Coding the Beams: Improving Beamforming Training in mmWave Communication System (1104.1007v4)

Abstract: The mmWave communication system is operating at a regime with high number of antennas and very limited number of RF analog chains. Large number of antennas are used to extend the communication range for recovering the high path loss while fewer RF analog chains are designed to reduce transmit and processing power and hardware complexity. In this regime, typical MIMO algorithms are not applicable. Before any communication starts, devices are needed to align their beam pointing angles towards each other. An efficient searching protocol to obtain the best beam angle pair is therefore needed. It is called BeamForming (BF) training protocol. This paper presents a new BF training technique called beam coding. Each beam angle is assigned unique signature code. By coding multiple beam angles and steering at their angles simultaneously in a training packet, the best beam angle pair can be obtained in a few packets. The proposed BF training technique not only shows the robustness in non-line-of-sight environment, but also provides very flat power variations within a packet in contrast to the IEEE 802.11ad standard whose scheme may lead to large dynamic range of signals due to beam angles varying across a training packet.

• The paper introduces a beam coding scheme that assigns signature codes to beam angles, allowing simultaneous training that minimizes complexity compared to traditional methods.
• It demonstrates that beam coding achieves stable power variations—with a power ratio consistently below 1—while significantly reducing the training overhead by up to 4000 bits.
• The approach is robust under both LOS and NLOS conditions using even two-bit phase quantization, simplifying hardware requirements for practical mmWave systems.

Improving Beamforming Training in mmWave Communication Systems through Beam Coding

The paper entitled "Coding the Beams: Improving Beamforming Training in mmWave Communication System" discusses an innovative technique to enhance beamforming (BF) training protocols in millimeter-wave (mmWave) communication systems. As the demand for higher data rates continues to grow, these systems, operating in high-frequency bands around 60 GHz, offer promising solutions with their capacity to handle multiple gigabits per second. However, one of the significant challenges remains the high signal path loss associated with these frequencies, necessitating the employment of a large number of antennas for effective transmission.

Beamforming Challenges and Beam Coding Solution

Traditional MIMO algorithms fall short in the mmWave regime due to the disparity between the number of antennas and the limited RF analog chains available. Within this context, antenna selection strategies only partially mitigate transmission range issues. Consequently, beamforming techniques, which enable antenna arrays to form highly directional patterns, are essential. These techniques necessitate efficient BF training protocols to identify optimal beam direction pairs, an area where existing methods exhibit limitations, particularly in non-line-of-sight (NLOS) conditions.

The paper introduces a beam coding scheme to address these limitations. This method assigns signature codes to each potential beam angle, allowing multiple beams to be trained simultaneously within a single packet. This strategy contrasts with the IEEE 802.11ad standard's in-packet training, which suffers from signal power dynamic variations due to beam switching, requiring complex AGC adjustments and synchronization challenges.

Key Numerical Results and Claims

The simulations show that the beam coding technique achieves stable power variations across packets, evaluating environments with typical NLOS and line-of-sight (LOS) scenarios. Beam coding significantly reduces signal fluctuations, yielding a power ratio ($\gamma$) where $\gamma<1$ for almost all training data, contrasting with the IEEE 802.11ad standard's ratio range from 0 to 14 in extreme LOS conditions. Therefore, beam coding can ensure reliable signal processing without additional AGC resetting, streamlining packet structures with a significant reduction of overhead—up to 4000 bits per beam direction trained.

Moreover, beam coding demonstrates excellent performance under uniformly weighted phased arrays, even with limited phase quantization. It closely matches the optimal exhaustive packet-by-packet training scheme performance in NLOS environments, making it feasible with only two-bit phase quantization, highlighting its robustness under practical constraints where hardware phase control might be limited.

Implications and Future Research Directions

The introduction of beam coding in mmWave systems presents significant improvements in BF training protocols—namely, reducing overheads and improving robustness in varied signal environments. Practically, it simplifies hardware requirements, making advanced beamforming accessible for more consumer devices. Furthermore, its compatibility with compressed sensing techniques opens avenues for further optimizations in training time.

Theoretically, the implication that beam coding can overcome known limitations of phased-array specifications indicates potential paradigms for other applications, such as in vehicular communication or high-density urban networks, where dynamic beam management is crucial.

Future research may explore adaptive coding strategies for dynamically changing environments, further reducing computational complexity, or integrating machine learning algorithms to anticipate environmental shifts and adjust beam configurations proactively. As mmWave technology continues to evolve, beam coding provides a robust framework that addresses current challenges and sets the foundation for further advancements in communication systems.