Joint Source-Channel Coding over a Fading Multiple Access Channel with Partial Channel State Information

In this paper we address the problem of transmission of correlated sources over a fast fading multiple access channel (MAC) with partial channel state information available at both the encoders and the decoder. We provide sufficient conditions for transmission with given distortions. Next these conditions are specialized to a Gaussian MAC (GMAC). We provide the optimal power allocation strategy and compare the strategy with various levels of channel state information. Keywords: Fading MAC, Power allocation, Partial channel state information, Correlated sources.Comment: 7 Pages, 3 figures. To Appear in IEEE GLOBECOM, 200

Random Matrix Theory applied to the Estimation of Collision Multiplicities

This paper presents two techniques in order to estimate the collision multiplicity, i.e., the number of users involved in a collision [1]. This estimation step is a key task in multi-packet reception approaches and in collision resolution techniques. The two techniques are proposed for IEEE 802.11 networks but they can be used in any OFDM-based system. The techniques are based on recent advances in random matrix theory and rely on eigenvalue statistics. Provided that the eigenvalues of the covariance matrix of the observations are above a given threshold, signal eigenvalues can be separated from noise eigenvalues since their respective probability density functions are converging toward two different laws: a Gaussian law for the signal eigenvalues and a Tracy-Widom law for the noise eigenvalues. The first technique has been designed for the white noise case, and the second technique has been designed for the colored noise case. The proposed techniques outperform current estimation techniques in terms of mean square error. Moreover, this paper reveals that, contrary to what is generally assumed in current multi-packet reception techniques, a single observation of the colliding signals is far from being sufficient to perform a reliable estimation of the collision multiplicities

Sending a Bi-Variate Gaussian over a Gaussian MAC

We study the power versus distortion trade-off for the distributed transmission of a memoryless bi-variate Gaussian source over a two-to-one average-power limited Gaussian multiple-access channel. In this problem, each of two separate transmitters observes a different component of a memoryless bi-variate Gaussian source. The two transmitters then describe their source component to a common receiver via an average-power constrained Gaussian multiple-access channel. From the output of the multiple-access channel, the receiver wishes to reconstruct each source component with the least possible expected squared-error distortion. Our interest is in characterizing the distortion pairs that are simultaneously achievable on the two source components. We present sufficient conditions and necessary conditions for the achievability of a distortion pair. These conditions are expressed as a function of the channel signal-to-noise ratio (SNR) and of the source correlation. In several cases the necessary conditions and sufficient conditions are shown to agree. In particular, we show that if the channel SNR is below a certain threshold, then an uncoded transmission scheme is optimal. We also derive the precise high-SNR asymptotics of an optimal scheme.Comment: submitted to the IEEE Transactions on Information Theor

Decentralized sequential change detection using physical layer fusion

The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors make noisy observations of a parameter that changes from an initial state to a final state at a random time where the random change time has a geometric distribution. The sensors amplify and forward the observations over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. The optimal transmission strategy at each stage is shown to be the one that maximizes a certain Ali-Silvey distance between the distributions for the hypotheses before and after the change. Simulations demonstrate that the proposed analog technique has lower detection delays when compared with existing schemes. Simulations further demonstrate that the energy-constrained formulation enables better use of the total available energy than the power-constrained formulation in the change detection problem.Comment: 10 pages, two-column, 10 figures, revised based on feedback from reviewers, accepted for publication in IEEE Trans. on Wireless Communication

Transmission of Analog Information Over the Multiple Access Relay Channel Using Zero-Delay Non-Linear Mappings

[Abstract]: We consider the zero-delay encoding of discrete-time analog information over the Multiple Access Relay Channel (MARC) using non-linear mapping functions. On the one hand, zero-delay non-linear mappings are capable to deal with the multiple access interference (MAI) caused by the simultaneous transmission of the information. On the other, the relaying operation is a Decode-and-Forward (DF) strategy where the decoded messages are merged into a single message using a specific continuous mapping depending on the correlation level of the source information. At the receiver, an approximated Minimum Mean Squared Error (MMSE) decoder is developed to obtain an estimate of the transmitted source symbols which exploits the information received from the relay node in combination with the messages received from the transmitters through the direct links. The resulting system provides better performance than the other alternative encoding strategies for the MARC with similar complexity and delay and also approaches the performance of theoretical strategies which require a significantly higher delay and computational cost.This work was supported in part by the Office of the Naval Research Global of United States under Grant N62909-15-1-2014, in part by the Xunta de Galicia under Grant ED431C 2016-045, Grant ED341D R2016/012, and Grant ED431G/01, in part by the Agencia Estatal de Investigación of Spain under Grant TEC2015-69648-REDC and Grant TEC2016-75067-C4-1-R, and in part by the ERDF funds of the EU (AEI/FEDER, UE).Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED341D R2016/012Xunta de Galicia; ED431G/0

Correlated Sources In Distributed Networks - Data Transmission, Common Information Characterization and Inferencing

Correlation is often present among observations in a distributed system. This thesis deals with various design issues when correlated data are observed at distributed terminals, including: communicating correlated sources over interference channels, characterizing the common information among dependent random variables, and testing the presence of dependence among observations. It is well known that separated source and channel coding is optimal for point-to-point communication. However, this is not the case for multi-terminal communications. In this thesis, we study the problem of communicating correlated sources over interference channels (IC), for both the lossless and the lossy case. For lossless case, a sufficient condition is found using the technique of random source partition and correlation preserving codeword generation. The sufficient condition reduces to the Han-Kobayashi achievable rate region for IC with independent observations. Moreover, the proposed coding scheme is optimal for transmitting a special correlated sources over a class of deterministic interference channels. We then study the general case of lossy transmission of two correlated sources over a two-user discrete memoryless interference channel (DMIC). An achievable distortion region is obtained and Gaussian examples are studied. The second topic is the generalization of Wyner\u27s definition of common information of a pair of random variables to that of N random variables. Coding theorems are obtained to show that the same operational meanings for the common information of two random variables apply to that of N random variables. We establish a monotone property of Wyner\u27s common information which is in contrast to other notions of the common information, specifically Shannon\u27s mutual information and G\u27{a}cs and K {o}rner\u27s common randomness. Later, we extend Wyner\u27s common information to that of continuous random variables and provide an operational meaning using the Gray-Wyner network with lossy source coding. We show that Wyner\u27s common information equals the smallest common message rate when the total rate is arbitrarily close to the rate-distortion function with joint decoding. Finally, we consider the problem of distributed test of statistical independence under communication constraints. Focusing on the Gaussian case because of its tractability, we study in this thesis the characteristics of optimal scalar quantizers for distributed test of independence where the optimality is both in the finite sample regime and in the asymptotic regime