International audience

Future wireless networks are expected to support ever increasing amounts of IoT data traffic, while satisfying heterogeneous and stringent Quality of Service (QoS) constraints. Although AI-based resource allocation approaches have shown great potential, current methods are unable to cope with the severe energy limitations of IoT devices. In this work, we investigate the IoT device-to-multi-Access Points (AP) association problem in heterogeneous Sub-6GHz/mmWave IoT networks. To reduce the complexity and latency issues inherent to centralized Deep Reinforcement Learning (DRL) methods, we design a solution based on Multi-Agent DRL (MADRL) where each IoT device selects its AP and band association, according to its local environment. This method is empowered by a Federated Learning (FL)-based aggregation process, enabling cooperation among agents with limited signaling costs. Unlike previous works, the proposed method fully adapts to the heterogeneous QoS demands and energy constraints of each IoT device. In particular, our approach is specifically designed to reduce energy consumption by exploiting DRL-tailored pruning, while handling the devices’ diverse requirements. Numerical results show that the proposed method outperforms benchmarks in terms of rate outage probabilities, while considerably reducing AI energy consumption.

In this paper, we investigate the information piggyback capability of the unmanned aerial vehicle (UAV) by encoding observed motion states. Specifically, at specific moments throughout the holistic navigation process, the distance between the UAV and the starting point, the position expressed by the three-dimensional (3D) Cartesian coordinates, the linear velocities, and the attitude angles are encoded into 16-bit digital symbols through a proposed codebook. In this way, covert data communications can be enabled, complementing conventional radio frequency (RF) communications in harsh electromagnetic environments. To achieve a well-designed flight controller that is necessary to enable fluent movement against external disturbances and accurate motion state encoding, we introduce the whole flight control structure and perform system dynamic analysis. The proposed motion control mechanism can mitigate the jitter and oscillation during the journey while ensuring the required motion status for information encoding purposes.

Future Internet-of-Things (IoT) applications are expected to require increasingly demanding Quality of Service (QoS) levels, jointly in terms of rate, delay, and reliability. In this context, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) based framework to flexibly orchestrate the use of Sub-6 GHz and millimeter Wave bands, while fulfilling such heterogeneous QoS demands. In particular, we design two Personalized Federated MADRL (F-MADRL) methods that enable each user to adapt their learning model to the specific local mobile environment, thereby improving the overall network performance in terms of QoS outages, while reducing signaling exchanges and costs. Numerical evaluations show that our methods largely outperform benchmark MADRL schemes, while closely approaching a fully centralized solution.

This paper addresses the power control problem of a cell-free uplink massive Multiple-Input Multiple-Output (MIMO) system with mobile users, aiming at global sum-rate maximization under individual user Quality of Service (QoS) constraints. To solve this problem, we propose a Deep Deter-ministic Policy Gradient (DDPG)-based power control algorithm, whose design is tailored given the static and mobile user cases, respectively. In particular, different partial state space designs are investigated for each mobility use case, so as to achieve the best tradeoff between network performance and required learning complexity. Numerical results validate the effectiveness of the proposed method, which outperforms benchmark schemes both in terms of sum-rate and number of QoS satisfied users. It is shown that it can combine the advantages of traditional uniform max power control and max-min power control schemes. Furthermore, the proposed method is flexible and adapts itself well to dynamic and mobile environments.

Terahertz (THz) wireless communications are identified as a crucial technology towards Beyond 5G and 6G. Although THz waves are amenable to the implementation of ultra-massive MIMO devices, they entail a large amount of energy consumption. In this work, we address the issue of energy efficiency optimization for a switch network-based dynamic Array of SubArray (AoSA) hybrid beamforming structure. The initial joint optimization problem, which is intractable, is tackled by decomposing it into user clustering, switch network, analog and digital beamforming optimization subproblems. The originality of the proposed approach lies in the activation of the best subset of RF chains, as well as their assignment to optimized user groups, such that the overall energy efficiency is maximized. Numerical results show that the proposed method outperforms the Fully-Connected (FC) and AoSA benchmarks, and provides a well-balanced tradeoff between sum-rate and energy consumption.

Long Range (LoRa) technology constitutes one of the major enablers of future Internet-of-Things (IoT) applications, such as monitoring of challenged environments and smart buildings. However, crucial issues in the context of multi-gateway LoRa networks have been overlooked. In particular, most existing methods did not consider the stringent constraint of limited number of demodulators at each gateway. Therefore, we devise a gateway selection method for uplink LoRa transmissions, where this limited availability of demodulators is fully considered. We propose an optimization approach based on the auction mechanism, where each IoT device is pre-assigned to a unique gateway, so as to maximize the total amount of demodulated transmissions without redundancy at the network server. Furthermore, a low complexity method is also designed, where devices are partitioned into groups and where auctions are parallelized. Numerical results show that the proposed methods largely outperform benchmark algorithms in terms of the network utility function and sum-rate, while approaching the upper bound performance. The proposed methods are particularly suited to cope with the inherent dynamics of mobile LoRa IoT networks, as highest gains are attained for demodulation latencies in the order of tens to hundreds of milliseconds.11This work was supported in part by the Grants-in-Aid for Scientific Research (Kakenhi 17K06453 and 20H00592) from the Ministry of Education, Science, Sports, and Culture of Japan and by the NII MoU Grant.

Major device nodes within the Internet of Things (IoT) system collects and store information in bit forms of 0’s and 1’s regardless of its repetition. The nodes do not possess the capability of processing redundant data information except for outright rejection/replacement of packets of similar sizes. This becomes a research problem since high volumes of packet redundancy are prevalent owing to repetitive information. Many optimal solutions have been provided to reprocess redundant packets and to store them in edge server for accessibility by other connected IoT systems and networks servers. To do so, major IoT platforms implements tier-based network layers which primarily aid seamless communication among nodes. These network layers perform near-similar tasks of guaranteeing packet sensing and exchange although at often-higher energy requirement. To mitigate the energy concerns, packets are clustered and compressed, allowing exchange of essential information only. But the approach continues to present heavy packet-losses and/or redundancies. In this article, two-tier layered network—where packet exchange is conducted at the top layer in order to lower energy consumption and promote system-reliability is investigated. All packets are first segregated into multiple clusters using the Voronoi cell-based correlation cluster formation (VC3F) technique. Cluster heads (CHs) are identified by their multipath (M-Score) value, thus, assuming sole responsibility of redundant packet removal within each cluster. The redundant packets are then moved to the edge-tier layer using optimized multiobjective flower pollination (MO-FPO) routing, and finally processed using hybrid models of novel fast-fully connected neural network (F2CNN) accelerator and Lempel–Ziv–Welch (LZW) data compression. The F2CNN and LZW models are harmonized to further collectively explore potential benefits of both models. These benefits include the neural capability to work on the sensitivity level of the packets determined by the packet classification and validation approaches. The system is evaluated with detailed experimental investigations where higher system throughput, packet delivery ratio (PDR), end-to-end delay, and system reliability are corroborated.

The cooperation between unmanned aerial vehicles (UAVs) and ground mobile-edge computing (MEC) servers in processing tasks is becoming one of the main research trends of MEC networks. Despite the advantages of UAV-assisted MEC, it is restricted by the limited battery capacity and sensitive energy consumption of UAVs. Unlike the previous works where UAVs are allowed to either process tasks locally or offload them to ground MEC servers, in this article, we propose a multihop task routing solution for Internet of Things (IoT) networks in which a UAV can also relay to another UAV with better connection to a ground MEC server. Furthermore, the UAV can make benefit of existing intelligent reflective surfaces (IRSs) to further improve task offloading and reduce energy consumption. We show that the problem of minimizing the total energy of UAVs is NP-hard, and we propose a graph-based heuristic solution to solve it. Simulation results show that the proposed graph-based solution outperforms the traditional no UAV–UAV relaying scheme, especially when IRSs are deployed. Furthermore, a convolutional neural network (CNN) is devised to reduce the delay of finding the decisions for the UAVs at the centralized coordinator. Simulations show that the CNN achieves very close energy consumption performance and a remarkable reduction in execution time compared to the graph-based heuristic solution.

LoRa is one of the most prominent low power wide area network technologies, and enables to interconnect thousands of devices distributed over areas of several square kilometers. However, the limited number of demodulators present in the hardware of LoRa gateways limits LoRa scalability. In this paper, we argue that scalability can be improved by having gateways collaborate, so that they attempt to demodulate different frames. We propose several algorithms in order to measure the benefits of random-based collaboration and deterministic collaboration. Our simulation results show that random-based protocols improve the baseline performance in most setups, while deterministic protocols improve the network performance when the number of gateways is large, and with many demodulators per gateway.

Realizing extreme reliability for Internet of Things (IoT) communications is one of the major milestones paving the way towards Beyond 5G (B5G) and 6G. In this work, we investigate the issue of improving the reliability of packet transmissions in the absence of prior knowledge of network statistics, nor of instantaneous Channel State Information (CSI), for B5G Sub-6GHz/mmWave integrated networks. Specifically, the aim is to maximize the global successful packet reception at devices, while guaranteeing their individual Packet Loss Rate (PLR) requirements. The proposed method exploits a newly developed approach of Risk-Averse Reinforcement Learning (RARL), for exploiting multi-connectivity over Sub-6Hz and mmWave interfaces. Namely, the Access Point (AP) is able to optimize its interface selection decisions despite the unknown dynamics of the wireless environment based on limited feedback from its associated devices, so as to increase reliability under low delay and resource consumption. Numerical results show that, the proposed method significantly improves the global reliability performance by rapidly learning and adapting its decisions as compared to baseline methods.

The joint provision of higher data rates and massive Internet of Things (IoT) connectivity has been identified as one of the key milestones toward beyond 5G (B5G). To this end, we investigate the issue of device selection and beamforming (BF) optimization assuming a large-scale IoT network using mmWaves. We formulate the considered problem as a network sum-rate maximization problem under Access Points’ load constraints, and where the BF parameters belong to discrete sets, as in practical cases. First, we mathematically prove the submodularity of the objective function, under specific yet reasonable assumptions. Based on the identified features of the problem at hand, we propose three different approaches to tackle this intricate optimization problem: 1) a Branch-and-Bound-based; 2) a Lagrangian Relaxation-based; and 3) a Greedy-based approach inspired by the submodular objective. The numerical results validate the three approaches, as they achieve a near-optimal sum rate in small network cases, and largely outperform benchmark schemes in terms of sum rate and individual rates. Among them, the proposed Greedy-based approach achieves the best sum rate with very low complexity, thereby providing excellent scalability.

In this work, we investigate the problem of joint user-to-access points (AP) association and beamforming in an integrated sub-6GHz/mmWave system. The goal is to maximize the long-term throughput of the system, while satisfying a large number of heterogeneous user QoS requirements in a distributed manner. We propose a method based on Deep Q-Networks (DQN), where each user self-optimizes its AP association and interface requests, and can be served by several APs simultaneously for supporting multiple applications. Based on these requests, each AP selects its associated users and applications served on each interface, while optimizing its mm Wave beamforming parameters. Simulation results show that, compared to baseline DQN schemes among which the Action Elimination (AE)-DQN, the proposed method enables to fine-tune the selection of APs and interfaces to the specific level of each required QoS, thereby achieving a high global throughput while notably reducing user outage probabilities1.1.This collaborative research project is funded by NTT Corporation, Japan.

This work investigates the design of a sustainable Deep Q-Network (DQN) implemented at the user device, whose purpose is to optimize the user’s association to multiple access points (AP) in a Beyond 5G (B5G) Sub-6GHz and mmWave integrated network. To better cope with dynamic mobile environments, we first propose an adaptive $\varepsilon$-greedy policy at each user’s DQN in order to maximize the long-term sum-rate while simultaneously satisfying the Quality of Service (QoS) constraints of different applications. We then provide the detailed analysis of the energy consumed by each user device, in particular the power for DQN processing and for data movement. The trade-off between network performance in terms of sum-rate and QoS outage probability, and energy consumption at the user side is evaluated. Numerical results not only show the effectiveness of the proposed method compared to baseline, but also reveal the tremendous energy costs required by the default user DQN, underscoring the paramount importance of the proposed trade-off aware user DQN design1.

Future wireless networks will be facing unprecedented difficulties arising from mobile traffic growth, network densification, as well as diversification of applications and services. Indeed, future user devices are expected to integrate diverse radio interfaces such as 5G, WBAN or IoT, enabling each user to be served a wide range of applications at any time. This poses significant challenges in terms of wireless resource sharing and interference management, as more and more stringent Quality of Service (QoS) constraints should be jointly satisfied in dense interfering environments. Furthermore, future networks are expected to be highly autonomous and decentralized. To meet these challenges, this work proposes distributed user-to-multiple Access Points (AP) association methods, where the objective is to maximize the long-term sum-rate subject to application QoS constraints, as well as to AP load constraints. Our distributed methods enable each user to leverage their Deep Reinforcement Learning (DRL) capabilities, in particular Deep Q-Learning (DQL), to self-optimize their APs’ selection solely based on their local network state knowledge, so as to best satisfy their diverse requirements. Numerical results show that, compared to baseline schemes, the proposed methods enable global throughput enhancements while reducing user QoS outage probabilities, even in large and dense networks.

To meet the stringent demands of Beyond 5G applications, an optimized and seamless usage of sub-6 GHz and mmWave networks under high user mobility is essential. In particular, to alleviate the heavy burdens of mmWave channel state information feedback, we propose a Deep Learning-based scheduler at the base station that predicts the future mmWave blockage status and optimal beamforming vectors of the mobile user, solely based on sub-6 GHz channel knowledge, i.e., out-of-band information. The designed Deep Neural Network (DNN) comprises Long Short-Term Memory layers to extract the temporal correlation from the low-frequency channel knowledge, for enabling an accurate blockage and beam prediction. We investigate the influence of the available past channel information and of the user speed on the prediction accuracy and on the achievable data rates. Simulation results show that the proposed method significantly improves the prediction accuracy of optimal mmWave beamformers compared to the benchmark DNN with much reduced complexity. Furthermore, the proposed DNN demonstrates high robustness for different user speeds, while approaching optimal rates achieved through exhaustive search and perfect blockage knowledge.

Long Range (LoRa) is a protocol that enables low-power wireless communications over long distances for a wide range of IoT applications. Its main drawback is its limited throughput, which is further reduced by the small number of demodulators in the hardware of the gateway. In this paper, we propose to use each demodulator as efficiently as possible. To do so, we reuse them for short frames during the preamble of long frames. By smartly planning the demodulation of multiple frames, the proposed method enables a recursive reuse of each demodulator. Compared to the benchmark packet arbiter policy, our method is shown to offer throughput and fairness enhancements even with a large number of users, thereby improving the scalability of LoRa systems. Our simulation results show that when the number of nodes is large, 6.5% more frames are decoded and the rate fairness among nodes is improved by 11%.

LoRa is becoming widely used in low-power wide area networks as it enables a communication range of several kilometers with low energy consumption, but with a low bitrate. Collisions in LoRa further reduce the overall performance of the network, and more specifically the throughput. In this paper, we propose a slotted MAC protocol that enables the decoding of colliding LoRa signals. It is based on a new decoding technique at the physical layer that is able to decode the symbols of many frames in collision. Simulation results show that our MAC protocol significantly increases the achievable performance of LoRa networks. For instance, for 25 nodes having a duty-cycle of 10% and with SF7, the throughput with our protocol is 11% larger than the existing protocols.

The LoRa physical layer is one of the most promising Low Power Wide-Area Network (LPWAN) technologies for future Internet of Things (IoT) applications. It provides a flexible adaptation of coverage and data rate by allocating different Spreading Factors (SFs) and transmit powers to end-devices. We focus on improving throughput fairness while reducing energy consumption. Whereas most existing methods assume perfect SF orthogonality and ignore the harmful effects of inter-SF interferences, we formulate a joint SF and power allocation problem to maximize the minimum uplink throughput of end-devices, subject to co-SF and inter-SF interferences and power constraints. This results into a mixed-integer non-linear optimization, which, for tractability, is split into two sub-problems: firstly, the SF assignment for fixed transmit powers, and secondly, the power allocation given the previously obtained assignment solution. For the first sub-problem, we propose a low-complexity many-to-one matching algorithm between SFs and end-devices. For the second one, given its intractability, we transform it using two types of constraints' approximation: a linearized and a quadratic version. Our performance evaluation demonstrates that the proposed SF allocation and power optimization methods enable to drastically enhance various performance objectives such as throughput, fairness and power consumption, and that they outperform baseline schemes.

This paper analyzes the integration of Non-Orthogonal Multiple Access (NOMA) in a Fog Radio Access Network (FRAN) architecture with limited fronthaul capacity. More precisely, it proposes methods for optimizing the resource allocation for the downlink of a NOMA-based FRAN with multiple resource blocks (RB). The resource allocation problem is formulated as a mixed-integer optimization problem, which determines the user-to-RB assignment, the power allocated to each RB, and the power split levels of the NOMA users served by each RB. The optimization problem maximizes a network-wide rate-based utility function subject to fronthaul-capacity constraints. The paper proposes a feasible decoupled solution for such a non-convex optimization problem using a three-step hybrid centralized/distributed approach, which in part relies on the edge-devices computation capabilities. The paper proposes and compares two distinct methods for solving the assignment problem, namely a Hungarian-based method, and a Multiple Choice Knapsack-based method. The power allocation to RBs and the NOMA power split optimization are solved using the alternating direction method of multipliers (ADMM). Simulations results illustrate the advantages of the proposed methods compared to different baseline schemes, including the conventional Orthogonal Multiple Access (OMA), for different utility functions and different network environments.

Recently, Fog-RANs have been introduced as the evolution of Cloud Radio Access Networks (CRAN) for enabling edge computing in 5G systems. By alleviating the fronthaul burden for data transfer, transport delays are expected to be greatly reduced. However, in order to support envisioned 5G real-time and delay-sensitive applications, tailored radio resource and interference management schemes become necessary. Therefore, this paper investigates the issues of user scheduling and beamforming for energy efficient Fog-RAN. We formulate the energy efficiency maximization problem, taking into account the local user clustering constraint specific to Fog-RANs. Given the difficulty of this non-convex optimization problem, we propose a strategy where the energy efficient user scheduling is split in two parts: first, we solve an equivalent sum-rate maximization problem, then, the most energy-efficient FogAPs are activated in a greedy manner. To meet the requirement of low computational complexity of FogAPs, local beamforming is performed given fixed user scheduling. Simulation results show that the proposed scheme not only provides similar levels of user rates and fairness, but also largely outperforms the system energy efficiency in comparison with the baseline scheme1.

In future wireless networks, more and more users will be requiring various applications provided by multiple wireless interfaces simultaneously. This poses significant challenges for enabling efficient wireless resource sharing while satisfying the diverse and stringent Quality of Service (QoS) constraints, especially in dense interfering networks. In such a context, this work proposes distributed user-to-multiple Access Points (AP) association methods, where a user requiring several applications may be served by several APs simultaneously. The problem is formulated as a network sum-rate maximization subject to the required QoS constraints for each user and application, and AP load constraints. In the proposed distributed association methods, each user can decide to associate to multiple APs simultaneously using its locally available network information, leveraging reinforcement learning techniques. Simulation results show that, compared to a baseline scheme, the proposed methods enable large throughput enhancements while satisfying the QoS constraints and AP load limitations, thereby reducing user outage probabilities.

Low-Power Wide-Area Network (LPWAN) based on LoRa physical layer is envisioned as one of the most promising technologies to support future Internet of Things (IoT) systems. LoRa provides flexible adaptations of coverage and data rates by allocating different Spreading Factors (SFs) to end-devices. Although most works so far had considered perfect orthogonality among SFs, the harmful effects of inter-SF interferences have been demonstrated recently. Therefore in this work, we consider the problem of SF allocation optimization under co-SF and inter-SF interferences, for uplink transmissions from end-devices to the gateway. To provide fairness, we formulate the problem as maximizing the minimum achievable average rate in LoRa, and propose a SF allocation algorithm based on matching theory. Numerical results show that our proposed algorithm enables to jointly enhance the minimal user rates, network throughput and fairness, compared to baseline SF allocation methods.

We propose an effective interference management and beamforming mechanism for uplink communication systems that yields fair allocation of rates. In particular, we consider a hotspot area of a millimeter-wave (mmWave) access network consisting of multiple user equipment (UE) in the uplink and multiple access points (APs) with directional antennas and adjustable beam widths and directions (beam configurations). This network suffers tremendously from multi-beam multi-user interference, and, to improve the uplink transmission performance, we propose a centralized scheme that optimizes the power, the beam width, the beam direction of the APs, and the UE - AP assignments. This problem involves both continuous and discrete variables, and it has the following structure. If we fix all discrete variables, except for those related to the UE-AP assignment, the resulting optimization problem can be solved optimally. This property enables us to propose a heuristic based on simulated annealing (SA) to address the intractable joint optimization problem with all discrete variables. In more detail, for a fixed configuration of beams, we formulate a weighted rate allocation problem where each user gets the same portion of its maximum achievable rate that it would have under non-interfered conditions. We solve this problem with an iterative fixed point algorithm that optimizes the power of UEs and the UE - AP assignment in the uplink. This fixed point algorithm is combined with SA to improve the beam configurations. Theoretical and numerical results show that the proposed method improves both the UE rates in the lower percentiles and the overall fairness in the network.

Heterogeneous Cloud Radio Access Network (H-CRAN) is a promising network architecture for the future 5G mobile communication system to address the increasing demand for mobile data traffic. In this work, we consider the design of efficient joint beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have different mobility profiles. Given the rapidly time-varying nature of such wireless environment, it becomes very challenging to enable optimized beamforming and user clustering without incurring large Channel State Information (CSI) and signaling overheads. The main objective of this work is to investigate and evaluate the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of different CSI feedback strategies given different user mobility profiles. We propose the Adaptive Beamforming and User Clustering (ABUC) algorithm which adapts its feedback parameters, namely the period of dynamic user clustering and the type of CSI feedback, in function of user mobility. Furthermore, we design a reinforcement-learning framework which enables the proposed ABUC algorithm to optimize its scheduling parameters on-the-fly, given each user mobility profile. Based on computer simulations, an analysis of the effect of mobility on system performance metrics is presented and conclusions are drawn regarding the algorithm’s adequate parameter tuning for different mobility scenarios.1

Simultaneous co-channel transmission and reception, denoted as in-band full-duplex (FD) communications, has been promoted as a solution to improve the spectral efficiency in wireless networks. For cellular networks, in addition to the existing aggregate interference in half-duplex transmission, the residual self-interference and cross-mode interference [i.e., between uplink (UL) and downlink (DL)] impose major obstacles for FD communications' deployment. Although the FD communication's promising impact on the overall network data rate has been established in the literature, the rate gains are achieved in the DL transmissions at the expense of marginal gain, or even degradation, for the UL transmissions. This paper, therefore, focuses on the analysis of UL ergodic rate in FD cellular networks where a minimum distance between BSs using the same time-frequency resource block is imposed. Hence, the mutually interfering BSs' locations are modeled by Matérn hard core point process. The distribution of the aggregate interference and the channel-to-interference-plus-noise ratio at the UL of a typical user are characterized using a stochastic geometry analysis. Several UL power control techniques are presented and their resulting ergodic rates are derived and compared. The simulation results suggest that the UL performance highly depends on the network parameters and the UL power control techniques.

We investigate the user pre-scheduling and beamforming design for 5G Fog Radio Access Networks (FogRANs). Conventional Cloud Radio Access Networks (CRANs) enabled centralized resource and power allocation optimization over all the small cells served by multiple Access Points (APs). However, the fronthaul links connecting each AP to the cloud introduce delays and cause outdated Channel State Information (CSI). By contrast, FogRAN enables lower latencies and better CSI qualities, at the cost of local optimization. To alleviate these issues, we propose a hybrid algorithm exploiting both the centralized feature of the cloud for globally-optimized pre-scheduling using outdated global CSIs, and the distributed nature of FogRAN for accurate beamforming with high quality local CSIs. The centralized phase enables to consider the interference patterns over the global network, while the distributed phase allows for latency reduction, in line with the requirements of FogRAN applications. Simulation results show that our hybrid algorithm for FogRAN outperforms the centralized algorithm under outdated CSI, both in terms of throughput and delays.

Non-Orthogonal Multiple Access (NOMA) is a promising technology for 5G that enables each resource unit to simultaneously serve multiple users. This work evaluates the potential benefit of joint scheduling and power adaptation in NOMA- based downlink in Fog-Radio Access Networks (FRAN). We consider the downlink of a FRAN, where the Fog Access Points (FAPs) are connected to central cloud baseband units (BBUs) through capacity-constrained fronthaul links. The FAPs adopt a two-user NOMA scheme, within each resource block (RB), to serve a common set of users. The paper formulates an optimization problem which maximizes a network-wide {rate-based utility} function subject to fronthaul- capacity constraints, so as to determine both the user- to-FAP assignment and the power levels of the users served by each FAP. The main contribution of the paper is solving this mixed-integer non-convex optimization problem using a two- step centralized-distributed approach, which is aligned with FRAN operation that {aims to} %relies on partially shifting the network control to the FAPs so as to overcome delays due to fronthaul rate constraints. The assignment step is first solved at the centralized BBU pool by reformulating the problem such that the Hungarian algorithm is applicable. The power {adaptation} is then solved at every FAP using a barrier method. Simulation results show that the proposed NOMA-based algorithm outperforms conventional Orthogonal Multiple Access (OMA) algorithms, even with stringent fronthaul limitations. The proposed algorithm further shows an appreciable {performance trade-off} between the rate and fairness metrics.

Long-range low-power wireless communications, such as LoRa, are used in many IoT and environmental monitoring applications. They typically increase the communication range to several kilometers, at the cost of reducing the bitrate to a few bits per seconds. Collisions further reduce the performance of these communications. In this paper, we propose two algorithms to decode colliding signals: one algorithm requires the transmitters to be slightly desynchronized, and the other requires the transmitters to be synchronized. To do so, we use the timing information to match the correct symbols to the correct transmitters. We show that our algorithms are able to significantly improve the overall throughput of LoRa.

Heterogeneous Cloud Radio Access Networks (H-CRANs) are a promising cost-effective architecture for 5G system which incorporates the cloud computing into Heterogeneous Networks (HetNets). We consider in this work the joint beamforming and clustering (user-to-Remote Radio Head (RRH) association) issue for downlink H-CRAN to solve the sum-rate maximization problem under fronthaul link capacity and per-RRH power constraints. The main objective is to address the beamforming and user association process over time by taking into account the user mobility as a key factor to tune the solution's parameters. More precisely, based on the mobility profile of users (mainly velocity), we propose an advanced Mobility-Aware Beamforming and User Clustering (MABUC) algorithm which selects the best Channel State Information (CSI) feedback strategy and periodicity to achieve the targeted sum-rate performance while ensuring the minimum possible cost (complexity and CSI signaling). MABUC inherits the behavior of our previously proposed Hybrid algorithm which periodically activates dynamic and static clustering strategies to manage the allocation process over time. MABUC algorithm, however, takes into account the user mobility by using a CSI estimation model which can improve the algorithm performance compared to reference schemes. Our proposed algorithm has the benefit to meet the targeted sum-rate performance while being aware and adaptive to practical system constraints such as mobility, complexity and signaling costs.

Envisioned for fifth generation (5G) systems, millimeter- wave (mmWave) communications are under very active research worldwide. Although pencil beams with accurate beamtracking may boost the throughput of mmWave systems, this poses great challenges in the design of radio resource allocation for highly mobile users. In this paper, we propose a joint adaptive beam-frequency allocation algorithm that takes into account the position uncertainty inherent to high mobility and/or unstable users as, e.g., Unmanned Aerial Vehicles (UAV), for whom this is a major problem. Our proposed method provides an optimized beamwidth selection under quality of service (QoS) requirements for maximizing system proportional fairness, under user position uncertainty. The rationale of our scheme is to adapt the beamwidth such that the best trade-off among system performance (narrower beam) and robustness to uncertainty (wider beam) is achieved. Simulation results show that the proposed method largely enhances the system performance compared to reference algorithms, by an appropriate adaptation of the mmWave beamwidths, even under severe uncertainties and imperfect channel state information (CSIs).

The design of energy- and spectrally efficient Wireless Sensor Networks (WSN) is crucial to support the upcoming expansion of Internet-of-Things (IoT) mobile data traffic. In this work, we consider an energy harvesting WSN where sensor data are periodically reported to a Fusion Center (FC) by a sparse set of active sensors. Unlike most existing works, the transmit power levels of each sensor are assumed to be unknown at the FC in this distributed setting. We address the inverse problem of joint signal / power restoration at the FC-a challenging under-determined separation problem. To regularize the ill-posed problem, we assume both a graph-signal smoothness prior (signal is smooth with respect to a graph modeling spatial correlations among sensors) and a sparsity power prior for the two unknown variables. We design an efficient algorithm by alternately fixing one variable and solving for the other until convergence. Specifically, when the signal is fixed, we solve for the power vector using Simplex pivoting in linear programming (LP) to iteratively identify sparse feasible solutions, locally minimizing an objective. Simulation results show that our proposal can achieve very low reconstruction errors and outperform conventional schemes.

Cloud Radio Access Network (CRAN) is a promising network architecture for 5G to address the increasing demand for mobile data traffic. We consider a joint beamforming and clustering (user-to-Remote Radio Head (RRH) association) issue for downlink CRAN to solve the sum-rate maximization problem under fronthaul link capacity and per-RRH power constraints. The main objective is to investigate and analyze the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of imperfect Channel State Information (CSI). We propose a hybrid algorithm which periodically activates dynamic and static clustering strategies to manage the allocation process over time. This algorithm has the benefit to approach the optimal performance while being aware of practical system constraints. Furthermore, we present an analysis of major cost metrics for the proposed and reference dynamic algorithms. The simulation results show that our proposed algorithm reduces significantly the complexity and signaling costs while approaching the performance of the optimal solution.

We propose a distributed radio resource allocation method based on macrocell/picocell partial Channel State Information (CSI) sharing for small cell networks with range expansion. The Macro Base Station (MBS) predicts the cell-edge Pico users' Resource Block (RB) allocation based on these shared CSIs, and reduces its transmit power only in RBs with high allocation probabilities. Unlike most previous works, we analyze the post-scheduling outage probability encompassing the effects of channel- based scheduling and random user positions. Thanks to the analysis, we can easily find the MBS power constraint that minimizes this outage probability. The results show that the proposed scheme largely improves outage probability and macrocell user offloading, compared to conventional methods.

This paper proposes a reduced complexity signal detection scheme for overloaded MIMO (Multiple-Input Multiple-Output) systems. The proposed scheme firstly divides the transmitted signals into two parts, the post-voting vector containing the same number of signal elements as of receive antennas, and the pre-voting vector containing the remaining elements. Secondly, it uses slab decoding to reduce the solution candidates of the pre-voting vector and determines the post-voting vectors for each pre-voting vector candidate by lattice reduction aided MMSE (Minimum Mean Square Error)-SIC (Successive Interference Cancellation) detection. Simulation results show that the proposed scheme can achieve almost the same performance as the optimal ML (Maximum Likelihood) detection while drastically reducing the required computational complexity.

In this paper, we propose a self-organized resource allocation method in a macrocell/picocell heterogeneous network employing Cell Range Expansion (CRE). To protect expanded Pico User Equipments (ePUEs) from severe Macro Base Station (MBS) interference in downlink, in the conventional method, MBS makes use of reduced power Almost Blank Subframes (ABSs) where ePUEs can be scheduled. However, this severely limits the amount of usable resources/power for the MBS. In the proposed scheme, MBS predicts the ePUE's Resource Block (RB) allocation based on their overheard Channel State Information (CSI) feedback intended to Pico Base Station (PBS), and reduces its transmit power in RBs where ePUE's allocation probability is estimated to be high for mitigating downlink interference. The simulation results show that the proposed scheme outperforms the conventional reduced power ABS scheme in terms of total sum throughput as well as fairness among all heterogeneous users.1

In this paper, we propose channel estimation methods for LTE (Long Term Evolution)-Advanced system based on compressed sensing. Taking advantage of the fact that typical LTE-Advanced downlink channels are composed of a small number of dominant taps, compressed sensing enables us to take the time-domain approach for the estimation using DM-RS (Demodulation Reference Signal), even when the amount of available DM-RSs to each UE is reduced, given the limited amount of allocated RBs. The effectiveness of the proposed schemes is confirmed through computer simulations, even in the case of dense channels.

This paper deals with the interference issue in a two-tier heterogeneous network. Although the co-channel spectrum allocation provides larger bandwidth for both macrocells and femtocells, the resulting cross-tier interference may prevent macrocell users to achieve the minimum required SINR. Therefore, in this work, we propose a centralized control strategy of downlink interference generated by femtocells to increase the performance requirement of macrocell users. Our mechanism presents two possible power control strategies for interference management which take benefit from some context information on femto and macrocell users positioning. System-level simulations show a better performance in terms of throughput for macrocell users while maintaining a targeted QoS for femtocell users.

To encourage traffic offloading from the Macrocell to its overlaid Picocells in a Heterogeneous Network (HetNet), Cell Range Expansion (CRE) has been widely envisioned, enabling more Macro users to be offloaded to the less saturated Picocells, and enhancing the throughput performance of the entire system. By doing so, users in the Picocell expanded area may experience high interference from the Macrocell Base Station (MBS). This effect is mitigated by enhanced Inter-Cell Interference Coordination (e-ICIC) schemes that reserve part of the MBS bandwidth to those Pico users exclusively. Here, we propose a mobility-aware e-ICIC scheme which turns-off some of the Resource Blocks (RBs) in the Macrocell in function of the mobility behaviour of the range-expanded Pico users, thereby providing an efficient trade-off between Macrocell and Picocell achievable throughputs. Our numerical results confirm that point, and show that our proposed method outperforms reference e-ICIC methods.

We consider a wireless system where multiple users are served in Downlink (DL) by one Base Station (BS) and one Relay Station (RS). Previous research has shown that, combining two users' messages via Superposition Coding (SC) could enhance their achievable rate and fairness, in both two-user and multi-user systems. In this work, we propose two novel SC schemes referred as Relayed/Direct (RD-SC) and Relayed/Relayed SC (RR-SC) schemes, that combine the messages to a pair of Relayed/Direct users or Relayed/Relayed users into three or four SC layers, where the number of SC layers is equal to the number of available links. For each scheme, power allocation under sum-rate maximization is analyzed, enabling to derive the optimal power ratios to each SC layer numerically. Then, we design the non-orthogonal Max Rate-Two User-SC (MR-TU-SC) Scheduler and Proportional Fair-Two User-SC (PF-TU-SC) Scheduler where a pair of selected users are allocated simultaneously based on RD-SC and RR-SC schemes. The simulation results show that the proposed schedulers significantly outperform the conventional orthogonal schedulers where a single user is allocated on a unit resource block, in terms of system throughput, fairness and outage, while approaching the upper bound performance.

We consider the problem of femtocell/macrocell interference mitigation in an OFDMA based Downlink (DL) heterogeneous system. Most conventional methods rely on the information of the interference channel states and Macrocell User (MU) allocation provided by a control channel, causing large overhead increase. In our proposed method, each Femtocell Base Station (FBS) is able to predict the subchannel allocation of MUs based on partial Channel State Information (CSI) feedback and overhearing from the MUs to the Macrocell Base Station (MBS). Based on this estimation, each FBS performs subchannel and power allocation to Femtocell Users (FUs). With this procedure, the proposed method achieves excellent MU/FU sum-rate while greatly reducing required overhead compared to reference schemes.

There are two basic principles used in wireless network coding to design throughput-efficient schemes: (1) aggregation of communication flows and (2) interference is embraced and subsequently cancelled or mitigated. These principles inspire design of many novel multi-flow transmission (MFT) schemes. Such are the Coordinated Direct/Relay (CDR) schemes, where each basic transmission involves two flows to a direct and a relayed user. Usage of MFT schemes as building blocks of more complex transmission schemes essentially changes the problem of scheduling, since some of the flows to be scheduled are coupled in a signal domain and they need to be assigned a communication resource simultaneously. In this paper we define a novel framework that can be used to analyze MFT schemes and assess the system-level gains. The framework is based on cellular wireless users with two-way traffic and it sets the basis for devising composite time-multiplexed MFT schemes, tailored to particular optimization criteria. Those criteria can be formulated by adapting well-known schedulers in order to incorporate MFT schemes. The results show rate advantages brought by the CDR schemes in pertinent scenarios. Another key contribution is the proposed framework, which can be used to evaluate any future multi-flow transmission scheme.

Anonymous tag set cardinality estimation problem of Radio Frequency IDentification (RFID) using Maximum A Posteriori (MAP) approach is studied in this paper. The posterior probability of the total number of tags, given the frame size and the observed number of non-empty slots, is firstly determined. Then, the total number of tags is estimated to maximize the posterior probability. Computer simulation results demonstrate the effectiveness of the proposed approach.

We consider the problem of Downlink (DL) power allocation at a Femtocell Base Station (FBS) in a macro-cell/femtocell coexisting system based on OFDMA transmission. To mitigate the interference to macrocell users, many conventional methods rely on the information of the interference channel states and macrocell user allocation provided by a control channel, causing large overhead increase. Instead, in our proposed method, each FBS is able to predict the subchannel allocation of macrocell users based on partially overheard Channel State Information (CSI) from the macrocell users to the Macrocell Base Station (MBS). Based on this estimation, the FBS performs subchannel and power allocation to femtocell users. With this procedure, the proposed method can mitigate the interference to macrocell users without largely increasing the control overhead. Simulations show the validity of the proposed method, providing sum-rate gains for both femtocells and macrocell users compared to reference schemes.

The paper proposes a subcarrier power allocation method for downlink OFDMA systems. The proposed method utilizes an iterative water-filling (IWF) algorithm with signal-to-leakage-plus-noise ratio (SLNR) rather than conventional signal-to-interference-plus-noise ratio (SINR), which enables us to considerably reduce the overhead. Computer simulation results show that the proposed IWF based on SLNR with 1-shot 1-bit feedback per subcarrier can achieve very close sum-rate performance to the conventional IWF based on SINR, while only replacing SINR to SLNR in the IWF algorithm results in poor performance.

A relay system where two users are served by one Base Station (BS) and one Relay Station (RS) is focused on. A scheme based on Superposition Coding (SC) for Decode-and-Forward (DF) half-duplex relaying is proposed. Thanks to its design and power allocation ratios derived analytically, the proposed scheme is able to serve both users simultaneously, while attaining the same sum rate as the optimal benchmark scheme that exclusively allocates the best user. Simulation results show considerable fairness improvement over a large range of SNRs.

The ideas of wireless network coding at the physical layer promise high throughput gains in wireless systems with relays and multi--way traffic flows. This gain can be ascribed to two principles: (1) joint transmission of multiple communication flows and (2) usage of a priori information to cancel the interference. In this paper we use these principles to devise new transmission schemes in wireless cellular systems that feature both users served directly by the base stations (direct users) and users served through relays (relayed users). We present four different schemes for coordinated transmission of uplink and downlink traffic in which one direct and one relayed user are served. These schemes are then used as building blocks in multi--user scenarios, where we present several schemes for scheduling pairs of users for coordinated transmissions. The optimal scheme involves exhaustive search of the best user pair in terms of overall rate. We propose several suboptimal scheduling schemes, which perform closely to the optimal scheme. The numerical results show a substantial increase in the system--level rate with respect to the systems with non--coordinated transmissions.

A Superposition Coding (SC) scheme using discrete Adaptive Modulation (AM) for a three-node wireless relay system is proposed. In the proposed method, 2/4-QAM and 4/16-QAM hierarchical modulations are used to generate the SC message composed of a basic and superposed messages. We derive the analytical throughput expression of the system and determine the necessary conditions for guaranteeing optimal power allocation. The simulation results show that the proposed power allocation achieves a throughput close to the one obtained by exhaustive search. Moreover, over a large range of SNRs, the proposed method largely outperforms the conventional relaying methods such as multi-hop and cooperative diversity, proving the substantial benefits of SC even under practical discrete AM and decoding errors during SIC.

Optimized resource allocation of the Downlink (DL) in wireless systems utilizing Multi-Carrier (MC) transmission requires Channel State Information (CSI) feedback for each user/subchannel to the Base Station (BS), consuming a high amount of Uplink (UL) radio resources. To alleviate this problem, several works have considered contention-based CSI feedback in the UL control channel. We propose such a feedback scheme for a generic MC system, based on the idea of variable collision protection, where the probability that a feedback information experiences a collision depends on its importance. By partitioning the CSI into orthogonal layers of priority, and allocating different numbers of feedback slots to each layer, this scheme ensures that the feedback success probability is higher for the CSI with better quality, which is more likely to be used by the scheduler. Furthermore, we present a theoretical performance analysis of the proposed scheme, assuming Maximum CSI (Max CSI) and normalized Proportional Fair Scheduler (PFS), where a tight approximation of the achievable throughput is obtained assuming discrete Adaptive Modulation (AM) and CSI feedback which are relevant for the practical systems. Analytical and simulation results show that our proposed scheme provides an excellent trade-off between system performance and feedback overhead.

This paper proposes a transmit beamforming and subcarrier power allocation method for orthogonal frequency division multiple access (OFDMA) systems based on ¿signal-to-leakage-plus-noise ratio¿ (SLNR). As the transmit beamforming vector control criterion, we employ the maximization of the SLNR at each base station, while the subcarrier power allocation is performed by iterative water-filling algorithm using the SLNR of each subcarrier. The SLNR based approach enables us to obtain closed form beamforming vector, and achieve power allocation without sending any signal to the mobile terminal. We also discuss the validity of the SLNR based beamforming vector in terms of Pareto optimality. Computer simulations show the promising performance of the proposed method and the validity of the analysis of the SLNR based beamforming vector.

This paper considers a transmit beamforming and subcarrier power allocation method for orthogonal frequency division multiple access (OFDMA) systems. As the beamforming vector control criterion, we employ the maximization of so-called signal-to-leakage-plus-noise ratio (SLNR) at each base station, which enable us to obtain closed form beamforming vector by using only locally available information. We also discuss the local optimality of the SLNR based beamforming vector. As for the subcarrier power allocation, two different approaches are employed, namely, the equalization of signal-to-interference-plus-noise ratio (SINR) for subcarriers and the maximization of sum rate of subcarriers. Computer simulation results show the validity of the transmit beamforming and power allocation method with highlighting the difference between the two power allocation algorithms.

We propose generic relay and subcarrier allocation schemes for multicarrier (MC) system with amplify-and-forward (AF) relays. The outage probability bounds are derived analytically for each scheme. Simulation results show that these bounds are very tight and better than the bounds obtained straightforwardly from the analysis in the Single-Carrier (SC) case. This is because in our analysis we reckon with the increased degree of freedom brought by the parallel channels. One of the proposed schemes, the Average Best Relay Selection scheme, is best suited for practical implementation since it approaches the best performance while minimizing the required amount of signaling.

Optimal Proportional Fair Scheduling (PFS) in a multi-carrier system is a prohibitively complex combinatorial problem. In this paper we consider practical time frames with multiple time slots, where this optimal allocation becomes even more complex. Therefore, we derive bounds for the optimal proportional fair allocation, by means of convex optimization, and propose approximation algorithms where several users can be time-multiplexed on a same subchannel. With a much lower complexity than the optimal allocation, these algorithms achieve an excellent tradeoff between throughput and proportional fairness, even with the increased signaling overhead.

We address the problem of radio resource allocation in the Downlink (DL) of relay-aided cellular system, based on OFDMA transmission technology. There has been little work on specific resource allocation algorithms for this system in the literature, although these are the key elements for realizing the potential capacity and coverage increase offered by the relay. Therefore, we propose two resource allocation algorithms which improve the overall throughput and coverage compared to a system without relay. The advantage of our algorithms is that they perform well while minimizing the complexity and the required amount of Channel State Information (CSI), making them suitable for practical use.

We address the problem of radio resource allocation in the downlink (DL) of a cellular system with relay stations (RS), based on orthogonal frequency division multiple access (OFDMA) transmission technology. There is a need for the design of resource allocation algorithms for this type of system, where practical yet efficient algorithms are required to exploit the potential capacity and coverage increase offered by the relays. We propose several resource allocation algorithms with different options such as time or frequency division. The evaluations give some directions about the suitable allocation schemes. One algorithm offers an overall improvement of throughput and coverage, compared to a system without relays. At the same time, the advantage of our algorithms is that their complexity and amount of information overhead are much reduced compared to an optimal algorithm.

This work addresses radio resource allocation for downlink (DL) transmissions in a cellular system based on Orthogonal Frequency Division Multiple (OFDM). While scheduling based on multi-user diversity increases throughput, it decreases fairness across the users. Fairness is usually provided by Proportional Fair Scheduling (PFS). However, it was shown that optimal PFS in a Multi-Carrier (MC) system is prohibitively complex. In this paper we consider the practical assumption where there are multiple time slots (OFDM symbols) per scheduling frame, for which the optimal allocation becomes even more complex. Therefore, we derive the upper and lower proportional fairness limits which bound the optimal solution, by means of convex optimization. The results show that those derived proportional fairness limits are in fact very close, which gives an excellent bounding of the optimal solution. We also propose two heuristic algorithms which exploit the fact that several users can be multiplexed on one subcarrier in a time-division manner. We investigate their performance in terms of throughput, proportional fairness metric and latency. The additional overhead due to user multiplexing is taken into account. The results show that the proposed algorithms achieve an excellent tradeoff between throughput and proportional fairness.

We address the problem of channel state information (CSI) feedback for scheduling the downlink (DL) data transmissions in a cellular system based on orthogonal frequency division multiple access (OFDMA). In order to benefit from multi-user diversity, the scheduler needs to know each user's CSI for each subchannel and time frame. However, this feedback information can become very high when the number of subchannels and/or users increase. Therefore, we have designed an adaptive feedback encoding method which can optimize the amount of feedback according to the variable amount of CSI requested by the base station (BS). The amount of useful CSI depends not only on the number of users, but also on the scheduling made at the BS. The simulation results show that with our adaptive encoding scheme, the performance of maximum CSI and proportional fair scheduling (PFS) with full CSI can be approached with a considerably reduced amount of feedback