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Massive multiple-input multiple-output systems use a nearly infinite number of high-quality antennas at the transmitter/receiver are anticipated to play a key role in “5G” systems. However, the large number of antenna elements in massive MIMO also posed interference a major deficiency for successful communication in wireless systems. This work deals with Interference Alignment (IA) technique for mitigating the interference, although massive-antennas ‘based’ transmitters coordinated transmission where each transmission creates interference at the unintended receivers, such that the interference signal lies in a reduced dimensional subspace at each receiver. Recent works deal with the feasibility of IA in terms of the numbers of antennas and assume full channel state information at the transmitter (CSIT) is defined by the perfect knowledge of the global channel matrix. In this paper, we develop an algorithm, which ensures IA with incomplete CSIT, which uses the antennas ensured by Massive MIMO to further minimize the size of the CSIT allocation provided that the feasibility of the interference alignment is conserved and make transmitter cooperation more practical. Where the size of the CIST allocation defined as the total of complex numbers transmitted to the transmitter.

Wireless communications systems are the most prosperous technologies of this decade. Driven by newfangled innovative applications such as internet of things and augmented reality, this trend certainly continues exponentially. Massive MIMO technique use almost infinite number of antennas at the base station are expected to play a key role in future communication systems as part of horizon 2020 project. However, the large number of antenna elements in massive MIMO also pose a major challenge, due to the interference is the main impairment to successful communication. In this paper, we propose an original massive MIMO system. Adaptive Beamforming algorithm’s used since it is a promising solution in order to avoid interference problems where conventional beamformers are susceptible to interference signals on the contrary the algorithm can account for interference signals, the simulate results evaluate the interference inter-beams by giving the coherence between the different beams which each beam is appropriate to a specific terminal in communication system and demonstrates the effectiveness of our solution to mitigate interference, thereby contributing to the increase of network capacity. Our research affords a suitable solution for the design of next-generation wireless communication systems.

International audience; When a node is moving in a wireless network, the routing metrics associated to its wireless links may reflect link quality degrada- tions and help the routing process to adapt its routes. Unfortunately, an important delay between the metric estimation and its inclusion in the routing process makes this approach inefficient. In this paper, we intro- duce an algorithm that predicts metric values a few seconds in advance, in order to compensate the delay involved by the link quality measurement and their dissemination by the routing protocol. We consider classical metrics, in particular ETX (Expected Transmission Count) and ETT (Expected Transmission Time), but we combine their computations to our prediction algorithm. Extensive simulations show the route enhance- ment as the Packet Delivery Ratio (PDR) is close to 1 in presence of mobility.

Layered decoding is well appreciated in Low-Density Parity-Check (LDPC) decoder implementation since it provides efficient and high-throughput implementation of LDPC decoders. Variable-Node Layered Belief Propagation (VL-BP) algorithm and their reduced-complexity derivatives for LDPC codes are presented. The VL-BP algorithm is a modification of Belief Propagation algorithm (BP), where the variable nodes are divided in subgroups called layers and each iteration is broken into multiple sub-iterations. Some simplifications can also be made to lower the complexity of both BP and CL-BP algorithms, and particularly the complexity of the check node update rule. In this paper, we consider Variable Node Layered BP (VL-BP) algorithm and propose an optimized min-sum VL-BP (MS VL-BP) algorithm for decoding LDPC code which has better performance not only from MS VL-BP algorithm but also from BP algorithm. In this decoding method, unlike other layered algorithms, we consider for the first layer a set of variable nodes that has a low value of the intrinsic information. Simulation results show that good performance can be achieved, and which can even be improved by the addition of either a normalization term or an additive offset term.

International audience; In wireless mobile ad hoc networks, routing protocols use metrics to select the best routes. Metrics may reflect the quality of the wireless link and help to manage mobility. But, there is a delay between link quality measurements performed by the routing protocol and their instantaneous values. To compensate this delay, the idea is to anticipate the value of the metric a few seconds in advance. The purpose of this paper is to propose a new technique to compute ETX metric, that is sensitive to mobility and which optimizes throughput at the same time. We show through simulations performed with NS-3 (Network Simulator version 3) that our approach leads to Packet Delivery Ratio (PDR) close to 1 in presence of mobility.

The Layered Belief Propagation L-BP algorithm is is a modified Belief Propagation BP algorithm, where the check nodes are divided in subgroups called layers and each iteration is broken into multiple sub-iterations. In this paper, we consider layered belief propagation decoding and propose an efficient variable node layering for decoding LDPC codes that performs well. We compare the performance of the first introduced LDPC decoding algorithm BP and Layered BP using check node layering with Layered BP using variable node layering in terms of bit error rate (BER). We show that the convergence for decoding LDPC codes is increasing by using a simple and efficient layering strategy.

Signal transmission over mobile channels undergoes interferences coming from neighboring symbols in time and frequency. In this paper, we consider a mobile transmission by the OFDM/BFDM technique. Single pulse modulation (SPM) or multi pulse modulation (MPM) combined with a partial equalization of few neighboring pulses (for SPM) or co-located pulses (for MPM) at the receiver can be suited efficiently in the time-frequency lattice structure to optimize the overall system performance.