Browsing by Author "Zhang, Xing"

Now showing 1 - 6 of 6
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    Analysis of scalable channel estimation in FDD massive MIMO
    (Springer Nature, 2023) Zhang, Xing; Sabharwal, Ashutosh
    One of the key ideas for reducing downlink channel acquisition overhead for FDD massive MIMO systems is to exploit a combination of two assumptions: (i) the dimension of channel models in propagation domain may be much smaller than the next-generation base-station array sizes (e.g., 64 or more antennas), and (ii) uplink and downlink channels may share the same low-dimensional propagation domain. Our channel measurements demonstrate that the two assumptions may not always hold, thereby impacting the predicted performance of methods that rely on the above assumptions. In this paper, we analyze the error in modeling the downlink channel using uplink measurements, caused by the mismatch from the above two assumptions. We investigate how modeling error varies with base-station array size and provide both numerical and experimental results. We observe that modeling error increases with the number of base-station antennas, and channels with larger angular spreads have larger modeling error. Utilizing our modeling error analysis, we then investigate the resulting beamforming performance rate loss. Accordingly, we observe that the rate loss increases with the number of base-station antennas, and channels with larger angular spreads suffer from higher rate loss.
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    Application of Hydrogels in Heart Valve Tissue Engineering
    (Begell House, 2015) Zhang, Xing; Xu, Bin; Puperi, Daniel S.; Wu, Yan; West, Jennifer L.; Grande-Allen, K. Jane
    With an increasing number of patients requiring valve replacements, there is heightened interest in advancing heart valve tissue engineering (HVTE) to provide solutions to the many limitations of current surgical treatments. A variety of materials have been developed as scaffolds for HVTE including natural polymers, synthetic polymers, and decellularized valvular matrices. Among them, biocompatible hydrogels are generating growing interest. Natural hydrogels, such as collagen and fibrin, generally show good bioactivity but poor mechanical durability. Synthetic hydrogels, on the other hand, have tunable mechanical properties; however, appropriate cell-matrix interactions are difficult to obtain. Moreover, hydrogels can be used as cell carriers when the cellular component is seeded into the polymer meshes or decellularized valve scaffolds. In this review, we discuss current research strategies for HVTE with an emphasis on hydrogel applications. The physicochemical properties and fabrication methods of these hydrogels, as well as their mechanical properties and bioactivities are described. Performance of some hydrogels including in vitro evaluation using bioreactors and in vivo tests in different animal models are also discussed. For future HVTE, it will be compelling to examine how hydrogels can be constructed from composite materials to replicate mechanical properties and mimic biological functions of the native heart valve.
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    Directional Training for FDD Massive MIMO
    (2016-12-05) Zhang, Xing; Sabharwal, Ashutosh
    To achieve the full array gain of massive MIMO in downlink trans- mission, the base station requires the knowledge of full downlink channel state information (CSI). In frequency-division duplexing (FDD) mode, full channel training in antenna space with feedback is required to obtain full downlink CSI and the overhead scales with the number of antennas at the base station. As a result, for large-antenna MIMO, the downlink CSI acquisition overhead will consume a large amount of coherence time and lead to much spectral efficiency loss. In this thesis, to reduce the large downlink CSI acquisition overhead and to let FDD still benefit from the array gain of massive MIMO, we propose directional training for FDD massive MIMO systems. Directional training exploits the fact that the number of angle-of-arrival/angle-of- departure (AoA/AoD) is much smaller than the number of antennas at the base station. Also, based on our measured channel data, we note that the number of AoD is nearly independent of the number of antennas at the base station. Directional training first leverages the possible AoA/AoD reciprocity between uplink and downlink to locate the AoD set of downlink channel utilizing uplink CSI only and then trains the downlink channel using the AoD set only. Therefore, the overhead of directional training will not scale with the number of antennas at the base station and will be much smaller than the overhead of full training. We conduct extensive channel measurement employing a 64-antenna base station at two different bands in the indoor environment to evaluate the downlink beamforming performance of directional training using zero-forcing beamforming. The results show that in the perfect CSI case, directional training performs close to full training in the line-of-sight scenarios and leads to about 17% achievable rate loss in the non-line-of-sight scenarios when serving two mobiles. In contrast, for the imperfect CSI case, directional training outperforms full training by 155% in the line-of-sight scenarios and 100% in the non-line-of-sight scenarios in terms of spectral efficiency when channel coherence symbols length is 200. Hence, directional training is a promising scheme for FDD massive MIMO to obtain downlink CSI.
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    Integrating valve-inspired design features into poly(ethylene glycol) hydrogel scaffolds for heart valve tissue engineering
    (Elsevier, 2015) Zhang, Xing; Xu, Bin; Puperi, Daniel S.; Yonezawa, Aline L.; Wu, Yan; Tseng, Hubert; Cuchiara, Maude L.; West, Jennifer L.; Grande-Allen, K. Jane
    The development of advanced scaffolds that recapitulate the anisotropic mechanical behavior and biological functions of the extracellular matrix in leaflets would be transformative for heart valve tissue engineering. In this study, anisotropic mechanical properties were established in poly(ethylene glycol) (PEG) hydrogels by crosslinking stripes of 3.4 kDa PEG diacrylate (PEGDA) within 20 kDa PEGDA base hydrogels using a photolithographic patterning method. Varying the stripe width and spacing resulted in a tensile elastic modulus parallel to the stripes that was 4.1-6.8 times greater than that in the perpendicular direction, comparable to the degree of anisotropy between the circumferential and radial orientations in native valve leaflets. Biomimetic PEG-peptide hydrogels were prepared by tethering the cell-adhesive peptide RGDS and incorporating the collagenase-degradable peptide PQ (GGGPQG↓IWGQGK) into the polymer network. The specific amounts of RGDS and PEG-PQ within the resulting hydrogels influenced the elongation, de novo extracellular matrix deposition and hydrogel degradation behavior of encapsulated valvular interstitial cells (VICs). In addition, the morphology and activation of VICs grown atop PEG hydrogels could be modulated by controlling the concentration or micro-patterning profile of PEG-RGDS. These results are promising for the fabrication of PEG-based hydrogels using anatomically and biologically inspired scaffold design features for heart valve tissue engineering.
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    Scalable Channel Estimation in FDD Massive MIMO
    (2020-04-24) Zhang, Xing; Sabharwal, Ashutosh
    Massive MIMO brings in key benefits that make it a key design in the next-generation wireless network. To fulfill the potential benefits, channel state information is essential to realize effective user selection and beamforming. In this thesis, we design and analyze scalable channel estimation schemes for FDD massive MIMO systems. First, to make downlink channel estimation scalable with the number of base-station antennas, one of the key ideas is to exploit the inherent sparsity of wireless channels, driven by two main assumptions: (i) the cardinality of channel models in propagation domain is much smaller than the expected base-station array sizes (64+ antennas), and (ii) uplink and downlink channels share the same spatial space. However, based on our channel measurement data, we find that the two assumptions may not always hold and hence FDD channel estimation schemes with the above assumptions may not result in maximal achievable performance. To understand the performance gap, we analyze the modeling mismatch regarding the above two assumptions to quantify the modeling error of approximating downlink channel with uplink dominant angles in the propagation domain. We derive modeling error convergence with growing base-station array size and provide both numerical and experimental results. We observe that modeling error increases with the number of base-station antennas before converging to a value that is independent of the base-station array size, and more distributed channel power leads to larger modeling error. Utilizing the modeling error, we then investigate the resulted beamforming performance rate loss. Accordingly, from both numerical and experimental results, we observe that rate loss increases with the number of base-station antennas before converging to a value that is independent of the base-station array size, and more distributed channel power results in higher rate loss. Second, to make downlink channel estimation scalable with the number of users, we propose a novel propagation domain-based user selection scheme, labeled as zero-measurement selection, for FDD massive MIMO systems. The key idea of approximate selection is to infer downlink user channel norm and inter-user channel orthogonality from uplink channel in propagation domain, which is proven effective with both experimental and numerical results. 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 will be 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-aid user selection on 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 experimental findings.
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    Scalable user selection in FDD massive MIMO
    (Springer Nature, 2021) Zhang, Xing; Sabharwal, Ashutosh
    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.