Redox functional groups of humic substances.

A new analytical technique based on palladium (Pd) and H2 catalytic system showed significant potential as a useful method for reliably assessing redox sites in humic substances. The technique identifies redox sites as a function of their resistance to the hydrogenolysis process. The test system consists of catalytic reduction process, the measurement of electron carrying capacity, and air oxidation. The extent of hydrogenolysis, which occurs during the catalytic reduction, can be controlled by pH and the type of catalyst used in the system. Verification of the reversibility of the redox sites is also permitted due to the use of a removable catalyst that allows the test to be repeated. Eight quinone compounds and fourteen humic substance samples were examined using this technique. The tests with quinone compounds demonstrated that hydrogenolysis occurring in the pH 6.5-Pd/Al2O3 redox system effectively removed quinone moieties in all model compounds. When the system's pH was increased to 8, the extent of hydrogenolysis became less intense. Quinones with an electron withdrawing substituent were left intact. As hydrogenolysis was further compromised by removing Al2O3 from the system, quinones without substituents and quinones with adjacent electron donating functional groups also remained intact. At that point, only quinones with an electron donating substituent located far away in a separate conjugated system suffered hydrogenolysis. The humic substance samples' tests showed that six landfill leachate humic substances, which were highly aliphatic, did not have redox sites. Eight other humic substance samples were capable of shuttling electrons, even in the pH 6.5-Pd/Al2O3 redox system, which had removed their quinone redox sites. The technique showed that redox sites in humic substance samples include both nonquinone (NQ) and quinone groups. Redox sites in the NQ group were responsible for 21%--56% of the electron carrying capacity (ECC) of the samples. The technique divided redox sites in the quinone group into two subgroups. The first subgroup includes redox sites with a neighboring electron withdrawing substituent which was liable for 13%--58% of the ECC. The second subgroup contains redox sites characterized by having an adjacent electron donating substituent and were accountable for 8%--50% of ECC

Machine-type communications: current status and future perspectives toward 5G systems

Machine-type communications (MTC) enables a broad range of applications from mission- critical services to massive deployment of autonomous devices. To spread these applications widely, cellular systems are considered as a potential candidate to provide connectivity for MTC devices. The ubiquitous deployment of these systems reduces network installation cost and provides mobility support. However, based on the service functions, there are key challenges that currently hinder the broad use of cellular systems for MTC. This article provides a clear mapping between the main MTC service requirements and their associated challenges. The goal is to develop a comprehensive understanding of these challenges and the potential solutions. This study presents, in part, a roadmap from the current cellular technologies toward fully MTC-capable 5G mobile systems.Peer reviewe

Implementing opportunistic spectrum access in LTE-Advanced

Long term evolution advanced (LTE-A) has emerged as a promising mobile broadband access technology aiming to cope with the increasing traffic demand in wireless networks. However, the enhanced spectral efficiency offered by LTE-A may become futile without a better management of scarce and overcrowded electromagnetic spectrum. In this sense, cognitive radio (CR) has been proposed as a potential solution to the problem of spectrum scarcity. Among all the mechanisms provided by CR, opportunistic spectrum access (OSA) aims at a dynamic and seamless use of certain licensed bands provided the licensee is not harmfully affected. This operation requires spectral awareness in order to avoid interferences with licensed systems. In spite of implementing some spectrum sensing mechanisms, LTE-A technology lacks other tools that are needed in order to improve the knowledge of the radio environment. This work studies the adoption of a Geo-located data base (Geo-DB) that cooperatively retrieves and maintains information regarding the location of unutilized portions of spectrum potentially available for OSA. Moreover, the potential benefit of this LTE-compliant OSA solution is evaluated using a calibrated simulation tool, by which numerical results allow us to optimally configure the system and show that the proposed opportunistic system is able to significantly improve its performance.The authors would like to thank the funding received from the Ministerio de Ciencia e Innovacion within the Project number TEC2011-27723-C02-02 and from the Ministerio de Industria, Turismo y Comercio TSI-020100-2011-266 funds. This article had been written in the framework of the CELTIC project CP08-001 COMMUNE. Study by X. Gelabert is funded by the BP-DGR 2010 scholarship (ref. 00192). The authors would like to acknowledge the contributions of their colleagues.Osa Ginés, V.; Herranz Claveras, C.; Monserrat Del Río, JF.; Gelabert, X. (2012). Implementing opportunistic spectrum access in LTE-Advanced. EURASIP Journal on Wireless Communications and Networking. 2012(99):1-17. https://doi.org/10.1186/1687-1499-2012-99S117201299Martín-Sacristán D, Monserrat JF, Cabrejas-Peñuelas J, Calabuig D, Garrigas S, Cardona N: On the way towards fourth-generation mobile: 3GPP LTE and LTE-Advanced. EURASIP J Wirel Commun Netw 2009, 2009: 1-10.Ratasuk R, Tolli D, Ghosh A: Carrier aggregation in LTE-Advanced. In IEEE 71st Vehicular Technology Conference (VTC 2010-Spring). Taipei; 2010:1-5.Wang H, Rosa C, Pedersen K: Performance of uplink carrier aggregation in LTE-advanced systems. In IEEE 72nd Vehicular Technology Conference Fall (VTC 2010-Fall). Ottawa; 2010:1-5.Tandra R, Sahai A, Mishra S: What is a spectrum hole and what does it take to recognize one? Proc IEEE 2009, 97(5):824-848.Mitola IJ, Maguire JGQ: Cognitive radio: making software radios more personal. IEEE Personal Commun 1999, 6(4):13-18. 10.1109/98.788210Haykin S: Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 2005, 23(2):201-220.IEEE 802.22 Working Group on Wireless Regional Area Networks. [ http://www.ieee802.org/22/ ]ITU-R BT1368: Planning criteria for digital terrestrial television services in the VHF/UHF bands.ITU-R BT1786: Criterion to assess the impact of interference to the terrestrial broadcasting service (BS).Kawade S, Nekovee M: Cognitive radio-based urban wireless broadband in unused TV bands. In 20th International Radioelektronika Conference. Brno; 2010:1-4.Modlic B, Sisul G, Cvitkovic M: Digital dividend--Opportunities for new mobile services. In International Symposium ELMAR 2009 (ELMAR'09). Zadar; 2009:1-8.Zhao X, Guo Z, Guo Q: A cognitive based spectrum sharing scheme for LTE advanced systems. In International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). Moscow; 2010:965-969.Hussain S, Fernando X: Spectrum sensing in cognitive radio networks: Up-to-date techniques and future challenges. In IEEE Toronto International Conference on Science and Technology for Humanity (TIC-STH). Toronto; 2009:736-741.Xu Y, Sun Y, Li Y, Zhao Y, Zou H: Joint sensing period and transmission time optimization for energy-constrained cognitive radios. EURASIP J Wirel Commun Netw 2010, 2010: 1-16.Yucek T, Arslan H: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 2009, 11: 116-130.Cabric D, Mishra S, Brodersen R: Implementation issues in spectrum sensing for cognitive radios. In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers. Volume 1. Pacific Grove; 2004:772-776.Zeng Y, Liang YC, Hoang A, Peh E: Reliability of spectrum sensing under noise and interference uncertainty. In IEEE International Conference on Communications Workshops, 2009. ICC Workshops. Dresden; 2009:1-5.Bixio L, Ottonello M, Raffetto M, Regazzoni CS: Comparison among cognitive radio architectures for spectrum sensing. EURASIP J Wirel Commun Netw 2011, 2011: 1-18.Mustonen M, Matinmikko M, Mammela A: Cooperative spectrum sensing using quantized soft decision combining. In 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2009 (CROWNCOM'09). Hannover; 2009:1-5.Xiao L, Liu K, Ma L: A weighted cooperative spectrum sensing in cognitive radio networks. In International Conference on Information Networking and Automation (ICINA). Volume 2. Kunming; 2010:45-48.Pan Q, Chang Y, Zheng R, Zhang X, Wang Y, Yang D: Solution of information exchange for cooperative sensing in cognitive radios. In IEEE Wireless Communications and Networking Conference, 2009 (WCNC'2009). Budapest; 2009:1-4.Masri A, Chiasserini CF, Perotti A: Control information exchange through UWB in cognitive radio networks. In 5th IEEE International Symposium on Wireless Pervasive Computing (ISWPC). Modena; 2010:110-115.Celebi H, Arslan H: Utilization of location information in cognitive wireless networks. IEEE Wirel Commun 2007, 14(4):6-13.FCC: Notice of Proposed Rulemaking, in the Matter of Unlicensed Operation in the TV Broadcast Bands (ET Docket no. 04-186) and Additional Spectrum for Unlicensed.Marcus MJ, Kolodzy P, Lippman A: Reclaiming the vast wasteland: why unlicensed use of the white space in the TV bands will not cause interference to DTV viewers. New America Foundation: wireless future program, tech rep 2005.Nam H, Ghorbel M, Alouini M: Proc. of the Fifth International Conference on Cognitive Radio Oriented. In Proc of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks Communications (CROWNCOM). Cannes; 2010:1-5.IEEE Std 80221-2008: IEEE Standard for Local and Metropolitan Area Networks-Part 21: Media Independent Handover. 2009.3GPP TS 36133: Evolved Universal Terrestrial Radio Access (E-UTRA); Requirements for support of radio resource management.Sesia S, Baker M, Toufik I: LTE, the UMTS long term evolution: from theory to practice. Wiley, New Haven; 2009.Digham FF, Alouini MS, Simon MK: On the energy detection of unknown signals over fading channels. In IEEE International Conference on Communications, 2003 (ICC'03). Volume 5. Anchorage; 2003:3575-3579.Ghasemi A, Sousa ES: Collaborative spectrum sensing for opportunistic access in fading environments. In First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). Baltimore; 2005:131-136.Gelabert X, Akyildiz IF, Sallent O, Agustí R: Operating point selection for primary and secondary users in cognitive radio networks. Comput Netw 2009, 53(8):1158-1170. 10.1016/j.comnet.2009.02.009Taniuchi K, Ohba Y, Fajardo V, Das S, Tauil M, Cheng YH, Dutta A, Baker D, Yajnik M, Famolari D: IEEE 802.21: media independent handover: features, applicability, and realization. IEEE Commun Mag 2009, 47: 112-120.3GPP TS 36305: Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Stage 2 functional specification of User Equipment (UE) positioning in E-UTRAN.3GPP TS 36355: Evolved Universal Terrestrial Radio Access; LTE Positioning Protocol (LPP).3GPP TS 36455: Evolved Universal Terrestrial Radio Access; LTE Positioning Protocol A (LPPa).Ren W, Zhao Q, Swami A: Power control in cognitive radio networks: how to cross a multi-lane highway. IEEE J Sel Areas Commun 2008, 27(7):1283-1296.3GPP R1-084424: Control Channel Design Issues for Carrier Aggregation in LTE-A.Dajie J, Haiming W, Malkamaki E, Tuomaala E: Principle and performance of semi-persistent scheduling for VoIP in LTE system. In International Conference on Wireless Communications, Networking and Mobile Computing, 2007 (WiCom 2007). Shanghai; 2007:2861-2864.Rajbanshi R, Wyglinski AM, Minden GJ: An efficient implementation of NC-OFDM transceivers for cognitive radios. In Proc of 1st Conf on Cognitive Radio Oriented Wireless Networks and Commun. Mykonos; 2006:1-5.Wellens M, Riihijarvi J, Mahonen P: Modeling primary system activity in dynamic spectrum access networks by aggregated ON/OFF-processes. In 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks Workshops, 2009. SECON Workshops'09. Rome; 2009:1-6.3GPP TS 36214: Physical layer; Measurements.Ofuji Y, Morimoto A, Abeta S, Sawahashi M: Comparison of packet scheduling algorithms focusing on user throughput in high speed downlink packet access. In 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. Volume 3. Lis-boa; 2002:1462-1466.ITU-R ITU M2135: Guidelines for evaluation of radio interface technologies for IMT-Advanced 2008

Gateways to Heaven : Observations and Predictions on the Software Architecture of IoT Gateways

The Internet of Things (IoT) enables connected devices that are an integral part of the physical world. The possibility to connect, manage, configure and dynamically reprogram remote devices through local and global cloud environments will open up a broad variety of new use cases, services, applications and device categories, and will enable entirely new product and application ecosystems as well. In this paper we discuss the software architecture options of IoT gateways as a follow-up to our earlier paper that defined a taxonomy of software architectures for IoT devices. We summarize several different software architecture options for IoT gateways. These options have a significant impact on the overall end-to-end architecture and topology of IoT systems, e.g., in determining how much computation can be performed on the edge of the network. Based on our observations and industry experiences we then make predictions on the future of gateway solutions and IoT systems more broadly. © 2019 ACM.Peer reviewe

Adaptive Multiuser Decision Feedback Demodulation for GSM

Demodulation of multiple users in a cellular TDMA system with large power imbalance among the received signals and limited side information is considered. Our approach is to combine an adaptive multiuser decision-feedback demodulator (DFD) with multiple antennas. The DFD contains (matrix) finite-impulse response feedforward and and feedback filters to mitigate both intersymbol and multiple-access interference. The MMSE filter coefficients are specified, and adaptive estimation algorithms are presented which rely on training data from each user. Simulation results show that by cancelling a relatively strong user, the DFD offers a significant improvement in performance for weak users relative to the linear MMSE receiver. 1 Introduction We consider adaptive space-time multiuser demodulation of digital signals in a cellular environment with limited side information. The communication system of interest is the forward link of a cellular Time-Division Multiple-Access (TDMA) network (in part..