Agapi Mesodiakaki

Performance Analysis of a Cognitive Radio Contention-Aware Channel Selection Algorithm

In cognitive radio (CR) networks, due to the ever increasing traffic demands and the limited spectrum resources, it is very likely for several secondary networks (SNs) to coexist and opportunistically use the same primary user (PU) resources. In such scenarios, the ability to distinguish whether a licensed channel is occupied by a PU or by other SNs can significantly improve the spectrum efficiency of the network, while the contention among the SNs already operating on licensed channels with no PU activity may further affect its throughput and energy efficiency. Therefore, the proper selection of licensed channels could result in notable performance gains. In this paper, we propose a novel contention-aware channel selection algorithm, where the SN under study 1) detects the licensed channels with no PU activity by exploiting cooperative spectrum sensing, 2) estimates the probability of collision in each one, and 3) selects the less contended to access. We provide a detailed analytical model for the throughput and the energy efficiency of the SN, and we validate it by means of simulation. We also show the significant performance gains of our proposal in comparison with other relevant state-of-the-art algorithms.

Energy-efficient context-aware user association for outdoor small cell heterogeneous networks

To meet the ever-increasing traffic demands, future cellular networks are about to include a plethora of small cells (SCs), with user equipments (UEs) being able of communicating via multiple bands. Given that SCs are expected to be eventually as close as 50 m apart, some of them will not have a direct connection to the core network, and thus will forward their traffic to the neighboring SCs until they reach it. In such architectures, the user association problem becomes challenging with backhaul (BH) energy consumption playing a key role. Thus, in this paper, we study the user association problem aiming at maximizing the network energy efficiency. The problem is formulated as an optimization problem, which is NP-hard. Therefore, we propose a cognitive heuristic algorithm that exploits context-aware information (i.e., UE measurements and requirements, the HetNet architecture knowledge and the available spectrum resources of each base station (BS)) to associate the UEs in an energy-efficient way, while considering both the access and the BH energy consumption. We evaluate the performance of the proposed algorithm under two study-case scenarios and we prove that it achieves significantly higher energy efficiency than the reference algorithms, while maintaining high spectral efficiency.

Energy- and Spectrum-Efficient User Association in Millimeter-Wave Backhaul Small-Cell Networks

Macrocells are expected to be densely overlaid by small cells (SCs) to meet increasing capacity demands. Due to their dense deployment, some SCs will not be connected directly to the core network, and thus, they may forward their traffic to the neighboring SCs until they reach it, thereby forming a multihop backhaul (BH) network. This is a promising solution, since the expected short length of BH links enables the use of millimeter-wave (mmWave) frequencies to provide high-capacity BH. In this context, user association becomes challenging due to the multihop BH architecture, and therefore, new optimal solutions should be developed. Thus, in this paper, we study the user association problem aiming at the joint maximization of network energy efficiency (EE) and spectrum efficiency (SE), without compromising the user quality of service (QoS). The problem is formulated as an

Energy impact of outdoor small cell backhaul in green heterogeneous networks

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Fairness evaluation of a secondary network coexistence scheme

-constraint problem, which considers the transmit energy consumption both in the access network, i.e., the links between the users and their serving cells, and the BH links. The optimal Pareto-front solutions of the problem are analytically derived for different BH technologies, and insights are gained into the EE and SE tradeoff. The proposed optimal solutions, despite their high complexity, can be used as a benchmark for the performance evaluation of user association algorithms. We also propose a heuristic algorithm, which is compared with reference solutions under different traffic scenarios and BH technologies. Our results motivate the use of mmWave BH, whereas the proposed algorithm achieves near-optimal performance.

Energy-efficient contention-aware channel selection in Cognitive Radio Ad-Hoc Networks

Small cells (SCs) are expected to be densely deployed during the next few years to enhance the network capacity of future heterogeneous networks (HetNets). Due to their dense deployment, not all SCs are expected to have a direct connection to the core network. As a result, some SCs will forward their traffic to the neighboring SCs until they reach the core network, thus forming a multi-hop backhaul (BH) network. Due to the large number of BH links, the BH is expected to be one of the main challenges that future HetNets will have to face. At the same time, traffic demands are growing exponentially resulting in higher energy consumption. Therefore, how to achieve high network energy efficiency becomes of utmost importance. To that end, in this paper, we study the role of BH in future outdoor HetNets aiming to answer to whether or not it could constitute an energy bottleneck for the HetNet. To gain insights, we study the BH energy consumption impact compared to the access network under different traffic distribution scenarios and BH technologies.

Joint uplink and downlink cell selection in cognitive small cell heterogeneous networks

Due to the ever-increasing traffic demands and the fact that the spectrum resources are limited, it is very likely that several secondary networks (SNs) will coexist and opportunistically use the same primary user (PU) resources. In such coexistence scenarios, it is fundamental to guarantee fairness among the coexisting SNs. To that end, in this paper, we evaluate the performance of an SN coexistence scheme in terms of fairness and we show that it can achieve throughput and energy efficiency gains, while maintaining fairness among the coexisting SNs in comparison to other state-of-the-art approaches.

Energy efficiency analysis of secondary networks in cognitive radio systems

In this paper, we propose a novel contention-aware channel selection algorithm that focuses on throughput and energy efficiency improvement in Cognitive Radio Ad-Hoc Networks (CRAHNs). Specifically, we study the operation and performance of a Secondary Network (SN) in a scenario where other non-cooperating CRAHNs are also using the primary resources. We prove that a channel categorization of the idle channels based on their contention level and the selection of the less contented ones can result in up to 70% improvement in throughput and up to 68% improvement in energy efficiency. Simulation results are presented for the performance evaluation of our proposed algorithm.

Energy efficient line-of-sight millimeter wave small cell backhaul: 60, 70, 80 or 140 GHz?

In the next few years, small cells (SCs) are expected to be densely deployed to achieve sustainable capacity enhancement. Due to the high SC density, some SCs will not have a direct connection to the core network, and thus will forward their traffic to their neighboring SCs through a multi-hop backhaul (BH). In such multi-hop architectures, the user association problem becomes challenging with BH energy consumption playing a key role. In parallel, the ever-increasing need to minimize the user equipment (UE) transmission power along with the uplink (UL) and downlink (DL) traffic asymmetry, predicate the joint study of UL and DL. Thus, in this paper, we study the joint UL and DL cell selection problem aiming at maximizing the total network energy efficiency, without compromising the UE quality of service. The problem is formulated as an optimization problem, which is NP-hard. Therefore, we propose a heuristic context-aware algorithm that associates the UEs in an energy-efficient way, while considering both access and BH energy consumption in UL and DL. We evaluate the proposed algorithm performance and we show that it can achieve significantly higher energy efficiency than the reference approaches, while maintaining high spectral efficiency and low UE power consumption.

Energy and spectrum efficient user association in 5G heterogeneous networks

In this paper, we evaluate a novel contention-aware channel selection algorithm that focuses on energy efficiency improvement of a secondary network (SN) in a scenario where other non-cooperating SNs are also using the primary resources. We present a detailed energy efficiency analysis and we study how the time between two consecutive sensing periods affects the energy efficiency. Our analysis proves that a categorization of the idle channels based on their contention level and the selection of the less contended ones can result in up to 70% gain in energy efficiency. The model is further evaluated through simulations.