{'fraction_non_alphanumeric': 0.029049295774647887, 'fraction_numerical': 0.009683098591549295, 'mean_word_length': 5.213114754098361, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Measurement-Based Massive MIMO Antenna Selection in Indoor Office Scenario at 3.52 GHz

Massive multiple-input-multiple-output (MIMO) offers huge advantages in terms of energy efficiency, spectral efficiency, robustness, and reliability and has been considered as a leading 5G technology candidate. However, high cost and energy consumption are major challenges in massive MIMO systems. Antenna selection at the base station side by reducing the number of radio-frequency (RF) chains is a practical and effective technology to solve high cost and energy consumption problems. In this paper, we investigate indoor massive MIMO antenna selection based on the measured data in a indoor office scenario at 3.52 GHz by using a uniform linear array (ULA) with 128 transmit (Tx) antennas and 7 receiving (Rx) users. A convex optimization algorithm is applied to select optimal Tx antenna subset by maximizing dirty-paper coding (DPC) capacity. Then, the antenna selection system performance is researched. The investigation shows that indoor antenna selection based on measured data can both improve the system performance and reduce the cost.