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International audience; Mission-critical communications (MCC) involve all communications between people in charge of the safety of the civil society. MCC have unique requirements that include improved reliability, security and group communication support. In this paper, we propose secure and robust Multiple-Input-Multiple-Output (MIMO) transceivers, designed for multiple Base Stations (BS) supporting multicast MCC in presence of multiple eavesdroppers. We formulate minimization problems with the Sum-Mean-Square-Error (SMSE) at legitimate users as an objective function, and a lower bound for the MSE at eavesdroppers as a constraint. Security is achieved thanks to physical layer security mechanisms, namely MIMO beamforming and Artificial Noise (AN). Reliability is achieved by designing a system which is robust to two types of channel state information errors: stochastic and norm-bounded. We propose a coordinate descent-based algorithm and a worst-case iterative algorithm to solve these problems. Numerical results at physical layer and system level reveal the crucial role of robust designs for reliable MCC. We show the interest of both robust design and AN to improve the security gap. We also show that full BS cooperation in preferred for highly secured and reliable MCC but dynamic clustering allows to trade-off security and reliability against capacity. Index Terms-mission critical communication (MCC), physical layer security, robust transceiver design I. INTRODUCTION Mission critical communications (MCC) are all communications between people in charge of the security and the safety of the civil society. Employees of public safety services, like policemen, firemen, rescue teams and ambulance nurses, but also from large companies managing critical infrastructures in the energy or transportation sectors require MCC for their operations [1]. MCC are conveyed by dedicated Private Mobile Radio (PMR) networks [2] that offer a group (or multicast) communication service. This is a one-to-many or many-tomany communication [3], which is one of the most important features of PMR networks and is essential to manage teams of employees. In 5G New Radio, group communication will be supported for MCC from Release R17 onwards [4]. Due to the critical aspects of their missions, MCC users also inherently require highly reliable and secure communication. In particular, sensitive information should not leak to unintended receivers although the broadcast nature of the wireless channel makes the network vulnerable to malicious eavesdroppers.

International audience; Full Duplex (FD) in cellular networks is expected to increase the cell spectral efficiency. However, while the downlink (DL) spectral efficiency (SE) increases with FD, the uplink (UL) SE decreases because of the Base Station to Base Station (BS) interference. In this paper, assuming a three-node model, we propose a method based on Successive Interference Cancellation (SIC) to reduce the BS-to-BS interference present in FD cellular networks. The approach consists in coordinating BSs to enable the decoding and the suppression of undesired signals that impair uplink transmissions. We analyze both distributed and Centralized Radio Access Networks (CRAN) architectures. Stochastic geometry is used to derive the coverage probability and mean data rate of the proposed scheme. In the distributed scenario, the FD UL average data rate is increased by 25% with our solution compared to a classical FD network, while our FD scheme still outperforms Half-Duplex (HD) on the DL. In the centralized scenario, our solution outperforms HD by 10% and classical FD by 78% on the UL, while preserving classical FD gains on the DL.

Vast volumes of time series are becoming more common in part to recent research advancements in big data analytics and sensor/monitoring networks. However, tranmission of time series for visualization services can cause serious bandwidth wastage and extensive network delays if there is no efficient data management. Existing work such as M4 aim to solve this problem by providing high data reduction rates through data aggregation and guaranteeing the reliability of visualization results at the same time. However, current work on M4 does not consider verification in a more practical environment; for example experimentation on web-based servers that are openly accessible by users. In this paper, we propose inter-pixel gradient-based M4 (IGM4) for enhancing the existing M4 scheme, and conduct a study to reduce the amount of data and delay without distorting the results of visualized graph. We build user-friendly web-based system to dealing with the data processing technique, and perform test on the empirical environment. Finally, we present the results of performance evaluation through comparison among the original data, M4, and IGM4 reflecting the various kinds of time series and resolutions.

International audience; Solar energy is currently a popular renewable resource, yet limited daily. In green cellular networks, multiple constraints optimization (MCO) problems arise naturally. For example, a typical objective is to control the power transmission of hybrid base stations (connected to both solar panels and electrical grid) in order to maximize user's average throughput, under the constraints of consumed grid energy and user's blocking rate. However, such problems have been generally proved to be NP-hard. In this paper, we formulate this generic MCO problem as a quantized Markovian cost-reward model, with no assumption on input data. We then propose a novel algorithm, namely Cost-constrained Viterbi Algorithm, which recursively returns the optimal policy with linear computational complexity for this model. As an application, we provide engineering rules for the design of hybrid base stations through extensive simulations. In comparison with brute force method for a simple scenario, we find that our algorithm does achieve the constrained optimal policy.

International audience; This paper studies the performance of two traditional schedulers, namely, proportional fair (PF) and round robin (RR), in the context of relay-enhanced Long-Term Evolution Advanced (LTE-A) networks. These two schedulers are natural candidates for implementation in relay nodes (RNs), and following the results obtained in single-hop networks, mobile operators could be tempted to adopt PF because of the good tradeoff it offers between cell capacity and fairness. Based on a statistical throughput evaluation model, we show that this is not necessarily the right option. The number of RNs, their locations in the cell, and backhaul link quality have a decisive influence on scheduler choice. In some scenarios, it is even not desirable to deploy relays as they degrade the network performance compared with the no-relay case. For the purpose of performance evaluation, we develop a realistic and computationally tractable statistical network model that takes into account fast fading, multiple interferers, cell range expansion bias, backhaul link quality, and traffic load. We also propose optimization of the radio frame structure and a suboptimal RN placement scheme to fairly compare RR and PF.

This work proposes a new radio resource allocation scheme for the green uplink Long-Term Evolution (LTE) networks. The proposed scheme aims at efficiently allocating the resource blocks (RBs) and the transmission power of the user equipment (UE). The allocation of RBs is based on the opportunistic and efficient RB allocation (OEA) algorithm, whose objective is to maximize the aggregate throughput while being subjected to single-carrier frequency-division multiple-access (SC-FDMA) constraints. An evolution of the algorithm, which is called quality-of-service (QoS)-based OEA, providing QoS differentiation, is also presented. It allocates a given maximum number of RBs to each UE, which is set according to the user's QoS requirements. Once the allocation of RBs is completed, the allocation of the UE transmission power is performed. The transmission power considers the UE channel conditions over its whole allocated RBs. Its value is reduced as much as possible and adjusted such that the user's required QoS satisfies throughput demand previously determined by the RB allocation. The proposed radio resource management algorithm has several interesting properties. It maximizes the aggregate throughput, decreases the ratio of inefficiently allocated RBs, cancels the RB's wastage, and increases the energy efficiency without affecting the user's throughput. Furthermore, the OEA algorithms can be implemented in LTE hardware equipment devices on account of their low computational complexity.

This paper considers the problem of receive antenna selection (AS) in a multiple-antenna communication system having a single radio-frequency (RF) chain. The AS decisions are based on noisy channel estimates obtained using known pilot symbols embedded in the data packets. The goal here is to minimize the average packet error rate (PER) by exploiting the known temporal correlation of the channel. As the underlying channels are only partially observed using the pilot symbols, the problem of AS for PER minimization is cast into a partially observable Markov decision process (POMDP) framework. Under mild assumptions, the optimality of a myopic policy is established for the two-state channel case. Moreover, two heuristic AS schemes are proposed based on a weighted combination of the estimated channel states on the different antennas. These schemes utilize the continuous valued received pilot symbols to make the AS decisions, and are shown to offer performance comparable to the POMDP approach, which requires one to quantize the channel and observations to a finite set of states. The performance improvement offered by the POMDP solution and the proposed heuristic solutions relative to existing AS training-based approaches is illustrated using Monte Carlo simulations.

This paper considers antenna selection (AS) at a receiver equipped with multiple antenna elements but only a single radio frequency chain for packet reception. As information about the channel state is acquired using training symbols (pilots), the receiver makes its AS decisions based on noisy channel estimates. Additional information that can be exploited for AS includes the time-correlation of the wireless channel and the results of the link-layer error checks upon receiving the data packets. In this scenario, the task of the receiver is to sequentially select (a) the pilot symbol allocation, i.e., how to distribute the available pilot symbols among the antenna elements, for channel estimation on each of the receive antennas; and (b) the antenna to be used for data packet reception. The goal is to maximize the expected throughput, based on the past history of allocation and selection decisions, and the corresponding noisy channel estimates and error check results. Since the channel state is only partially observed through the noisy pilots and the error checks, the joint problem of pilot allocation and AS is modeled as a partially observed Markov decision process (POMDP). The solution to the POMDP yields the policy that maximizes the long-term expected throughput. Using the Finite State Markov Chain (FSMC) model for the wireless channel, the performance of the POMDP solution is compared with that of other existing schemes, and it is illustrated through numerical evaluation that the POMDP solution significantly outperforms them.

We present a novel approach for distributed load balancing in heterogeneous networks that use cell range expansion (CRE) for user association and almost blank subframe (ABS) for interference management. First, we formulate the problem as a minimization of an $\alpha$ -fairness objective function with load and outage constraints. Depending on $\alpha$ , different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a near-potential game, in which the potential function is the $\alpha$ -fairness function. The optimal pure Nash equilibrium (PNE) of the game is found by using distributed learning algorithms. We propose log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. We give a detailed proof of convergence of learning algorithms for a near-potential game. We provide sufficient conditions under which the learning algorithms converge to the optimal PNE. By running extensive simulations, we show that the proposed algorithms converge within few hundreds of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. Finally, we show that outage can be controlled and a better load balancing can be achieved by introducing ABS.