research-article
Authors: Yiwei Liu, Shengchu Wang, Mengxia He
IEEE Transactions on Wireless Communications, Volume 23, Issue 8_Part_2
Pages 9361 - 9374
Published: 12 February 2024 Publication History
Metrics
Total Citations0Total Downloads0Last 12 Months0
Last 6 weeks0
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
- View Options
- References
- Media
- Tables
- Share
Abstract
This paper proposes a novel equalizer for a hybrid-MIMO system combining both linear and nonlinear magnitude-only radio-frequency chains. A dropout-and-constellation-deletion parametric bilinear generalized approximate message passing (DCDP-BIG-AMP) algorithm is developed, and then applied for joint channel and data estimation (JCDE) in the hybrid-MIMO. Compared to parametric bilinear generalized approximate message passing (P-Bi-GAMP), DCDP-BIG-AMP randomly selects only a portion of variables to be updated and deletes constellation points which are changed slightly during the DCDP-BIG-AMP iterations. Consequently, the computational complexity is decreased significantly with the cost of minor performance degradation based on our experimental results. Its core operation is cyclic convolution, which is accelerated by fast Fourier transform (FFT). Subsequently, it is suitable for parallel hardware implementation. Moreover, the expectation maximization (EM) framework is combined to learn the unknown prior distribution parameters of channel. Simulation results show that DCDP-BIG-AMP successfully exploits the nonlinear magnitude measurements, and enables hybrid-MIMO to outperform the traditional MIMO on communication performance and energy-efficiency. DCDP-BIG-AMP based JCDE alternatively enhances the channel estimation (CE) and multiuser detection (MUD) performances, and converges quickly after only about 5 iterations. The dropout and constellation deletion mechanisms well balance between computational complexity and performance degradation.
References
[1]
H. Yin, X. Wei, Y. Tang, and K. Yang, “Diagonally reconstructed channel estimation for MIMO-AFDM with inter-Doppler interference in doubly selective channels,” 2022, arXiv:2206.12822.
[2]
D. Tse and P. Viswanath., Fundamentals of Wireless Communications. Cambridge, U.K.: Cambridge Univ. Press, 2005.
Digital Library
[3]
A. S. Gharagezlou, J. Pourrostam, and M. Nangir, “Secure energy efficient power allocation in massive multiple-input multiple-output systems with an eavesdropper using cell division technique,” Int. J. Commun. Syst., vol. 35, no. 3, p. e5040, Feb. 2022.
[4]
F. Boccardi, R. W. Heath Jr., A. Lozano, T. L. Marzetta, and P. Popovski, “Five disruptive technology directions for 5G,” IEEE Commun. Mag., vol. 52, no. 2, pp. 74–80, Feb. 2014.
[5]
E. Björnson, L. Sanguinetti, J. Hoydis, and M. Debbah, “Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer?” IEEE Trans. Wireless Commun., vol. 14, no. 6, pp. 3059–3075, Jun. 2015.
Digital Library
[6]
S. Wang, Y. Li, and J. Wang, “Multiuser detection in massive spatial modulation MIMO with low-resolution ADCs,” IEEE Trans. Wireless Commun., vol. 14, no. 4, pp. 2156–2168, Apr. 2015.
Digital Library
[7]
L. N. Ribeiro, S. Schwarz, M. Rupp, and A. L. F. de Almeida, “Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs,” IEEE J. Sel. Topics Signal Process., vol. 12, no. 2, pp. 298–312, May 2018.
[8]
J. Zhang, L. Dai, X. Li, Y. Liu, and L. Hanzo, “On low-resolution ADCs in practical 5G millimeter-wave massive MIMO systems,” IEEE Commun. Mag., vol. 56, no. 7, pp. 205–211, Jul. 2018.
[9]
S. Wang, L. Zhang, Y. Li, J. Wang, and E. Oki, “Multiuser MIMO transmission aided by massive one-bit magnitude measurements,” IEEE Trans. Wireless Commun., vol. 15, no. 10, pp. 7058–7073, Oct. 2016. 10.1109/TWC.2016.2596718.
Digital Library
[10]
S. Wang, L. Zhang, Y. Li, J. Wang, and E. Oki, “Multiuser MIMO communication under quantized phase-only measurements,” IEEE Trans. Commun., vol. 64, no. 3, pp. 1083–1099, Mar. 2016.
[11]
S. Wang, M. He, and Y. Liu, “Nonlinear MIMO communication with p-periodic phase measurements,” IEEE Trans. Wireless Commun., vol. 21, no. 7, pp. 4856–4870, Jul. 2022.
Digital Library
[12]
S. Wang, L. Zhang, and X. Jing, “Phase retrieval motivated nonlinear MIMO communication with magnitude measurements,” IEEE Trans. Wireless Commun., vol. 16, no. 8, pp. 5452–5466, Aug. 2017.
Digital Library
[13]
O. Orhan, E. Erkip, and S. Rangan, “Low power analog-to-digital conversion in millimeter wave systems: Impact of resolution and bandwidth on performance,” in Proc. Inf. Theory Appl. Workshop (ITA), 2015, pp. 191–198.
[14]
J. Liu, J. Ding, C. Wang, K. Wang, T. Zheng, and J. Yu, “8192QAM signal transmission by an IM/DD system at W-band using delta-sigma modulation,” IEEE Photon. Technol. Lett., vol. 35, no. 4, pp. 207–210, Feb. 15, 2023.
[15]
W. Zeng, N. Do, M. Lambert, J. Gong, B. Cazzolato, and M. Stephens, “Linear phase detector for detecting multiple leaks in water pipes,” Appl. Acoust., vol. 202, Jan. 2023, Art. no.
[16]
N. Shaik and P. K. Malik, “Modified entropy based least square channel estimation technique for 5G massive multiple input multiple output universal filtered multicarrier systems,” Eng. Res. Exp., vol. 5, no. 1, Mar. 2023, Art. no.
[17]
A. Mezghani, F. Antreich, and J. A. Nossek, “Multiple parameter estimation with quantized channel output,” in Proc. Int. ITG Workshop Smart Antennas (WSA), Feb. 2010, pp. 143–150.
[18]
J.-C. Chen, “Low-resolution MMSE equalization for massive MU-MIMO systems,” IEEE Trans. Veh. Technol., vol. 72, no. 6, pp. 8164–8169, Jun. 2023. 10.1109/TVT.2023.3241231.
[19]
J. Mo, P. Schniter, N. G. Prelcic, and R. W. Heath Jr., “Channel estimation in millimeter wave MIMO systems with one-bit quantization,” in Proc. 48th Asilomar Conf. Signals, Syst. Comput., Nov. 2014, pp. 957–961.
[20]
S. Rangan, “Generalized approximate message passing for estimation with random linear mixing,” in Proc. IEEE Int. Symp. Inf. Theory, Jul. 2011, pp. 2168–2172.
[21]
M. He and S. Wang, “Sparse channel estimation in nonlinear MIMO with magnitude/phase measurements,” in Proc. IEEE 31st Annu. Int. Symp. Pers., Indoor Mobile Radio Commun., Aug. 2020, pp. 1–6.
[22]
W. Hachem, “Approximate message passing for sparse matrices with application to the equilibria of large ecological lotka-volterra systems,” 2023, arXiv:2302.09847.
[23]
F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, “Factor graphs and the sum-product algorithm,” IEEE Trans. Inf. Theory, vol. 47, no. 2, pp. 498–519, Feb. 2001.
Digital Library
[24]
L. V. Nguyen, A. Lee Swindlehurst, and D. H. N. Nguyen, “Variational Bayes for joint channel estimation and data detection in few-bit massive MIMO systems,” 2022, arXiv:2212.01717.
[25]
Y. Xionget al., “Joint effective channel estimation and data detection for RIS-aided massive MIMO systems with low-resolution ADCs,” IEEE Commun. Lett., vol. 27, no. 2, pp. 721–725, Feb. 2023.
[26]
D. L. Li and A. Farhang, “Joint channel estimation and equalization in massive MIMO using a single pilot subcarrier,” 2022, arXiv:2212.07781.
[27]
R. Prasad, C. R. Murthy, and B. D. Rao, “Joint channel estimation and data detection in MIMO-OFDM systems: A sparse Bayesian learning approach,” IEEE Trans. Signal Process., vol. 63, no. 20, pp. 5369–5382, Oct. 2015.
Digital Library
[28]
V. Savaux, Y. Louët, M. Djoko-Kouam, and A. Skrzypczak, “Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems,” EURASIP J. Adv. Signal Process., vol. 2013, no. 1, pp. 1–11, Dec. 2013.
[29]
C.-K. Wen, C.-J. Wang, S. Jin, K.-K. Wong, and P. Ting, “Bayes-optimal joint channel-and-data estimation for massive MIMO with low-precision ADCs,” IEEE Trans. Signal Process., vol. 64, no. 10, pp. 2541–2556, May 2016.
Digital Library
[30]
J. T. Parker, P. Schniter, and V. Cevher, “Bilinear generalized approximate message passing—Part I: Derivation,” IEEE Trans. Signal Process., vol. 62, no. 22, pp. 5839–5853, Nov. 2014.
[31]
J. T. Parker, P. Schniter, and V. Cevher, “Bilinear generalized approximate message passing—Part II: Applications,” IEEE Trans. Signal Process., vol. 62, no. 22, pp. 5854–5867, Nov. 2014.
[32]
S. Zhang, Y. Cui, and W. Chen, “Joint detection for massive grant-free access via BiGAMP,” in Proc. Int. Symp. Wireless Commun. Syst. (ISWCS), Oct. 2022, pp. 1–6.
[33]
X. Meng and J. Zhu, “Bilinear adaptive generalized vector approximate message passing,” IEEE Access, vol. 7, pp. 4807–4815, 2019.
[34]
M. Akrout, A. Housseini, F. Bellili, and A. Mezghani, “BiG-VAMP: The bilinear generalized vector approximate message algorithm,” in Proc. 56th Asilomar Conf. Signals, Syst., Comput., Oct. 2022, pp. 1377–1384.
[35]
J. T. Parker and P. Schniter, “Parametric bilinear generalized approximate message passing,” IEEE J. Sel. Topics Signal Process., vol. 10, no. 4, pp. 795–808, Jun. 2016.
[36]
F. Burkhardt, S. Jaeckel, E. Eberlein, and R. Prieto-Cerdeira, “QuaDRiGa: A MIMO channel model for land mobile satellite,” in Proc. 8th Eur. Conf. Antennas Propag. (EuCAP), Apr. 2014, pp. 1274–1278.
[37]
P.-I. Mak, S.-P. U and R. P. Martins, “Transceiver architecture selection: Review, state-of-the-art survey and case study,” IEEE Circuits Syst. Mag., vol. 7, no. 2, pp. 6–25, 2nd Quart., 2007. 10.1109/MCAS.2007.4299439.
[38]
F. Krzakala, M. Mézard, F. Sausset, Y. Sun, and L. Zdeborová, “Probabilistic reconstruction in compressed sensing: Algorithms, phase diagrams, and threshold achieving matrices,” J. Stat. Mech., Theory Exp., vol. 2012, no. 8, Aug. 2012, Art. no.
[39]
P. Schniter and S. Rangan, “Compressive phase retrieval via generalized approximate message passing,” IEEE Trans. Signal Process., vol. 63, no. 4, pp. 1043–1055, Feb. 2015.
Digital Library
[40]
J. Vila and P. Schniter, “Expectation-maximization Bernoulli–Gaussian approximate message passing,” in Proc. Conf. Rec. 45th Asilomar Conf. Signals, Syst. Comput. (ASILOMAR), Nov. 2011, pp. 799–803.
Index Terms
Dropout and Constellation Deletion Parametric Bilinear Generalized Approximate Message Passing-Based Equalization in Hybrid-MIMO
Hardware
Communication hardware, interfaces and storage
Signal processing systems
Mathematics of computing
Information theory
Coding theory
Networks
Network types
Mobile networks
Wireless access networks
Security and privacy
Cryptography
Mathematical foundations of cryptography
Theory of computation
Computational complexity and cryptography
Communication complexity
Index terms have been assigned to the content through auto-classification.
Recommendations
- Spatial multiplexing with transmit antenna and constellation selection for correlated MIMO fading channels
We consider spatial multiplexing systems in correlated multiple-input multiple-output (MIMO) fading channels with equal power allocated to each transmit antenna. Under this constraint, the number and subset of transmit antennas together with the ...
Read More
- Adaptive modulation and decision feedback equalization for frequency-selective MIMO channels
In this paper, an adaptive modulation scheme for the multiple-input multiple-output MIMO frequency-selective channels is investigated. We consider a scenario with precoded block-based transceivers over spatially correlated Rayleigh multipath MIMO ...
Read More
- SVD-based MIMO Precoding and Equalization Schemes for Realistic Channel Knowledge: Design Criteria and Performance Evaluation
Multiple-input multiple-output (MIMO) based communication systems with precoding, bit-loading and equalization procedures are considered in this paper. Applications of precoding schemes, which are based on Singular Value Decomposition (SVD) of the ...
Read More
Comments
Information & Contributors
Information
Published In
IEEE Transactions on Wireless Communications Volume 23, Issue 8_Part_2
Aug. 2024
1198 pages
Issue’s Table of Contents
1536-1276 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Press
Publication History
Published: 12 February 2024
Qualifiers
- Research-article
Contributors
Other Metrics
View Article Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
Total Citations
Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Sep 2024
Other Metrics
View Author Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Publication
Media
Figures
Other
Tables
