Wireless networks keep evolving to handle an increase of the number of users equipment and services. One of the main challenge is to enhance spectral efficiency as it lead to higher throughput and system capacity. Traditional scheduler, such as Maximum Signal-to-Noise Ratio (MaxSNR), Proportional Fair (PF) perform well in specific context, but can be outperformed in others. Moreover, there is many possible scheduler to try in order to find the best ones for diverse contexts. An approach that uses Artificial Intelligence (AI) becomes a conceivable solution. In this paper we introduce a new AI tool that selects and evaluates schedulers that enhance spectral efficiency for different contexts. Our approach is to train the AI model on several traffic load, to evaluate and select schedulers based on a general scheduling formula, resulting in an explainable model for traffic scheduling. After the training, the appropriate scheduler is used for each traffic load conditions. In addition, our tool detects terms of the formula that does not have a positive impact on enhancing spectral efficiency. Simulation results show that the obtained scheduler outperform state of the art algorithms such as RR, MaxSNR and PF.

Energy consumption reduction in 5G networks and beyond is a significant challenge. Discontinuous Reception (DRX) established by 3GPP is the current state of the art energy-saving strategy. With DRX, users that neither receive nor transmit for a certain duration are set in a sleep cycle. These users periodically wake up in order to consult the Physical Downlink Control Channel in case of potential transmissions. DRX timings are negotiated during the initial connection. Despite the inherent variability of user traffic patterns, DRX configuration parameters remain static, leading to energy waste. In addition, DRX does not consider delay constraints, which may incur QoS degradation due to inadequate sleep duration. This paper proposes Meta Sleep Scheduler (MSS) as an alternative to DRX. User sleep duration is dynamically adjusted to meet application time constraints while reducing the time users spend in high power states. Similarly to DRX, MSS is compatible with all radio resources allocation schedulers. MSS achieves an average reduction of up to 90% in user standby power consumption compared to DRX, while maintaining or surpassing DRX’s Quality of Experience.

In the evolving landscape of Fifth-generation (5G) and beyond, Integrated Access and Backhaul (IAB) networks have emerged as a promising solution to extend coverage and enhance capacity without additional wired infrastructure. However, the dynamic allocation of radio resources on the shared access-backhaul spectrum in IAB networks presents challenges, particularly in meeting the diverse Quality of Service (QoS) requirements of today's services such as voice, video, and data. This paper introduces a heuristic-based, QoS-aware downlink scheduler specifically designed for radio resource scheduling in IAB networks. The proposed scheduler aims to enhance users' satisfaction by meeting users' delay requirements. We evaluate its performance using discrete-event simulations, demonstrating that our scheduler ensure the best Packet Delay Outage Ratio (PDOR) and the highest percentage of satisfied users compared to other tested schedulers.

With the increased demands for 5G networks and the limited radio resources, providing high spectral efficiency, low delay, low energy consumption, and other Key Performance Indicators (KPIs) is a challenging task. Extensive research has been conducted to propose efficient solutions for specific objectives and contexts. Although these solutions (often heuristic-based) are highly effective in specific contexts, their performances diminish when applied in different conditions. This implies difficulties in adapting to environmental variations and/or changes in objectives. In order to overcome this problem, we propose an approach employing reinforcement learning to dynamically derive the formula for a scheduler that can be adapted to any context and objective. The proposed solution is validated with a Proof of Concept (PoC), which highlights the Artificial Intelligence (AI) ability to identify the adequate scheduler to optimize spectral efficiency in different traffic loads contexts.

Recently, we have witnessed an operational deployment of 5G in big cities to increase the communication capacity of users. However, with the ever-increasing of devices in dense networks, the current 5G could fail due to its unscalable issues. Scientists in academia and industry are actively analyzing this open issue within the context of Beyond 5G (B5G) and 6G before their realistic deployment. Moreover, the high densities of devices lead to the challenges of over-energy consumption and unfair Quality of Service (QoS) caused by the distances from the base station and existing obstacles separating the communicating devices. The network relay election method can be seen as an appropriate option to overcome these challenges, but this solution should be optimized according to the environment's dynamics. Therefore, in this paper, we propose an energy-efficient election-based method that selects relays so that it maximizes the overall network lifetime while maintaining a certain level of QoS. Considering that our system is deployed in an unknown dynamic environment, we employ a deep reinforcement learning technique that aims to optimize the network relay selection, maximize the residual energy levels of devices, and ensure fair and high data rates. Our system has been tested through a discrete-event simulator, and its performances have been evaluated and compared to a set of baseline and benchmark methods. It has been shown that our proposed system outperforms existing relevant solutions.

The 3rd Generation Partnership Project (3GPP) has proposed recently the Integrated Access and Backhaul (IAB) to simplify the deployment of new base stations, where the connection between the User Equipment (UE) and the Core Network (CN) is provided by a multi-hop 5G wireless connection between the IAB nodes. The main contribution of this paper is to propose a cell selection policy specific to IAB, that aims to guarantee the lowest possible total transmission bandwidth cost (including the backhaul cost which is a significant factor in an IAB context) while respecting the base stations capacities, in order to boost IAB network capacity. In this context, a comparative performance study is performed between our proposed policy and state-of-the-art policies. System level simulation results show that our solution provides the best system capacity via a trade-off between spectral efficiency and load balancing.

Radio resource allocation is a major research field in wireless networks. The main challenge is to find the most suitable user for each time and frequency resource to ensure the Quality of Service (QoS) requirements. The use of Massive Multiple-Input-Multiple-Output (MIMO) makes it possible to simultaneously schedule several users on the same time and frequency resources. This is a new paradigm for resource scheduling. In this context, resource allocation algorithms have to account for the choice of users served on the same time and frequency resource in their decision mechanism. In this paper we analyze the impact of different group sizes on system capacity using several user selection algorithms and we propose a new user selection algorithm which performs closer to the optimal algorithm proposed in the literature.

In wireless networks, the transmission efficiency is highly impacted by attenuations such as: path-loss, shadowing, multi-path fading and interference. At cell edges, mobiles are far from their access point and close to neighboring cells, leading to high path-loss and high magnitude of interference. Consequently, ensuring high spectral efficiency is necessary to guarantee an appropriate Quality of Service (QoS), especially at edges. To cope with this crucial issue, this work investigates the benefits of the Joint Transmission Coordinated Multi-Point (JT-CoMP) clustering to mitigate Inter Cell Interference (ICI). This paper proposes the Hybrid Joint-Transmission Coordinated MultiPoint algorithm (H-JT-CoMP). This solution dynamically performs its ICI management according to the Chanel State Information (CSI). This allows to make a wise CoMP usage according to the magnitude of interference received. Performance evaluation highlights an increased QoS and system capacity with a better fairness between inner and edges of the cell.

Radio resources are limited and subject to interferences, multi-path fading, shadowing and path-loss which highly impact the transmission efficiency. Optimizing the usage of these resources is the main issue in wireless networks. The resource management can be performed at different levels: in intracellular context, it is carried out by scheduling algorithms while in multi-cellular context, it is done by interference management strategies. This paper aims to merge these two steps. The proposed solution is compatible with the most acknowledged schedulers in order to optimize spectrum usage in intra-cell domain while efficiently decreasing the interferences in multicell domain. An innovative strategy is to consider the network as an "hyper-cell" rather than a sum of cells quite independent. This strategy often called "Cell-less" is especially effective in a Cloud Radio Access Network (C-RAN) architecture because it allows to centralize the decision making. This is more suitable for interference management as it provides a better flexibility to the system by retrieving and managing data from several cells that allows an overall performance increase. The proposed solution called Dynamic Cell-less Radio Access Network Meta-Scheduler (DC-RAN-MS) dynamically handles for each cell the management of radio resources depending on the interferences potentially experienced by users. Performance evaluation shows that the DC-RAN-MS offers an increased system capacity by optimizing the usage of bandwidth while reducing the magnitude of interferences received.

The amount of data represented by the Internet of Things (IoT) is continually growing and is expected to reach more than one third of the total amount of Internet traffic by 2020. Wireless sensors pose a great challenge because of the varying radio conditions and the limited energy and computational capabilities they have. This is why we have developed an Objective Function (OF) that uses fuzzy logic to dynamically adapt to variable environments in wireless networks. Simulation results will show that the proposed algorithm increases performance compared to other solutions by up to 15% in terms of throughput and by up to 14% in terms of Packet Delivery Ratio (PDR) without compromising energy consumption.

Even though system energy and spectral efficiency are major issues in wireless network, reaching these objectives conjointly seems very difficult and requires the usage of tradeoffs. Moreover, depending on the context, the importance of either varies. In underloaded context, guaranteeing high Quality of Service (QoS) is easily achievable due to large surplus of available radio resources and focus should be put on energy rather than system throughput. On the contrary, in an overloaded context, the lack of available radio resources required that resources allocation algorithms focus on system capacity in order to preserve QoS. Since the major issue of the network is to satisfy users, in this specific case, energy consumption must become lesser important. Many specialized solutions have been proposed that focus either on energy saving or on throughput maximization. They provide high performances, respectively, on their specific network traffic load context, previously described, but are not optimized outside. Other solutions that proposed static tradeoffs provide average performances but can not be fully efficient in all scenarios. In this paper, we propose a Dynamic Tradeoff between energy and throughput efficiency that adapts the scheduler priorities to the network context and particularly to the traffic load. Considering the context, the scheduler is able to adjust its behavior in order to maintain high QoS while reducing as much energy as possible. Performance evaluation will show that the proposed solution succeeds to minimize energy consumption better than energy focused scheduler in underloaded context while being able to reach the same spectral efficiency as throughput oriented scheduler in highly loaded context.

Inter-Cell Interference Coordination (ICIC) techniques are proposed as solutions to alleviate the negative impact of interference on system performance, while enhancing the provided Quality of Service (QoS). Typically, the available bandwidth is divided into inner and edge sub-bands. Users are also classified into interior and edge users. The available resources in each zone are exclusively allocated to users belonging to this zone. Mobile users classification is usually based on a threshold that can be either a given mean SINR value or a given distance. However, ICIC approaches based on these static parameters cannot efficiently manage non-homogeneous distribution of users. In this paper, we introduce a dynamic handoff algorithm that aims to adapt static ICIC schemes to uneven distribution of users. Our new solution dynamically computes the classification of active users into interior and edge users, based on a heuristic load balancing algorithm. In our proposal, each cell autonomously reconfigures its bandwidth allocation constraints without modifying bandwidth repartition across the cellular network. This makes the solution well adapted to the non-uniform repartition of users at the multicell scale. Simulation results show that the proposed scheme improves bandwidth usage, reduces packet delay, and increases user satisfaction compared to state-of-the-art ICIC techniques.

In this paper, we focus on the resource allocation based on scheduling algorithms. Unlike the existing solutions, we introduce the concept of Proportional-Integral-Derivative (PID) controller in the scheduling algorithm called PID-Scheduler in order to reach the system stability. The stability is considered in terms of throughput and delay which meet the nodes needs. In order to allocate the resources to the mobile nodes while meeting their needs in terms of throughput and delay, the proposed PID-Scheduler is not only based on the network parameters obtained in the present time, but also from the past and those which may be obtained in the future. The PID-Scheduler reacts immediately to deviation from the QoS required by the mobile. Moreover, it remembers and integrates the deviation history and corrects slowly. The prediction of the future deviation is taken into account in order to make a fast correction. The simulation results show that the proposed solution PID-Scheduler gives better results than the well-known scheduling algorithms like MaxSNR and WFO.

In the last decade, many research efforts have been done in order to increase the spectral efficiency of wireless communications. From now on, opportunistic resource allocations have emerged as the best way to reach this objective. They take into consideration the radio conditions in the allocation process. This allows to guarantee high system throughput and high Quality of Service (QoS). However, today, it is not sufficient anymore. Many climate problems have been underlined by the majority of world scientists and decreasing world greenhouse gas emission has become a necessity for the world's environment preservation. This requires to also reduce energy consumption in as much sectors as possible including wireless communication networks. This paper proposes to extend the opportunistic approach with the proposition of a new scheduling solution enables to significantly decrease the system energy consumption. Performance evaluations will show that the global energy consumption can be divided by 2 compared to existing schedulers without jeopardizing system efficiency.

In this paper, we propose a new scheduler able to extend the wireless coverage by using an incentive approach for potential mobile relaying nodes. Indeed, the cost of cooperation can be expensive in terms of QoS and energy consumption which do not motivate the nodes to cooperate. Our incentive approach rewards the cooperative nodes. The percentage of cooperation is considered in the QoS management in order to incite the border nodes to cooperate and then to extend the wireless area. Moreover, the monitoring mechanism is proposed to correctly evaluate the cooperation rate of each node. The results show that not only the proposed solution allows the border nodes to cooperate without the negative impact but also enhance the QoS parameters.

MaxSNR scheduling is aknowledged as the reference resource allocation for multiuser OFDM wireless networks. Well adapted to the wireless environment, MaxSNR opportunistically considers the channel state and fights the multipath fading negative effects. Taking advantage of multiuser diversity, MaxSNR scheduling is well-known for maximizing the global cell throughput. However, throughput maximization is made at the expense of fairness. Indeed, severe fairness deficiencies appear especially when mobiles experience unequal path loss due to unequal position. In this paper, we propose to solve this fairness issue with a modified MaxSNR scheme that introduces distance compensation factors while keeping the original MaxSNR system throughput maximization properties. Simulation results show that this well-balanced resource allocation outperforms other existing scheduling schemes and jointly provides both high system throughput and high fairness.

Efficient support of multimedia services in next generation wireless networks requires advanced scheduling schemes that achieve both system capacity maximization and full QoS differentiation. This paper proposes a new MAC scheduling scheme for multiuser OFDM wireless networks. Designed in an opportunistic cross-layer approach, this scheme uses a system of weights that dynamically adjusts the priority of the flows considering the transmission conditions and the QoS that they currently experience. This results in a very efficient management of the bandwidth. Performance evaluation shows that the proposed scheduling outperforms existing wireless OFDM based scheduling schemes providing full QoS differentiation, high fairness and system throughput maximization.

Providing multimedia services in wireless networks requires maximizing the system throughput without sacrificing fairness. Previous works focus on system capacity optimization but fail to jointly ensure adequate fairness. This paper proposes a new MAC scheduling scheme which dynamically takes in consideration the QoS experienced by the mobiles and the transmission conditions in an extended cross-layer design. Based on a weighted opportunistic algorithm, our resource allocation takes a maximum advantage of multiuser diversity optimizing fairness and system throughput. This provides an efficient support of multimedia services in OFDM wireless networks. Performance evaluation shows that the proposed scheduling widely outperforms the best existing wireless OFDM based scheduling schemes.

This paper proposes a new MAC scheduling scheme for efficient support of multimedia services in multiuser OFDM wireless networks, both in the uplink and in the downlink. This scheme is based on a system of weights that dynamically accounts for the experienced QoS and the transmission conditions in an extended higher layers/MAC/PHY cross layer design. This approach solves the crucial issue of wireless multiple access schemes: ensuring fairness without sacrificing the throughput offered by the scarce bandwidth resource. Performance evaluation shows that the proposed scheduling outperforms existing wireless OFDM based scheduling schemes and demonstrates that choosing between high fairness and high system throughput is not required.

In wireless networks, providing a fair bandwidth allocation without too much reducing the system throughput is very challenging. Path loss attenuations induce unequal spectral efficiencies and in terms unequal throughtput for mobiles with different geographical positions. In the litterature, Proportional Fair (PF) is acknowledged as the reference scheduler in multiuser OFDM wireless networks. Opportunistically considering the channel state, PF is adapted to the wireless environment fighting the multipath fading negative effects. PF takes advantage of multiuser diversity and globally maximizes the throughput. Additionally, PF scheduling currently makes the best tradeoff between fairness and throughput maximization. However, severe fairness deficiencies appear when the mobiles experience unequal path loss. In this paper, we propose to solve this fairness issue with a modified PF scheme that introduces distance compensation factors. Simulation results show that this well-balanced resource allocation outperforms other existing scheduling schemes and jointly provides both high system throughput and high fairness.