论文标题
使用上行链路下行链接二元性与1位转换器的多用户下行链路形成,用于平坦的褪色通道
Multi-user Downlink Beamforming using Uplink Downlink Duality with 1-bit Converters for Flat Fading Channels
论文作者
论文摘要
高分辨率数据转换器在较高载体频率和较大带宽方面增加的功耗正在成为通信系统的瓶颈。在本文中,我们考虑了一个完全数字基站,该基站配备了每个射频链上配备了1位类似物到数字(在上行链路)和数字到Analog(在下行链路中)的转换器。基站与具有单个SINR约束的多个单个天线用户进行通信。我们首先在不相关的量化噪声假设下,在1位硬件约束下建立上行链路下行二重性原理。然后,我们根据上行链路下行链接二元性原理为多用户下行链接界定问题提供线性解决方案。提出的解决方案考虑了硬件约束,并共同优化了分配给每个用户的下行链路界定器和功率。通过向真实的系统用户添加虚拟用户获得的优化抖动可确保在现实设置下不相关的量化噪声假设是真实的。使用Quadriga生成的3GPP频道模型进行的详细模拟表明,我们所提出的解决方案的表现优于最低的总和和最低速率,尤其是当用户数量较大时。我们还证明,所提出的解决方案可大大减少非线性解决方案的性能差距,从而在计算复杂性的一部分下未编码的位错误率。
The increased power consumption of high-resolution data converters at higher carrier frequencies and larger bandwidths is becoming a bottleneck for communication systems. In this paper, we consider a fully digital base station equipped with 1-bit analog-to-digital (in uplink) and digital-to-analog (in downlink) converters on each radio frequency chain. The base station communicates with multiple single antenna users with individual SINR constraints. We first establish the uplink downlink duality principle under 1-bit hardware constraints under an uncorrelated quantization noise assumption. We then present a linear solution to the multi-user downlink beamforming problem based on the uplink downlink duality principle. The proposed solution takes into account the hardware constraints and jointly optimizes the downlink beamformers and the power allocated to each user. Optimized dithering obtained by adding dummy users to the true system users ensures that the uncorrelated quantization noise assumption is true under realistic settings. Detailed simulations carried out using 3GPP channel models generated from Quadriga show that our proposed solution outperforms state of the art solutions in terms of the ergodic sum and minimum rate especially when the number of users is large. We also demonstrate that the proposed solution significantly reduces the performance gap from non-linear solutions in terms of the uncoded bit error rate at a fraction of the computational complexity.