Utility Maximization for Uplink MU-MIMO: Combining Spectral-Energy Efficiency and Fairness

Driven by green communications, energy efficiency (EE) has become a new important criterion for designing wireless communication systems. However, high EE often leads to low spectral efficiency (SE), which spurs the research on EE-SE tradeoff. In this paper, we focus on how to maximize the utility in physical layer for an uplink multi-user multiple-input multipleoutput (MU-MIMO) system, where we will not only consider EE-SE tradeoff in a unified way, but also ensure user fairness. We first formulate the utility maximization problem, but it turns out to be non-convex. By exploiting the structure of this problem, we find a convexization procedure to convert the original nonconvex problem into an equivalent convex problem, which has the same global optimum with the original problem. Following the convexization procedure, we present a centralized algorithm to solve the utility maximization problem, but it requires the global information of all users. Thus we propose a primal-dual distributed algorithm which does not need global information and just consumes a small amount of overhead. Furthermore, we have proved that the distributed algorithm can converge to the global optimum. Finally, the numerical results show that our approach can both capture user diversity for EE-SE tradeoff and ensure user fairness, and they also validate the effectiveness of our primal-dual distributed algorithm

Assessment of topsoil removal as an effective method for vegetation restoration in farmed peatlands

Peatland areas have dramatically declined in the past century because of the demand for agriculture. Therefore, it is necessary to develop suitable techniques to preserve these unique ecosystems. We studied the effects of topsoil removal on vegetation restoration in silt- and sand-amended peatlands in Changbai Mountain, China. We observed that topsoil removal effectively improved soil nutrient levels and water holding capacity in the silt-amended peatland but exhibited no significant effect on the sand-amended peatland. Topsoil removal decreased the species richness in both silt- and sand-amended peatlands but did not have any effect on the plant cover and biomass in the sand-amended peatland. The coverage, density, and aboveground biomass of dominant species, namely, Carex schmidtii, significantly increased after topsoil removal in the silt-amended peatland. The target Carex species was absent from the sand-amended peatland. Redundancy analysis identified that the soil water content, soil organic carbon, total nitrogen, and total phosphorus explained the most variance in vegetation composition in the silt-amended peatland. Our results demonstrated that topsoil removal is necessary to reduce the weed seeds and promote the recolonization of peatland species, particularly the tussock-forming Carex, in the silt-amended peatland during restoration

Backward magnetostatic surface spin waves in exchange coupled Co/FeNi bilayers

Propagation of backward magnetostatic surface spin waves (SWs) in exchange coupled Co/FeNi bilayers are studied by using Brillouin light scattering (BLS) technique. Two types of SWs modes were identified in our BLS measurements. They are magnetostatic surface waves (MSSWs) mode and perpendicular standing spin waves (PSSWs) mode. The dispersion relations of MSSWs obtained from the Stokes and Anti-Stokes measurements display respectively positive and negative group velocities. The Anti-Stokes branch with positive phase velocities and negative group velocities, known as backward magnetostatic surface mode originates from the magnetostatic interaction of the bilayer. The experimental data are in good agreement with the theoretical calculations. Our results are useful for understanding the SWs propagation and miniaturizing SWs storage devices

Interpretable Machine Learning for COVID-19:An Empirical Study on Severity Prediction Task

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at S\~ao Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.Comment: 14 pages, 10 figure

Throughput and delay scaling laws for mobile overlaid wireless networks

In this paper, we study the throughput and delay scaling laws over two coexisting mobile networks. The primary network consists of n randomly distributed primary nodes which can operate as if the secondary network is absent. However, the secondary network with a higher density m=nβ, β>1 is required to adjust its protocol. By considering that both the primary and the secondary networks move according to random walk mobility model, we propose a multi-hop transmission scheme, and show that the secondary network can achieve the same throughput and delay tradeoff scaling law as in stand-alone network Ds(m)=Θ(mλs(m)). Furthermore, for primary network, it is shown that the tradeoff scaling law is given by Dp(n)=Θ(√nlognλp(n)), when the primary node is chosen as relay node. If the relay node is a secondary node, the scaling law is Dp(n)=Θ(√nβlognλp(n)). The novelties of this paper lie in: (i) detailed study of the delay scaling law for the primary network in the complex scenario where both the primary and the secondary networks are mobile; (ii) the impact of buffer delay on the two networks due to the presence of preservation region. We explicitly analyze the buffer delay and obtain an expression as DsrII(m)=Θ(1/√nβ-1αs(m))

Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks

In this paper, joint optimization of throughput and error rate via cooperative spectrum sensing in cognitive radio networks is investigated. An optimization problem is formulated, which aims to maximize the average achievable throughput of cooperating cognitive users while keeping the error rate at a lower level. This is a multi-variable nonconvex optimization problem. Instead of solving it directly, we propose an iterative algorithm which jointly optimizes the threshold and sensing time together to decrease the effect of the error and to increase the achievable throughput. We first prove that the local error rate of the cognitive user is a convex function of energy threshold and determine a closed-form for the optimal threshold which minimizes the error rate. Then we show that the AND rule is the optimal fusion rule to maximize the achievable throughput. Furthermore we determine the least number of cooperating cognitive users that can guarantee a minimum target error rate. This initial nonconvex problem is converted into a single variable convex optimization problem which can be successfully solved by common methods e.g. Newton’s method. Simulation results illustrate the fast convergence and effectiveness of the joint iterative algorithm

Cluster-based adaptive multispectrum sensing and access in cognitive radio networks

Spectrum sensing and access have been widely investigated in cognitive radio network for the secondary users to efficiently utilize and share the spectrum licensed by the primary user. We propose a cluster-based adaptive multispectrum sensing and access strategy, in which the secondary users seeking to access the channel can select a set of channels to sense and access with adaptive sensing time. Specifically, the spectrum sensing and access problem is formulated into an optimization problem, which maximizes the utility of the secondary users and ensures sufficient protection of the primary users and the transmitting secondary users from unacceptable interference. Moreover, we explicitly calculate the expected number of channels that are detected to be idle, or being occupied by the primary users, or being occupied by the transmitting secondary users. Spectrum sharing with the primary and transmitting secondary users is accomplished by adapting the transmission power to keep the interference to an acceptable level. Simulation results demonstrate the effectiveness of our proposed sensing and access strategy as well as its advantage over conventional sensing and access methods in terms of improving the achieved throughput and keeping the sensing overhead low.Published versio

A MAC sensing protocol design for data transmission with more protection to primary users

MAC protocols to sense channels for data transmission have been widely investigated for the secondary users to efficiently utilize and share the spectrum licensed by the primary user. One important issue associated with MAC protocols design is how the secondary users determine when and which channel they should sense and access without causing harmful interference to the primary user. In this paper, we jointly consider the MAC-layer spectrum sensing and channel access. Normal Spectrum Sensing (NSS) is required to be carried out at the beginning of each frame to determine whether the channel is idle. On detecting the available transmission opportunity, the secondary users employ CSMA for channel contention. The novelty is that, Fast Spectrum Sensing (FSS) is inserted after channel contention to promptly detect the return of the primary users. This is unlike most other MAC protocols which do not incorporate FSS. Having FSS, the primary user can benefit from more protection. A concrete protocol design is provided in this paper, and the throughput-collision tradeoff and utility-collision tradeoff problems are formulated to evaluate its performance. Simulation results demonstrate the efficiency of the proposed MAC protocol with FSS