<p>This thesis investigates how to reach and recruit cutting-edge users to user experience studies. The recruitment of cutting-edge users is difficult since these users usually are not registered in recruitment databases. Cutting-edge users are advanced, early-adopters of technology and sometimes referred to as opinion leaders. Telecom research projects performed at Ericsson Research involve products and services 2-3 years ahead of the market; early-adopters and cutting edge users are therefore an important user group.</p><p> </p><p>To test recruitment methods a user experience study was performed of Ericsson Labs developer portal. Ericsson Labs offers Application Programming Interfaces for mobile and web applications development. Internet marketing theories were used to form a recruitment method. Respondents were recruited from the Ericsson Labs user database and they were contacted individually via email. The users were invited to share their thoughts and ideas about the portal to help improve and possibly influence the direction of the site.</p><p> </p><p>This thesis also assessed different online qualitative research methods applied for user experience research. Online focus groups such as bulletin boards were used to interact with users in addition to individual chat and voice interviews. Performing user experience research on the Internet is a cost-efficient way to interact with users in geographically dispersed areas.</p><p> </p><p>The findings from the study show that recruitment is hard; it is especially difficult to recruit active and conversational respondents from a user database. Providing incentives and using personal communication were shown to be successful strategies to convince users to participate in a study.</p>

This thesis investigates how to reach and recruit cutting-edge users to user experience studies. The recruitment of cutting-edge users is difficult since these users usually are not registered in recruitment databases. Cutting-edge users are advanced, early-adopters of technology and sometimes referred to as opinion leaders. Telecom research projects performed at Ericsson Research involve products and services 2-3 years ahead of the market; early-adopters and cutting edge users are therefore an important user group.   To test recruitment methods a user experience study was performed of Ericsson Labs developer portal. Ericsson Labs offers Application Programming Interfaces for mobile and web applications development. Internet marketing theories were used to form a recruitment method. Respondents were recruited from the Ericsson Labs user database and they were contacted individually via email. The users were invited to share their thoughts and ideas about the portal to help improve and possibly influence the direction of the site.   This thesis also assessed different online qualitative research methods applied for user experience research. Online focus groups such as bulletin boards were used to interact with users in addition to individual chat and voice interviews. Performing user experience research on the Internet is a cost-efficient way to interact with users in geographically dispersed areas.   The findings from the study show that recruitment is hard; it is especially difficult to recruit active and conversational respondents from a user database. Providing incentives and using personal communication were shown to be successful strategies to convince users to participate in a study.

Cloud Gaming systems are among the most challenging networked-applications, since they deal with streaming high-quality and bulky video in real-time to players’ devices. While all industry solutions today are centralized, we introduce an AI-assisted hybrid networking architecture that, in addition to the central cloud servers, also uses some players’ computing resources as additional points of service. We describe the problem, its mathematical formulation, and potential solution strategy. Edge computing is a promising paradigm that brings servers closer to users, leading to lower latencies and enabling latency-sensitive applications such as cloud gaming, virtual/augmented reality, telepresence, and telecollaboration. Due to the high number of possible edge servers and incoming user requests, the optimum choice of user-server matching has become a difficult challenge, especially in the 5G era where the network can offer very low latencies. In this thesis, we introduce the problem of fair server selection as not only complying with an application's latency threshold but also reducing the variance of the latency among users in the same session. Due to the dynamic and rapidly evolving nature of such an environment and the capacity limitation of the servers, we propose as solution a Reinforcement Learning method in the form of a Quadruple Q-Learning model with action suppression, Q-value normalization, and a reward function that minimizes the variance of the latency. Our evaluations in the context of a cloud gaming application show that, compared to a existing methods, our proposed method not only better meets the application's latency threshold but is also more fair with a reduction of up to 35\% in the standard deviation of the latencies while using the geo-distance, and it shows improvements in fairness up to 18.7\% compared to existing solutions using the RTT delay especially during resource scarcity. Additionally, the RL solution can act as a heuristic algorithm even when it is not fully trained. While designing this solution, we also introduced action suppression, Quadruple Q-Learning, and normalization of the Q-values, leading to a more scalable and implementable RL system. We focus on algorithms for distributed applications and especially esports, but the principles we discuss apply to other domains and applications where fairness can be a crucial aspect to be optimized.

Inter-cell interference coordination in 3GPP Long Term Evolution system received much attention in recent years. However, most of the studies are based on ideal system with regular hexagon-shaped cell. The indoor environment has special characteristics that the building shape and BS locations are irregular; the traffic load has great variation compared to urban and rural area. So, conventional ICIC scheme may not be used in indoor situation directly. In this thesis, ICIC scheme is employed for indoor environment. Based on different quality of backhaul, static and dynamic schemes will be proposed. The performances of proposed schemes and the performance of system without ICIC will be simulated and compared. At last, how much the improvement of the system can acquire after applying ICIC schemes will be analyzed, and the question about whether it is good to apply ICIC scheme in indoor environment will be answered.

This paper introduces a new way of solving an edge user allocation problem. The problem is to be solved with a network of spiking neurons. This network should quickly and with low energy cost solve the optimization problem of allocating users to servers and minimizing the amount of servers hired to reduce the related hiring cost. The demonstrated method is a simulation of a method which could be implemented onto neuromorphic hardware. It is written in Python using the Brian2 spiking neural network simulator. The core of the method involves simulating an energy function through the use of circuit motifs. The dynamics of these circuit motifs mimic a search for the lowest energy point in an energy landscape, corresponding to a valid solution for the edge user allocation problem. The paper also shows the results of testing this network within the Brian2 environment.

Στην παρούσα διδακτορική διατριβή προτείνονται τεχνικές για την βελτιστοποίηση της ποιότητας των παρεχομένων υπηρεσιών στους χρήστες σύγχρονων ασύρματων τηλεπικοινωνιακών συστημάτων που ως τεχνολογίες πρόσβασης έχουν την πολλαπλή πρόσβαση ορθογωνικής διαίρεσης συχνότητας και την πολλαπλή πρόσβαση διαίρεσης κώδικα. Οι τεχνικές που αναπτύχθηκαν αφορούν επαναληπτικούς αλγόριθμους κατανομής των διαθέσιμων ραδιοπόρων και εφαρμόζοναι κυρίως στην κατερχόμενη των ασύρματων συστημάτων. Ως παράμετροι της ποιότητας των παρεχόμενων υπηρεσιών θεωρούνται: το ελάχιστο απαιτούμενο επίπεδο ρυθμού μετάδοσης των δεδομέων, ο ρυθμός των λανθασμέων bit, και η ελάχιστη απαιτούμενη ποσότητα ραδιοπόρων σε κάθε χρήστη. Η αξιολόγηση των τεχνικών που προτείνονται γίνεται μέσω δεικτών της απόδοσής τους, οι οποίοι είναι: η πιθανότητα παραβίασης της ποιότητας της υπηρεσίας, ο δείκτης δικαιοσύνης του συστήματος, ο ρυθμός μετάδοσης δεδομένων στα άκρα της κυψέλης και η χωρητικότητα της κυψέλης. Για την εξαγωγή των δεικτών αυτών είναι απαραίτητα στατιστικά δεδομένα, τα οποία συλλέγονται μέσα από μεγάλο αριθμό προσομοιώσεων. / This doctoral thesis proposes QoS optimization techniques in modern wireless telecommunication systems, whereby orthogonal frequency division multiple access and code division are used. The proposed techniques are iterative resource allocation algorithms which are mainly suitable for the downlink of wireless networks. The minimum required level of data rate, the bit error rate and the minimum number of resources per user are considered as quality of service parameters. The validation of the proposed techniques is done through the performance of performance metrics like the : the quality of service violation probability, the system fairness index, the cell-edge data rate and the cell capacity. Statistical data are required which are collected through extensive simulation