Efficient communication and computing resource allocation is becoming a fundamental issue in wireless networks. Efficiency is most often defined in terms of throughput, utilization and spectral efficiency, while the required computational effort is often overlooked. In this paper, we focus on efficient and computationally lightweight downlink scheduling, and we propose a semi-persistent scheduler based on adaptive short term traffic prediction. We evaluate the performance of the proposed scheduler in terms of throughput, fairness, latency, and scheduling complexity. Our numerical results show that scheduling with prediction is a promising approach in improving network performance. The proposed semi-persistent scheduler performs equally well in terms of throughput, fairness, and latency as traditional proportional-fair scheduling, but at a significantly reduced computational cost.

In today's cellular networks, an increasing number of connected devices on-board in fast-moving vehicles would require more efficient handover (HO) procedures. To this end, we investigate the utilization of mobile relay nodes (MRNs) in vehicles to facilitate efficient HO and HO-related power consumption reductions for all on-board user equipments (UEs). In particular, the potential gains in terms of HO rate, HO failure ratio (HOFR), ping-pong (PP) rate, and total power consumption are studied for different UE cluster sizes. To eliminate the measurement power-consuming procedure, uplink (UL) reference signals (RS) transmitted by UEs are exploited. Four different case scenarios are simulated utilizing both the DL and UL RS based HO procedure, with and without deploying MRNs on the buses traveling along the cell edges of surrounding macro BSs. Simulation results indicate that the UL RS based HO procedure can improve HO performance significantly because it reduces the air-interface signaling messages, namely the measurement report (MeasReport) transmission and reception. Also, in terms of power consumption, deploying MRNs is a more attractive solution with substantial power reduction for onboard UEs of higher cluster size.

Mobile relay node (MRN) is one of the cheaper options for reliable communication when users are moving by public transport (i.e. bus, tram, train, subway, etc.), especially in urban areas. Critically, MRNs need to maintain a backhaul connection with the fixed infrastructure via a donor eNB, (DeNB). If the MRN fails to successfully handover (HO) from one DeNB to another, it will create a single point of failure, i.e. the connection of all UEs connected to MRN will be dropped. In this paper, we address the HO performance of a MRN including a power consumption analysis thereof. We investigate the potential gains in terms of HO rate, HO failure ratio (HOFR), ping-pong (PP) rate and power consumption (both at UE and eNB) when a MRN is deployed on a bus traveling along the cell edges of surrounding macro BSs. We also look over the MRN HO failure cases to identify the causes of HO failures that are more critical for the UEs onboard. Numerical results indicate that deploying a MRN on the roof-top of a bus improves the HO rates 15%, HOFR 8%, PP rate 17%, UE power consumption 21%, and eNB power consumption 14% on average for all simulated cases. We have also established that UL transmission errors are the most dominant causes of turning MRN to a single point of failure during a HO.

The introduction of large antenna arrays facilitating massive multiple-input-multiple output (M-MIMO), and the addition of a tier of pico and femto base stations (BS), implementing an heterogeneous network, provides means to improve network throughput and capacity in 5G networks. However, the addition of antennas and BSs implies additional hardware and is associated with higher energy consumption. Improving the energy efficiency (EE) while reducing the power consumption of such heterogeneous M-MIMO dense networks can be performed by switching off base stations that have few users to serve and redistribute those users among the active neighboring base stations. One promising solution to intelligently map user spatial distribution to the optimal set of active BSs is by utilizing radio service maps (RSM). In this paper we propose a novel approach that effectively switches off base stations by combining reinforcement learning with RSM data. The proposed approach is evaluated through computer simulations using a 3D ray tracing model. The simulation results show the benefits of RSM and machine learning use for the improvements in EE of the considered heterogeneous M-MIMO networks.

Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The prediction of network traffic at the user level is particularly challenging, because the traffic characteristics emerge from a complex interaction of user level and application protocol behavior. In this work we address the problem of predicting the network traffic at the user level over a short horizon, motivated by its applications in cellular scheduling. Motivated by recent works on robust adversarial learning, we treat the prediction problem for non-stationary traffic in an adversarial context, and propose a meta-learning scheme that consists of a set of predictors, each optimized to predict a particular kind of traffic, and of a master policy that is trained for choosing the best fit predictor dynamically based on recent prediction performance, using deep reinforcement learning. We evaluate the proposed meta-learning scheme on a variety of traffic traces consisting of video and non-video traffic. Our results show that it consistently outperforms state-of-the-art predictors, and can adapt to before unseen traffic without the need for retraining the individual predictors.

Over the last decade there has been a large research interest in exploiting terminal positions for various cellular network services and communication aspects. However, the relevance of terminal coordinates for resource allocation is relatively unexplored to date. In this work, we thus take a first step in that direction by studying coordinates-based resource allocation in an arguably favorable, and straightforward set-up. In particular, we consider the usage of supervised machine learning for resource allocation. Our results show that for the studied scenario, coordinates-based resource allocation can achieve a comparable performance to a CSI-based comparison scheme. While the main limiting factors are channel uncertainty as well as the accuracy of the terminal coordinates, in particular more complex machine learning schemes like Random Forests are able to provide some robustness despite the above mentioned noisy features.

In this paper, we address the signaling overhead and the power consumption that results from the transmission of handover-related signaling in a Long Term Evolution (LTE) cellular network. Specifically, we analyze the contribution of the different signaling messages over the air-interface, and their impact on power consumption both at the eNB and at the User Equipment (UE) during handover (HO). A quantitative analysis is provided using system level simulations. We observe that, within the HO process, the largest contributor to air-interface signaling overhead is the transmission of the measurement report by the UE. This uplink (UL) transmission suffers from different channel impairments, due to interference and transmission range, for particular cell sizes. As a consequence, uplink signaling retransmissions are triggered causing higher signaling load and consequently higher power consumption, this being especially detrimental to battery-powered devices.

Position-based estimation of radio signals in future dense wireless networks is expected to significantly improve the efficiency of radio resource allocation and the radio service coverage. In this paper we propose a new approach that effectively combines path-loss models with interpolation for the generation of radio signal maps (RSM) based on a radio signal characterization of user positions. In particular, a path-loss model is derived by utilizing positioning information and associated radio signal measurements. The path-loss model is used to determine a radio signal estimate which is then combined with an interpolation based estimate to derive the radio signal for a certain position. The combination depends on a characterization of a position as being line-of-sight (LOS) or non-line-of-sight (NLOS). The method is evaluated by means of simulations and compared to two reference scenarios based on path-loss estimate or interpolation estimate, respectively. From the results it is shown that the combined method outperforms both reference methods for the prediction of coverage service maps (CSM).

Due to non-negligible amount of energy consumption of state-of-the-art small cells at idle mode, energy efficiency of the overall network may decrease with densification. Therefore, energy efficiency in ultra-dense networks (UDNs) is one of the key challenges for mobile network operators (MNOs) to reduce their operative expenditure (OPEX), and to mitigate the carbon footprint. Low-power and low-cost dense networks are vital to extend next generation cellular network functionalities by improving network capacity in hotspot areas, and to deploy networks in short time periods. In energy harvesting networks, access points (APs) may perform both backhaul and access link data communication, simultaneously, removing UDN dependency on optical distribution network and electrical grid. In this paper, different power modes and essential signaling for operation of such APs are introduced, aiming to reduce energy consumption of UDNs by integrating energy harvesters into APs, equipped with wireless backhaul.

Dynamic spectrum sharing can provide many benefits to wireless networks operators. However, its efficiency requires sophisticated control mechanisms. The more context information is used by it, the higher performance of networks is expected. A facility for collecting this information, processing it, and controlling base stations managed by various network operators is a so‐called Radio Environment Map (REM) subsystem. This paper proposes REM‐based schemes for the allocation of base stations power levels in 4G/5G networks, while considering interference generated to a licensed network. It is assumed that both networks have different profiles of served users, e.g., area of their positions and movement, which opens opportunities for spectrum sharing. The proposed schemes have been evaluated by means of extensive system‐level simulations and compared with two widely adopted policy‐based spectrum sharing reference schemes. Simulation results show that dynamic schemes utilizing rich context information outperforms static, policy‐based spectrum sharing schemes.

In this paper, we extend our previous work on user assignment in Cloud-RAN, where we proposed an algorithm for user assignment (UA). We motivate the inherent fairness issue that is present in the latter UA scheme, since some users in the system will never get served. For that purpose, we propose two schemes to be used in conjunction with aforementioned UA scheme, to improve its fairness. The first scheme aims at improving the minimum throughput (MT), by selecting users with lowest throughput, as input to the UA algorithm, and to be (potentially) scheduled in the next time slot. The second scheme is based on round-robin (RR) scheduling, where the set of potentially scheduled users (for the next slot), is done by excluding all the previously served users, in that round. Moreover, the subset of actual users to be served, is determined using the UA algorithm. We evaluate their fairness and sum-rate performance, via extensive simulations. While one might have expected a tradeoff between the sum-rate performance and fairness, our results showed that MT improves both metric, when compared to the original UA algorithm (without fairness), for some choice of parameter values.

Provision of high data rates with always-on connectivity to high mobility users is one of the motivations for design of fifth generation (5G) systems. High system capacity can be achieved by coordination between large number of antennas, which is done using the cloud radio access network (CRAN) design in 5G systems. In terms of baseband processing, allocation of appropriate resources to the users is necessary to achieve high system capacity, for which the state of the art uses the users' channel state information (CSI); however, they do not take into account the associated overhead, which poses a major bottleneck for the effective system performance. In contrast to this approach, this paper proposes the use of machine learning for allocating resources to high mobility users using only their position estimates. Specifically, the `random forest' algorithm, a supervised machine learning technique, is used to design a learning-based resource allocation scheme by exploiting the relationships between the system parameters and the users' position estimates. In this way, the overhead for CSI acquisition is avoided by using the position estimates instead, with better spectrum utilization. While the initial numerical investigations, with minimum number of users in the system, show that the proposed learning-based scheme achieves 86% of the efficiency achieved by the perfect CSI-based scheme, if the effect of overhead is factored in, the proposed scheme performs better than the CSI-based approach. In a realistic scenario, with multiple users in the system, the significant increase in overhead for the CSI-based scheme leads to a performance gain of 100%, or more, by using the proposed scheme, and thus proving the proposed scheme to be more efficient in terms of system performance.

The next generation of radio access networks is expected to offer users flexible radio access to a wide range of frequency bands and radio access technologies (RATs) in dense deployments. In this context, we investigate novel lightweight algorithms for spectrum access in ultra-dense networks. We pose the problem as a network utility maximization and apply Lagrange duality to devise distributed algorithms for users to jointly select access nodes and spectrum bands. Leveraging our theoretical framework, we further propose a heuristic scheme for spectrum access based on the expected long-term user data rate per RAT, hence on a measure of the RATs' traffic load. Numerical examples show that our schemes achieve significant throughput gains on top of the gains achievable by network densification.

Massive densification of the radio network infrastructure is considered to be a promising solution to meet a rapidly increasing demand in future radio access networks. In this study we investigate the feasibility of a centralized approach for the allocation of radio network resources by studying the performance of a network scheduler that implements access node on/off scheduling in ultra-dense networks. The evaluation is performed by means of simulations for different distributions of mobile nodes and for different network densities. Simulation results show that considerable improvements in the network throughput and energy efficiency can be sought with an increasing network density. The improvements are independent of the mobile node distribution and diminish steadily as the network density increases. It is also shown that the gains are higher when mobile nodes are uniformly distributed as compared to a distribution where mobile nodes are clustered due to a higher number of mobile nodes per access node.

Cooperative technology is deemed to be one of the promising solutions for the next-generation wireless communications. By virtue of the space time coding and diversity combination, an enhanced cooperative relay (ECoR) scheme is designed for the uplink of wireless cellular network, where two users are served by two relay nodes and one base station in a cooperative way. The objective function of the ECoR scheme guides optimisation towards the minimal energy consumption of the whole network under the assumption that the Bit Error Ratio (BER) yielded by the ECoR scheme is lower than the BER yielded by the baseline scheme. The baseline scheme is based on a macro network topology with no relay nodes. The performance of the ECoR scheme is evaluated and analysed by numerical simulations and compared to the baseline, the simple relay (SR) scheme and the cooperative relay (CoR) scheme in terms of capacity and energy efficiency. In the SR scheme a user is served either by a relay node or the base station whilst in the CoR scheme a user is served by the relay node or the base station or both by means of diversity combination. Simulation results show that the ECoR scheme outperforms the baseline, SR and CoR scheme on both capacity and energy efficiency. The gains of capacity and energy efficiency achieved by the ECoR scheme over the baseline reach up to 40% and 107 % respectively.

In this paper, we study a mobile cooperative scheme by taking advantage of network coding to enhance the throughput and energy efficiency for mobile terminals. The mobile terminals are clustered into cooperation groups in each of which one of the mobile terminals acts as the group head to coordinate the cooperative signals transmitted among mobile terminals. The objective function of the cooperative scheme targets to seek the minimal energy consumption of mobile terminals under the assumption that a certain constraint on the bit error rate is met. The numerical simulation results indicate that the network coding scheme is advantageous compared to the reference direct transmit scheme where all the mobile terminals transmit signals to the base station directly without cooperation. However, for the cooperation to be really beneficial the cooperation cluster should be formed such that the head of the group is not constrained by energy consumption.

The effect of relaying in terms of energy efficiency is an important and interesting issue in cellular communications. In this paper, two relaying schemes for uplink are analyzed and compared in the context of cellular systems: Simple relaying (SR), where a user is served either by a relay node or the base station, and Cooperative Relaying (CR), where a user is served by the relay node, the base station or both in a cooperative manner. For the relaying a specific relay network topology is proposed where the relay nodes are deployed at the edge of each cell. The two schemes are analyzed in terms of cell capacity, provided through bit error analysis, and energy efficiency, and are compared to a baseline scheme that is based on a macro network topology with no relays. Based on the proposed relay network topology and the Decode-and-Forward relaying mechanism, this study focuses on finding out the optimal access path between a user and its serving base station. The optimization is performed according to a utility function which targets to maximizing cell capacity and energy efficiency. By choosing the optimal access path while decreasing the energy consumption per user, both cell capacity and energy efficiency of the wireless network are improved. By means of numerical simulations, it is shown that the relay schemes fully outperform the baseline wireless network without relays in both cell capacity and energy efficiency. In all simulation scenarios the CR scheme is the best performing scheme showing a gain around 33% in terms of cell capacity and a gain around 190% in terms of energy efficiency on average over the baseline. Further improvements are expected by utilizing more efficient cooperative relay schemes which will be exploited in the future work.

In this paper two schemes of distributed antenna selection, Pathloss-based Antenna Selection (P-AS) and Energy-efficient Antenna Selection (E-AS), are studied for the purpose of improving energy efficiency in Distributed Antenna Systems (DAS). Path loss information is exploited in P-AS, whilst the energy consumption of antenna nodes is taken into account in E-AS for the Pre-coding Matrix Index (PMI) selection and resource scheduling. The performance of the two algorithms is evaluated by means of numerical simulations based on two different DAS topology models of node layouts; a homogeneous topology model where distributed directional antennas are deployed at equidistant positions on the cell edges, and a heterogeneous topology model where omni-directional antennas are positioned at the cell centers. Simulation results show that both P-AS and E-AS outperform the Single Frequency Network (SFN) where all the transmission antennas transmit the same signal continuously in terms of energy efficiency. When compared to SFN, energy efficiency is improved by more than 50% for both schemes and for both homogeneous and heterogeneous layouts. In the case of the homogeneous topology model, P-AS scheme improves the energy efficiency by 54% at a rather low throughput loss of 7%, whilst E-AS improves energy efficiency considerably by nearly 70% without any loss of throughput.

The design of centralized algorithms and techniques allowing for an efficient utilisation of infrastructure in terms of energy consumption is one of the key challenges in heterogeneous networks (HetNets). In this study the energy efficiency in the HetNet scenario is formulated as an optimisation problem and an iterative improvement algorithmic approach to power on-off of network cells is devised and evaluated. The algorithm is based on the simulated annealing search approach and the obtained network configuration solutions are compared to a baseline configuration scenario where all cells are powered on. The optimization search is guided by an objective function which is defined on outage throughput and energy efficiency. Simulation results show that significant energy reductions gains can be achieved by switching off macro cells and with no loss of the cell edge user throughput. In some scenarios the algorithm generates solutions which considerably increase network throughput. The gains are more pronounced in configurations where pico cells are deployed at hot zones of user clusters.

Autonomic networking and cooperative communications paradigms have emerged as promising technologies that can pave the way for next generation networks. CONSERN project is focusing on cooperation and collaboration enablers, cooperative decision and control algorithms that will maximise the expected gains by utilising cooperative problem solving for improving energy efficiency in wireless networks. In this paper, cooperative and/or autonomous solutions are captured and a mapping to the envisaged technologies is made. Based on this mapping an initial analysis of the requirements for energy-efficient cooperation is presented. The cooperation deals with information exchange, actions coordination and decision making within and between different types of network nodes. Based on the requirements analysis a set of key cooperation enablers are identified and further elaborated upon.

In this paper we describe an LTE based demonstrator of the Universal Link Layer API (ULLA) and Cognitive Resource Manager (CRM) modules that are developed in ARAGORN project. The demonstrated LTE system comprises one LTE TDD eNode B and one User Equipment (UE). We first introduce ULLA and CRM framework and then demonstrate their suitability to be implemented with the existing LTE equipments. We show how, through ULLA, CRM is able to obtain PHY/MAC status information of the link between the eNode B and UE, and in turn change system parameters to achieve better resource utilization and transmission efficiency. The control logic can be implemented with simple adaptation or policy-based intelligent methods. The platform clearly shows the feasibility to use ULLA/CRM architecture for radio resource management in a LTE network. It also shows the neutrality of ULLA/CRM mechanisms towards PHY/MAC characteristics of LTE technology platform; hence the platform is viable to flexibly switch between technology platforms (e.g. between LTE access and WiFi access) under the control of ULLA/CRM.

Coverage and capacity optimization is considered as one of the most important optimization items for cellular network. It is beneficial from energy consumption and interference deduction perspective if the downlink base station power can be regulated according to the UEs distribution and service requirements. A cellular base station downlink power regulation scheme is studied where the power regulation scheme makes use of UE (user equipment) measurements and an optimization method Annealed Gibbs Sampling. A central node (or function entity) is involved as for the purpose of performance monitoring and coordinating the optimization process. Simulation with a LTE network configuration shows that while the measurements and optimization computation are distributed across base stations, a network performance can be optimized with this scheme especially for the cell edge users. Further characteristics of this scheme are presented and discussed.

This paper describes the optimization of the frequency allocation in the downlink of an Orthogonal Frequency Division Multiple Access (OFDMA) cellular network by means of centralized adaptive frequency management. The adaptive frequency allocation is performed off-line based on a Genetic Algorithm (GA) which computes for each cell an optimal number of radio resource blocks as guided by the geometry and the interference induced from adjacent cells. The objective of the optimization algorithm is to reduce interference and improve cell edge user throughput while preserving overall system throughput performance. For certain scenarios simulation results demonstrate gains up to 20% for the cell-edge user throughput as compared to a partial frequency 3-reuse scheme.

The effect of the TCP congestion control mechanism over rapidly fluctuating wireless links is a deterioration of the TCP performance. The utilization of multi-radio accesses in future networks by means of packet scheduling and dynamic access selection enriches the arsenal of solutions that mitigate radio channel variations. Packet scheduling over multiple radio accesses at link level provides a novel diversity scheme referred to as Multi-Radio Transmission Diversity (MRTD). This work evaluates, through simulations, the effects of different MRTD schemes for TCP flows over heterogeneous radio links. Results indicate that MRTD schemes provide substantial gains in terms of goodput and show a significant reduction in file download times. These gains are due to the diversity obtained and the suppression of the unwanted duplicate acknowledgements that frequently cause degradation in TCP performance over a radio channel.

Integration and seamless networking of different wireless technologies is one of the driving scenarios in the contemporary research. In recent years, mobile phones have seen an influx of attractive on-demand multimedia services with strict QoS requirements. This work investigates the QoS for delay sensitive services under the design framework of a heterogeneous network. Multi Radio Transmission Diversity (MRTD), which exploits the benefits of dynamic usage of different access technologies, is tested using simulations for Voice-over-IP on multi-radio wireless devices. The network technologies in this study include HSDPA on UMTS and IEEE 802.11. By applying various MRTD schemes, gains in performance have been observed in terms of average packet delay, packet loss and goodput as compared to a legacy system which operates under only one network access for a given session. The MRTD schemes have been analyzed for various simulation scenarios characterized by varying traffic load and radio propagation conditions.

In comparison to traditional voice based circuit switched mobile systems, the multimedia content puts forward significantly strict quality of service requirements. In this perspective, a noticeable proportion of contemporary research has been driven towards exploring the possibilities of simultaneously utilizing geographically co-existing mobile networks belonging to a wide range of wireless technologies. This paper evaluates such co-existence by means of a novel macro diversity scheme referred to as Multi Radio Transmission Diversity (MRTD) which exploits the dynamic cooperation between the networks at the radio link level. Relevant QoS parameters for an MPEG4 encoded video streaming service have been analyzed by means of simulations in a hybrid network topology consisting of IEEE 802.11b WLAN and UMTS HSDPA. The performance gains have been observed for various load and radio propagation conditions in comparison to a benchmark system which is not capable of utilizing such cooperation between multiple network interfaces.

This paper describes the steps and the procedures involved to establish access flows for a user data session in multi-radio access networks. The description is evolved around the overall connectivity abstraction model defined in the ambient networks (AN) project. This model uses access flows as its fundamental objects which are the result of two main access selection procedures: network attachment and flow setup. All access abstractions and procedures have been developed to handle the requirements of efficient multi-access resource management through all stages of access connectivity. The paper discusses some design trade-off for the establishment of access flows concerning setup delays and resource efficiency.

In alignment to an "always best experience" scenario, we assess gains on user traffic in the context of a multi-radio access environment where heterogeneous radio technologies are integrated at link layer and users are jointly scheduled across multiple radio accesses (RAs). Such integration allows for an implementation of multi-radio transmission diversity (MRTD) which switches transmission of a user's data over the available RAs. The RA selection is based on measurements of radio link quality as reported by the radio interface. The application of an ARQ scheme utilising multiple RAs (MR-ARQ) in conjunction with MRTD provides added gains on user delay and neutralizes parts of degradations in spectral efficiency caused by reporting delays. Such multi-radio ARQ mechanism exploits the diversity across independent RAs, and results in gains in throughput over those achieved by RA-specific ARQ. MRTD when combined with MR-ARQ shows gains of up to 35% in spectral efficiency compared to the case where the radio accesses operate independently.

We evaluate the spectral efficiency gains observed through multi-radio transmission diversity (MRTD), whereby packets of data are jointly scheduled for downlink transmission over multiple independent radio accesses. We specifically address downlink switched MRTD employed across macro- and pico-cellular radio accesses with non-collocated base stations in a hierarchical cell structure. It is shown that while significant gains can be achieved via MRTD among collocated macro-cell (or pico-cell) base stations, tight cooperation across non-collocated macro-and pico-cell base stations is only beneficial for a small subset of possible geometries. The impact of CQI reporting delays is also investigated

Integration of heterogeneous access technologies to provide multi-radio access allows for dynamic access selection. The "always best connected" paradigm can increase system capacity and end user throughput, as well as, reduce the network deployment costs for multi-radio wireless networks. In this paper we present the functional entities generic link layer and multi-radio resource management, and describe their interactions for access selection. A focus of the paper is to show - with an example given for 3G and WLAN - how existing wireless networks can be migrated to provide multi-radio access functionality. This will be explained for a single operator integrating different radio access standards, as well as, for multiple cooperating network operators.