Abstract-Macrocells are expected to be densely overlaid by small cells (SCs) to meet the 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 multi-hop 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 and spectrum efficiency, without compromising the user quality of service. The problem is formulated as an ε-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 energy and spectrum efficiency trade-off. 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 distribution scenarios and BH technologies. Our results motivate the use of mmWave BH, while the proposed algorithm is shown to achieve near-optimal performance.

Due to the ever increasing data traffic demands, which are directly connected to increased energy consumption, it becomes challenging for operators to achieve capacity enhancement while limiting their electric bill. To that end, exploiting the context awareness of future cognitive networks is expected to play a key role. Next generation cellular networks are about to include a plethora of small cells, with users being able to communicate via multiple bands. Given that small cells are expected to be eventually as close as 50 m apart, not all of them will have a direct connection to the core network; thus, multihop communication through neighboring small cells may be required. In such architectures, the user association problem becomes challenging, with backhaul energy consumption being a definitive parameter. Thus, in this article, we study the user association problem in cognitive heterogeneous networks. We evaluate the existing approaches in terms of energy efficiency and show the potential of exploiting the available context-aware information (i.e., users' measurements and requirements, knowledge of the network architecture, and the available spectrum resources of each base station) to associate the users in an energy-efficient way, while maintaining high spectrum efficiency. Our study considers both the access network and backhaul energy consumption, while the performance of the association algorithms is evaluated under two different case study scenarios.Peer ReviewedPostprint (published version

MmWave radio, although instrumental for achieving the required 5G capacity Key Performance Indicators (KPIs), necessitates the need for a very large number of Access Points, which places an immense strain on the current network infrastructure. In this article, we try to identify the major challenges that inhibit the design of the Next Generation Fronthaul Interface in two upcoming distinctively highly dense environments: i) in Urban 5G deployments in metropolitan areas and ii) in ultra-dense Hotspot scenarios. Secondly, we propose a novel centralized and converged analog Fiber-Wireless Fronthaul architecture, specifically designed to facilitate mmWave access in the above scenarios. The proposed architecture leverages optical transceivers, optical add/drop multiplexers and optical beamforming integrated photonics towards a Digital Signal Processing analog fronthaul. The functional administration of the fronthaul infrastructure is achieved by means of a packetized Medium Transparent Dynamic Bandwidth Allocation protocol. Preliminary results show that the protocol can facilitate Gbps-enabled data transport while abiding to the 5G low-latency KPIs in various network traffic conditions.

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.Peer ReviewedPostprint (published version

Abstract-Spectrum scarcity together with high capacity demands make the use of millimeter wave (mmWave) frequencies an interesting alternative for next generation, i.e., fifth generation (5G), networks. Although mmWave is expected to play a key role for both access network and backhaul (BH), its initial use in the BH network seems more straight-forward. This stems from the fact that, in the BH case, its deployment is less challenging due to the fixed locations of BH transceivers. Still, provided that mmWave spectrum consists of several subbands, each one with different characteristics and thus different deployment constraints (e.g., channel bandwidth, maximum transmission power), a comparison is required in order to gain a better insight into the potentials of each solution. To that end, in this paper, the main mmWave candidate frequency bands are compared in terms of range, throughput and energy consumption. In our results, the bandwidth availability, the maximum transmission power as well as the antenna gains of each BH technology are taken into account, as defined by the Federal Communications Commission. The results are also compared with current industry-oriented state-of-the-art transceiver characteristics in order to gain further insights into the maximum achievable gains of each subband.

Next generation, i.e., fifth generation (5G), cellular networks will provide a significant higher capacity per area to support the ever-increasing tra c demands. In order to achieve that, many small cells need to be deployed that are connected using a combination of optical fiber links and millimeter-wave (mmWave) backhaul architecture to forward heterogeneous tra c over mesh topologies. In this paper, we present a general optimization framework for the design of policies that optimally solve the problem of where to associate a user, over which links to route its tra c towards which mesh gateway, and which base stations and backhaul links to switch o↵ in order to minimize the energy cost for the network operator and still satisfy the user demands. We develop an optimal policy based on mixed integer linear programming (MILP) which considers di↵erent user distribution and tra c demands over multiple time periods. We develop also a fast iterative two-phase solution heuristic, which associates users and calculates backhaul routes to maximize energy savings. Our strategies optimize the backhaul network configuration at each timeslot based on the current demands and user locations. We discuss the application of our policies to backhaul management of mmWave cellular networks in light of current

Abstract-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: i) detects the licensed channels with no PU activity by exploiting cooperative spectrum sensing, ii) estimates the probability of collision in each one, and iii) 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.

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