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In this paper, the problem of energy efficiency in cellular heterogeneous networks (HetNets) is investigated using radio resource and power management combined with the base station (BS) ON/OFF switching. The objective is to minimize the total power consumption of the network while satisfying the quality of service requirements of each connected user. We consider the case of coexisting macrocell BS, small cell BSs, and private femtocell access points (FAPs). Three different network scenarios are investigated, depending on the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs, and HetNets with semiclosed FAPs. A unified framework is proposed to simultaneously allocate spectrum resources to users in an energy efficient manner and switch OFF redundant small cell BSs. The high complexity dual decomposition technique is employed to achieve optimal solutions for the problem. A low complexity iterative algorithm is also proposed and its performances are compared to those of the optimal technique. The particularly interesting case of semiclosed FAPs, in which the FAPs accept to serve external users, achieves the highest energy efficiency due to increased degrees of freedom. In this paper, a cooperation scheme between FAPs and mobile operator is also investigated. The incentives for FAPs, e.g., renewable energy sharing and roaming prices, enabling cooperation are discussed to be considered as a useful guideline for interoperator agreements. [ABSTRACT FROM PUBLISHER]

A high-speed railway system equipped with moving relay stations placed on the middle of the ceiling of each train wagon is investigated. The users inside the train are served in two hops via orthogonal frequency-division multiple-access (OFDMA) technology. In this paper, we first focus on minimizing the total downlink power consumption of the base station (BS) and the moving relays while respecting specific quality-of-service (QoS) constraints. We first derive the optimal resource-allocation solution, in terms of OFDMA subcarriers and power allocation, using the dual decomposition method. Then, we propose an efficient algorithm based on the Hungarian method to find a suboptimal but low-complexity solution. Moreover, we propose an OFDMA planning solution for high-speed trains by finding the maximal inter-BS distance, given the required user data rates to perform seamless handover. Our simulation results illustrate the performance of the proposed resource-allocation schemes in the case of Third-Generation Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) and compare them with previously developed algorithms, as well as with the direct transmission scenario. Our results also highlight the significant planning gain obtained, owing to the use of multiple relays instead of the conventional single-relay scenario. [ABSTRACT FROM PUBLISHER]

In this paper, we study a multiple-antenna two-way relaying (TWR) cognitive radio (CR) system. A space alignment (SA) technique is adopted by the secondary users (SUs) to avoid interference with the primary users (PUs). We derive the optimal power allocation that maximizes the TWR achievable SU sum-rate while respecting the total power budget and the relay power constraints. We also analyze the case in which the relay is able to optimize its gain matrix structure to enhance the SU sum-rate. In the numerical results, we quantify the sum-rate gain of using the SA in the TWR CR and we show that the SU sum-rate is very limited when the relay power is low or the PU power and its resulting interference are high. In addition, we optimize the relay gain using an iterative algorithm and compare between different relay matrix structures. [ABSTRACT FROM PUBLISHER]

We investigate the spectral efficiency gain of an uplink cognitive radio (CR) multi-input-multi-output system in which the secondary user (SU) is allowed to share the spectrum with the primary user (PU) using a specific precoding scheme to communicate with a common receiver. The proposed scheme exploits, at the same time, the free eigenmodes of the primary channel after a space alignment procedure and the interference threshold tolerated by the PU. At the common receiver, we adopt a successive interference cancellation (SIC) technique to eliminate the effect of the detected primary signal transmitted through the exploited eigenmodes. Furthermore, we analyze the SIC operation inaccuracy as well as the CSI estimation imperfection on the PU and SU throughputs. Numerical results show that our proposed scheme enhances considerably the cognitive achievable rate. For instance, in case of a perfect detection of the PU signal, the CR rate remains non-zero for high signal to noise ratio, which is usually impossible when we only employ a space alignment technique. We show that a modified water-filling power allocation policy at the PU can increase the secondary rate with a marginal degradation of the primary rate. Finally, we investigate the behavior of the PU and SU rates through the study of the rate achievable region. [ABSTRACT FROM PUBLISHER]

We study the secondary sum-rate of an underlay cognitive multiple access channel consisting of a primary user and multiple secondary users (SUs) communicating with a common destination. We propose a particular linear precoding and SU selection scheme that maximize the cognitive sum-rate. This scheme is based on space alignment strategy allowing SUs to share the spectrum without interfering with each other. We derive the optimal power allocation for each selected SU after applying perfect or imperfect successive interference cancellation. Numerical results show that the proposed scheme provides a significant sum-rate improvement as the number of SUs increases. In addition, it achieves almost the same performance as an exhaustive search selection, mainly in low and high power ranges. [ABSTRACT FROM PUBLISHER]

Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the \CO2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods. [ABSTRACT FROM AUTHOR]