Power-Aware Planning and Design for Next Generation Wireless Networks

Mobile network operators have witnessed a transition from being voice dominated to video/data domination, which leads to a dramatic traffic growth over the past decade. With the 4G wireless communication systems being deployed in the world most recently, the fifth generation (5G) mobile and wireless communica- tion technologies are emerging into research fields. The fast growing data traffic volume and dramatic expansion of network infrastructures will inevitably trigger tremendous escalation of energy consumption in wireless networks, which will re- sult in the increase of greenhouse gas emission and pose ever increasing urgency on the environmental protection and sustainable network development. Thus, energy-efficiency is one of the most important rules that 5G network planning and design should follow. This dissertation presents power-aware planning and design for next generation wireless networks. We study network planning and design problems in both offline planning and online resource allocation. We propose approximation algo- rithms and effective heuristics for various network design scenarios, with different wireless network setups and different power saving optimization objectives. We aim to save power consumption on both base stations (BSs) and user equipments (UEs) by leveraging wireless relay placement, small cell deployment, device-to- device communications and base station consolidation. We first study a joint signal-aware relay station placement and power alloca- tion problem with consideration for multiple related physical constraints such as channel capacity, signal to noise ratio requirement of subscribers, relay power and network topology in multihop wireless relay networks. We present approximation schemes which first find a minimum number of relay stations, using maximum transmit power, to cover all the subscribers meeting each SNR requirement, and then ensure communications between any subscriber and a base station by ad- justing the transmit power of each relay station. In order to save power on BS, we propose a practical solution and offer a new perspective on implementing green wireless networks by embracing small cell networks. Many existing works have proposed to schedule base station into sleep to save energy. However, in reality, it is very difficult to shut down and reboot BSs frequently due to nu- merous technical issues and performance requirements. Instead of putting BSs into sleep, we tactically reduce the coverage of each base station, and strategi- cally place microcells to offload the traffic transmitted to/from BSs to save total power consumption. In online resource allocation, we aim to save tranmit power of UEs by en- abling device-to-device (D2D) communications in OFDMA-based wireless net- works. Most existing works on D2D communications either targeted CDMA- based single-channel networks or aimed at maximizing network throughput. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize total power consumption while meeting user data rate requirements. We propose to solve it using a joint optimization approach by presenting two effective and efficient algorithms, which both jointly determine mode selection, channel allocation and power assignment. In the last part of this dissertation, we propose to leverage load migration and base station consolidation for green communications and consider a power- efficient network planning problem in virtualized cognitive radio networks with the objective of minimizing total power consumption while meeting traffic load demand of each Mobile Virtual Network Operator (MVNO). First we present a Mixed Integer Linear Programming (MILP) to provide optimal solutions. Then we present a general optimization framework to guide algorithm design, which solves two subproblems, channel assignment and load allocation, in sequence. In addition, we present an effective heuristic algorithm that jointly solves the two subproblems. Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions, compared to several baseline methods

Wireless Backhaul Node Placement for Small Cell Networks

Small cells have been proposed as a vehicle for wireless networks to keep up with surging demand. Small cells come with a significant challenge of providing backhaul to transport data to(from) a gateway node in the core network. Fiber based backhaul offers the high rates needed to meet this requirement, but is costly and time-consuming to deploy, when not readily available. Wireless backhaul is an attractive option for small cells as it provides a less expensive and easy-to-deploy alternative to fiber. However, there are multitude of bands and features (e.g. LOS/NLOS, spatial multiplexing etc.) associated with wireless backhaul that need to be used intelligently for small cells. Candidate bands include: sub-6 GHz band that is useful in non-line-of-sight (NLOS) scenarios, microwave band (6-42 GHz) that is useful in point-to-point line-of-sight (LOS) scenarios, and millimeter wave bands (e.g. 60, 70 and 80 GHz) that are recently being commercially used in LOS scenarios. In many deployment topologies, it is advantageous to use aggregator nodes, located at the roof tops of tall buildings near small cells. These nodes can provide high data rate to multiple small cells in NLOS paths, sustain the same data rate to gateway nodes using LOS paths and take advantage of all available bands. This work performs the joint cost optimal aggregator node placement, power allocation, channel scheduling and routing to optimize the wireless backhaul network. We formulate mixed integer nonlinear programs (MINLP) to capture the different interference and multiplexing patterns at sub-6 GHz and microwave band. We solve the MINLP through linear relaxation and branch-and-bound algorithm and apply our algorithm in an example wireless backhaul network of downtown Manhattan.Comment: Invited paper at Conference on Information Science & Systems (CISS) 201

Deploy-As-You-Go Wireless Relay Placement: An Optimal Sequential Decision Approach using the Multi-Relay Channel Model

We use information theoretic achievable rate formulas for the multi-relay channel to study the problem of as-you-go deployment of relay nodes. The achievable rate formulas are for full-duplex radios at the relays and for decode-and-forward relaying. Deployment is done along the straight line joining a source node and a sink node at an unknown distance from the source. The problem is for a deployment agent to walk from the source to the sink, deploying relays as he walks, given that the distance to the sink is exponentially distributed with known mean. As a precursor, we apply the multi-relay channel achievable rate formula to obtain the optimal power allocation to relays placed along a line, at fixed locations. This permits us to obtain the optimal placement of a given number of nodes when the distance between the source and sink is given. Numerical work suggests that, at low attenuation, the relays are mostly clustered near the source in order to be able to cooperate, whereas at high attenuation they are uniformly placed and work as repeaters. We also prove that the effect of path-loss can be entirely mitigated if a large enough number of relays are placed uniformly between the source and the sink. The structure of the optimal power allocation for a given placement of the nodes, then motivates us to formulate the problem of as-you-go placement of relays along a line of exponentially distributed length, and with the exponential path-loss model, so as to minimize a cost function that is additive over hops. The hop cost trades off a capacity limiting term, motivated from the optimal power allocation solution, against the cost of adding a relay node. We formulate the problem as a total cost Markov decision process, establish results for the value function, and provide insights into the placement policy and the performance of the deployed network via numerical exploration.Comment: 21 pages. arXiv admin note: substantial text overlap with arXiv:1204.432

QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks

We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that number of hops in the path from each sensor to its BS is bounded by hmax, and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios