Energy Efficient Radio Resource Management in Wireless Networks The contemporary extension of telecommunication industry and the hike towards more and more data rates have a profound impact on the power disbursement. More sensitive of the disadvantages associated with this power budget are the climatic perils along with the economic bounds. Consequently, the concept of Energy Efficiency (EE) for attaining green communication is evolved that may help to address the above affairs in current and future wireless networks. Enormous efforts have been done in this regard for the attainment of EE that include efficient power control, channel allocation, interference management and data rate maximization, etc. In addition, the incorporation of new techniques like Device to Device (D2D) and Cognitive Radio (CR) in existing wireless communication will increase the EE of the wireless systems. In this thesis, the notion of EE has been thoroughly investigated, with the basic as well as advanced approaches for its attainment. Therefore, resource allocation problems for maximization of EE have been focused in different wireless frameworks like Single Carrier Frequency Division Multiple Access (SC-FDMA), D2D and CR networks. Resource allocation is the field of wireless communication in which the wireless resources like bandwidth, data rates, transmission power etc., are cleverly distributed among users. Therefore, I have considered channel allocation, transmission power allocation, user admission and mode selection in cellular communication frameworks for the achievement of my goal of increasing the EE. Firstly, I have considered resource allocation problem in uplink SCFDMA for maximization of EE. The joint power and subchannel allocation problem is formulated to maximize the EE metric under constraints on the minimum achieved data rate of each user, the maximum transmit power budget of each user, and the exclusive as well as the consecutive allocation of sub-channels among the users. This problem is a nonlinear and combinatorial optimization problem, which has been transformed to equivalent binary integer programming problem for obtaining the optimal solution. Also, a heuristic suboptimal solution with less computational complexity has been presented. This EE utilization of resources may further be extended to advanced wireless techniques like D2D and CR to complement our goal. D2D is one of the promising technologies that can be used in association with cellular networks for the provision of high data rates, which is the foremost demand of today''s cellular users. These high data rates can further be increased through efficient management of resources, which may also increase the EE of such systems. Therefore, I have also address the mode selection, user admission control and the resource allocation in a pooled D2D and cellular network optimization model, for energy efficient resource allocation. The problem formulated is a Mixed Integer Nonlinear Problem (MINLP), whereas such problems belong to a class of problems whose computational complexity is NP-hard. Finding the optimal solution of such hard problems by the brute force and searching the feasible space is also challenging since the complexity increases exponentially by increasing the admitted users. Therefore, a Mesh Adaptive Direct Search (MADS) algorithm has been proposed to find the EE resource allocation with reasonable computational complexity. The D2D connectivity is nonetheless a game changing technique while considering the high data rates, however, more smart utilization of resources in CR assisted D2D networks, may result in more data rates and EE. Therefore, the problem of maximizing EE of CR assisted D2D networks has been considered, subject to compliance of transmit powers of cellular users and interference constraints of primary users of broadcast network. CR network users are cellular users comprising of cellular and D2D users. Cellular users can opt any of the cellular or D2D mode. The cognitive radio network users opportunistically utilize spectrum of Television (TV) white spaces. The problem thus formulated is NP-complete. MADS algorithm has been used to find near optimal solution. The results of MADS have been compared with the exhaustive search algorithm, which clearly proves MADS to be an ideal algorithm, and can be a choice for real life large cellular networks, due to its low computational complexity.