Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks

The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements

Rate Optimal design of a Wireless Backhaul Network using TV White Space

The penetration of wireless broadband services in remote areas has primarily been limited due to the lack of economic incentives that service providers encounter in sparsely populated areas. Besides, wireless backhaul links like satellite and microwave are either expensive or require strict line of sight communication making them unattractive. TV white space channels with their desirable radio propagation characteristics can provide an excellent alternative for engineering backhaul networks in areas that lack abundant infrastructure. Specifically, TV white space channels can provide "free wireless backhaul pipes" to transport aggregated traffic from broadband sources to fiber access points. In this paper, we investigate the feasibility of multi-hop wireless backhaul in the available white space channels by using noncontiguous Orthogonal Frequency Division Multiple Access (NC-OFDMA) transmissions between fixed backhaul towers. Specifically, we consider joint power control, scheduling and routing strategies to maximize the minimum rate across broadband towers in the network. Depending on the population density and traffic demands of the location under consideration, we discuss the suitable choice of cell size for the backhaul network. Using the example of available TV white space channels in Wichita, Kansas (a small city located in central USA), we provide illustrative numerical examples for designing such wireless backhaul network

Hierarchical Cooperation for Operator-Controlled Device-to-Device Communications: A Layered Coalitional Game Approach

Device-to-Device (D2D) communications, which allow direct communication among mobile devices, have been proposed as an enabler of local services in 3GPP LTE-Advanced (LTE-A) cellular networks. This work investigates a hierarchical LTE-A network framework consisting of multiple D2D operators at the upper layer and a group of devices at the lower layer. We propose a cooperative model that allows the operators to improve their utility in terms of revenue by sharing their devices, and the devices to improve their payoff in terms of end-to-end throughput by collaboratively performing multi-path routing. To help understanding the interaction among operators and devices, we present a game-theoretic framework to model the cooperation behavior, and further, we propose a layered coalitional game (LCG) to address the decision making problems among them. Specifically, the cooperation of operators is modeled as an overlapping coalition formation game (CFG) in a partition form, in which operators should form a stable coalitional structure. Moreover, the cooperation of devices is modeled as a coalitional graphical game (CGG), in which devices establish links among each other to form a stable network structure for multi-path routing.We adopt the extended recursive core, and Nash network, as the stability concept for the proposed CFG and CGG, respectively. Numerical results demonstrate that the proposed LCG yields notable gains compared to both the non-cooperative case and a LCG variant and achieves good convergence speed.Comment: IEEE Wireless Communications and Networking Conference 201