Multiobjective auction-based switching-off scheme in heterogeneous networks: to bid or not to bid?

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction.Peer ReviewedPostprint (author's final draft

Modeling the relationship between network operators and venue owners in public Wi-Fi deployment using non-cooperative game theory

Wireless data demands keep rising at a fast rate. In 2016, Cisco measured a global mobile data traffic volume of 7.2 Exabytes per month and projected a growth to 49 Exabytes per month in 2021. Wi-Fi plays an important role in this as well. Up to 60% of the total mobile traffic was off-loaded via Wi-Fi (and femtocells) in 2016. This is further expected to increase to 63% in 2021. In this publication, we look into the roll-out of public Wi-Fi networks, public meaning in a public or semi-public place (pubs, restaurants, sport stadiums, etc.). More concretely we look into the collaboration between two parties, a technical party and a venue owner, for the roll-out of a new Wi-Fi network. The technical party is interested in reducing load on its mobile network and generating additional direct revenues, while the venue owner wants to improve the attractiveness of the venue and consequentially generate additional indirect revenues. Three Wi-Fi pricing models are considered: entirely free, slow access with ads or fast access via paid access (freemium), and paid access only (premium). The technical party prefers a premium model with high direct revenues, the venue owner a free/freemium model which is attractive to its customers, meaning both parties have conflicting interests. This conflict has been modeled using non-cooperative game theory incorporating detailed cost and revenue models for all three Wi-Fi pricing models. The initial outcome of the game is a premium Wi-Fi network, which is not the optimal solution from an outsider's perspective as a freemium network yields highest total payoffs. By introducing an additional compensation scheme which corresponds with negotiation in real life, the outcome of the game is steered toward a freemium solution

The design and optimization of cooperative mobile edge

As the world is charging towards the Internet of Things (IoT) era, an enormous amount of sensors will be rapidly empowered with internet connectivity. Besides the fact that the end devices are getting more diverse, some of them are also becoming more powerful, such that they can function as standalone mobile computing units with multiple wireless network interfaces. At the network end, various facilities are also pushed to the mobile edge to foster internet connections. Distributed small scale cloud resources and green energy harvesters can be directly attached to the deployed heterogeneous base stations. Different from the traditional wireless access networks, where the only dynamics come from the user mobility, the evolving mobile edge will be operated in the constantly changing and volatile environment. The harvested green energy will be highly dependent on the available energy sources, and the dense deployment of a variety of wireless access networks will result in intense radio resource contention. Consequently, the wireless networks are facing great challenges in terms of capacity, latency, energy/spectrum efficiency, and security. Equivalently, balancing the dynamic network resource demand and supply is essential to the smooth network operation. Leveraging the broadcasting nature of wireless data transmission, network nodes can cooperate with each other by either allowing users to connect with multiple base stations simultaneously or offloading user workloads to neighboring base stations. Moreover, grid facilitated and radio frequency signal enabled renewable energy sharing among network nodes are introduced in this dissertation. In particular, the smart grid can transfer the green energy harvested by each individual network node from one place to another. The network node can also transmit energy from one to another using radio frequency energy transfer. This dissertation addresses the cooperative network resource management to improve the energy efficiency of the mobile edge. First, the energy efficient cooperative data transmission scheme is designed to cooperatively allocate the radio resources of the wireless networks, including spectrum and power, to the mobile users. Then, the cooperative data transmission and wireless energy sharing scheme is designed to optimize both the energy and data transmission in the network. Finally, the cooperative data transmission and wired energy sharing scheme is designed to optimize the energy flow within the smart grid and the data transmission in the network. As future work, how to motivate multiple parties to cooperate and how to guarantee the security of the cooperative mobile edge is discussed. On one hand, the incentive scheme for each individual network node with distributed storage and computing resources is designed to improve network performance in terms of latency. On the other hand, how to leverage network cooperation to balance the tradeoff between efficiency (energy efficiency and latency) and security (confidentiality and privacy) is expounded

Multi-tenant slicing for spectrum management on the road to 5G

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The explosive data traffic demand in the context of the 5G revolution has stressed the need for network capacity increase. As the network densification has almost reached its limits, mobile network operators are motivated to share their network infrastructure and the available resources through dynamic spectrum management. Although some initial efforts have been made in this direction by concluding sharing agreements at a coarse granularity (i.e., months or years), the 5G developments require fine timescale agreements, mainly enabled by network slicing. In this article, taking into account the radical changes foreseen for next generation networks, we provide a thorough discussion of the challenges that network slicing brings in the different network parts, while introducing a new entity capable of managing the end-to-end slicing in a coherent manner. In addition, according to the paradigm shift of operators sharing their resources in a common centralized pool, we design a cooperative game to study the potential cooperation aspects among the participants. The experimental results highlight the performance and financial gains achievable by operators through multi-tenant slicing, providing them with the necessary incentives for network upgrade toward 5G.Peer ReviewedPostprint (author's final draft

Distributed optimisation techniques for wireless networks

Alongside the ever increasing traffic demand, the fifth generation (5G) cellular network architecture is being proposed to provide better quality of service, increased data rate, decreased latency, and increased capacity. Without any doubt, the 5G cellular network will comprise of ultra-dense networks and multiple input multiple output technologies. This will make the current centralised solutions impractical due to increased complexity. Moreover, the amount of coordination information that needs to be transported over the backhaul links will be increased. Distributed or decentralised solutions are promising to provide better alternatives. This thesis proposes new distributed algorithms for wireless networks which aim to reduce the amount of system overheads in the backhaul links and the system complexity. The analysis of conflicts amongst transmitters, and resource allocation are conducted via the use of game theory, convex optimisation, and auction theory. Firstly, game-theoretic model is used to analyse a mixed quality of service (QoS) strategic non-cooperative game (SNG), for a two-user multiple-input single-output (MISO) interference channel. The players are considered to have different objectives. Following this, the mixed QoS SNG is extended to a multicell multiuser network in terms of signal-to-interference-and-noise ratio (SINR) requirement. In the multicell multiuser setting, each transmitter is assumed to be serving real time users (RTUs) and non-real time users (NRTUs), simultaneously. A novel mixed QoS SNG algorithm is proposed, with its operating point identified as the Nash equilibrium-mixed QoS (NE-mixed QoS). Nash, Kalai-Smorodinsky, and Egalitarian bargain solutions are then proposed to improve the performance of the NE-mixed QoS. The performance of the bargain solutions are observed to be comparable to the centralised solutions. Secondly, user offloading and user association problems are addressed for small cells using auction theory. The main base station wishes to offload some of its users to privately owned small cell access points. A novel bid-wait-auction (BWA) algorithm, which allows single-item bidding at each auction round, is designed to decompose the combinatorial mathematical nature of the problem. An analysis on the existence and uniqueness of the dominant strategy equilibrium is conducted. The BWA is then used to form the forward BWA (FBWA) and the backward BWA (BBWA). It is observed that the BBWA allows more users to be admitted as compared to the FBWA. Finally, simultaneous multiple-round ascending auction (SMRA), altered SMRA (ASMRA), sequential combinatorial auction with item bidding (SCAIB), and repetitive combinatorial auction with item bidding (RCAIB) algorithms are proposed to perform user offloading and user association for small cells. These algorithms are able to allow bundle bidding. It is then proven that, truthful bidding is individually rational and leads to Walrasian equilibrium. The performance of the proposed auction based algorithms is evaluated. It is observed that the proposed algorithms match the performance of the centralised solutions when the guest users have low target rates. The SCAIB algorithm is shown to be the most preferred as it provides high admission rate and competitive revenue to the bidders