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Transmission of Spatio-Temporal Correlated Sources Over Fading Multiple Access Channels With DQLC Mappings

O. Fresnedo, P. Suárez-Casal, L. Castedo

TL;DR

This work tackles zero-delay joint source-channel coding for transmitting spatio-temporally correlated analog sources over fading MACs. It proposes DQLC mappings at the transmitters and a sphere-decoding MMSE decoder enhanced by a nonlinear Kalman Filter to exploit both spatial and temporal correlations, with a lattice-based reformulation that enables efficient search over quantization-indicator vectors. The main contributions are a practical MMSE decoding strategy for arbitrary numbers of users, a parameter-optimization framework for DQLC mappings under power and decoding robustness constraints, and substantial performance gains over uncoded transmission at medium to high SNRs, especially when sources are highly correlated. The approach yields scalable, low-delay JSCC suitable for wireless sensor networks and related multiuser systems, offering significant practical impact in energy- and latency-constrained wireless networks.

Abstract

The design of zero-delay Joint Source-Channel Coding (JSCC) schemes for the transmission of correlated information over fading Multiple Access Channels (MACs) is an interesting problem for many communication scenarios like Wireless Sensor Networks (WSNs). Among the different JSCC schemes so far proposed for this scenario, Distributed Quantizer Linear Coding (DQLC) represents an appealing solution since it is able to outperform uncoded transmissions for any correlation level at high Signal-to-Noise Ratios (SNRs) with a low computational cost. In this paper, we extend the design of DQLC-based schemes for fading MACs considering sphere decoding to make the optimal Minimum Mean Squared Error (MMSE) estimation computationally affordable for an arbitrary number of transmit users. The use of sphere decoding also allows to formulate a practical algorithm for the optimization of DQLC-based systems. Finally, non-linear Kalman Filtering for the DQLC is considered to jointly exploit the temporal and spatial correlation of the source symbols. The results of computer experiments show that the proposed DQLC scheme with the Kalman Filter decoding approach clearly outperforms uncoded transmissions for medium and high SNRs.

Transmission of Spatio-Temporal Correlated Sources Over Fading Multiple Access Channels With DQLC Mappings

TL;DR

This work tackles zero-delay joint source-channel coding for transmitting spatio-temporally correlated analog sources over fading MACs. It proposes DQLC mappings at the transmitters and a sphere-decoding MMSE decoder enhanced by a nonlinear Kalman Filter to exploit both spatial and temporal correlations, with a lattice-based reformulation that enables efficient search over quantization-indicator vectors. The main contributions are a practical MMSE decoding strategy for arbitrary numbers of users, a parameter-optimization framework for DQLC mappings under power and decoding robustness constraints, and substantial performance gains over uncoded transmission at medium to high SNRs, especially when sources are highly correlated. The approach yields scalable, low-delay JSCC suitable for wireless sensor networks and related multiuser systems, offering significant practical impact in energy- and latency-constrained wireless networks.

Abstract

The design of zero-delay Joint Source-Channel Coding (JSCC) schemes for the transmission of correlated information over fading Multiple Access Channels (MACs) is an interesting problem for many communication scenarios like Wireless Sensor Networks (WSNs). Among the different JSCC schemes so far proposed for this scenario, Distributed Quantizer Linear Coding (DQLC) represents an appealing solution since it is able to outperform uncoded transmissions for any correlation level at high Signal-to-Noise Ratios (SNRs) with a low computational cost. In this paper, we extend the design of DQLC-based schemes for fading MACs considering sphere decoding to make the optimal Minimum Mean Squared Error (MMSE) estimation computationally affordable for an arbitrary number of transmit users. The use of sphere decoding also allows to formulate a practical algorithm for the optimization of DQLC-based systems. Finally, non-linear Kalman Filtering for the DQLC is considered to jointly exploit the temporal and spatial correlation of the source symbols. The results of computer experiments show that the proposed DQLC scheme with the Kalman Filter decoding approach clearly outperforms uncoded transmissions for medium and high SNRs.
Paper Structure (10 sections, 1 theorem, 62 equations, 8 figures, 1 algorithm)

This paper contains 10 sections, 1 theorem, 62 equations, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Contributions
  3. System Model
  4. DQLC scheme
  5. KF-based decoding using the Sphere Decoder
  6. Radius of the Sphere Decoder
  7. Analysis of the Computational Cost
  8. Parameter Optimization
  9. Results
  10. Conclusion

Key Result

Lemma 1

The term $\Omega(\boldsymbol{l}, \tilde{\boldsymbol{s}}_{\boldsymbol{l}})$ can be rewritten in a lattice form as

Figures (8)

  • Figure 1: Block diagram of the considered MAC scenario.
  • Figure 2: Example of quantized mapping in DQLC with $\Delta_k=1$ and $\alpha_k=0.9$. The source symbol $s_k=-1.7$ is mapped to the interval corresponding to $l_k=-2$.
  • Figure 3: Performance of the DQLC-based and uncoded schemes for $K=3$ users, with $\varphi=0$ and a spatial correlation $\rho=0.95$.
  • Figure 4: Performance of the different DQLC-based systems and the uncoded scheme for $K=3$ users and considering uncorrelated symbols in both the spatial domain and the temporal domain, i.e. $\varphi=\rho =0$.
  • Figure 5: Performance of the different transmission schemes for $K=3$ users and two different spatio-temporal correlation factors: $\varphi=\rho =0.99$ (top) and $\varphi=\rho =0.90$ (bottom).
  • ...and 3 more figures

Theorems & Definitions (1)

  • Lemma 1