Abstract
User subset selection requires full downlink channel state information to realize effective multi-user beamforming in frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. However, the channel estimation overhead scales with the number of users in FDD systems. In this paper, we propose a novel propagation domain-based user selection scheme, labeled as zero-measurement selection, for FDD massive MIMO systems with the aim of reducing the channel estimation overhead that scales with the number of users. The key idea is to infer downlink user channel norm and inter-user channel correlation from uplink channel in the propagation domain. In zero-measurement selection, the base-station performs downlink user selection before any downlink channel estimation. As a result, the downlink channel estimation overhead for both user selection and beamforming is independent of the total number of users. Then, we evaluate zero-measurement selection with both measured and simulated channels. The results show that zero-measurement selection achieves up to 92.5% weighted sum rate of genie-aided user selection on the average and scales well with both the number of base-station antennas and the number of users. We also employ simulated channels for further performance validation, and the numerical results yield similar observations as the experimental findings.
Highlights
Massive multi-input multi-output (MIMO) improves wireless communication, which is a key component of next-generation wireless networks [1,2,3,4,5,6,7,8,9,10,11,12,13]
To reduce the large downlink channel estimation overhead that scales with the number of users in frequency-division duplexing (FDD) massive MIMO systems, we develop a novel propagation domain-based user selection scheme, labeled as zero-measurement selection
2.4 Proposed scheme: zero‐measurement selection we answer the second question - Can the base-station perform effective user selection based on partial downlink channel information that is inferred from uplink only? Inspired by the finding that downlink inter-user channel correlation and user channel norm can be effectively inferred from free uplink channel information in the propagation domain, we propose a scalable user selection scheme, labeled as zero-measurement selection for FDD massive MIMO systems
Summary
Massive multi-input multi-output (MIMO) improves wireless communication, which is a key component of next-generation wireless networks [1,2,3,4,5,6,7,8,9,10,11,12,13]. A key challenge for operating massive MIMO in frequency-division duplex mode is the large channel overhead in acquiring channel state information. A significant fraction of spectrum allocations worldwide are for frequency-division duplexing (FDD) operation. There is a significant demand to enable massive MIMO operation in FDD mode. The scaling challenge of channel measurement in FDD massive MIMO systems is twofold. First is measuring a large number of channels per user. In [14], we proposed directional training, a scalable channel estimation method, that addressed the per-user channel measurement scalability challenge
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