HySIM: A Hybrid Spectrum and Information Market for TV White Space Networks

We propose a hybrid spectrum and information market for a database-assisted TV white space network, where the geo-location database serves as both a spectrum market platform and an information market platform. We study the inter- actions among the database operator, the spectrum licensee, and unlicensed users systematically, using a three-layer hierarchical model. In Layer I, the database and the licensee negotiate the commission fee that the licensee pays for using the spectrum market platform. In Layer II, the database and the licensee compete for selling information or channels to unlicensed users. In Layer III, unlicensed users determine whether they should buy the exclusive usage right of licensed channels from the licensee, or the information regarding unlicensed channels from the database. Analyzing such a three-layer model is challenging due to the co-existence of both positive and negative network externalities in the information market. We characterize how the network externalities affect the equilibrium behaviours of all parties involved. Our numerical results show that the proposed hybrid market can improve the network profit up to 87%, compared with a pure information market. Meanwhile, the achieved network profit is very close to the coordinated benchmark solution (the gap is less than 4% in our simulation).Comment: This manuscript serves as the online technical report of the article published in IEEE International Conference on Computer Communications (INFOCOM), 201

WAVEFORM DESIGN AND NETWORK SELECTION IN WIDEBAND SMALL CELL NETWORKS

The explosion in demand for wireless data traffic in recent years has triggered rapid development and pervasive deployment of wireless communication networks. To meet the exponentially increasing demand, a promising solution is the concept of wideband small cells, which is based on the idea of using broader frequency bandwidth and employing more efficient radio frequency resource reuse by dense deployment of wideband, short-range, low cost and low power base-stations. Broader bandwidth provides substantial degrees of freedom as well as challenges for system design due to the abundant multipaths and thus interference in high speed systems under large delay spread channels. Reducing the transmission range and increasing the number of cells permit better spatial reuse of spectrum. With the proliferation of wideband small cells, the strategy of selection among multiple networks has significant impacts to the performance of users and to the load balance of the system. In this dissertation, we address these problems with a focus on waveform design and network selection. In time-reversal communication systems, the time-reversal transmit waveform can boost the signal-to-noise ratio at the receiver with simple single-tap detection by utilizing channel reciprocity with very low transmitter complexity. However, the large delay spread gives rise to severe inter-symbol interference when the data rate is high, and the achievable transmission rate is further degraded in the multiuser downlink due to the inter-user interference. We study the weighted sum rate optimization problem by means of waveform design in the time-reversal multiuser downlink. We propose a new power allocation algorithm, which is able to achieve comparable sum rate performance to that of globally optimal power allocation. Further, we study the joint waveform design and interference pre-cancellation by exploiting the symbol information to further improve the performance by utilizing the information of previous symbols. In the proposed joint design, the causal interference is subtracted using interference pre-cancellation and the anti-causal interference can be further suppressed by waveform design with more degrees of freedom. The second part of this dissertation is concerned with the wireless access network selection problem considering the negative network externality, i.e, the influence of subsequent users' decisions on an individual's throughput due to the limited available resources. We formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modelled as a multi-dimensional Markov decision process. A modified value iteration algorithm is proposed to efficiently obtain the optimal decision rule with a simple threshold structure, which enables us to reduce the storage space of the strategy profile. We further investigate the mechanism design problem with incentive compatibility constraints, which enforce the networks to reveal the truthful state information. We analyze a data set of wireless LAN traces collected from campus networks, from which we observe that the number of user arrivals is approximately Poisson distributed; the session time and the waiting time to switch network can be approximated by exponential distributions. Based on the analysis, we formulate a wireless access network association game with both arriving strategy and switching strategy and validate the effectiveness of the proposed best response strategy

Network association strategies for an energy harvesting aided super-wifi network relying on measured solar activity

The super-WiFi network concept has been proposed for nationwide Internet access in the United States. However, the traditional mains power supply is not necessarily ubiquitous in this large-scale wireless network. Furthermore, the non-uniform geographic distribution of both the based-stations and the tele-traffic requires carefully considered user association. Relying on the rapidly developing energy harvesting techniques, we focus our attention on the sophisticated access point (AP) selection strategies conceived for the energy harvesting aided super-WiFi network. Explicitly, we propose a solar radiation model relying on the historical solar activity observation data provided by the University of Queensland, followed by a beneficial radiation parameter estimation method. Furthermore, we formulate both a Markov decision process (MDP) as well as a partially observable MDP (POMDP) for supporting the users’ decisions on beneficially selecting APs. Moreover, we conceive iterative algorithms for implementing our MDP and POMDP-based AP-selection, respectively. Finally, our performance results are benchmarked against a range of traditional decision-making algorithms