In this paper, we study linear precoding for multiple-input multiple-output (MIMO) multiple access channels (MAC) with discrete-constellation inputs. We derive the constellation-constrained capacity region for the MIMO MAC with an arbitrary number of users. Due to the non-concavity of the objective function, we obtain the necessary conditions for the weighted sum rate (WSR) maximization problem through Karush-Kuhn-Tucker (KKT) analysis. To find the optimal precoding matrices, we propose an iterative algorithm utilizing alternating optimization strategy and gradient descent update. Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio (SNR) is in the medium range, our proposed algorithm offers significantly higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity. Furthermore, the bit error rate (BER) results of a low-density parity-check (LDPC) coded system also indicate that the system with the proposed linear precoder achieves significant gains over other methods.
ABSTRACT Carbapenem-resistant Enterobacteriaceae have become widely prevalent globally because of antibiotic misuse and the spread of drug-resistant plasmids, where carbapenem-resistant Escherichia coli (CREC) is one of the most common and prevalent pathogens. Furthermore, E. coli has been identified as a member of normal gut flora and does not cause disease under normal circumstances. However, certain strains of E. coli , due to the expression of virulence genes, can cause severe intestinal and extra-intestinal infections. Therefore, clinically, drug resistance and pathogenic E. coli strains are significantly challenging to treat. In this study, a novel CREC strain DC8855 was isolated from the ascites of a patient with intestinal perforation, identified as a novel sequence type 12531 (ST12531) and an unreported serotype O8:H7. It was revealed that the resistance of ST12531 CREC was predominantly conferred by an IncFII(K) plasmid carrying bla NDM-4 . Furthermore, phylogenetic analysis indicated that this is the first discovery of such plasmids in China and the first identification in E. coli . Moreover, regarding virulence, the swimming assays, qRT-PCR, and in vitro intestinal barrier model indicated that DC8855 had significantly higher motility, flagella gene expression, and intestinal epithelial cell barrier migration ability than the other sequence types CREC strains (ST167 and ST410). In conclusion, this study identified novel CREC which was multidrug resistant as well as enteropathogenic and therefore requires continuous monitoring.
Travelers’ route choice behavior has become one of the crucial issues in urban transportation system, given the fact that the network-wide traffic state may be influenced by the route choice behavior. There are often multiple routes between an origin-destination (OD) pair. Travelers may choose different routes based on the individual preferences. Many factors may influence travelers’ route choice, such as distance, average travel time, travel time reliability, comfortableness, safety, fuel consumption, etc. We usually assume that a decision-making of route choice is a reflection of potential preferences for each available route and the traveler chooses the route with the highest utility. Most of the current studies analyzed the route choice behavior based on Stated Preference (SP) questionnaire survey data. The individual characteristics and route choice preferences in hypothetical situations can be collected by SP. However, respondents have to assume choice set instead of experiencing the route choice practically in SP survey. Actual route choice behavior on real-world transportation network can not be adequately investigated. Respondents may simply answer questions that they would not realistically pursue. Also, inherent limitations of questionnaire survey related to honest, accurate and bias-free reporting are difficult to avoid. In recent years, advancements in traffic information collection technologies, including trip records from in-vehicle GPS device, can facilitate the investigation of influences dominates route choice decisions. In this study, rather than questionnaire dataset, we aim to explore travelers’ route choice behavior from large-scale GPS trip records. There are still two main challenges introduced by using general GPS trip records. First, there are usually a large number of alternatives between an OD pair, resulting in an intractable problem of estimation of a discrete choice model with a full choice set. How to sample alternatives from the full choice set and the size of the sampled choice set needs to be tackled. Second, different from data collected from hypothesis-oriented questionnaires, how to indicate the impact of factors on route choice behavior in different situations is crucial. To overcome these difficulties, we propose to apply the multinomial logit (MNL) model with sampling of alternatives to explore travelers’ route choice preference from real-world GPS trajectories. First, we generate the route choice set by using a stochastic path generation algorithm in an experienced transportation network. We assume that local commuters are good at choosing a path due to their experiences accumulated over years. When facing multiple route choices, most of the people who are unfamiliar with the local traffic condition may simply select the shortest path while the experienced commuters may utilize their driving experience to choose the best one based on their preferences. Naturally, the link usage frequency reflects the driving experience which can be expressed by the degree of familiarity. We propose to penalize the link travel times by the link usage frequency. Then, the experienced transportation network can be constructed by using the penalized link travel time. A biased random walk algorithm is applied to generate a set of shortest paths with selection probabilities on the experienced transportation network with penalized link travel times. Second, we estimate the MNL model with sampling of alternatives using classical conditional maximum likelihood estimation (MLE). The conditional probability that an individual n will choose alternative i conditional on the sampling subset for the individual can be derived using the Bayes theorem.Since the traditional MNL model is restricted by the independence from irrelevant alternative (IIA) property, which does not hold the correlation problem of overlapping routes, we use the modified MNL model, i.e., the path size logit (PSL) model in which an additional term is introduced to capture the correlation of routes, to overcome the overlapping problem. We take the GPS trajectories recorded in Toyota city, Japan as the experiment data. The data is collected from 153 private cars in 1 month, from March 1 st to March 31 st , 2011. After map-matching and some basic data cleaning work, 7245 trip records with 5667 OD pairs are extracted. The route characteristics include path length, average path travel time, path travel time variability, fuel consumption, and average speed. A correlation matrix is computed to detect potential collinearity between all pairs of these explanatory variables included in model estimation. We only choose path length, average speed, and path travel time variability as the explanatory variables because path length, average path travel time, and fuel consumption are highly correlated. To investigate the impact of route characteristics on utility function, we normalize the path length, average speed, and path travel time variability so that a larger estimated coefficient indicates the corresponding variable has a greater impact on travelers’ utility. To investigate the impact of individual heterogeneity and journey attributes on route choice preference, we group the OD pairs by gender, age, departure time, and OD Euclidean distance, and then estimate the route choice models respectively. We compare the choice probabilities of four representative paths, i.e., shortest distance, maximum average speed, and least path travel time variability. Finally, the effect of individual heterogeneity and journey attributes on route choice preference can be demonstrated by the difference of choice probability.
This study aims to find an experientially reliable path considering travel time uncertainty and driving experience of local probe vehicle drivers. Accordingly, a two‐stage route‐finding procedure is proposed. First, a set of candidate paths is built by using the hyperpath algorithm, where the choice probability is assigned to each link with uncertain travel time. Second, the shortest path algorithm is applied to find the experientially reliable path on the graph of hyperpath where the modified link cost is penalised based on the link choice probability derived from hyperpath algorithm and the driving experience of local drivers. Four kinds of optimal path in a real‐world network are compared with the observed one. It is found that the proposed path has the most similarity with the observed path and it has a higher degree of familiarity and reasonable time and distance.
Conflict analysis using surrogate safety measures (SSMs) has become an efficient approach to investigate safety issues. The state-of-the-art studies largely resort to video images taken from high buildings. However, it suffers from heavy labor work, high cost of maintenance, and even security restrictions. Data collection and processing remains a common challenge to traffic conflict analysis. Unmanned Aerial Systems (UASs) or Unmanned Aerial Vehicles (UAVs), known for easy maneuvering, outstanding flexibility, and low costs, are considered to be a novel aerial sensor. By taking full advantage of the bird’s eye view offered by UAV, this study, as a pioneer work, applied UAV videos for surrogate safety analysis of pedestrian-vehicle conflicts at one urban intersection in Beijing, China. Aerial video sequences for a period of one hour were analyzed. The detection and tracking systems for vehicle and pedestrian trajectory data extraction were developed, respectively. Two SSMs, that is, Postencroachment Time (PET) and Relative Time to Collision (RTTC), were employed to represent how spatially and temporally close the pedestrian-vehicle conflict is to a collision. The results of analysis showed a high exposure of pedestrians to traffic conflict both inside and outside the crosswalk and relatively risking behavior of right-turn vehicles around the corner. The findings demonstrate that UAV can support intersection safety analysis in an accurate and cost-effective way.
To mitigate the range anxiety problem of electric bus system, wireless power transfer is regarded as one of the emerging technologies for long-term range extension. Previous studies have discussed the optimization problem of the power track deployment. However, the en-route charging strategy also significantly influences the operation cost besides the power track, which is yet to be investigated sufficiently. To fill this gap, a new wireless charging model for optimizing the energy cost is proposed. In particular, the cost of battery and the time-of-use electricity price are taken into account. Firstly, a microscopic power consumption model considering passenger flows and automobile dynamics is developed to estimate the charging cost. Then, a relaxation approach based on penalty function and grey wolf optimization (GWO) algorithm is developed to solve the non-deterministic polynomial-hard (NP-hard) problem with complex multidimensional variables and multiple inequality constraints. And the performance of the proposed charging strategy is verified in a real-world bus line via numerical simulation. A sensitivity analysis is conducted to quantify the marginal impact of the unit cost of battery capacity on the total energy cost. Finally, the computational performance of the proposed algorithm (GWO) is validated by comparing other outstanding methods such as genetic algorithm (GA), particle swarm optimization (PSO) and CPLEX solvers.
In this paper, we investigate the mutual information maximization for a multiple-input multiple-output (MIMO) system with simultaneous wireless information and power transfer (SWIPT), where perfect channel state information (CSI) is known at the transmitter. We formulate an optimization problem under the constraints of transmit power and harvested energy. Different from previous work, we assume the input signals to be taken from finite-alphabet inputs instead of Gaussian signals. The formulated problem is NP-hard, so a global optimal solution can not be found with polynomial time complexity. However, by exploiting the structure of the problem, this optimization problem can be transformed into a semidefinite programming (SDP) problem, and then a near optimal algorithm based on semidefinite relaxation (SDR) technique is developed. Simulation results show the efficacy of the proposed algorithm.
This paper considers the precoder design for dual-hop amplify-and-forward relay networks and formulates the design from the standpoint of finite-alphabet inputs. In particular, the mutual information is employed as the utility function, which, however, results in a nonlinear and nonconcave problem. This paper exploits the structure of the optimal precoder that maximizes the mutual information and develops a two-step algorithm based on convex optimization and optimization on the Stiefel manifold. By doing so, the proposed algorithm is insensitive to initial point selection and able to achieve a near global optimal precoder solution. Besides, it converges fast and offers high mutual information gain. These advantages are verified by numerical examples, which also show the large performance gain in mutual information also represents the large gain in the coded bit-error rate.
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