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In this work, we propose an uplink power control (PC) framework compliant with the technical specifications of the fifth generation (5G) wireless networks. We apply the fundamentals of reinforcement learning (RL) to develop a power control algorithm able to learn a strategy that enhances the total data rate on the uplink channel and mitigates the neighbor cell interference. The base station (BS) uses a set of commands to specify by how much the user equipment (UE) transmit power should change. After implementing such commands, each UE reports a set of information to its serving BS, and this, in turn, predicts the next commands to achieve a suitable UE transmit power level. The BS converts the UE reports into rewards according to a predefined cost function, which impacts the longterm behavior of the UE transmit power. The simulation results indicate a near-optimum performance of the proposed framework in terms of total transmit power, total data rate, and network energy efficiency. It provides a self-exploratory power control strategy that overcomes soft dropping power control (SDPC) with similar signaling levels.

This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three parts – clustering, grouping, and scheduling – and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput.

The success of 5G networks relies on well-designed scheduling strategies that can tackle the increasing complexity of emerging mobile networks. Consequently, scheduling solutions are becoming more complicated, requiring high computational power and communication with very low latency. in this context, this work studies how to enhance existing scheduling solutions considering cellular networks with multi-connectivity, where users can have two or more simultaneous connections to different cells. Starting with a literature overview, we identify the main drawbacks of existing scheduling solutions for multi-connectivity scenarios. Then, we propose a novel scheduling concept that simplifies the scheduling task and overcomes several issues of existing scheduling solutions. The proposed solution logically splits the existing scheduling responsibilities across two simpler entities, thus it is named SRS. Furthermore, we propose a scheduling algorithm for practical SRS deployments, which is suitable for current and future multi-connectivity scenarios. Simulations show that the proposed solution has near-optimal performance in 5G multi-connectivity scenarios, when running in centralized and distributed architectures, while outperforming existing solutions by increasing the minimum user rate by 95 percent and the percentage of satisfied users by 50 percent at high system loads.

While the problems of sum-rate maximization and sum-power minimization subject to quality of service (QoS) constraints in the multiple input multiple output interference broadcast channel (MIMO IBC) have been widely studied, most of the proposed solutions have neglected the user scheduling aspect assuming that a feasible set of users has been previously selected. However, ensuring QoS for each user in the MIMO IBC involves the joint optimization of transmit/receive beamforming vectors, transmit powers, and user scheduling variables. To address the full problem, we propose a novel formulation of a rate-constrained sum-utility maximization problem which allows to either deactivate users or minimize the QoS degradation for some scheduled users in infeasible scenarios. Remarkably, this is achieved avoiding the complexity of traditional combinatorial formulations, but rather by introducing a novel expression of the QoS constraints that allows to solve the problem in a continuous domain. We propose centralized and decentralized solutions, where the decentralized solutions focus on practical design and low signaling overhead. The proposed solutions are then compared with benchmarking algorithms, where we show the effectiveness of the joint scheduling and transceiver design as well as the flexibility of the proposed solution performing advantageously in several MIMO IBC scenarios.

In this article, we study a Radio Resource Allocation (RRA) that was formulated as a non-convex optimization problem whose main aim is to maximize the spectral efficiency subject to satisfaction guarantees in multiservice wireless systems. This problem has already been previously investigated in the literature and efficient heuristics have been proposed. However, in order to assess the performance of Machine Learning (ML) algorithms when solving optimization problems in the context of RRA, we revisit that problem and propose a solution based on a Reinforcement Learning (RL) framework. Specifically, a distributed optimization method based on multi-agent deep RL is developed, where each agent makes its decisions to find a policy by interacting with the local environment, until reaching convergence. Thus, this article focuses on an application of RL and our main proposal consists in a new deep RL based approach to jointly deal with RRA, satisfaction guarantees and Quality of Service (QoS) constraints in multiservice celular networks. Lastly, through computational simulations we compare the state-of-art solutions of the literature with our proposal and we show a near optimal performance of the latter in terms of throughput and outage rate.

In this paper, a new stochastic channel model (SCM) is proposed for fifth-generation (5G) systems. By means of the sum-of-sinusoids (SoS) method to generate spatially consistent random variables (SCRVs), the proposed model extends the 3rd Generation Partnership Project (3GPP)-SCM by considering three important features for accurate simulations in 5G, i.e., support for dual mobility, spatial correlation at both ends of the link and considerable reductions of the required memory consumption when compared with existing models. A typical problem presented in existing channel models, namely the generation of uncorrelated large scale parameters (LSPs) and small scale parameters (SSPs) for close base stations (BSs), is solved, then allowing for more realistic numerical evaluations in most of the 5G scenarios characterized by a large density of BSs and user equipments (UEs) per unit of area, such as ultra-dense networks (UDNs), indoor environments, device-to-device (D2D) and vehicular-to-vehicular (V2V). The proposed model emerges as the first SCM, and therein lower complexity when compared with ray-tracing (RT)-based models, that comprises all the following features: support for single and dual mobility with spatial consistency, smooth time evolution, dynamic modeling, large antenna array, frequency range up 100 GHz and bandwidth up to 2 GHz. Some of the features are calibrated for single mobility in selected scenarios and have shown a good agreement with the calibration results found in the literature.

Heterogeneous Networks (HetNets) are considered a technology option capable of improving system capacity and spatial spectrum reuse. However, high spectrum reuse causes high inter-cell interference among macro and small cells. Almost Blank Subframe (ABS) is a method of the Enhanced Inter-Cell Interference Coordination (e-ICIC) framework proposed for LTE systems as a means to mitigate interference among macro and small cells. ABS mutes some of macro cell transmissions in selected subframes to reduce interference to small cells, orthogonalizing macro and small cell transmissions in time-domain. In this work, we use Moving Average Crossover (MAC) based on trading know-how to propose a new dynamic ABS e-ICIC algorithm. Using system-level simulations, we attest that the proposed algorithm outperforms traditional e-ICIC by 6.4% in terms of capacity.

The Dual Connectivity (DC) technology has gained a lot of momentum in the LTE Release 12 as a means to enhance the per-user throughput and provide mobility robustness. Some studies in the literature have discussed the interworking between the LTE and the air interface of the upcoming Fifth Generation (5G) in a DC scenario. That integration may provide some benefits to meet the high throughput, reliability and availability requirements of the 5G networks. This work firstly presents a brief overview of the DC technology considering the integration between 4G e 5G. Then, we highlight some open issues and challenges for future investigation involving Radio Resource Management (RRM) in such a scenario, which includes: (i) user-cell association, (ii) interaction between base stations and (iii) resource allocation. After that, we propose an extension of a utility-based resource allocation algorithm for DC scenarios. Finally, a performance evaluation is conducted in multi-Radio Access Technology (RAT) LTE-NR scenarios considering the bearer split configuration of the DC technology, where the gains provided by the proposed algorithm are demonstrated.

Efficient Radio Resource Allocation (RRA) is of utmost importance for achieving maximum capacity in mobile networks. However, the performance assessment should take into account the main constraints of these networks. This letter presents important enhancements to RRA algorithms proposed in [1]. Prior work [1] ignores some important system constraints such as the impact of inter-cell interference and granularity of frequency allocation blocks. Here we show the performance degradation when these system constraints are assumed on the algorithms in [1] as well as propose some improvements on these algorithms in order to achieve better performance.

In this article, we study relay selection and resource allocation in a cooperative network that employs Single Carrier - Frequency Division Multiple Access (SC-FDMA) in the uplink direction such as in Long Term Evolution - Advanced (LTE-A) system. With SC-FDMA, the frequency resources assigned to a transmitter should be adjacent to each other in the frequency domain (adjacency constraint). We formulate the total data rate maximization problem as a non-linear combinatorial optimization problem. Through algebraic manipulations, we managed to convert the original problem in a Mixed Integer Linear Problem (MILP) that can be optimally solved by standard computational solvers. So as to reduce the computational complexity, we propose a low-complexity suboptimal solution to this problem. Additionally, we propose a transmit power adjustment in order to reduce the power consumption in the system and improve Energy Efficiency (EE) by exploiting the characteristics of the forwarding mechanism in the relays. Finally, we present simulation results so as to evaluate the performance of the proposed solutions and obtain insights on the effect of the main variables presented in the system model.

In the fifth-generation 5G mobile networks, it is expected that users experience high throughputs with an ultra-low-latency network, while a massive number of devices are connected to the network. However, in remote rural areas, there is still a large number of people that do not have access to broadband Internet. To overcome this issue, a possible strategy is to exploit the excellent propagation conditions of very high frequency (VHF) and ultra-high frequency (UHF) bands by allowing secondary spectrum reuse in the TV white space (TVWS) channels. But for that, a reliable channel model is required to perform valid coverage and data rate prediction studies. In this paper, a channel model is proposed that takes into account large and small scale fading effects, as well as the particularities of such remote rural areas. The proposed model makes use of measurements along with the clustered delay line CDL profiles from 3rd Generation Partnership Project (3GPP). It is a simple model to implement and can be used to provide fast link and system-level simulations. Numerical and analytical results are provided to validate the proposed model and a data rate evaluation is carried out for single-input single-output (SISO) and multipleinput multiple-output (MIMO) configurations, as well as for single-polarized (SP) and dual-polarized (DP) antennas.

The joint scheduling of cellular and D2D communications to share the same radio resource is a complex task. In one hand, D2D links provide very high throughputs. In the other hand, the intra-cell interference they cause impacts on the performance of cellular communications. Therefore, designing algorithms and mechanisms that allow an efficient reuse of resources by the D2D links with a reduced impact on cellular communications is a key problem. In general, traditional Radio Resource Management (RRM) schemes (D2D grouping and mode selection) focus on finding the most compatible D2D pair for an already scheduled cellular User Equipment (UE). However, such approach limits the number of possible combinations to form the group (composed by a cellular UE and a D2D pair) to be scheduled in the radio resource. To overcome that, in this work a unified Joint Opportunistic Scheduling (JOS) of cellular and D2D communications, which is able to improve the total system throughput by exploiting the spatial compatibility among cellular and D2D UEs, is proposed. But more complexity is brought to the scheduling problem. Thus, a low-complexity suboptimal heuristic Joint Opportunistic Assignment and Scheduling (JOAS) is also elaborated. Results show that it is possible to reduce the computational complexity but still improve the overall performance in terms of cellular fairness and total system throughput with less impact on cellular communications.

Massive multiple-input multiple-output technology has been considered a breakthrough in wireless communication systems. It consists of equipping a base station with a large number of antennas to serve many active users in the same time-frequency block. Among its underlying advantages is the possibility to focus transmitted signal energy into very short-range areas, which will provide huge improvements in terms of system capacity. However, while this new concept renders many interesting benefits, it brings up new challenges that have called the attention of both industry and academia: channel state information acquisition, channel feedback, instantaneous reciprocity, statistical reciprocity, architectures, and hardware impairments, just to mention a few. This paper presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research.

In this paper, we formulate and solve two Energy Efficiency (EE) problems, namely the Power Minimization Problem (PMP) and the Maximization of Energy Efficiency Problem (MEEP), for a wireless system using power and frequency resource allocation considering Quality of Service (QoS) requirements and multiple services. Despite those problems are nonlinear, they can be converted into Integer Linear Problems (ILPs). Therefore, the optimal solution for both PMP and MEEP can be obtained by well-known methods. Additionally, we propose two fast suboptimal algorithms as to avoid the high computational complexity of obtaining optimal solution for MEEP. Our results show that the MEEP has a better trade-off between transmitted data rate and power saving than the PMP solution. Moreover, the suboptimal algorithms present good performance compared to the optimal solution for moderated loads but with a much lower computational complexity, thus achieving a remarkable trade-off between performance and computational complexity.

International audience; The development of Machine-Type Communication (MTC) in the Long Term Evolution (LTE) standard can lead to an overuse of the Physical Random Access Channel (PRACH). An efficient way to avoid it consists in reducing the number of requests done by end-devices. In this paper, we first study the probability of collision in a congested LTE-MTC network. As a second step, we analyze the latency in the network as a function of the request access rate. Then, we propose a closed-form for a sub-optimal request access rate. We finally conduct numerical simulations in order to assess the performance of the proposed approximation.

In this work, we study the problem of allocating resources in a multi-service cellular network aiming at maximizing the total system rate while providing suitable Quality of Experience (QoE) to the network users. In our formulation, we try to satisfy at least a certain number of users per service plan, which is an important constraint from the mobile network operators' perspective. We manage to reformulate this nonlinear optimization problem as an Integer Linear Problem (ILP), that can be solved by standard methods. However, due to the exponentially high complexity to solve large instances of this problem, we propose and evaluate a suboptimal algorithm with a much lower complexity, called Rate Maximization under Experience Constraints (RMEC), whose main idea is to divide the problem into three smaller subproblems with reduced complexity. By means of computational simulations, we show that our proposed algorithm presents a near optimal performance and outperforms the state-of-art solution of the literature.

Radio Resource Allocation (RRA) in cellular systems is a relevant and difficult task that shouldassign the system resources in the most efficient manner while fulfilling different constraints suchas Quality of Service (QoS). In wireless systems that employ Single Carrier - Frequency DivisionMultiple Access (SC-FDMA) as in Long Term Evolution (LTE) uplink, RRA is even more difficultsince the frequency resources should be assigned in contiguous blocks of subcarriers to eachterminal. In this work we study two important RRA problems in SC-FDMA: the total data ratemaximization and the total data rate maximization with minimum user satisfaction constraints. Weformulate them as optimization problems and show that the optimal solutions are not practical tobe employed in real systems due to the high computational complexity. Therefore, we propose twoefficient heuristic solutions for those problems. By simulation results, we show that both solutionsare near optimal and subject only to small performance degradation compared to the complex andimpractical optimal solutions.

We formulate the resource and power assignment problem of maximizing the spectral efficiency of a wireless system subject to user satisfaction constraints in the multiservice scenario. We show that although this optimization problem is nonlinear, it can be converted to an integer linear program. In this way, standard techniques can be used to obtain the optimal solution. Motivated by the high computational complexity of the optimal solution, we propose a fast suboptimal algorithm. Simulation results show that our proposal achieves near-optimal performance in low and medium loads with a much lower computational complexity compared with the algorithm used to obtain the optimal solution. Therefore, our proposed algorithm achieves a good tradeoff between performance and computational complexity. We also show that the addition of adaptive power allocation renders significant performance gains in the considered scenario.

This article presents a QoE-aware resource assignment and power allocation framework aiming at maximizing the minimum MOS of the users in a wireless network. It considers a satisfaction factor, that is, a requirement related to the minimum number of users that should be satisfied. This requirement is an important aspect from the operators’ point of view and has not yet been studied in this context, as far as we know. The proposed method is evaluated by means of simulations, and the obtained results show that it outperforms existing solutions, and performs close to the optimal solution as well. The performance evaluation demonstrates that the satisfaction factor is a good solution to deal with heavily loaded systems.

We formulate the radio resource allocation problem of maximizing throughput in an OFDMA (Orthogonal Frequency Division Multiple Access) downlink where the BS (Base Station) adapts the power allocation according to non-causal (offline) knowledge of the harvested energy and channel state. The offline case is important from a theoretical point of view since it provides a bound on the performance of the online problem (causal). Differently from previous works that consider a continuous mapping between SNR (Signal-to-Noise Ratio) and transmit data rate, we employ a discrete mapping that depends on the required MCSs (Modulation and Coding Schemes). Also, we propose a heuristic algorithm that provides near-optimal results and achieves a good complexity/performance trade-off. In addition, we analyze the online version of the problem, and we propose two novel solutions to solve the problem only with causal information. Furthermore, we reformulate our problem to satisfy QoS (Quality of Service) constraints for each user in a hybrid power system where the BS is powered by a fixed power source from the electric grid and by a stochastic power source from renewable energy sources. Lastly, we present an offline solution to this new problem that also achieves a considerable complexity/performance gain.

In order to keep and/or expand its share of the wireless communication market and decrease churn, it is important for network operators to keep their users clients satisfied. The problem to be solved is how to increase the number of satisfied non-real time NRT and real time RT users in the downlink of the radio access network of an orthogonal frequency division multiple access system. In this context, the present work proposes a method to solve the referred problem using a unified radio resource allocation RRA framework based on utility theory. This unified RRA framework is particularized into two RRA policies that use sigmoidal utility functions based on throughput or delay and are suitable for NRT and RT services, respectively. It is demonstrated by means of system-level simulations that a step-shaped sigmoidal utility function combined with a channel-aware opportunistic scheduling criterion is effective toward the objective of user satisfaction maximization. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we study the downlink resource assignment problem of maximizing the total data rate subject to minimum satisfaction guarantees in multiservice scenarios assuming the use of multiuser (MU) multiple-input-multiple-output (MIMO) techniques. With the use of MU MIMO techniques, the same frequency resource can be shared by different terminals at the same time by assigning different spatial channels to each terminal. Different from single-user (SU) access per frequency resource where the resource assignment problem consists of searching for the best association between frequency resources and terminals, with MU access per resource, we aim at finding which space-division multiple-access (SDMA) group should be associated with each frequency resource. The first contribution of this paper is the formal presentation of this generalized problem as an optimization problem. Then, after some mathematical manipulations, we managed to convert this problem from a nonlinear integer to an integer linear problem (ILP) that can be optimally solved by standard techniques instead of resorting to brute-force methods. Motivated by the high computational complexity of the optimal solution, we propose a low-complexity algorithm. By analyzing outage rate and total data rate performance metrics, we show that the proposed solution presents a good performance-complexity tradeoff compared with the method for obtaining the optimal solution.

The combination of multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiple access (OFDMA) is a promising solution to the flexible and spectrally efficient provision of data services in future wireless communication systems. However, adaptive resource allocation (RA) in frequency, time, and space in multiuser MIMO-OFDMA systems is very complex due to the inclusion of the space dimension and the large number of resources to be managed. Indeed, an optimal RA for maximizing the sum rate is usually too complex for practical application, and suboptimal strategies are required. In this paper, the performance, complexity, and fairness of suboptimal RA strategies aiming at the maximization of the sum rate are investigated. A model for suboptimal RA strategies is proposed, and two new RA strategies are introduced. The proposed strategies are compared in terms of the sum rate, complexity, and throughput fairness and are shown to present better performance-complexity and performance-fairness tradeoffs than some existing suboptimal strategies, as well as to achieve almost the same sum rate as that obtained through an exhaustive search.

In this paper, we present a Quality of Experience (QoE)-aware scheduler aiming at maximizing the minimum Mean Opinion Score (MOS) in a system subject to attaining at least a minimum number of satisfied users. Different of the benchmarking solution, the proposed one takes into account all the data that the User Equipments (UEs) have already received, and not only the data of a specific instant of time, thus suitably excluding UEs already satisfied. According to simulation results, the proposed solution not only improves the users’ satisfaction and the minimum experienced MOS in the system, but it also doubles the capacity in terms of supported number of users compared to benchmarking solution. It is also able to handle imperfections on the Channel State Information (CSI).