Direct device-to-device (D2D) communication presents as an effective technique to reduce the load at the base station (BS) while ensuring reliable localized communication. In this paper, we propose a large-scale M2M data Aggregation and Trunking (MAT) scheme, whereby the user equipments (UEs) aggregate M2M data from the nearby MTDs and trunk this data along with their own data to the BS in the cellular uplink. We develop a comprehensive stochastic geometry framework by considering a Poisson hard sphere model for UE coverage. The main motivation of this model is to capture the fact that a UE can gather data from short range, low-power MTDs located only in its close proximity while ensuring that an MTD is associated to at most one UE. We explore the inherent trade-off between the time reserved for aggregation and successful trunking of data to the BS and compare our results with the baseline case where no aggregation mechanism is used. We show that while the baseline case of connecting a bulk of MTDs directly with the BS is prohibitive, MAT scheme can efficiently gather data from selected MTDs in a distributed manner.

Taking plenty benefit of the new frequency bands in 5G networks requires the design of efficient carrier aggregation (CA) methods compliant with the expected large resource pool dimensions, especially when realized jointly with Multi-User (MU)-MEMO feature. In this paper, we propose a MU-MIMO aware CA (MMCA) resource allocation solution for SG systems that assigns the available resources in time, frequency and MEMO dimensions under realistic traffic and wireless system architecture constraints. We design a novel user grouping approach that accounts for available input constraints defined by the system design. These constraints are generated by higher layers such as user priorities based on their requests and previously allocated rates as well as lower layers such as coupling factors between users based on the used beamformers and physical layer signal processing. In addition, a hierarchical resource scheme is proposed in order to cope with the high computational complexity through optimization space reduction and parallelization. Obtained simulation results under 5G assumptions and realistic constraints clearly assess the large gains achieved by our solution in terms of user throughput and fairness comparing to a set of baseline schemes.

In this article, we investigate how the performance of Massive MIMO cellular systems can be enhanced by introducing D2D communication. We consider a scenario where the base station (BS) is equipped with large, but finite number of antennas and the total number of UEs is kept fixed. The key design question is that what fraction of users should be offloaded to D2D mode in order to maximize the aggregate cell level throughput. We demonstrate that there exists an optimal user offload fraction, which maximizes the overall capacity. This fraction is strongly coupled with the network parameters such as the number of antennas at the BS, D2D link distance and the transmit SNR at both the UE and the BS and careful tuning of the offload fraction can provide up to 5× capacity gains.1

We propose a coordination scheme to minimize the interference between users in a cellular network with full-duplex base stations and half-duplex user devices. Our scheme exploits signal attenuation from obstacles between the users by (i) extracting spatially isolated regions from a radio map and (ii) assigning simultaneous co-channel uplink and downlink transmissions to users in these regions such that inter-user interference is minimized. While adding low computational complexity and insignificant signaling overhead to existing deployments, evaluating our solution with real coverage data shows impressive gains compared to conventional half-duplex and full-duplex operation.

In this paper, the problem of interference coordination in heterogeneous networks, comprising macro base stations (MBS) and several underlay small cells (SCS) is analyzed. Such problem is normally solved by coordinating and orthogonalizing the transmissions of the MBS and the SCS in order to reduce the global interference level, at the price of underutilized network resources. To solve this problem, an interference coordination scheme, based on the exploitation of device-to-device (D2D) enabled relaying, and allowing full spectrum usage is presented. This scheme is based on the idea of substituting long-range transmission with short-range D2D communications, which are characterized by low power transmissions, thus introducing low interference to the overall system. The proposed algorithm is compared to standard enhanced inter cell interference coordination (eICIC) scheme and its performance gain is assessed through numerical simulations.

In this paper, we present a framework for including geographical context information within the process of relay selection, where both the relay and destination can be user equipment (UE) hence enabling relaying via device-to-device (D2D) communication. Geographical information, acquired through positioning systems and radio maps, are used to infer pathloss, link budgets and relay generated interference. Thus, this information becomes available at the core network without the aid of extra signaling and channel sounding. Tools from image processing can be exploited to analyze the radio map and determine so called isolated regions. Then, these regions are used by the proposed framework to effectively select the relay. Three different relay selection policies that exploit geographical context via the knowledge of the spatial isolated regions are introduced. Each of these policies is analyzed via simulation and performance is compared with respect to baseline schemes. The pertinence of adopting geographical information is confirmed by the gain in terms of useful power and reduced interference level generated.

In non-reciprocal wireless channels, the performance of link adaptation heavily deteriorates with outdated measurement reports. To provide an effective solution to this problem, we construct an accurate channel gain predictor by combining the Kalman Filter (KF) with Expectation-Maximization (EM). This joint design minimizes the mismatch between the autoregressive model of the KF and the true channel gain. After deriving the filter parameters in closed form, we compare the accuracy of our solution to two relevant alternatives in a 3GPP-compliant scenario. Impressive gains at low computational complexity make our solution a promising candidate to improve the next generation of link adaptation functions.

Streaming services come with their own challenges and technical issues that still need to be addressed for satisfying the target quality of experience (QoE) of the end-users in mobile environments. In this paper, we explore the idea of combining users' context information with the packed prefetching process features to enhance users' QoE in heterogeneous networks. More specifically, we propose a scheduling mechanism for video streaming traffic, in which the access to the network resources is restricted to users with a signal-to-noise plus interference ratio (SINR) above a given threshold. This scheme benefits from the fact that, as users are in permanent motion, they may experience different SINR values during the same video streaming session offering the opportunity to only schedule users with good channel conditions. The proposed scheduling approach (subsequently referred to as context-aware mode switching (CAMS)) not only allows to achieve overall network spectral efficiency improvement, but also guarantees fairness and QoE among users. Our simulation results show that CAMS achieves almost 1 bit per second per Hertz gain compared to the conventional scheduler (without CAMS), and up to 87% improvement in the probability of no starvations when users move at 40 kmph.

In this paper, the problem of resource allocation and interference minimization within a device-to-device (D2D) network is analyzed. A clustered structure for the D2D network is proposed along with a two-level distributed scheme operating both at cluster and link levels. By exploiting the peculiarities of the network, the proposed scheme is shown to efficiently configure the transmission parameters. At regime, the algorithm operating at the cluster-level is proved to minimize the average multiple access interference (MAI) arising in the network. A conjecture linking this interference with the algorithm parameters is presented and validated through numerical simulations. The algorithm operating at the link-level is shown to efficiently set the transmit sub-channel and power level satisfying the quality of service (QoS) requirements for the largest possible set of devices using the minimum amount of power. The pertinence of the proposed scheme is highlighted by numerical simulations validating the theoretical findings and assessing the algorithm performance.

Heterogeneous Networks (HetNets) are seen as an effective solution for improving network capacity, offloading traffic and optimizing coverage. However, the transmit power difference between the introduced low power nodes and the legacy macro cells essentially leads to two challenges: Cell Association and Inter-cell interference management. The above issues can be mitigated through the enhanced Inter Cell Interference Coordination (eICIC) feature introduced in LTE release 10. eICIC involves two techniques: 1-Cell Range Extension (CRE) using Cell Individual Offset (CIO) to offload a set of users to the small cells (SC), and 2-Almost Blank Sub-frames (ABS) patterns to "protect" SC users from interfering macro eNodeBs (eNBs). In this paper, we propose a dynamic solution performing a joint optimization of both CIO and ABS in order to maximize any predefined network utility function. Simulation results illustrate the important performance gains brought by this joint optimization, especially for cell edge users as their averaged throughput is increased up to 300% of their initial performance.

In this work, we address the problem of power allocation for interfering transmitter-receiver pairs so that the probability that each queue length exceeds a specified threshold is fixed at a desired value. One application is satisfying QoS requirements in a dense cellular network. We deal with this problem using heavy traffic approximation techniques which lead to an asymptotic model of a (controlled) stochastic differential equation. The proposed power control strategy consists of allocating most of the power according to the states of the channel and a smaller fraction according to the queue lengths, for which we find a closed-form expression. We first consider a scenario where all channel realizations and queue lengths are known instantaneously to every transmitter. Then, the algorithm is extended to the case where only local SINR feedback is available and when queue length information is shared with delays among the transmitters. These models and results are also extended to the case where the transmitters are equipped with multiple antennas. Finally, the applicability in practical system settings are discussed and simulation results are provided to illustrate the performance of the proposed method.

In this paper, we investigate the interaction between two major technical challenges for LTE small cells deployment, in order to face the explosive increase of the traffic growth. These challenges are 1-Inter-Cell Interference Management which becomes critical in dense deployment of small cells and 2-Mobility Management since the handover frequency between close-by cells increases considerably. These two features, often analyzed separately are intertwined. The main reason is that handover occurs in overlapping cellular areas, where interference level is the highest. In this paper, interference is managed by a decentralized approach based on Multi Armed Bandit solutions. Handover performances are evaluated thanks to standard Mobility Robustness Optimization counters. Thus, we evaluate the impact of inter-cell interference management on handover performances and we demonstrate through simulations that the optimal configuration of interference management shall jointly account for performances indicators directly impacted by the interference management mechanism performances like convergence duration and throughput but also for handover-related performance metrics.

This articles proposes a smart resource sharing algorithm to manage interference in LTE networks. Relying on reinforcement learning theory, the proposed Cyclic Multi Armed Bandit (CMAB) algorithm steers each cell in the choice of the most suitable frequency band portions, in autonomous manner. Thanks to the traffic aware feature, the algorithm adapts as well to the real needs of the cell. Adding to that, a refinement of the decision function is proposed to speed up the convergence time. The proposed approach is tested in LTE compliant simulator and the results shows its efficiency compared to conventional static reuse schemes.

Resource allocation is an important prerequisite for the effective deployment of Pico Cells (PCs). This topic becomes even more challenging in the case of heterogeneous networks, where autonomous interference management mechanisms are necessary. In this article, we propose a resource sharing method inspired from the reinforcement learning theory and particularly the methods used to solve the Multi Armed Bandit (MAB) problem. The main goal resides in giving the ability for each cell to make its decision autonomously while dynamically taking into account the resource occupation of each surrounding cell. We set up the global framework for the MAB based resource allocation strategies in the case of total frequency overlapping PCs. The performances of the proposed method are evaluated in the case of Long Term Evolution (LTE) Pico Cells deployment and compared to static allocation schemes. The results demonstrate the efficiency of our method.

In this paper, we address the problem of spectrum sharing for LTE pico cells (PCs) networks with heterogeneous traffic loads. Distributed mechanisms are designed to steer the PCs in selecting the most suitable resources so that the overall throughput is maximized. The goal is to respond to the real traffic needs, while ensuring users fairness. Dynamic Cooperative Algorithms (DCA) relying on information exchange between the PCs and Non Cooperative Algorithms (DNCA) relying only on local measurements are elaborated and compared through extensive simulations. Different propagation conditions and varied resource needs between the PCs are also considered. We demonstrate in this paper the performance gain brought by the cooperative solution comparing to the non cooperative one and we show the capability of the algorithm to dynamically adjust the allocated resources to the real traffic loads.