

2013

2013 IEEE Globecom Workshops (GC Wkshps)

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“…The user and its behavior is naturally not subject to a precise science. In order to get some usable properties of the input/output system response, two surveys have been conducted in [79,80]. A fitting mathematical model derived from the empirical distributions follows an exponential shape for the function between incentive and the probability p d expp¡βdq of using the service where d is the distance between current user location and suggested location and β depends on individuals, incentives, and many other parameters.…”

Section: Basic Uilmentioning

confidence: 99%

“…For this thesis, for the sake of simplicity, we assume that the p d function has the same shape (β parameter) for all users. Figure 25 illustrates the probability of movement and the fitting functions based on the surveys conducted in article [79,80].…”

Section: Basic Uilmentioning

confidence: 99%

HetHetNets: Heterogeneous Wireless Cellular Networks with Heterogeneous Traffic

Mirahsan¹

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One of the main expected characteristics of the envisioned 5G wireless cellular networks is heterogeneity. Heterogeneity is expected in both supply and demand. In the supply side, the network access part will be comprised of heterogeneous base stations (BSs) with different transmit powers, antenna heights, and radio technologies including macro-BSs, pico-BSs, femto-BSs, and wi-fi access points (HetNets). The spatial distribution of BSs is also heterogeneous (non-uniform) since the deployment of BSs is not carefully planned anymore and follows the customer requirements. In the demand side, the distribution of user equipments (UEs) is heterogeneous in the time domain as well as in the space domain due to the emergence of various applications with different rate requirements such as machine type communications (MTC) and also the heterogeneity of population density specially in municipal areas.Nevertheless, an enormous majority of the existing literature on traffic modeling in wireless cellular networks consider only homogeneous (uniform) traffic scenarios. In particular, two independent Poisson point processes (PPPs) are excessively used to model the spatial distribution of UEs and BSs. PPP might be a fitting process for BSs but it is not an accurate model for the UE distributions. The assumption of independence between BSs and UEs is also not realistic since BSs (specially small-cell BSs) are usually deployed in UE hotspots.In this thesis, we propose an accurate, realistic, simple, and adjustable modeling for the future heterogeneous wireless cellular networks with heterogeneous traffic distributions (HetHetNets). First, we propose a traffic modeling process describing a systematic approach to traffic modeling. According to the proposed process, we introduce a traffic modeling in which the heterogeneity of the UE distribution as well as the correlation between UEs and BSs are adjustable. Then, we show the impact of the traffic heterogeneity and the UE-BS correlation on the performance of HetHet-Nets. Finally, we present algorithms and applications in wireless networks which can exploit this realistic traffic modeling to enhance the network performance.ii I would like to dedicate my thesis to my lovely wife, Ghazaleh, who gave me the wings to fly, the courage to soar, and the reason to live.iii

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“…The UIL concept [46][47][48][49][50][51][52][53][54] aims at controlling the user ("layer-8") behavior in a wireless system to achieve a better performance of both the user and the network by convincing the users to move from one location to a better one or to avoid traffic congestion by postponing session traffic out of the busy hours. Based on the impact dimension, the approach is called spatial or temporal UIL control (this work only involves spatial UIL).…”

Section: Related Workmentioning

confidence: 99%

Resource Management in Wireless Cellular Networks with Heterogeneous Capacity Supply and Heterogeneous Traffic Demand (HetHetNets)

Wang¹

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The term HetNets (heterogeneous networks) is used to describe cellular networks in which the capacity supply is heterogeneous due to the overlaid architecture of macrocells and small-cells.In this thesis we use the term HetHetNets to refer to cellular networks in which the traffic demand is also heterogeneous due to reasons such as user spatial clustering and diversified user traffic types, in addition to the heterogeneity in the capacity supply.The main objective of this thesis is to study the essential problem of matching the traffic demand ("load") with the capacity supply ("capacity") in resource-limited HetHetNets; this problem is expected to be a major concern in the fifth generation (5G) cellular networks.This thesis proposes two approaches to address this problem. One approach is to deploy small-cells to the centers of user clusters (put supply where needed), and another approach is to guide users to the cells that are lightly loaded (load balancing via spatial traffic shaping).We first investigate how to model the spatial non-uniform (i.e., heterogeneous) user distribution, and then the impact of user spatial heterogeneity on HetNets is evaluated. Simulation-based analyses show that network performance deteriorates significantly with the increase in user spatial heterogeneity if the user locations are uncorrelated with the locations of the macro and small-cell base stations. Cluster analysis on the non-uniform user points is then utilized to find the cluster centroids as the potential locations for small-cells. Simulation results show that the network performance can improve substantially with increasing user spatial heterogeneity if we deploy small-cells in the appropriate locations.The second approach is enabled by the recently developed user-in-the-loop (UIL) paradigm. In the literature, there have been several investigations on load-aware cell association as an approach to match traffic demand with the capacity supply, in which a user may associate to a less loaded cell, even though that cell does not iii necessarily provide the maximum SINR. In other words, a user is associated with a cell to get more share of resources at the cost of lower spectral efficiency. While, the UIL approach can increase the user received SINR and the share of allocated resources at the same time, which encourages a user to move to a new location that maximizes the utility function considering received SINR, cell load and the probability of moving.Numerical results show that the UIL can double the mean user rate in comparison to the load-aware cell association strategy, and also results in a more balanced load across the network.iv

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First Survey Results of Quantified User Behavior in User-in-the-Loop Scenarios for Sustainable Wireless Networks

Cited by 9 publications

References 10 publications

Erlang analysis of cellular networks using stochastic Petri nets and user-in-the-loop extension for demand control

Erlang analysis of cellular networks using stochastic Petri nets and user-in-the-loop extension for demand control

HetHetNets: Heterogeneous Wireless Cellular Networks with Heterogeneous Traffic

Resource Management in Wireless Cellular Networks with Heterogeneous Capacity Supply and Heterogeneous Traffic Demand (HetHetNets)

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