Asynchronous Distributed Averaging on Communication Networks

Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packet-switched communication networks such as the Internet. In this paper, we present several implementable algorithms that are robust to asynchronism and dynamic topology changes. The algorithms are completely distributed and do not require any global coordination. In addition, they can be proven to converge under very general asynchronous timing assumptions. Our results are verified by both simulation and experiments on Planetlab, a real-world TCP/IP network. We also present some extensions that are likely to be useful in applications

Multipair Two-Way DF Relaying with Cell-Free Massive MIMO

We consider a two-way half-duplex decode-and-forward (DF) relaying system with multiple pairs of single-antenna users assisted by a cell-free (CF) massive multiple-input multiple-output (mMIMO) architecture with multiple-antenna access points (APs). Under the practical constraint of imperfect channel state information (CSI), we derive the achievable sum spectral efficiency (SE) for a finite number of APs with maximum ratio (MR) linear processing for both reception and transmission in closed-form. Notably, the proposed CF mMIMO relaying architecture, exploiting the spatial diversity, and providing better coverage, outperforms the conventional collocated mMIMO deployment. Moreover, we shed light on the power-scaling laws maintaining a specific SE as the number of APs grows. A thorough examination of the interplay between the transmit powers per pilot symbol and user/APs takes place, and useful conclusions are extracted. Finally, differently to the common approach for power control in CF mMIMO systems, we design a power allocation scheme maximizing the sum SE.Comment: 15 pages, 8 figures, This work was accepted in IEEE Trans. Green Commun. Net. Copyright may be transferred without notice, after which this version may no longer be accessibl

Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.Peer reviewe

Adaptive Polarization Contrast Techniques for Through-Wall Microwave Imaging Applications

In this paper, we describe and utilize polarization contrast techniques of the adaptive polarization difference imaging algorithm and its transient modification for through-wall microwave imaging (TWMI) applications. Originally developed for optical imaging and sensing of polarization information in nature, this algorithm is modified to serve for target detection purposes in a through-wall environment. The proposed techniques exploit the polarization statistics of the observed scene for the detection and identification of changes within the scene and are not only capable of mitigating and substantially removing the wall effects but also useful in detecting motion, when conventional Doppler techniques are not applicable. Applications of the techniques to several TWMI scenarios including both homogeneous and periodic wall cases are presented

SDN-enabled MIMO Heterogeneous Cooperative Networks with Flexible Cell Association

Accepted in IEEE TWCSmall-cell densification is a strategy enabling the offloading of users from macro base stations (MBSs), in order to alleviate their load and increase the coverage, especially, for cell-edge users. In parallel, as the network increases in density, the BS cooperation emerges as an efficient design method towards the demands for drastic improvement of the system performance against the detrimental overall interference. We, therefore, model and scrutinize a heterogeneous network (HetNet) of two tiers (macro and small cells) with multiple-antenna BSs serving a multitude of users, which differ with respect to their basic design parameters, e.g., the deployment density, the number of transmit antennas, and transmit power. In addition, the tiers are enhanced with cell association policies by introducing the concept of the association probability. Above this and motivated by the advantages of cooperation among BSs, the small base stations (SBSs) are enriched with this property in their design. The SBS cooperation allows shedding light into its impact on the cell selection rules in multi-antenna HetNets. Under these settings, software-defined networking (SDN) is introduced smoothly to play the leading role in the orchestration of the network. In particular, heavy operations such as the coordination and the cell association are undertaken by virtue of an SDN controller performing and managing efficiently the corresponding computations due to its centralized adaptability and dynamicity towards the enhancement and potential scalability of the network. In this context, we derive the coverage probability and the mean achievable rate. Not only we show the outperformance of BS cooperation over uncoordinated BSs, but we also demonstrate that the SBS cooperation enables the admittance of more users from the macro-cell BSs (MBSs). Furthermore, we show that by increasing the number of BS antennas, the system performance is improved as the metrics under study reveal. Moreover, we investigate the performance of different transmission techniques, and we identify the optimal bias in each case when SBSs cooperate. Finally, we depict that the SBS densification is beneficial until a specific density value since a further increase does not increase the coverage probability.Peer reviewedFinal Accepted Versio

Scalable cell-free massive MIMO systems with hardware impairments

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/PIMRC48278.2020.9217151Despite the deleterious effect of hardware impairments (HWIs) on wireless systems, most prior works in cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems have not accounted for their impact. In particular, the effect of phase noise (PN) has not been investigated at all in CF systems. Moreover, there is no work investigating HWIs in scalable CF (SCF) mMIMO systems, encountering the prohibitively demanding fronthaul requirements of large networks with many users. Hence, we derive the uplink spectral efficiency (SE) under HWIs with minimum mean-squared error (MMSE) combining in closed-form by means of the deterministic equivalent (DE) analysis. Notably, previous works, accounted for MMSE decoding, studied the corresponding SE only by means of simulations. Numerical results illustrate the performance loss due to HWIs and result in insightful conclusions

Efficient T-CONT-agnostic Bandwidth and Wavelength Allocation for NG-PON2

Dynamic bandwidth and wavelength allocation are used to demonstrate high quality of service (QoS) in time wavelength-division multiplexed–passive optical networks (TWDM-PONs). Both bandwidth and wavelength assignment are performed on the basis of transmission containers (T-CONTs) and therefore by means of upstream service priority traffic flows. Our medium access control (MAC) protocol therefore ensures consistency in processing alike classes of service across all optical network units (ONUs) in agreement with their QoS figures. For evaluation of the MAC protocol performance, a simulator has been implemented in OPNET featuring a 40 km, 40 Gbps TWDM-PON with four stacked wavelengths at 10 Gbps each and 256 ONUs. Simulation results have confirmed the efficiency of allocating bandwidth to each wavelength and the significant increase of network traffic flow due to adaptive polling from 9.04 to 9.74 Gbps. The benefit of T-CONT-centric allocation has also been measured with respect to packet delay and queue occupancy, achieving low packet delay across all T-CONTs. Therefore, improved NG-PON2 performance and greater efficiency are obtained in this first demonstration of T-CONTs allocated to both wavelength and time.Peer reviewe

1959 Ruby Yearbook

A digitized copy of the 1959 Ruby, the Ursinus College yearbook.https://digitalcommons.ursinus.edu/ruby/1062/thumbnail.jp

Intelligent Reflecting Surface-assisted MU-MISOSystems with Imperfect Hardware: ChannelEstimation and Beamforming Design

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improvingthe spectral and energy efficiency of the fifth-generation (5G)and beyond networks. It is also noteworthy that an IRS canshape the reflected signal propagation. Most works in IRS-assisted systems have ignored the impact of the inevitable residualhardware impairments (HWIs) at both the transceiver hardwareand the IRS while any relevant works have addressed only simplescenarios, e.g., with single-antenna network nodes and/or withouttaking the randomness of phase noise at the IRS into account.In this work, we aim at filling up this gap by considering ageneral IRS-assisted multi-user (MU) multiple-input single-output(MISO) system with imperfect channel state information (CSI)and correlated Rayleigh fading. In parallel, we present a generalcomputationally efficient methodology for IRS reflect beamforming(RB) optimization. Specifically, we introduce an advantageouschannel estimation (CE) method for such systems accounting forthe HWIs. Moreover, we derive the uplink achievable spectralefficiency (SE) with maximal-ratio combining (MRC) receiver,displaying three significant advantages being: 1) its closed-formexpression, 2) its dependence only on large-scale statistics, and3) its low training overhead. Notably, by exploiting the first twobenefits, we achieve to perform optimization with respect to thethat can take place only at every several coherence intervals,and thus, reduces significantly the computational cost comparedto other methods which require frequent phase optimization.Among the insightful observations, we highlight that uncorrelatedRayleigh fading does not allow optimization of the SE, whichmakes the application of an IRS ineffective. Also, in the case thatthe phase drifts, describing the distortion of the phases in theRBM, are uniformly distributed, the presence of an IRS providesno advantage. The analytical results outperform previous worksand are verified by Monte-Carlo (MC) simulations

Evolutionary History and Novel Biotic Interactions Determine Plant Responses to Elevated CO2 and Nitrogen Fertilization

A major frontier in global change research is predicting how multiple agents of global change will alter plant productivity, a critical component of the carbon cycle. Recent research has shown that plant responses to climate change are phylogenetically conserved such that species within some lineages are more productive than those within other lineages in changing environments. However, it remains unclear how phylogenetic patterns in plant responses to changing abiotic conditions may be altered by another agent of global change, the introduction of non-native species. Using a system of 28 native Tasmanian Eucalyptus species belonging to two subgenera, Symphyomyrtus and Eucalyptus, we hypothesized that productivity responses to abiotic agents of global change (elevated CO2 and increased soil N) are unique to lineages, but that novel interactions with a nonnative species mediate these responses. We tested this hypothesis by examining productivity of 1) native species monocultures and 2) mixtures of native species with an introduced hardwood plantation species, Eucalyptus nitens, to experimentally manipulated soil N and atmospheric CO2. Consistent with past research, we found that N limits productivity overall, especially in elevated CO2 conditions. However, monocultures of species within the Symphyomyrtus subgenus showed the strongest response to N (gained 127% more total biomass) in elevated CO2 conditions, whereas those within the Eucalyptus subgenus did not respond to N. Root:shoot ratio (an indicator of resource use) was on average greater in species pairs containing Symphyomyrtus species, suggesting that functional traits important for resource uptake are phylogenetically conserved and explaining the phylogenetic pattern in plant response to changing environmental conditions. Yet, native species mixtures with E. nitens exhibited responses to CO2 and N that differed from those of monocultures, supporting our hypothesis and highlighting that both plant evolutionary history and introduced species will shape community productivity in a changing world