Resource Management for Cellular-Assisted Device-to-Device (D2D) Communications

Device-to-Device (D2D) communication has become a promising candidate for future wireless communication systems to improve the system spectral efficiency, while reducing the latency and energy consumption of individual communication. With the assistance of cellular network, D2D communications can greatly reduce the transmit distance by utilizing the spatial dispersive nature of ever increasing user devices. Further, substantial spectrum reuse gain can be achieved due to the short transmit distance of D2D communication. It, however, significantly complicates the resource management and performance analysis of D2D communication underlaid cellular networks. Despite an increasing amount of academic attention and industrial interests, how to evaluate the system performance advantages of D2D communications with resource management remains largely unknown. On account of the proximity requirement of D2D communication, the resource management of D2D communication generally consists of admission access control and resource allocation. Resource allocation of cellular assisted D2D communications is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large amount of signaling overheads for channel state information (CSI) acquisition. Hence, the first part of this thesis is devoted to the resource allocation of cellular assisted D2D communication and the performance analysis. A novel resource allocation scheme for cellular assisted D2D communication is developed with low signaling overhead, while maintaining high spectral efficiency. By utilizing the spatial dispersive nature of D2D pairs, a geography-based sub-cell division strategy is proposed to group the D2D pairs into multiple disjoint clusters, and sub-cell resource allocation is performed independently for the D2D pairs within each sub-cell without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation scheme, tractable approximation for the inter D2D interference modeling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells that maximizes the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, the system capacity/spectral efficiency can be improved significantly by adopting the proposed resource allocation scheme, especially in dense D2D deployment scenario. The investigation of use cases for cellular assisted D2D communication is another important topic which has direct effect on the performance evaluation of D2D communication. Thanks to the spatial dispersive nature of devices, D2D communication can be utilized to harvest the vast amount of the idle computation power and storage space distributed at the devices, which yields sufficient capacities for performing computation-intensive and latency-critical tasks. Therefore, the second part of this thesis focuses on the D2D communication assisted Mobile Edge Computing (MEC) network. The admission access control of D2D communication is determined by both disciplines of mobile computing and wireless communications. Specifically, the energy minimization problem in D2D assisted MEC networks is addressed with the latency constraint of each individual task and the computing resource constraint of each computing entity. The energy minimization problem is formed as a two-stage optimization problem. At the first stage, an initial feasibility problem is formed to maximize the number of executed tasks, and the global energy minimization problem is tackled in the second stage while maintaining the maximum number of executed tasks. Both of the optimization problems in two stages are NP-hard, therefore a low-complexity algorithm is developed for the initial feasibility problem with a supplementary algorithm further proposed for energy minimization. Simulation results demonstrate the near-optimal performance of the proposed algorithms and the fact that the number of executed tasks is greatly increased and the energy consumption per executed task is significantly reduced with the assistance of D2D communication in MEC networks, especially in dense user scenario

Efficiency Resource Allocation for Device-to-Device Underlay Communication Systems: A Reverse Iterative Combinatorial Auction Based Approach

Peer-to-peer communication has been recently considered as a popular issue for local area services. An innovative resource allocation scheme is proposed to improve the performance of mobile peer-to-peer, i.e., device-to-device (D2D), communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which as bidders compete to obtain business while the packages of the D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit, as a basis of the proposed auction. And then a detailed non-monotonic descending price auction algorithm is explained depending on the utility function that accounts for the channel gain from D2D and the costs for the system. Further, we prove that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds. We explain non-monotonicity in the price update process and show lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.Comment: 26 pages, 6 fgures; IEEE Journals on Selected Areas in Communications, 201

Spectral Efficient and Energy Aware Clustering in Cellular Networks

The current and envisaged increase of cellular traffic poses new challenges to Mobile Network Operators (MNO), who must densify their Radio Access Networks (RAN) while maintaining low Capital Expenditure and Operational Expenditure to ensure long-term sustainability. In this context, this paper analyses optimal clustering solutions based on Device-to-Device (D2D) communications to mitigate partially or completely the need for MNOs to carry out extremely dense RAN deployments. Specifically, a low complexity algorithm that enables the creation of spectral efficient clusters among users from different cells, denoted as enhanced Clustering Optimization for Resources' Efficiency (eCORE) is presented. Due to the imbalance between uplink and downlink traffic, a complementary algorithm, known as Clustering algorithm for Load Balancing (CaLB), is also proposed to create non-spectral efficient clusters when they result in a capacity increase. Finally, in order to alleviate the energy overconsumption suffered by cluster heads, the Clustering Energy Efficient algorithm (CEEa) is also designed to manage the trade-off between the capacity enhancement and the early battery drain of some users. Results show that the proposed algorithms increase the network capacity and outperform existing solutions, while, at the same time, CEEa is able to handle the cluster heads energy overconsumption

Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources construct a distributed system, where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Analytically, it is established that this approach not only yields vanishing regret (in comparison to the global optimal solution), but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.Comment: 31 pages (one column), 9 figure