This paper proposes a Bayesian prediction framework for the dynamic interference, caused by the movement of the interfering device. The framework relies on a hidden Markov model where the hidden state is the device mobility and the observable is the measured interference. To efficiently predict the interference, we consider jointly exploiting the mobility correlation and the radio correlation in a probabilistic manner. The benefits in doing so is to keep all available information about the estimation meanwhile to have the flexible to handle the algorithm output. The numerical evaluations shows that the proposed approach outperforms the conventional algorithm such as the Auto-Regressive prediction.

We consider the following positioning problem where several base stations (BS) try to locate a user equipment (UE): The UE sends a positioning signal to several BS. Each BS performs Angle of Arrival (AoA) measurements on the received signal. These AoA measurements as well as a 3D model of the environment are then used to locate the UE. We propose a method to exploit not only the geometrical characteristics of the environment by a ray-tracing simulation, but also the statistical characteristics of the measurements to enhance the positioning accuracy.

We consider a multi-agent system where agents compete for the access to the radio resource. By combining some application-level parameters, such as the resilience, with a knowledge of the radio environment, we propose a new way of modeling the scheduling problem as an optimization problem. We design accordingly a low-complexity solver. The performance are compared with state-of-the-art schedulers via simulations. The numerical results show that this application-oriented scheduler performs better than standard schedulers. As a result, it offers more space for the selection of the application-level parameters to reach any arbitrary performance.

This paper investigates the joint interference source separation and geolocation problem, where the receiver is a vehicle with a known trajectory. We consider communications on a public band where contention-based protocols ensure that two interference sources close to each other do not transmit concurrently. No other prior knowledge on the interference sources is known. The vehicle collects the received power from several positions along its trajectory, and observes a random mixture of the interference sources, which can be modeled by a Dirichlet mixture. A Machine Learning technique relying on Bayesian inference is designed to jointly solve the interference source separation and geolocation. On the one hand, the source separation can be done by three approaches: decision making technique, sampling technique and hybrid decision-sampling technique. On the other hand, the geolocation is carried out via bayesian updates of the posterior distribution of interference sources' positions. The proposed method has the advantage of addressing various situations thanks to a non-parametric modeling.

In this paper, we analyze the high-SNR regime of the$M\times K$Network MISO channel in which each transmitter has access to a different channel estimate, possibly with different precision. It has been recently shown that, for some regimes, this setting attains the same Degrees-of-Freedom as the ideal centralized setting with perfect Channel State Information (CSI) sharing, in which all the transmitters are endowed with the best estimate available at any transmitter. This result is restricted by the limitations of the Degrees-of-Freedom metric, as it only provides information about the slope of growth of the capacity as a function of the SNR, without any insight about the possible performance at a given SNR. In order to overcome this limitation, we analyze the affine approximation of the rate on the high-SNR regime for this decentralized Network MISO setting for the antenna configurations in which it achieves the Degrees-of-Freedom of the centralized setting. We show that, for a regime of antenna configurations, it is possible to asymptotically attain the same achievable rate as in the ideal centralized scenario. Consequently, it is possible to achieve the beamforming gain of the ideal perfect-CSI-sharing setting even if only a subset of transmitters is endowed with precise CSI, which can be exploited in scenarios such as distributed massive MIMO where the number of transmit antennas is much bigger than the number of served users. This outcome is a consequence of the synergistic compromise between CSI precision at the transmitters and consistency between the locally-computed precoders, which is an inherent trade-off of decentralized settings that does not exist in the centralized CSI configuration. We propose a precoding scheme achieving the previous result, which is built on an uneven structure in which some transmitters reduce the precision of their own precoding vector for the sake of using transmission parameters that can be more easily predicted by the other transmitters.

In this work, we study the impact of imperfect sharing of the Channel State Information (CSI) available at the transmitters on a Network MIMO setting in which a set of M transmit antennas, possibly not co-located, jointly serve two multi-antenna users endowed with N1and N2antennas, respectively. We consider the case where only a subset of k transmit antennas have access to perfect CSI, whereas the other M - k transmit antennas have only access to finite precision CSI. The analysis of this configuration aims to answer the question of how much an extra informed antenna can help. We model this scenario as a Decentralized MIMO Broadcast Channel (BC) and characterize the Degrees-of-Freedom (DoF) region, showing that only k = max(N1, N2) antennas with perfect CSI are needed to achieve the DoF of the conventional BC with ubiquitous perfect CSI.

Multi-hop relaying with mobile relay nodes mounted at the top of train cabins is a promising technology to overcome the high propagation loss, blockage and mobility sensitivity in millimeter wave band communication for high speed train. In this context, we aim to maximize the data rate for passenger-oriented applications under multi-hop mobile relay architecture. Adopting the system deployment and antenna configuration in 3GPP, based on the transmitting power configuration and instantaneous train position, we propose an approach to optimize the average transmission data rate by selecting the optimal multi-hop relaying scheme. The analysis and numerical results exhibit that the proposed multi-hop relaying strategies can outperform a single hop 5G link communication under certain transmitting power regimes and train position ranges, which can serve as feasibility analysis and guideline for a possible architecture in next generation train radio deployment.

In this work, we consider the sum rate performance of joint processing coordinated multi-point transmission network (JP-CoMP, a.k.a Network MIMO) in a so-called distributed channel state information (D-CSI) setting. In the D-CSI setting, the various transmitters (TXs) acquire a local, TX-dependent, estimate of the global multi-user channel state matrix obtained via terminal feedback and limited backhauling. The CSI noise across TXs can be independent or correlated, so as to reflect the degree to which TXs can exchange information over the backhaul, hence allowing to model a range of situations bridging fully distributed and fully centralized CSI settings. In this context we aim to study the price of CSI distributiveness in terms of sum rate at finite SNR when compared with conventional centralized scenarios. We consider the family of JP-CoMP precoders known as regularized zero-forcing (RZF). We conduct our study in the large scale antenna regime, as it is currently envisioned to be used in real 5G deployments. It is then possible to obtain accurate approximations on so-called deterministic equivalents of the signal to interference and noise ratios. Guided by the obtained deterministic equivalents, we propose an approach to derive a RZF scheme that is robust to the distributed aspect of the CSI, whereby the key idea lies in the optimization of a TX-dependent power level and regularization factor. Our analysis confirms the improved robustness of the proposed scheme with respect to CSI inconsistency at different TXs, even with moderate number of antennas and receivers (RXs).

We study the Degrees-of-Freedom (DoF) in a wireless setting in which K Transmitters (TXs) aim at jointly serving K users. The performance is studied when the TXs are faced with a distributed Channel State Information (CSI) configuration in which each TX has access to its own multi-user imperfect channel estimate based on which it designs its transmit coefficients. The channel estimates are not only imperfectly acquired but they are also imperfectly shared between the TXs. Our first contribution consists of computing a genie-aided upper bound for the DoF of that setting. Our main contribution is then to develop a new robust transmission scheme that leverages the different qualities of CSI available at the TXs to improve the achieved DoF. We show the surprising result that there is a CSI regime, coined the Weak-CSIT regime, in which the genie-aided upper bound is achieved by the proposed transmission scheme. Interestingly, the optimal DoF in the Weak-CSIT regime only depends on the CSI quality at the best informed TX and not on the CSI quality at all other TXs.

In this paper1, we analyze a Network MIMO channel with 2 Transmitters (TXs) jointly serving 2 users, where each TX has a different multi-user Channel State Information (CSI), potentially with a different accuracy. Recently it was shown the surprising result that this decentralized setting can attain the same Degrees-of-Freedom (DoF) as its genie-aided centralized counterpart in which both TXs share the best-quality CSI. However, the DoF derivation alone does not characterize the actual rate and the question was left open as to how big the rate gap between the centralized and the decentralized settings was going to be. In this paper, we considerably strengthen the previous intriguing DoF result by showing that it is possible to achieve asymptotically the same sum rate as that attained by Zero-Forcing (ZF) precoding in a centralized setting endowed with the best-quality CSI. This result involves a novel precoding scheme which is tailored to the decentralized case. The key intuition behind this scheme lies in the striking of an asymptotically optimal compromise between i) realizing high enough precision ZF precoding while ii) maintaining consistent-enough precoding decisions across the non-communicating cooperating TXs.

This paper analyzes the impact of the imperfect Channel State Information (CSI) on the Gaussian input mutual information between the channel input and output. The existing bounds of the mutual information under imperfect CSI depend on the estimated channel realization, while for physical layer abstraction, the goal is to provide such bounds as a function of the perfect CSI variables without performing a complicated channel estimation procedure to obtain the imperfect channel. Indeed, the main motivation is to provide an efficient physical layer (PHY) abstraction tool in large system level simulations. In particular, performance taking into account CSI inaccuracies can be obtained without relying on time-consuming Monte-Carlo simulations. The PHY abstraction model with imperfect CSI is derived for the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems and particularly target the 3GPP-5G millimeter wave Massive-MIMO system level simulations. The optimization of pilot density and power boosting of OFDM modulations according to the proposed bounds lead to the same results as the one proposed in the 3GPP standards, which illustrates the applicability of the results presented in this paper.

We consider the transmission of a common message from a transmitter to three receivers over a broadcast channel, referred to as a multicast channel in this case. All the receivers are allowed to cooperate with each other over full-duplex non-orthogonal cooperation links. We investigate the information-theoretic upper and lower bounds on the transmission rate. In particular, we propose a three-receiver fully interactive cooperation scheme (3FC) based on superpositions of compress-forward (CF) and decode-forward (DF) at the receivers. In the 3FC scheme, the receivers interactively perform CF simultaneously to initiate the scheme, and then DF sequentially to allow a correlation of each layer of the DF superposition in cooperation with the transmitter toward the next receiver in the chain to improve the achievable rate. The analysis leads to a closed-form expression that allows for numerical evaluation, and also gives some insight on key points to design interactive schemes. The numerical results provided in the Gaussian case show that the proposed scheme outperforms the existing schemes and show the benefits of interaction.

We consider the multicast channel (MC), in which a transmitter broadcasts only a common message to two receivers. In our previous work, we have derived information-theoretic upper and lower bounds and showed that the two-round receiver cooperation (2RC) scheme, in which the receivers interactively perform compress-forward and then decode-forward, improves the MC achievable rate over different schemes from the literature, in the general case of full-duplex bi-directional non-orthogonal cooperation links. In this paper, we concentrate on the orthogonal cooperation to identify the properties of this interactive cooperation. We show that the full-duplexing between the cooperation links is not essential to take advantage of most of the gain offered by the 2RC full-duplex scheme, while the full-duplexing between the MC and the cooperation links brings most of the gain.

This work1analyzes the Generalized Degrees-of-Freedom (GDoF) of the 2-User Multiple-Input Single-Output (MISO) Broadcast Channel (BC) in the so-called Distributed CSIT regime, with application to decentralized wireless networks. This regime differs from the classical limited CSIT one in that the CSIT is not just noisy but also imperfectly shared across the transmitters (TXs). Hence, each TX precodes data on the basis of local CSIT and statistical quality information at other TXs. We derive the GDoF result and obtain the surprising outcome that by specific accounting of the pathloss information, it becomes possible for the decentralized precoded network to reach the same performance as a genie-aided centralized network where the central node has obtained the estimates of both TXs. The key idea allowing this surprising robustness is to let the TXs have asymmetrical roles such that the most informed TX is able to balance the lower CSIT quality at the other TX.

Obtaining accurate global channel state information (CSI) at multiple transmitter devices is critical to the performance of many coordinated transmission schemes. Practical CSI local feedback often leads to noisy and partial CSI estimates at each transmitter. With rate-limited bi-directional backhaul, transmitters have the opportunity to exchange few CSI-related bits to establish global channel state information at transmitter (CSIT). This paper investigates possible strategies toward this goal. We propose a novel decentralized algorithm that produces minimum mean square error-optimal global channel estimates at each device from combining local feedback and information exchanged through backhauls. The method adapts to arbitrary initial information topologies and feedback noise statistics and can do that with a combination of closed-form and convex approaches. Simulations for coordinated multi-point transmission systems with two or three transmitters exhibit the advantage of the proposed algorithm over conventional CSI exchange mechanisms when the coordination backhauls are limited.

This paper considers the problem of transmitter (TX) cooperation with distributed channel state information (CSI), where two or more transmitters seek to jointly precode messages while communicating over a rate-limited coordination link. Specifically we address a so-called master-slave scenario where one master (M-) TX is endowed with perfect CSI while K slave (S-) TXs have zero prior CSI information. We are interested in possible strategies for how the M-TX may efficiently guide the S-TXs over the coordination links so as to maximize the network's figure of merit. Strategies related to communicating quantized CSI or quantized precoding decisions are described and compared. Optimal and sub-optimal low complexity approaches are shown, exhibiting gains over conventional methods.

In wireless mesh networks, a robust synchronization is an enabler for optimizing performance, for example allowing powerful radio resource management and energy saving. In this paper, we combine a consensus algorithm, already proven efficient for the synchronization of wireless mesh networks, with a random broadcast for fully distributing its implementation. Unfortunately, when the distances between the wireless devices increase, the accumulation of non-negligible propagation delays resulting from the consensus algorithm introduce a drift in addition to the clock skew and offset of each node. We introduce a monitoring and broadcasting of the drift correction by a reference node that does not participate to the consensus algorithm, and show this allows for both solving the clock drift and aligning all nodes to the reference node's clock.

In this paper, we propose a practical implementation of the Dynamic Decode and Forward (DDF) protocol based on rateless codes and HARQ. We define the macro diversity order of a transmission from several intermittent sources to a single destination. Considering finite symbol alphabet used by the different sources, upper bounds on the achievable macro diversity order are derived. We analyse the diversity behavior of several relaying schemes for the DDF protocol, and we propose the Patching technique to increase both the macro and the micro diversity orders. The coverage gain for the open-loop transmission case and the spectral efficiency gain for the closed loop transmission case are illustrated by simulation results.

In this paper, a full duplex relay using a Dynamic Decode and Forward (DDF) protocol is considered for improving the performance of multiple source-destination pairs interfering one on each other. A relay precoder is optimized as a function of the symbols correctly decoded by the relay in order to improve the channel capacity. Furthermore, a DDF-Patching technique allows for increasing the number of precoded symbols by the relay and providing highly improved performance in interference-limited scenarios.

In this paper, we consider the tracking of mobile terminals based on the received signal strength (RSS) measured from several base stations. The spatial correlation of the random shadowing is exploited in order to improve the position tracking. We define an auto-regressive (AR) model of the temporal evolution of the shadowing. This model allows for performing a joint tracking of the position and the shadowing by applying a Rao- Blackwellized (RB) particle filter approximating the posterior probability distributions numerically. The simulation results show that the tracking can be improved by considering sufficiently high auto-regressive orders.

This paper addresses the optimization of the linear precoder of a relay shared between several source/destination links interfering one with the others, in the aim of reaching fairness between users. The sources are not aware of the relay existence, and the destinations behave as in a relay channel with interference. Thus, the destinations do not try to decode other sources' messages and the relay is the only enabler for user cooperation. We consider several figures of merit for each user, such as the channel capacity or the residual interference level after MMSE filtering, which are combined by a user fairness utility function.

Inter-cell interference is a major issue in current wireless cellular systems, in particular with the development of femto-cells. We propose a semi-centralized uplink femto-macro ICIC approach with low network signalling in which a coordinator defines a power control function for all femto base stations in a given area based on long-term statistics they build. The power control function takes path gains as arguments and the transmit power of each femto mobile terminal is set according to the short-term path gains it reports. The proposed power control function achieves good results in terms of femto-macro performance trade-off and mobile terminal transmit power.

Inter-cell interference is a major issue in current wireless cellular systems, in particular with the development of femto-cells. Indeed, macro-femto inter-cell interference coordination is not an easy task and should be performed with a minimum communication between macro- and femto-base stations. We propose a blind inter-cell interference coordination approach, in which each femto base station configures its transmission power autonomously. This power setting aims at maintaining a constant macro-cell performance impact of the femto base station, whatever its location in the macro-cell, i.e., it equalises the macro-degradation. In a 3GPP-LTE context, this approach exhibits a good femto-macro performance trade-off compared to fixed femto base station transmission power.

In this paper, we propose a new decentralized consensus-based energy saving technique that improves the energy efficiency of a cellular network, with a focus on femto base station networks. The energy saving is obtained by a log barrier optimization on the sum transmit power of the network with a long-term or short-term coverage constraint for each cell. The common coordination parameter of the log-barrier method is approximated at each node by the result of a consensus algorithm, which allows for tracking the topology variations in a fully decentralized fashion. Simulation shows that the proposed technique doubles the energy saving efficiency of the centralized energy saving.

In this paper, we consider a broadcast transmission from a source to multiple destinations with an OFDM-based system. In order to improve the broadcast services of a cellular system, we consider the use of relays with Dynamic Decode and Forward protocols allowing the source to ignore the existence of relays in the system. A new algorithm executed at the relay is proposed. It allows for maximizing the diversity order at the destinations side by selecting the best relaying protocol and modulation size as function of the relay correct decoding time. No feedback is assumed on any link between the source, relays and destinations.

In this paper, we propose Patched Distributed Space-Time Block Codes (DSTBC) for the Dynamic Decode and Forward (DDF) Relaying protocol, in which the relay transmits combination of information from the first and second phase of the DDF protocol, and the destination makes a linear combination of symbols received in the two phases. Thus, from the error correcting code point of view, the number of coded bits seeing a diversity-one channel reduces proportionnally as the number of symbols sent by the relay increases, which is not the case with known schemes. In other words, the length of the phase two of the DDF protocol needed to achieve full diversity can be reduced with respect to known schemes. Three examples of Patched DSTBC are presented, the Patched Alamouti, the Patched Golden Code and the Patched Silver Code whose outage analyses prove the diversity enhancement offered by the proposed scheme.

In this paper, we consider the problem of coding for the half-duplex nonorthogonal amplify-and-forward (NAF) cooperative channel where the transmitter to relay and the interrelay links are highly reliable. We derive bounds on the diversity order of the NAF protocol that are achieved by a distributed space-time bit-interleaved coded modulation (D-ST-BICM) scheme under iterative APP detection and decoding. These bounds lead to the design of space-time precoders that ensure maximum diversity order and high coding gains. The word error rate performance of D-ST-BICM are also compared to outage probability limits.

In this paper, we design bit interleaved coded modulations including a partial algebraic precoder in order to transmit several spatial streams with full diversity over a transmit beamformed MIMO channel. The achievable diversity orders with the coded modulation are derived, and allow for efficiently choosing the system parameters for optimizing the performance. Additionally, a very efficient low complexity soft output detector based on algebraic reduction is presented. With a low complexity at the receiver, the precoded BICM system allows achieving high performance at very high efficiencies, e.g., 12 bits per second per hertz.

The space-time bit-interleaved coded modulation (ST-BICM) is an efficient technique to obtain high diversity and coding gain on a block-fading multiple-input multiple-output (MIMO) channel. Its maximum-likelihood (ML) performance is computed under ideal interleaving conditions, which enables a global optimization taking into account channel coding. Thanks to a diversity upper bound derived from the Singleton bound, an appropriate choice of the time dimension of the space-time coding is possible, which maximizes diversity while minimizing complexity. Based on the analysis, an optimized interleaver and a set of linear precoders, called dispersive nucleo algebraic (DNA) precoders are proposed. The proposed precoders have good performance with respect to the state of the art and exist for any number of transmit antennas and any time dimension. With turbo codes, they exhibit a frame error rate which does not increase with frame length.

This paper investigates the combination of spatial multiplexing and overloading as an evolution of the UMTS-HSDPA (high speed downlink packet access) toward very high spectral efficiencies in the case of no (or very limited) channel state information at transmitter. Overloaded spreading is carried out in the time domain only (independent per antenna), transmit space diversity is partially recovered employing Space-Time BICM for each user. A low complexity soft interference cancellation based iterative receiver for channel estimation, space-time chip-equalization and multiuser detection is proposed. Two scenarios are considered for the intracell interference: either the spreading codes and the modulation of the interfering users are known or no-knowledge of the interfering signals is available at the receiver whatsoever.

This paper investigates the combination of spatial multiplexing and overloading as an evolution of the UMTSHSDPA (High Speed Downlink Packet Access) toward very high spectral efficiencies. The base station employs single-carrier space-time bit-interleaved coded modulations with linear precoding for each user. From an information-theoretic point of view, this scheme is potentially capacity achieving but puts high stakes on the receiver complexity. Channel estimation is made particularly difficult by the superimposition of pilot symbols and data. Indeed, the classical channel estimation method based on matched filtering and accumulation clearly fails to give satisfactory performance in a spatial multiplexing and overloading context. Thus, we propose a sophisticated iterative receiver which includes channel estimation, MMSE-based chip-equalization and MMSE-based multiuser detection together with decoding of the turbo-code. Simultaneous downlink transmission to several users is investigated in this paper which brings out the overloading flexibility in terms of Space-Time Modulation and Coding Scheme (ST-MCS) design and radio resource allocation.

This paper investigates the combination of spatial multiplexing and overloading as an evolution of the UMTS-HSDPA (high speed downlink packet access) toward very high spectral efficiencies. The base station employs single-carrier space-time bit-interleaved coded modulations with linear preceding for each user. From an information-theoretic point of view, this scheme is potentially capacity achieving but puts high stakes on the receiver complexity. Channel estimation is made particularly difficult by the superimposition of pilot symbols and data. Indeed, the classical channel estimation method based on matched filtering and accumulation clearly fails to give satisfactory performance in a spatial multiplexing and overloading context. Thus, we propose a sophisticated iterative receiver which includes channel estimation, MMSE-based chip-equalization and MMSE-based multiuser detection together with decoding of the turbo-code. Simultaneous downlink transmission to several users is investigated in this paper which brings out the overloading flexibility in terms of space-time modulation and coding scheme (ST-MCS) design and radio resource allocation

Multidimensional binary mappings for bit-interleaved coded modulations (BICMs) on ergodic multiple-antenna channels with iterative decoding are presented. After derivation of a closed-form expression for the pairwise error probability under ideal maximum-likelihood (ML) decoding, the design criterion for mapping optimization is established from the ML performance of the ideally interleaved channel. It coincides with the figure of merit derived from the genie condition when the iterative receiver converges to perfect a priori information. Multidimensional mapping constructions that exhibit high signal-to-noise ratio (SNR) gains without increasing the complexity of the a posteriori probability (APP) detection are proposed. They allow for a reduced decoding complexity as they achieve near turbo code performance with a single convolutional code.

We present a linear preceding solution to achieve full diversity with iteratively decoded bit-interleaved coded modulation on multiple antenna channels while minimizing the detection complexity.

Soft output detection for signals transmitted on linear channels is investigated. A particular emphasis is made for signal detection on multiple antenna channels. The a posteriori information at the detector output is evaluated from a shifted spherical list of point candidates. The spherical list is centered on the maximum likelihood point, which has the great advantage of stabilizing the list size. Thus, the sphere radius is selected in order to control the list size and to cope with the boundaries of the finite multiple antenna constellation. Our new soft output sphere decoder is then applied to the computation of constrained channel capacity and to the iterative detection of a coded transmission. For example, we achieved a signal-to-noise ratio at 1.25 dB from capacity limit on a 4/spl times/4 MIMO channel with 16-QAM modulation and a 4-state rate 1/2 parallel turbo code.