Research On Energy-Efficient Radio Resource Management

Energy consumption of wireless networks has been rapidly increasing, with the development of wireless communications technology and the expansion of networks. The mobile communications industry has now become a high-energy consuming industry, and therefore energy-efficient design in communications networks has become increasingly important. On the other hand, radio resources are limited in wireless transmission, such as power, time, frequency and space. How to allocate the allocation effectively has always been an important part of the study of wireless communications. However, high capacity or high spectral efficiency is currently no longer the only goal in radio resource management (RRM). This dissertation investigates the RRM from the perspective of energy efficiency (EE) optimization. The EE is maximized while satisfying the needs of the wireless networks by effective resource allocations.First, we study the energy-efficient coordinated transmission in multi-cell multiple-input multiple-output (MIMO) systems. We design a joint beamforming and power allocation, aiming to maximize the EE while satisfying the target data rates of all users. We formulate the optimization problem as a non-linear fractional programming issue with non-convex constraints. Due to the existence of interference and the fractional form of the objective function, we first adopt a zero-forcing coordinated beamforming scheme to eliminate inter-ference and simplify the complex forms of the objective function and the constraints. Then, we transform the fractional programming problem into a non-fractional problem by a parameterized method, and compute the auxiliary parameters by an iterative algorithm. To avoid the complex null space decomposition, a simplified zero-forcing coordinated beamforming scheme using beam tracing is given. For a given beam forming matrix, we derive an optimal power allocation solution for maximizing the EE. Simulation results demonstrate that the proposed algorithm has a better EE performance than the traditional capacity maximizing method. With a lower complexity, the proposed method using beam tracing approaches the EE performance of the null space decomposition method in slowly varying channels.Then, we investigate an energy-efficient resource allocation for the multi-cell cooperation in orthogo-nal frequency division multiple access (OFDMA) networks with imperfect channel state information. The co-channel interference is dealt with via the cooperation of multiple base stations sharing channel state infor-mation but not user data. The EE is maximized by a joint user scheduling, data rate adaptation and power allocation algorithm. To solve the probabilistic optimization problem, we derive rate function expression with respect to the power and user scheduling variables. And then we propose an iterative algorithm to deal with the mixed problem incorporating both integer and continuous variables. We also prove that the convergence of the iterative process. To solve the power allocation sub-problem, we transform it into a parameterized optimization problem and provide a lower bound approximation, and obtain an energy-efficient power allo-cation scheme via two iterative algorithms. We also show some interesting trade-offs in the proposed method with regard to spectral efficiency, EE and fairness. Simulation results show that the proposed algorithm can achieve an EE performance near to the upper bound. And the proposed algorithm has a higher EE than the conventional methods, such as the spectral efficiency maximization algorithm, the naive algorithm without regard to channel error, and the round-robin scheduling and equal power allocation algorithm. Also, the pro-posed algorithm incurs little spectral efficiency loss relative to the EE gains, under the multi-cell environment with co-channel interference.Next, we propose a resource allocation strategy in OFDMA heterogeneous networks for maximizing weighted sum EE. Such schemes can allow different BSs to have different weight factors of the EEs, and can also balance the EEs between macrocells and low power base stations. We formulate the optimization problem as a nonlinear sum-of-ratios programming problem, where the data rate requirements of users are protected with minimum rate constraints. To make the problem tractable, we consider a two-step scheme. First we present a heuristic subchannel allocation algorithm to maximize the weighted sum EE. To satisfy the minimum rate requirements of users, we select the user whose rate is the farthest from its requirement, and allocate the subchannel with the highest signal to interference and noise ratio to the user. After the rate requirements of all users are satisfied, we then allocate subchannels based on maximizing sum-rate of each cell. Then we solve the power allocation problem by a parameterized transformation and a first-order approximation based on an iterative process. Simulation results show that the proposed algorithm converges within a small number of iterations. Also, the proposed method can achieve a higher weighted sum EE performance than the EE maximization and the spectral efficiency maximization methods. An EE tradeoff exists between the macrocell and picocells for the proposed method.Additionally, we address the energy-efficient resource allocation scheme for downlink relay-assisted OFDMA networks. We consider a multi-user cellular system with multiple fixed relay stations using half-duplex decode-and-forward protocol. We aim to maximize the system EE by jointly optimizing the transmit power, data rate, and subcarrier allocation with imperfect channel state information. To this end, we formulate the EE maximization problem as a probabilistic mixed non-convex optimization problem, where the minimum data rate constraint and the outage probability requirement are taken into account. To make the problem tractable, we transform it into a standard convex problem by using the variable relaxation, the parameterized transformation and auxiliary variables, which leads to an efficient iterative resource allocation algorithm based on the bi-section method. In addition, we adopt a proportional fairness design to balance the EE and user fairness. Simulation results show that the proposed method can achieve a higher EE than the conventional goodput maximization method. And there exists a tradeoff between the EE and the sum goodput, as well as between the EE and the fairness.Finally, we study the low complexity energy-efficient power and rate adaptation algorithm for amplify-and-forward relay systems. In the proposed scheme, the system EE is maximized under the maximum transmit power constraint, the minimum system data-rate constraint and the target packet outage probability constrain-t. Due to the non-convexity of the optimization problem, we consider the approximation with high signal to noise ratio. Then, to solve the EE maximization problem, we proposed a low-complexity iterative algorithm. Numerical results illustrate that the proposed algorithm has a lower complexity without EE performance dete-rioration, especially for the strict minimum rate or maximum transmit power constraints. Also, the proposed method can achieve a higher EE than the traditional sum goodput maximization scheme and outperforms the method based on estimated channels.