Uplink Macro Diversity of Limited Backhaul Cellular Network (0805.4620v1)

Abstract: In this work new achievable rates are derived, for the uplink channel of a cellular network with joint multicell processing, where unlike previous results, the ideal backhaul network has finite capacity per-cell. Namely, the cell sites are linked to the central joint processor via lossless links with finite capacity. The cellular network is abstracted by symmetric models, which render analytical treatment plausible. For this idealistic model family, achievable rates are presented for cell-sites that use compress-and-forward schemes combined with local decoding, for both Gaussian and fading channels. The rates are given in closed form for the classical Wyner model and the soft-handover model. These rates are then demonstrated to be rather close to the optimal unlimited backhaul joint processing rates, already for modest backhaul capacities, supporting the potential gain offered by the joint multicell processing approach. Particular attention is also given to the low-SNR characterization of these rates through which the effect of the limited backhaul network is explicitly revealed. In addition, the rate at which the backhaul capacity should scale in order to maintain the original high-SNR characterization of an unlimited backhaul capacity system is found.

• The paper derives achievable rates for uplink macro diversity in cellular networks considering realistic finite backhaul capacity, moving beyond infinite capacity assumptions.
• The study provides closed-form expressions for low-SNR behavior and identifies scaling requirements for backhaul capacity to maintain high-SNR performance.
• Analysis of fading channels shows a wideband protocol is optimal, and insights into inter-cell interference mitigation are provided for different scenarios.

An Analysis of Uplink Macro Diversity in Limited Backhaul Cellular Networks

The paper under consideration explores the complexities and performance potentials of uplink macro diversity in cellular networks constrained by finite backhaul capacity. Unlike earlier assumptions that treat the backhaul network as having infinite capacity, this paper introduces a more realistic finite-capacity model. Specifically, the cell sites are connected to a central joint processor via lossless links with finite capacity, challenging prior models which presuppose an error-free, unlimited backhaul. This investigation unfolds through a symmetric model framework, supporting analytical processing and providing closed-form achievable rates for Gaussian and fading channels within the Wyner and soft-handover models.

Main Contributions and Findings

1. Rate Derivations Under Finite Backhaul: The authors derive new rates achievable when backhaul capacity is limited. Two primary scenarios are examined: the "oblivious" scheme where cell-sites, unaware of users' codebooks, compress their received signals before forwarding them, and the "partial local decoding" scheme, which integrates local decoding with the compress-and-forward strategy. Both scenarios reveal rates that, under moderate backhaul conditions, approach the ideals of infinite capacity systems, underscoring the efficacy of joint multicell processing (MCP).
2. Low and High SNR Analysis: The low-SNR region is characterized with closed-form expressions for minimum energy per bit and the rate's slope, highlighting the detrimental effects of backhaul limitations in simplified scenarios. Furthermore, the paper elucidates the scaling behavior of backhaul capacity needed to preserve the original high-SNR characterizations.
3. Fading Channel and Gaussian Channel Performance: The paper examines both Gaussian and Rayleigh fading channels, providing comprehensive insights into how channel conditions impact the derived rates. The introduction of fading alters the optimal transmission protocol from TDMA to a wideband (WB) protocol due to the latter's superior capacity realization.
4. Impact of Inter-Cell Interference: By leveraging setups like the Wyner and soft-handoff models, the paper highlights the influence of inter-cell interference, demonstrating that optimal local decoding strategies can mitigate these effects under certain conditions, primarily when interference is minimal or when backhaul capacity is particularly constrained.

Implications for Future Research and Practice

The implications of limited backhaul capacity are profound, particularly as networks strive to balance cost with performance in real-world deployments. The findings advocate for tailored MCP strategies, exploiting both joint processing and intelligent local decoding, to maximize throughput under capacity constraints. From a theoretical perspective, these models enhance prediction accuracy for cellular systems, portraying realistic scenarios that practitioners encounter.

Future developments could further refine these models by incorporating adaptive coding schemes that react to dynamic backhaul and channel conditions. Additionally, integrating machine learning mechanisms could optimize the allocation of backhaul resources in real-time, enhancing throughput and efficiency under fluctuating loads and diverse network configurations.

Conclusion

This paper significantly advances our understanding of multicell processing capabilities in networks endowed with only finite backhaul capacity. By moving beyond idealistic infinite capacity assumptions, it establishes a foundation for more robust and applicable MCP strategies that can be theorized and tested in practical setups. These insights offer a valuable reference for both current and future cellular network designs needing efficient and effective data handling under resource limitations.