Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces (1707.02887v1)

Abstract: In this paper, we consider the potential of data-transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which conceptually goes beyond contemporary massive MIMO technology, that arises from our vision of a future where man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent". We consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array. Under the condition that the surface-area is sufficiently large, the received signal after a matched-filtering (MF) operation can be closely approximated by a sinc-function-like intersymbol interference (ISI) channel. We analyze the capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show that the number of independent signal dimensions per m deployed surface is 2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2 for two and three dimensional terminal-deployments. Lastly, we consider implementations of the LIS in the form of a grid of conventional antenna elements and show that, the sampling lattice that minimizes the surface-area of the LIS and simultaneously obtains one signal space dimension for every spent antenna is the hexagonal lattice. We extensively discuss the design of the state-of-the-art low-complexity channel shortening (CS) demodulator for data-transmission with the LIS.

• The paper demonstrates that large intelligent surfaces can achieve a normalized capacity of ˆP/(2N0) per volume unit and enable independent signal dimensions across different deployment geometries.
• The paper applies sampling theory to show that a hexagonal antenna grid can reduce required surface area by 23% compared to conventional rectangular layouts.
• The paper develops a low-complexity receiver using a channel shortening demodulator that trades off performance near optimal BCJR detection with reduced computational load.

Overview of "Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces"

The paper by Sha Hu, Fredrik Rusek, and Ove Edfors, titled "Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces," explores the concept of a large intelligent surface (LIS) and its implications for wireless communication. The LIS represents a shift from traditional massive MIMO systems by envisioning man-made structures equipped with integrated electronics and wireless communications, transforming the environment into an "intelligent" space. This paper explores the theoretical and practical aspects of LIS, providing insights into its capacity and potential implementations.

Conceptual Foundation and Capacity Analysis

The LIS is proposed as a vast surface composed of numerous radiating and sensing elements, functioning as a receiving antenna array. This paper investigates the capacity limits and information-theoretical properties of LIS when interacting with single-antenna autonomous terminals. Two scenarios are particularly analyzed: one-dimensional terminal deployment and two- or three-dimensional deployments.

Key findings include:

• Capacity per Volume Unit: The paper establishes that, as the wavelength approaches zero, the normalized capacity per volume unit is $\hat{P}/(2N_0)$ [nats/s/Hz/volume-unit], where $\hat{P}$ denotes transmit power per volume unit and $N_0$ corresponds to the noise spectral density.
• Independent Signal Dimensions: For one-dimensional terminal deployment, LIS offers $2/\lambda$ independent signal dimensions per meter when terminal density increases, while for two- and three-dimensional deployments, $\pi/\lambda^2$ independent dimensions per square meter can be harvested.

Implementation and Sampling Theory

The paper addresses the practical aspect of deploying LIS as a grid of conventional antennas based on sampling theory. By leveraging lattice theory, the authors propose that the optimal sampling lattice to minimize surface area while maintaining one signal dimension per antenna is the hexagonal lattice, potentially reducing surface area requirements by 23% compared to rectangular lattices.

Low-Complexity Receiver Design

The paper also extends to practical receiver implementations by exploring a channel shortening (CS) demodulator design. This approach allows for a complexity-performance trade-off between linear minimum-mean-square-error (LMMSE) receivers and the optimal BCJR demodulator. It provides a nuanced examination of receiver design, emphasizing low complexity while maintaining robust performance.

Implications and Future Developments

The implications of this research are substantial for the development of next-generation communication systems. By demonstrating the potential for efficient interference suppression and significant energy focusing through LIS, the paper presents this technology as a promising avenue for enhancing data transmission capabilities. Future developments in artificial intelligence and advanced wireless communication frameworks could be influenced significantly by the adoption of LIS.

Conclusion

Overall, this paper provides a comprehensive theoretical framework and practical insights into utilizing large intelligent surfaces in data transmission. By moving beyond the confines of traditional massive MIMO with LIS technology, the research suggests a transformative approach to wireless communication, aligning with the evolving demands of high-capacity and energy-efficient systems. The implications for integrating such an approach into future communication networks, especially regarding the Internet of Things and smart environments, are profound and warrant further exploration.