Ghazzai, Hakim; Farooq, Muhammad Junaid; Alsharoa, Ahmad; Yaacoub, Elias; Kadri, Abdullah; Alouini, Mohamed-Slim(IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers (IEEE), 2016-12-07)[Article]
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 (QoS) requirements of each connected user. We consider the case of co-existing 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 semi-closed 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 semi-closed 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 inter-operator agreements.
Ghazzai, Hakim; Yaacoub, Elias E.; Alouini, Mohamed-Slim; Abu-Dayya, Adnan A.(IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers (IEEE), 2014-11)[Article]
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.
Ghazzai, Hakim; Bouchoucha, Taha; Alsharoa, Ahmad; Yaacoub, Elias; Alouini, Mohamed-Slim; Al-Naffouri, Tareq Y.(IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers (IEEE), 2016-03-15)[Article]
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 the orthogonal frequency-division multiple access (OFDMA) technology. In this work, 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 in order to find a suboptimal but low complexity solution. Moreover, we propose an OFDMA planning solution for high-speed train by finding the maximal inter-BS distance given the required user data rates in order to perform seamless handover. Our simulation results illustrate the performance of the proposed resource allocation schemes in the case of the 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 thanks to the use of multiple relays instead of the conventional single relay scenario.
Ghazzai, Hakim; Yaacoub, Elias; Alouini, Mohamed-Slim; Dawy, Zaher; Abu Dayya, Adnan(IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers (IEEE), 2015-03-09)[Article]
Base station deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation (4G) cellular networks using meta-heuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterwards, we implement a meta-heuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently-proposed grey wolf optimizer) to find suboptimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regards to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality of service targets are always reached even for large-scale problems.
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