An Experimental Evaluation of Rate Adaptation for Multi-Antenna Systems

Abstract

Abstract-Increasingly wireless networks use multi-antenna nodes as in IEEE 802.11n and 802.16. The Physical layer (PHY) in such systems may use the antennas to provide multiple streams of data (spatial multiplexing) or to increase the robustness of fewer streams. These physical layers also provide support for sending packets at different rates by changing the modulation and coding of transmissions. Rate adaptation is the problem of choosing the best transmission mode for the current channel and in these systems requires choosing both the level of spatial multiplexing and the modulation and coding.Hydra is an experimental wireless network node prototype in which both the MAC and PHY are highly programmable. Hydra's PHY is essentially the 802.11n PHY, and currently supports two antennas and the same modulations and codings as 802.11n. Because of limitations of our hardware platform, the actual rates are a factor of 10 smaller than 802.11n. The MAC is essentially the 802.11 MAC with extensions, including the ability to feedback channel state or rate information from the receiver. Hydra was designed to allow experimentation with real radios, PHYs, and network stacks over real-world channels and it is well suited to studying rate adaptation in multi-antenna systems. To allow controlled experimentation, we also have the ability to perform experiments over emulated channels using exactly the same MAC and PHY used for RF transmissions.We present rate control experiments based on transmission over both real and emulated channels. Our experiments include measurements for single antenna systems and two antenna systems using a single or multiple spatial streams. We study rate adaptation algorithms using both explicit and implicit feedback from the receiver. A novel aspect of our results is the first experimental study of adaptation between single and multiple spatial streams for 802.11n style systems.Increasingly wireless networking technologies, including IEEE 802.11n and IEEE 802.16, support radios with multiple antennas. These antennas can be used to support multiple data streams (spatial multiplexing) or to increase robustness by taking advantage of channel diversity [1], [2]. Choosing between

Joint rate and channel width adaptation for 802.11 MIMO wireless networks

“…Existing 802.11n solutions require either costly CSI [22], [2] or some form of a guided search (e.g., by probing candidate rates) to determine the best operating rate [5], which is inefficient when the search space is large. Other algorithms for MIMO environments do not consider other 802.11n features, such as channel bonding [23], or consider alternative energy efficiency goals [21].…”

A practical approach to rate adaptation for multi-antenna systems

“…In [19], the authors address the complexity of deciding between spatial multiplexing and diversity by proposing the Demmel condition, which is based on instantaneous channel state and the computed minimum Euclidean distance at the receiver, as an indicator of spatial structure of MIMO channels [20]. The proposed rate adaptation is based on a twodimensional look-up table of the average SNR per subcarrier and the average Demmel condition number per subcarrier, which are exchanged through RTS and CTS packets [19].…”

“…The proposed rate adaptation is based on a twodimensional look-up table of the average SNR per subcarrier and the average Demmel condition number per subcarrier, which are exchanged through RTS and CTS packets [19]. Evaluation of rate adaptation based on the Demmel condition is limited to a small subset of features of IEEE 802.11n.…”

“…In [19], the authors point out that spatial multiplexing works best when there exists a multiplexing structure; otherwise, it may reduce the reliability and worsen the throughput. But how can we identify this multiplexing structure?…”

A practical framework for 802.11 MIMO rate adaptation

“…Existing 802.11n solutions require either costly CSI [27,7] or some form of a guided search (e.g., by probing candidate rates) to determine the best operating rate [3], which is inefficient when the search space is large. Other algorithms for MIMO environments do not consider 735 other 802.11n features, such as channel bonding [28,26], or consider alternative energy efficiency goals [25].…”