OFDM Synthetic Aperture Radar Imaging with Sufficient Cyclic Prefix

The existing linear frequency modulated (LFM) (or step frequency) and random noise synthetic aperture radar (SAR) systems may correspond to the frequency hopping (FH) and direct sequence (DS) spread spectrum systems in the past second and third generation wireless communications. Similar to the current and future wireless communications generations, in this paper, we propose OFDM SAR imaging, where a sufficient cyclic prefix (CP) is added to each OFDM pulse. The sufficient CP insertion converts an inter-symbol interference (ISI) channel from multipaths into multiple ISI-free subchannels as the key in a wireless communications system, and analogously, it provides an inter-range-cell interference (IRCI) free (high range resolution) SAR image in a SAR system. The sufficient CP insertion along with our newly proposed SAR imaging algorithm particularly for the OFDM signals also differentiates this paper from all the existing studies in the literature on OFDM radar signal processing. Simulation results are presented to illustrate the high range resolution performance of our proposed CP based OFDM SAR imaging algorithm.Comment: This version has been accepted by IEEE Transactions on Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing 201

New Combinatorial Construction Techniques for Low-Density Parity-Check Codes and Systematic Repeat-Accumulate Codes

This paper presents several new construction techniques for low-density parity-check (LDPC) and systematic repeat-accumulate (RA) codes. Based on specific classes of combinatorial designs, the improved code design focuses on high-rate structured codes with constant column weights 3 and higher. The proposed codes are efficiently encodable and exhibit good structural properties. Experimental results on decoding performance with the sum-product algorithm show that the novel codes offer substantial practical application potential, for instance, in high-speed applications in magnetic recording and optical communications channels.Comment: 10 pages; to appear in "IEEE Transactions on Communications

Trusted-HB: a low-cost version of HB+ secure against Man-in-The-Middle attacks

Since the introduction at Crypto'05 by Juels and Weis of the protocol HB+, a lightweight protocol secure against active attacks but only in a detection based-model, many works have tried to enhance its security. We propose here a new approach to achieve resistance against Man-in-The-Middle attacks. Our requirements - in terms of extra communications and hardware - are surprisingly low.Comment: submitted to IEEE Transactions on Information Theor

A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems

[EN] Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. Simulation results show that the proposed scheme can effectively reduce the deviation, improve the channel estimation accuracy, mitigate the impact of pilot contamination and obtain accurate CSI with low overhead and computational complexity.This research was funded by Ministerio de Economia, Industria y Competitividad, Gobierno de Espana grant number BIA2017-87573-C2-2-P.Bangash, K.; Khan, I.; Lloret, J.; León Fernández, A. (2018). A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems. Electronics. 7(10). https://doi.org/10.3390/electronics7100218S710Gao, Z., Dai, L., Lu, Z., Yuen, C., & Wang, Z. (2014). Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations. IEEE Communications Letters, 18(7), 1266-1269. doi:10.1109/lcomm.2014.2325027Biswas, S., Masouros, C., & Ratnarajah, T. (2016). Performance Analysis of Large Multiuser MIMO Systems With Space-Constrained 2-D Antenna Arrays. IEEE Transactions on Wireless Communications, 15(5), 3492-3505. doi:10.1109/twc.2016.2522419Khan, I., Zafar, M., Jan, M., Lloret, J., Basheri, M., & Singh, D. (2018). Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems. Entropy, 20(2), 92. doi:10.3390/e20020092Khan, I., & Singh, D. (2018). Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems. AEU - International Journal of Electronics and Communications, 89, 181-190. doi:10.1016/j.aeue.2018.03.038Khan, I., Singh, M., & Singh, D. (2018). Compressive Sensing-based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems. Applied Sciences, 8(5), 754. doi:10.3390/app8050754Arshad, M., Khan, I., Lloret, J., & Bosch, I. (2018). A Novel Multi-User Codebook Design for 5G in 3D-MIMO Heterogeneous Networks. Electronics, 7(8), 144. doi:10.3390/electronics7080144Shahjehan, W., Shah, S., Lloret, J., & Bosch, I. (2018). Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting. Applied Sciences, 8(8), 1237. doi:10.3390/app8081237Jose, J., Ashikhmin, A., Marzetta, T. L., & Vishwanath, S. (2011). Pilot Contamination and Precoding in Multi-Cell TDD Systems. IEEE Transactions on Wireless Communications, 10(8), 2640-2651. doi:10.1109/twc.2011.060711.101155Jose, J., Ashikhmin, A., Marzetta, T. L., & Vishwanath, S. (2009). Pilot contamination problem in multi-cell TDD systems. 2009 IEEE International Symposium on Information Theory. doi:10.1109/isit.2009.5205814Jose, J., Ashikhmin, A., Whiting, P., & Vishwanath, S. (2011). Channel Estimation and Linear Precoding in Multiuser Multiple-Antenna TDD Systems. IEEE Transactions on Vehicular Technology, 60(5), 2102-2116. doi:10.1109/tvt.2011.2146797Marzetta, T. L. (2010). Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas. IEEE Transactions on Wireless Communications, 9(11), 3590-3600. doi:10.1109/twc.2010.092810.091092Rusek, F., Persson, D., Buon Kiong Lau, Larsson, E. G., Marzetta, T. L., & Tufvesson, F. (2013). Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays. IEEE Signal Processing Magazine, 30(1), 40-60. doi:10.1109/msp.2011.2178495Chang, Z., Wang, Z., Guo, X., Han, Z., & Ristaniemi, T. (2017). Energy-Efficient Resource Allocation for Wireless Powered Massive MIMO System With Imperfect CSI. IEEE Transactions on Green Communications and Networking, 1(2), 121-130. doi:10.1109/tgcn.2017.2696161Prasad, K. N. R. S. V., Hossain, E., & Bhargava, V. K. (2017). Energy Efficiency in Massive MIMO-Based 5G Networks: Opportunities and Challenges. IEEE Wireless Communications, 24(3), 86-94. doi:10.1109/mwc.2016.1500374wcFodor, G., Rajatheva, N., Zirwas, W., Thiele, L., Kurras, M., Guo, K., … De Carvalho, E. (2017). An Overview of Massive MIMO Technology Components in METIS. IEEE Communications Magazine, 55(6), 155-161. doi:10.1109/mcom.2017.1600802Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014). An Overview of Massive MIMO: Benefits and Challenges. IEEE Journal of Selected Topics in Signal Processing, 8(5), 742-758. doi:10.1109/jstsp.2014.2317671Larsson, E. G., Edfors, O., Tufvesson, F., & Marzetta, T. L. (2014). Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 52(2), 186-195. doi:10.1109/mcom.2014.6736761Yi Xu, Guosen Yue, & Shiwen Mao. (2014). User Grouping for Massive MIMO in FDD Systems: New Design Methods and Analysis. IEEE Access, 2, 947-959. doi:10.1109/access.2014.2353297Duly, A. J., Kim, T., Love, D. J., & Krogmeier, J. V. (2014). Closed-Loop Beam Alignment for Massive MIMO Channel Estimation. IEEE Communications Letters, 18(8), 1439-1442. doi:10.1109/lcomm.2014.2316157Choi, J., Love, D. J., & Bidigare, P. (2014). Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory. IEEE Journal of Selected Topics in Signal Processing, 8(5), 802-814. doi:10.1109/jstsp.2014.2313020Noh, S., Zoltowski, M. D., & Love, D. J. (2016). Training Sequence Design for Feedback Assisted Hybrid Beamforming in Massive MIMO Systems. IEEE Transactions on Communications, 64(1), 187-200. doi:10.1109/tcomm.2015.2498184Jiang, Z., Molisch, A. F., Caire, G., & Niu, Z. (2015). Achievable Rates of FDD Massive MIMO Systems With Spatial Channel Correlation. IEEE Transactions on Wireless Communications, 14(5), 2868-2882. doi:10.1109/twc.2015.2396058Adhikary, A., Junyoung Nam, Jae-Young Ahn, & Caire, G. (2013). Joint Spatial Division and Multiplexing—The Large-Scale Array Regime. IEEE Transactions on Information Theory, 59(10), 6441-6463. doi:10.1109/tit.2013.2269476Talwar, S., Viberg, M., & Paulraj, A. (1996). Blind separation of synchronous co-channel digital signals using an antenna array. I. Algorithms. IEEE Transactions on Signal Processing, 44(5), 1184-1197. doi:10.1109/78.502331Comon, P., & Golub, G. H. (1990). Tracking a few extreme singular values and vectors in signal processing. Proceedings of the IEEE, 78(8), 1327-1343. doi:10.1109/5.5832

Low Complexity Time Synchronization Algorithm for OFDM Systems with Repetitive Preambles

In this paper, a new time synchronization algorithm for OFDM systems with repetitive preamble is proposed. This algorithm makes use of coarse and fine time estimation; the fine time estimation is performed using a cross-correlation similar to previous proposals in the literature, whereas the coarse time estimation is made using a new metric and an iterative search of the last sample of the repetitive preamble. A complete analysis of the new metric is included, as well as a wide performance comparison, for multipath channel and carrier frequency offset, with the main time synchronization algorithms found in the literature. Finally, the complexity of the VLSI implementation of this proposal is discussed. © 2011 Springer Science+Business Media, LLC.This work was supported by the Spanish Ministerio de Educacion y Ciencia under grants TEC2006-14204-C02-01 and TEC2008-06787.Canet Subiela, MJ.; Almenar Terre, V.; Flores Asenjo, SJ.; Valls Coquillat, J. (2012). Low Complexity Time Synchronization Algorithm for OFDM Systems with Repetitive Preambles. Journal of Signal Processing Systems. 68(3):287-301. doi:10.1007/s11265-011-0618-6S287301683IEEE 802.11a standard (1999). Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: high-speed physical layer in the 5 GHz band.IEEE 802.11 g standard (2003). Wireless LAN specifications: Further higher data rate extension in the 2.4 GHz band.IEEE 802.16-2004 (2004). Standard for local and metropolitan area networks, part 16: Air interface for fixed broadband wireless access systems.Lee, D., & Cheun, K. (2002). Coarse symbol synchronization algorithms for OFDM systems in multipath channels. IEEE Communications Letters, 6(10), 446–448.Park, B., Cheon, H., Ko, E., Kang, C., & Hong, D. (2004). A blind OFDM synchronization algorithm based on cyclic correlation. IEEE Signal Processing Letters, 11(2), 83–85.Beek, J. J., Sandell, M., & Börjesson, P. O. (1997). ML estimation of time and frequency offset in OFDM system. IEEE Transactions on Signal Processing, 45(7), 1800–1805.Ma, S., Pan, X., Yang, G., & Ng, T. (2009). Blind symbol synchronization based on cyclic prefix for OFDM systems. IEEE Transactions on Vehicular Technology, 58(4), 1746–1751.Schmidl, T., & Cox, D. (1997). Robust frequency and timing synchronization for OFDM. IEEE Transactions on Communications, 45(12), 1613–1621.Coulson, A. J. (2001). Maximum likelihood synchronization for OFDM using a pilot symbol: Algorithms. IEEE Journal on Selected Areas in Communications, 19(12), 2495–2503.Tufvesson, F., Edfors, O., & Faulker, M. (1999). Time and frequency synchronization for OFDM using PN-sequence preambles. Proceedings of the Vehicular Technology Conference (VTC), 4, 2203–2207.Shi, K., & Serpedin, E. (2004). Coarse frame and carrier synchronization of OFDM systems: a new metric and comparison. IEEE Transactions on Wireless Communications, 3(4), 1271–1284.Minn, H., Zeng, M., & Bhargava, V. K. (2000). On timing offset estimation for OFDM Systems. IEEE Communications Letters, 4, 242–244.Minn, H., Bhargava, V. K., & Letaief, K. B. (2003). A robust timing and frequency synchronization for OFDM systems. IEEE Transactions on Wireless Communications, 2(4), 822–839.Minn, H., Bhargava, V. K., & Letaief, K. B. (2006). A combined timing and frequency synchronization and channel estimation for OFDM. IEEE Transactions on Communications, 54(3), 416–422.Park, B., Cheon, H., Ko, E., Kang, C., & Hong, D. (2003). A novel timing estimation method for OFDM systems. IEEE Communications Letters, 7(5), 239–241.Chang, S., & Kelley, B. (2003). Time synchronization for OFDM-based WLAN systems. Electronics Letters, 39(13), 1024–1026.Wu, Y., Yip, K., Ng, T., & Serpedin, E. (2005). Maximum-likelihood symbol synchronization for IEEE 802.11a WLANs in unknown frequency-selective fading channels. IEEE Transactions on Wireless Communications, 4(6), 2751–2763.Larsson, E. G., Liu, G., Li, J., & Giannakis, G. B. (2001). Joint symbol timing and channel estimation for OFDM based WLANs. IEEE Communications Letters, 5(8), 325–327.Troya, A., Maharatna, K., Krstic, M., Grass, E., Jagdhold, U., & Kraemer, R. (2007). Efficient inner receiver design for OFDM-based WLAN systems: algorithm and architecture. IEEE Transactions on Wireless Communications, 6(4), 1374–1385.Yang, J., & Cheun, K. (2006). Improved symbol timing synchronization in IEEE 802.11a/g wireless LAN systems in multipath channels. International Conference on Consumer Electronics. doi: 10.1109/ICCE.2006.1598425 .Manusani, S. K., Hshetrimayum, R. S., & Bhattacharjee, R. (2006). Robust time and frequency synchronization in OFDM based 802.11a WLAN systems. Annual India Conference. doi: 10.1109/INDCON.2006.302775 .Zhou, L., & Saito, M. (2004). A new symbol timing synchronization for OFDM based WLANs under multipath fading channels. 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. doi: 10.1109/PIMRC.2004.1373890 .Kim, T., & Park, S.-C. (2007). A new symbol timing and frequency synchronization design for OFDM-based WLAN systems. 9th Conference on Advanced Communication Technology. doi: 10.1109/ICACT.2007.358691 .Baek, J. H., Kim, S. D., & Sunwoo, M. H. (2008). SPOCS: Application specific signal processor for OFDM communication systems. Journal of Signal Processing Systems, 53(3), 383–397.Van Kempen, G., & van Vliet, L. (2000). Mean and variance of ratio estimators used in fluorescence ratio imaging. Cytometry, 39(4), 300–305.J. Melbo, J., & Schramm, P. (1998). Channel models for HIPERLAN/2 in different indoor scenarios. 3ERI085B, HIPERLAN/2 ETSI/BRAN contribution.Abramowitz, M., & Stegun, I. A. (1972). Handbook of mathematical functions. Dover.López-Martínez, F. J., del Castillo-Sánchez, E., Entrambasaguas, J. T., & Martos-Naya, E. (2010). Iterative-gradient based complex divider FPGA core with dynamic configurability of accuracy and throughput. Journal of Signal Processing Systems. doi: 10.1007/s11265-010-0464-y .Angarita, F., Canet, M. J., Sansaloni, T., Perez-Pascual, A., & Valls, J. (2008). Efficient mapping of CORDIC Algorithm for OFDM-based WLAN. Journal of Signal Processing Systems, 52(2), 181–191

Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications

[EN] The standard of in-body communications is limited to the use of narrowband systems. These systems are far from the high data rate connections achieved by other wireless telecommunication services today in force. The UWB frequency band has been proposed as a possible candidate for future in-body networks. However, the attenuation of body tissues at gigahertz frequencies could be a serious drawback. Experimental measurements for channel modeling are not easy to carry out, while the use of humans is practically forbidden. Sophisticated simulation tools could provide inaccurate results since they are not able to reproduce all the in-body channel conditions. Chemical solutions known as phantoms could provide a fair approximation of body tissues¿ behavior. In this work, the Time Reversal technique is assessed to increase the channel performance of in-body communications. For this task, a large volume of experimental measurements is performed at the low part of UWB spectrum (3.1-5.1 GHz) by using a highly accurate phantom-based measurement setup. This experimental setup emulates an in-body to in-body scenario, where all the nodes are implanted inside the body. Moreover, the in-body channel characteristics such as the path loss, the correlation in transmission and reception, and the reciprocity of the channel are assessed and discussed.This work was supported by the Programa de Ayudas de Investigacion y Desarrollo (PAID-01-16) from Universitat Politecnica de Valencia and by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Andreu-Estellés, C.; Garcia-Pardo, C.; Castelló-Palacios, S.; Cardona Marcet, N. (2018). Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications. Wireless Communications and Mobile Computing (Online). (8927107):1-12. https://doi.org/10.1155/2018/8927107S1128927107Fireman, Z. (2003). Diagnosing small bowel Crohn’s disease with wireless capsule endoscopy. Gut, 52(3), 390-392. doi:10.1136/gut.52.3.390Burri, H., & Senouf, D. (2009). Remote monitoring and follow-up of pacemakers and implantable cardioverter defibrillators. Europace, 11(6), 701-709. doi:10.1093/europace/eup110Scanlon, W. G., Burns, B., & Evans, N. E. (2000). Radiowave propagation from a tissue-implanted source at 418 MHz and 916.5 MHz. IEEE Transactions on Biomedical Engineering, 47(4), 527-534. doi:10.1109/10.828152Chavez-Santiago, R., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., Vermeeren, G., Joseph, W., … Cardona, N. (2015). Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2015.2418757Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2010). Ultra-wideband pulse-based data communications for medical implants. IET Communications, 4(15), 1889. doi:10.1049/iet-com.2009.0692Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2011). Ultra-wideband statistical propagation channel model for implant sensors in the human chest. IET Microwaves, Antennas & Propagation, 5(15), 1805. doi:10.1049/iet-map.2010.0537Kurup, D., Scarpello, M., Vermeeren, G., Joseph, W., Dhaenens, K., Axisa, F., … Vanfleteren, J. (2011). In-body path loss models for implants in heterogeneous human tissues using implantable slot dipole conformal flexible antennas. EURASIP Journal on Wireless Communications and Networking, 2011(1). doi:10.1186/1687-1499-2011-51Floor, P. A., Chavez-Santiago, R., Brovoll, S., Aardal, O., Bergsland, J., Grymyr, O.-J. H. N., … Balasingham, I. (2015). In-Body to On-Body Ultrawideband Propagation Model Derived From Measurements in Living Animals. IEEE Journal of Biomedical and Health Informatics, 19(3), 938-948. doi:10.1109/jbhi.2015.2417805Shimizu, Y., Anzai, D., Chavez-Santiago, R., Floor, P. A., Balasingham, I., & Wang, J. (2017). Performance Evaluation of an Ultra-Wideband Transmit Diversity in a Living Animal Experiment. IEEE Transactions on Microwave Theory and Techniques, 65(7), 2596-2606. doi:10.1109/tmtt.2017.2669039Anzai, D., Katsu, K., Chavez-Santiago, R., Wang, Q., Plettemeier, D., Wang, J., & Balasingham, I. (2014). Experimental Evaluation of Implant UWB-IR Transmission With Living Animal for Body Area Networks. IEEE Transactions on Microwave Theory and Techniques, 62(1), 183-192. doi:10.1109/tmtt.2013.2291542Chou, C.-K., Chen, G.-W., Guy, A. W., & Luk, K. H. (1984). Formulas for preparing phantom muscle tissue at various radiofrequencies. Bioelectromagnetics, 5(4), 435-441. doi:10.1002/bem.2250050408Cheung, A. Y., & Koopman, D. W. (1976). Experimental Development of Simulated Biomaterials for Dosimetry Studies of Hazardous Microwave Radiation (Short Papers). IEEE Transactions on Microwave Theory and Techniques, 24(10), 669-673. doi:10.1109/tmtt.1976.1128936YAMAMOTO, H., ZHOU, J., & KOBAYASHI, T. (2008). Ultra Wideband Electromagnetic Phantoms for Antennas and Propagation Studies. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E91-A(11), 3173-3182. doi:10.1093/ietfec/e91-a.11.3173Lazebnik, M., Madsen, E. L., Frank, G. R., & Hagness, S. C. (2005). Tissue-mimicking phantom materials for narrowband and ultrawideband microwave applications. Physics in Medicine and Biology, 50(18), 4245-4258. doi:10.1088/0031-9155/50/18/001Yilmaz, T., Foster, R., & Hao, Y. (2014). Broadband Tissue Mimicking Phantoms and a Patch Resonator for Evaluating Noninvasive Monitoring of Blood Glucose Levels. IEEE Transactions on Antennas and Propagation, 62(6), 3064-3075. doi:10.1109/tap.2014.2313139Gezici, S., Zhi Tian, Giannakis, G. B., Kobayashi, H., Molisch, A. F., Poor, H. V., & Sahinoglu, Z. (2005). Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE Signal Processing Magazine, 22(4), 70-84. doi:10.1109/msp.2005.1458289Marinova, M., Thielens, A., Tanghe, E., Vallozzi, L., Vermeeren, G., Joseph, W., … Martens, L. (2015). Diversity Performance of Off-Body MB-OFDM UWB-MIMO. IEEE Transactions on Antennas and Propagation, 63(7), 3187-3197. doi:10.1109/tap.2015.2422353SHI, J., ANZAI, D., & WANG, J. (2012). Channel Modeling and Performance Analysis of Diversity Reception for Implant UWB Wireless Link. IEICE Transactions on Communications, E95.B(10), 3197-3205. doi:10.1587/transcom.e95.b.3197Pajusco, P., & Pagani, P. (2009). On the Use of Uniform Circular Arrays for Characterizing UWB Time Reversal. IEEE Transactions on Antennas and Propagation, 57(1), 102-109. doi:10.1109/tap.2008.2009715Chavez-Santiago, R., Sayrafian-Pour, K., Khaleghi, A., Takizawa, K., Wang, J., Balasingham, I., & Li, H.-B. (2013). Propagation models for IEEE 802.15.6 standardization of implant communication in body area networks. IEEE Communications Magazine, 51(8), 80-87. doi:10.1109/mcom.2013.6576343Andreu, C., Castello-Palacios, S., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., & Cardona, N. (2016). Spatial In-Body Channel Characterization Using an Accurate UWB Phantom. IEEE Transactions on Microwave Theory and Techniques, 64(11), 3995-4002. doi:10.1109/tmtt.2016.2609409Pahlavan, K., & Levesque, A. H. (2005). Wireless Information Networks. doi:10.1002/0471738646Qiu, R. C., Zhou, C., Guo, N., & Zhang, J. Q. (2006). Time Reversal With MISO for Ultrawideband Communications: Experimental Results. IEEE Antennas and Wireless Propagation Letters, 5, 269-273. doi:10.1109/lawp.2006.875888Ando, H., Takizawa, K., Yoshida, T., Matsushita, K., Hirata, M., & Suzuki, T. (2016). Wireless Multichannel Neural Recording With a 128-Mbps UWB Transmitter for an Implantable Brain-Machine Interfaces. IEEE Transactions on Biomedical Circuits and Systems, 10(6), 1068-1078. doi:10.1109/tbcas.2016.251452