Spatial Coded Modulation

In this paper, we propose a spatial coded modulation (SCM) scheme, which improves the accuracy of the active antenna detection by coding over the transmit antennas. Specifically, the antenna activation pattern in the SCM corresponds to a codeword in a properly designed codebook with a larger minimum Hamming distance than its counterpart conventional spatial modulation. As the minimum Hamming distance increases, the reliability of the active antenna detection is directly enhanced, which in turn improves the demodulation of the modulated symbols and yields a better system reliability. In addition to the reliability, the proposed SCM scheme also achieves a higher capacity with the identical antenna configuration compared to the conventional spatial modulation technique. Moreover, the proposed SCM scheme strikes a balance between spectral efficiency and reliability by trading off the minimum Hamming distance with the number of available codewords. The optimal maximum likelihood detector is first formulated. Then, a low-complexity suboptimal detector is proposed to reduce the computational complexity, which has a two-step detection. Theoretical derivations of the channel capacity and the bit error rate are presented in various channel scenarios, i.e., Rayleigh, Rician, Nakagami-m, imperfect channel state information, and spatial correlation. Further derivation on performance bounding is also provided to reveal the insight of the benefit of increasing the minimum Hamming distance. Numerical results validate the analysis and demonstrate that the proposed SCM outperforms the conventional spatial modulation techniques in both channel capacity and system reliability.Comment: 30 pages, 17 figure

A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming

This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD

NiSn bimetallic nanoparticles as stable electrocatalysts for methanol oxidation reaction

Nickel is an excellent alternative catalyst to high cost Pt and Pt-group metals as anode material in direct methanol fuel cells. However, nickel presents a relatively low stability under operation conditions, even in alkaline media. In this work, a synthetic route to produce bimetallic NiSn nanoparticles (NPs) with tuned composition is presented. Through co-reduction of the two metals in the presence of appropriate surfactants, 3–5¿nm NiSn NPs with tuned Ni/Sn ratios were produced. Such NPs were subsequently supported on carbon black and tested for methanol electro-oxidation in alkaline media. Among the different stoichiometries tested, the most Ni-rich alloy exhibited the highest electrocatalytic activity, with mass current density of 820¿mA¿mg-1 at 0.70¿V (vs. Hg/HgO). While this activity was comparable to that of pure nickel NPs, NiSn alloys showed highly improved stabilities over periods of 10,000¿s at 0.70¿V. We hypothesize this experimental fact to be associated to the collaborative oxidation of the byproducts of methanol which poison the Ni surface or to the prevention of the tight adsorption of these species on the Ni surface by modifying its surface chemistry or electronic density of states.Peer ReviewedPostprint (author's final draft

Outage Constrained Robust BeamformingOptimization for Multiuser IRS-AssistedAnti-Jamming Communications With Incomplete Information

Malicious jamming attacks have been regarded asa serious threat to Internet of Things (IoT) networks, which cansignificantly degrade the quality of service (QoS) of users. Thispaper utilizes an intelligent reflecting surface (IRS) to enhanceanti-jamming performance due to its capability in reconfiguringthe wireless propagation environment via dynamicly adjustingeach IRS reflecting elements. To enhance the communicationperformance against jamming attacks, a robust beamformingoptimization problem is formulated in a multiuser IRS-assistedanti-jamming communications scenario with or without imperfectjammer’s channel state information (CSI). In addition, we furtherconsider the fact that the jammer’s transmit beamforming cannot be known at BS. Specifically, with no knowledge of jammerstransmit beamforming, the total transmit power minimizationproblems are formulated subject to the outage probability re-quirements of legitimate users with the jammer’s statistical CSI,and signal-to-interference-plus-noise ratio (SINR) requirementsof legitimate users without the jammer’s CSI, respectively.By applying the Decomposition-based large deviation inequal-ity (DBLDI), Bernstein-type inequality (BTI), Cauchy-Schwarzinequality, and penalty non-smooth optimization method, weefficiently solve the initial intractable and non-convex problems.Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performanceand lower power-consumption compared to the non-IRS schemeand reveal the impact of key parameters on the achievable systemperformance

Compositionally tuned NixSn alloys as anode materials for lithium-ion and sodium-ion batteries with a high pseudocapacitive contribution

Nickel tin alloy nanoparticles (NPs) with tuned composition NixSn (0.6 ≤ x ≤ 1.9) were synthesized by a solution-based procedure and used as anode materials for Li-ion batteries (LIBs) and Na-ion batteries (SIBs). Among the compositions tested, Ni₀₉Sn-based electrodes exhibited the best performance in both LIBs and SIBs. As LIB anodes, Ni₀₉Sn-based electrodes delivered charge-discharge capacities of 980 mAh g⁻¹ after 340 cycles at 0.2 A g⁻¹ rate, which surpassed their maximum theoretical capacity considering that only Sn is lithiated. A kinetic characterization of the charge-discharge process demonstrated the electrode performance to be aided by a significant pseudocapacitive contribution that compensated for the loss of energy storage capacity associated to the solid-electrolyte interphase formation. This significant pseudocapacitive contribution, which not only translated into higher capacities but also longer durability, was associated to the small size of the crystal domains and the proper electrode composition. The performance of NixSn-based electrodes toward Na-ion storage was also characterized, reaching significant capacities above 200 mAh g⁻¹ at 0.1 A g⁻¹ but with a relatively fast fade over 120 continuous cycles. A relatively larger pseudocapacitive contribution was obtained in Ni Sn-based electrodes for SIBs when compared with LIBs, consistently with the lower contribution of the Na ion diffusion associated to its larger size

Co–Sn nanocrystalline solid solutions as anode materials in lithium-ion batteries with high pseudocapacitive contribution

Co–Sn solid-solution nanoparticles with Sn crystal structure and tuned metal ratios were synthesized by a facile one pot solution-based procedure involving the initial reduction of a Sn precursor followed by incorporation of Co within the Sn lattice. These nanoparticles were used as anode materials for Li-ion batteries. Among the different compositions tested, Co0.7Sn and Co0.9Sn electrodes provided the highest capacities with values above 1500 mAh¿g-1 at a current density of 0.2 A¿g-1 after 220 cycles, and up to 800 mAh¿g-1 at 1.0 A¿g-1 after 400 cycles. Up to 81¿% pseudocapacitance contribution was measured for these electrodes at a sweep rate of 1.0 mV¿s-1, thereby indicating fast kinetics and long durability. The excellent performance of Co–Sn nanoparticle alloy-based electrodes was attributed to both the small size of the crystal domains and their suitable composition, which buffered volume changes of Sn and contributed to a suitable electrode restructuration.Postprint (author's final draft

Superior methanol electrooxidation performance of (110)-faceted nickel polyhedral nanocrystals

We present the synthesis of (110)-faceted nickel polyhedral nanocrystals (NCs) and their characterization as electrocatalysts for the methanol oxidation reaction (MOR). Ni NCs were produced at 180 °C through the reduction in solution of a Ni salt. They were combined with carbon black and Nafion and deposited over glassy carbon to study their electrocatalytic properties. Electrodes based on (110)-faceted Ni NCs displayed a first order reaction with KOH in the concentration range from 0.1 M to 1.0 M. These electrodes were characterized by higher coverages of active species, but lower diffusion coefficients of the species limiting the reaction rate when compared with electrodes prepared from spherical Ni NCs. Overall, electrodes based on faceted Ni NCs displayed excellent performance with very high current densities, up to 61 mA cm, and unprecedented mass activities, up to 2 A mg, at 0.6 V vs. Hg/HgO in 1.0 M KOH containing 1.0 M methanol. These electrodes also displayed a notable stability. While they suffered an activity loss of ca. 30% during the first 10000 s of operation, afterward activity stabilized at very high current densities, ∼35 mA cm, and mass activities, ∼1.2 A mg, with only a 0.5% decrease during operation from 20000 to 30000 s

Distributed Stochastic Power Control in Ad-hoc Networks: A Nonconvex Case

Utility-based power allocation in wireless ad-hoc networks is inherently nonconvex because of the global coupling induced by the co-channel interference. To tackle this challenge, we first show that the globally optimal point lies on the boundary of the feasible region, which is utilized as a basis to transform the utility maximization problem into an equivalent max-min problem with more structure. By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing. The proposed distributed power control algorithm can guarantee global optimality at the cost of slow convergence due to simulated annealing involved in the global optimization. The geometric cooling scheme and suitable penalty parameters are used to improve the convergence rate. Next, by integrating the stochastic power control approach with the back-pressure algorithm, we develop a joint scheduling and power allocation policy to stabilize the queueing systems. Finally, we generalize the above distributed power control algorithms to multicast communications, and show their global optimality for multicast traffic.Comment: Contains 12 pages, 10 figures, and 2 tables; work submitted to IEEE Transactions on Mobile Computin