Minimization of Sum Inverse Energy Efficiency for Multiple Base Station Systems

A sum inverse energy efficiency (SIEE) minimization problem is solved. Compared with conventional sum energy efficiency (EE) maximization problems, minimizing SIEE achieves a better fairness. The paper begins by proposing a framework for solving sum-fraction minimization (SFMin) problems, then uses a novel transform to solve the SIEE minimization problem in a multiple base station (BS) system. After the reformulation into a multi-convex problem, the alternating direction method of multipliers (ADMM) is used to further simplify the problem. Numerical results confirm the efficiency of the transform and the fairness improvement of the SIEE minimization. Simulation results show that the algorithm convergences fast and the ADMM method is efficient

On the Energy-Efficiency of Hybrid Analog-Digital Transceivers for Single- and Multi-carrier Large Antenna Array Systems

Hybrid Analog-Digital transceivers are employed with the view to reduce the hardware complexity and the energy consumption in millimeter wave/large antenna array systems by reducing the number of their Radio Frequency (RF) chains. However, the analog processing network requires power for its operation and it further introduces power losses, dependent on the number of the transceiver antennas and RF chains, that have to be compensated. Thus, the reduction in the power consumption is usually much less than it is expected and given that the hybrid solutions present in general inferior spectral efficiency than a fully digital one, it is possible for the former to be less energy efficient than the latter in several cases. Existing approaches propose hybrid solutions that maximize the spectral efficiency of the system without providing any insight on their energy requirements/efficiency. To that end, in this paper, a novel algorithmic framework is developed based on which energy efficient hybrid transceiver designs are derived and their performance is examined with respect to the number of RF chains and antennas. Solutions are proposed for fully and partially connected hybrid architectures and for both single- and multi-carrier systems under the Orthogonal Frequency Division Multiplexing (OFDM) modulation. Simulations and theoretical results provide insight on the cases where a hybrid transceiver is the most energy efficient solution or not

Energy efficient precoder design for MIMO-OFDM with rate-dependent circuit power

This paper studies an energy efficient design of pre-coder for point-to-point Multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Dif-ferently from traditional approaches, the optimal power allo-cation strategy is studied by modelling the circuit power as a rate-dependent function. We show that if the circuit power is a constant plus an increasing and convex function of the transmission rate, the energy efficiency maximization problem can be reformulated as a convex fractional program and solved by means of a simple bisection algorithm. The impact of the system parameters is mathematically analyzed and then confirmed by means of computational results

Cognitive Radio Systems: Performance Analysis and Optimal Resource Allocation

Rapid growth in the use of wireless services coupled with inefficient utilization of scarce spectrum resources has led to the analysis and development of cognitive radio systems. Cognitive radio systems provide dynamic and more efficient utilization of the available spectrum by allowing unlicensed users (i.e., cognitive or secondary users) to access the frequency bands allocated to the licensed users (i.e., primary users) without causing harmful interference to the primary user transmissions. The central goal of this thesis is to conduct a performance analysis and obtain throughput- and energy-efficient optimal resource allocation strategies for cognitive radio systems. Cognitive radio systems, which employ spectrum sensing mechanisms to learn the channel occupancy by primary users, generally operate under sensing uncertainty arising due to false alarms and miss-detections. This thesis analyzes the performance of cognitive radio systems in a practical setting with imperfect spectrum sensing. In the first part of the thesis, optimal power adaptation schemes that maximize the achievable rates of cognitive users with arbitrary input distributions in underlay cognitive radio systems subject to transmit and interference power constraints are studied. Simpler approximations of optimal power control policies in the low-power regime are determined. Low-complexity optimal power control algorithms are proposed. Next, energy efficiency is considered as the performance metric and power allocation strategies that maximize the energy efficiency of cognitive users in the presence of time-slotted primary users are identified. The impact of different levels of channel knowledge regarding the transmission link between the secondary transmitter and secondary receiver, and the interference link between the secondary transmitter and primary receiver on the optimal power allocation is addressed. In practice, the primary user may change its status during the transmission phase of the secondary users. In such cases, the assumption of time-slotted primary user transmission no longer holds. With this motivation, the spectral and energy efficiency in cognitive radio systems with unslotted primary users are analyzed and the optimal frame duration and energy-efficient optimal power control schemes subject to a collision constraint are jointly determined. The second line of research in this thesis focuses on symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. General formulations for the optimal decision rule and error probabilities for arbitrary modulation schemes are provided. The optimal decision rule for rectangular quadrature amplitude modulation (QAM) is characterized, and closed-form expressions for the average symbol error probability attained with the optimal detector under both transmit power and interference constraints are derived. Furthermore, throughput of cognitive radio systems for both fixed-rate and variable-rate transmissions in the finite-blocklength regime is studied. The maximum constant arrival rates that the cognitive radio channel can support with finite blocklength codes while satisfying statistical quality of service (QoS) constraints imposed as limitations on the buffer violation probability are characterized. In the final part of the thesis, performance analysis in the presence of QoS requirements is extended to general wireless systems, and energy efficiency and throughput optimization with arbitrary input signaling are studied when statistical QoS constraints are imposed as limitations on the buffer violation probability. Effective capacity is chosen as the performance metric to characterize the maximum throughput subject to such buffer constraints by capturing the asymptotic decay-rate of buffer occupancy. Initially, constant-rate source is considered and subsequently random arrivals are taken into account