Distributed Computation of A Posteriori Bit Likelihood Ratios in Cell-Free Massive MIMO (2111.15568v1)

Abstract: This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forwards the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically.

• The paper introduces a novel method for distributed computation of a posteriori bit likelihood ratios (LLRs) in uplink cell-free massive MIMO networks, enabling efficient soft detection with reduced fronthaul signaling.
• The proposed method involves access points (APs) computing partial statistics, fusing them sequentially through fronthaul, and a central processing unit (CPU) performing final LLR computation.
• Numerical results demonstrate significant fronthaul signaling reduction, proving the method's scalability and efficiency for practical distributed cell-free massive MIMO deployments.

Distributed Computation of A Posteriori Bit Likelihood Ratios in Cell-Free Massive MIMO

The paper "Distributed Computation of A Posteriori Bit Likelihood Ratios in Cell-Free Massive MIMO" introduces a novel approach to soft detection in uplink cell-free massive multiple-input-multiple-output (mMIMO) networks. Given the increasing interest in decentralized solutions for next-generation wireless networks, this paper makes an important contribution by focusing on distributed computation of bit likelihoods with a specific application to networks utilizing sequential fronthaul, such as radio stripes.

Key Contributions and Methodology

The core contribution of this work lies in proposing a method for decentralized computation of a posteriori bit likelihood ratios (LLRs), essential for reliable decoding of transmitted information in a cell-free mMIMO environment. Prior decentralized implementations of mMIMO mainly focused on efficient algorithm design to manage spatial diversity and enhance spectral efficiency (SE) or bit-error-rate (BER) performance. This paper shifts the focus towards ensuring reliability at the bit level, which has significant practical importance.

The authors propose that each access point (AP) in the network locally computes partial sufficient statistics from uplink signals received from user equipment, subsequently fusing these with information from other APs in a daisy chain-like manner. This cumulative data reaches a central processing unit (CPU), where final soft demodulation and LLR computation occur. The LLR values, which are critical for channel decoding (e.g., Turbo decoding), are derived analytically and computed efficiently, demonstrating the feasibility of decentralized detection processes in cell-free mMIMO with minimal fronthaul signaling requirement.

Numerical Results and Theoretical Claims

The analysis within the paper highlights significant reductions in fronthaul signaling compared to a centralized implementation. In particular, the sequential architecture's communication load with the CPU is independent of the number of APs, proving the configuration's scalability and robustness. Experimental results indicate substantial savings in the order of 90% in fronthaul requirements for large setups, attesting to the proposed architecture's efficiency.

Implications and Future Directions

The introduction of a decentralized LLR computation method allows for efficient utilization of the distributed nature of cell-free mMIMO networks. The reduction in fronthaul signaling and processing load, while maintaining detection performance, posits this approach as a viable solution for real-world implementations of distributed networks. The paper underscores the potential of radio stripes and similar architectures in scenarios where moving massive data to a centralized node may not be feasible or is costly.

The paper opens up several avenues for future research, including extensions to more complex network topologies beyond sequential formations, potential enhancements for systems with varied modulation schemes, and integration with advanced error control coding mechanisms.

In conclusion, by focusing on the practical challenges associated with distributed computation in cell-free mMIMO and addressing these through innovative architectural choices and algorithms, this paper contributes meaningfully to theoretical and practical advancements in the design of beyond 5G network infrastructures. These findings align with current needs for scalable, reliable communication systems that can handle massive data throughput while remaining economically viable.