Effects of Channel Aging in Massive MIMO Systems

Abstract: MIMO communication may provide high spectral efficiency through the deployment of a very large number of antenna elements at the base stations. The gains from massive MIMO communication come from the use of multi-user MIMO on the uplink and downlink, but with a large excess of antennas at the base station compared to the number of served users. Initial work on massive MIMO did not fully address several practical issues associated with its deployment. This paper considers the impact of channel aging on the performance of massive MIMO systems. The effects of channel variation are characterized as a function of different system parameters assuming a simple model for the channel time variations at the transmitter. Channel prediction is proposed to overcome channel aging effects. The analytical results on aging show how capacity is lost due to time variation in the channel. Numerical results in a multicell network show that massive MIMO works even with some channel variation and that channel prediction could partially overcome channel aging effects.

• The paper identifies that channel aging reduces desired signal power and increases inter-cell interference in massive MIMO systems.
• It employs an AR(1) model with a linear FIR Wiener predictor to estimate channel states and partially restore achievable rates.
• Numerical results show that channel prediction offers modest gains, particularly at lower Doppler shifts and under moderate aging conditions.

Effects of Channel Aging in Massive MIMO Systems

The paper investigates the impact of channel aging in massive MIMO systems, exploring how time-varying channel conditions affect system performance. It introduces methods to account for these dynamic changes, specifically proposing channel prediction techniques to mitigate the negative effects of channel aging.

Introduction to Massive MIMO and Channel Aging

Massive MIMO technology leverages a large number of antennas at base stations to provide high spectral efficiency and improved system capacity. However, practical deployment of massive MIMO faces challenges, such as channel aging, where the channel conditions vary over time between the initial estimation and actual usage for signal processing (Figure 1).

Figure 1: The massive MIMO system under consideration. There are C cells, each having one base station (BS) and U single-antenna users. Each base station has $N_{}$ antennas.

The concept of channel aging arises from time-varying propagation environments, often exacerbated by user mobility, leading to outdated channel state information (CSI). This paper generalizes existing frameworks to incorporate channel aging effects, revealing how aging primarily impacts desired signal power and inter-cell interference.

System Model and Channel Aging Analysis

The analysis considers a cellular network where each cell contains one base station and multiple user devices. The channels are modeled using a quasi-static block fading approach that integrates both fast fading and deterministic elements. Channel aging is modeled using an autoregressive (AR) process, which describes the temporal correlation of fading coefficients (Figure 2).

Figure 2: Base stations are located at the center of cells, illustrated by red squares. The focus is on the average achievable sum-rates in the center cell.

The AR(1) model is utilized for simplification: $h[n] = \alpha h[n-1] + w[n]$, where $\alpha$ is the temporal correlation coefficient, and $w[n]$ is Gaussian noise. This approach provides a tractable way to study how aging affects system performance, particularly on the uplink and downlink achievable rates.

Channel Prediction and Mitigation Strategies

To counter the negative impacts of channel aging, the paper proposes a linear finite impulse response (FIR) Wiener predictor. This predictor aims to estimate future channel realizations using current and past information, thereby reducing the estimation errors introduced by aging.

Analysis shows that channel prediction is partially effective in mitigating aging effects, improving achievable rates modestly. The performance benefit depends on the accuracy of prediction, influenced by factors such as the model order and the temporal correlation in the channel.

Numerical Results

Simulations demonstrate the impact of channel aging on system performance, comparing scenarios with and without channel prediction. Results indicate that while channel aging significantly degrades both uplink and downlink rates, the system maintains some performance under moderate aging conditions (Figure 3).

Figure 3: Downlink average achievable sum-rates of users in the center cell versus normalized Doppler shifts, for different base station antenna numbers.

Furthermore, channel prediction provides small gains in performance, especially at lower Doppler shifts, suggesting its potential utility in systems with predictable temporal dynamics (Figure 4).

Figure 4: Uplink average achievable sum-rates as a function of the number of antennas at base stations, for various normalized Doppler shifts.

Conclusion

The study concludes that while channel aging negatively impacts massive MIMO performance, the degradation is manageable under certain conditions. Techniques like channel prediction can offer partial mitigation, though further research is needed to refine these methods for larger gains.

Overall, the paper highlights the importance of considering dynamic channel conditions in the design and deployment of massive MIMO systems, paving the way for more robust communication strategies in time-varying environments.