Cognitive Hierarchy Theory for Distributed Resource Allocation in the Internet of Things

In this paper, the problem of distributed resource allocation is studied for an Internet of Things (IoT) system, composed of a heterogeneous group of nodes compromising both machine-type devices (MTDs) and human-type devices (HTDs). The problem is formulated as a noncooperative game between the heterogeneous IoT devices that seek to find the optimal time allocation so as to meet their quality-of-service (QoS) requirements in terms of energy, rate and latency. Since the strategy space of each device is dependent on the actions of the other devices, the generalized Nash equilibrium (GNE) solution is first characterized, and the conditions for uniqueness of the GNE are derived. Then, to explicitly capture the heterogeneity of the devices, in terms of resource constraints and QoS needs, a novel and more realistic game-theoretic approach, based on the behavioral framework of cognitive hierarchy (CH) theory, is proposed. This approach is then shown to enable the IoT devices to reach a CH equilibrium (CHE) concept that takes into account the various levels of rationality corresponding to the heterogeneous computational capabilities and the information accessible for each one of the MTDs and HTDs. Simulation results show that the proposed CHE solution keeps the percentage of devices with satisfied QoS constraints above 96% for IoT networks containing up to 10,000 devices without considerably degrading the overall system performance.Comment: To appear in IEEE Transactions on Wireless Communications, 201

Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach

The leading role of the HetNet (Heterogeneous Networks) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current cell selection mechanisms used in cellular networks. The max-SINR algorithm, although effective historically for performing the most essential networking function of wireless networks, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs. The association and routing decisions, in the context of single-RAT or multi-RAT connections, need to be optimized to efficiently exploit the benefits of the architecture. However, the high computational complexity required for multi-parametric optimization of utility functions, the difficulty of modeling and solving Markov Decision Processes, the lack of guarantees of stability of Game Theory algorithms, and the rigidness of simpler methods like Cell Range Expansion and operator policies managed by the Access Network Discovery and Selection Function (ANDSF), makes neither of these state-of-the-art approaches a favorite. This Thesis proposes a framework that relies on Machine Learning techniques at the terminal device-level for Cognitive RAT Selection. The use of cognition allows the terminal device to learn both a multi-parametric state model and effective decision policies, based on the experience of the device itself. This implies that a terminal, after observing its environment during a learning period, may formulate a system characterization and optimize its own association decisions without any external intervention. In our proposal, this is achieved through clustering of appropriately defined feature vectors for building a system state model, supervised classification to obtain the current system state, and reinforcement learning for learning good policies. This Thesis describes the above framework in detail and recommends adaptations based on the experimentation with the X-means, k-Nearest Neighbors, and Q-learning algorithms, the building blocks of the solution. The network performance of the proposed framework is evaluated in a multi-agent environment implemented in MATLAB where it is compared with alternative RAT selection mechanisms

Matching Theory for Future Wireless Networks: Fundamentals and Applications

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this paper, the first comprehensive tutorial on the use of matching theory, a Nobelprize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then, the key solution concepts and algorithmic implementations of this framework are exposed. Then, the developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed

Resource Allocation for Device-to-Device Communications Underlaying Heterogeneous Cellular Networks Using Coalitional Games

Heterogeneous cellular networks (HCNs) with millimeter wave (mmWave) communications included are emerging as a promising candidate for the fifth generation mobile network. With highly directional antenna arrays, mmWave links are able to provide several-Gbps transmission rate. However, mmWave links are easily blocked without line of sight. On the other hand, D2D communications have been proposed to support many content based applications, and need to share resources with users in HCNs to improve spectral reuse and enhance system capacity. Consequently, an efficient resource allocation scheme for D2D pairs among both mmWave and the cellular carrier band is needed. In this paper, we first formulate the problem of the resource allocation among mmWave and the cellular band for multiple D2D pairs from the view point of game theory. Then, with the characteristics of cellular and mmWave communications considered, we propose a coalition formation game to maximize the system sum rate in statistical average sense. We also theoretically prove that our proposed game converges to a Nash-stable equilibrium and further reaches the near-optimal solution with fast convergence rate. Through extensive simulations under various system parameters, we demonstrate the superior performance of our scheme in terms of the system sum rate compared with several other practical schemes.Comment: 13 pages, 12 figure