Soft Pilot Reuse and Multi-Cell Block Diagonalization Precoding for Massive MIMO Systems

The users at cell edge of a massive multiple-input multiple-output (MIMO) system suffer from severe pilot contamination, which leads to poor quality of service (QoS). In order to enhance the QoS for these edge users, soft pilot reuse (SPR) combined with multi-cell block diagonalization (MBD) precoding are proposed. Specifically, the users are divided into two groups according to their large-scale fading coefficients, referred to as the center users, who only suffer from modest pilot contamination and the edge users, who suffer from severe pilot contamination. Based on this distinction, the SPR scheme is proposed for improving the QoS for the edge users, whereby a cell-center pilot group is reused for all cell-center users in all cells, while a cell-edge pilot group is applied for the edge users in the adjacent cells. By extending the classical block diagonalization precoding to a multi-cell scenario, the MBD precoding scheme projects the downlink transmit signal onto the null space of the subspace spanned by the inter-cell channels of the edge users in adjacent cells. Thus, the inter-cell interference contaminating the edge users' signals in the adjacent cells can be efficiently mitigated and hence the QoS of these edge users can be further enhanced. Our theoretical analysis and simulation results demonstrate that both the uplink and downlink rates of the edge users are significantly improved, albeit at the cost of the slightly decreased rate of center users.Comment: 13 pages, 12 figures, accepted for publication in IEEE Transactions on Vehicular Technology, 201

Multi-user MIMO-OFDM for indoor visible light communication systems

In this paper, we investigate an indoor multiuser visible light communication system employing multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM). For each subcarrier in OFDM, the corresponding precoding matrix is calculated in the frequency domain to eliminate multi-user interference. The distances of the multiple transmitter-receiver links are different, which results in various temporal delays and phase differences in the frequency domain. Phase information is firstly considered, whereby complex instead of real channel matrices are used for precoding, which reduces the channel correlation and achieves better performance. Moreover, two DC bias and scaling factor calculation schemes are proposed, and their performances are compared with zero forcing and minimum mean-squared error (MMSE) precoding techniques

A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data

The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving PR-AUC of 0.891 (95$$ CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.908 (95$$ CI: 0.870-0.935) in predicting ICU admission.Comment: 14 page

Ultrahigh mobility and efficient charge injection in monolayer organic thin-film transistors on boron nitride

Organic thin-film transistors (OTFTs) with high mobility and low contact resistance have been actively pursued as building blocks for low-cost organic electronics. In conventional solution-processed or vacuum-deposited OTFTs, due to interfacial defects and traps, the organic film has to reach a certain thickness for efficient charge transport. Using an ultimate monolayer of 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT) molecules as an OTFT channel, we demonstrate remarkable electrical characteristics, including intrinsic hole mobility over 30 cm2/Vs, Ohmic contact with 100 Ω · cm resistance, and band-like transport down to 150 K. Compared to conventional OTFTs, the main advantage of a monolayer channel is the direct, nondisruptive contact between the charge transport layer and metal leads, a feature that is vital for achieving low contact resistance and current saturation voltage. On the other hand, bilayer and thicker C8-BTBT OTFTs exhibit strong Schottky contact and much higher contact resistance but can be improved by inserting a doped graphene buffer layer. Our results suggest that highly crystalline molecular monolayers are promising form factors to build high-performance OTFTs and investigate device physics. They also allow us to precisely model how the molecular packing changes the transport and contact properties

Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

Background Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression are yet to be fully explored. Objective: This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. Methods We used two ambulatory datasets (N=71 and N=215) whose acceleration signals were collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effect models were used to explore the associations between daily-life gait features and depression symptom severity measured by GDS-15 and PHQ-8 self-reported questionnaires. The likelihood ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. Results Higher depression symptom severity was found to be significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both datasets. The linear regression model with long-term daily-life gait features ( =0.30) fitted depression scores significantly better (LR test: P value = .001) than the model with only laboratory gait features ( =0.06). Conclusion This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no context. Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression: No Expectations, Sleep, Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic emergence and associations with depression, we compared behavioral (from wearables) and linguistic characteristics across identified topics. The correlation between topic shifts and changes in depression severity over time was also investigated, indicating the importance of longitudinally monitoring language use. We also tested the BERTopic model on a similar smaller dataset (356 speech recordings from 57 participants), obtaining some consistent results. In summary, our findings demonstrate specific speech topics may indicate depression severity. The presented data-driven workflow provides a practical approach to collecting and analyzing large-scale speech data from real-world settings for digital health research

Shuffled iterative receiver for LDPC-coded MIMO systems

In this paper, we consider the low density parity check (LDPC) coded multi-input multi-output (MIMO) system with iterative detection and decoding (IDD). Since the traditional frame-by-frame receiver scheme suffers from a huge decoding delay, we propose an efficient scheme with a shuffled structure between the demapper and decoder, which adopts group vertical shuffled belief propagation (BP) algorithm. The proposed shuffled iterative receiver converges faster and significantly reduces the delay introduced by the IDD process. Simulation results demonstrate that our proposed shuffled iterative receiver exhibits several tenths dB of signal-to-noise ratio gain in comparison to the existing schemes, while imposing a much lower average number of iterations for the IDD process

Location-based channel estimation and pilot assignment for massive MIMO systems

In this paper, a location-based channel estimation algorithm is proposed for massive multi-input multi-output (MIMO) systems. By utilizing the property of the steering vector, a fast Fourier transform (FFT)-based post-processing is introduced after the conventional pilot-aided channel estimation. Under the condition that different users with the same pilot sequence have non-overlapping angle-of-arrivals (AOAs), the proposed channel estimation algorithm is capable of distinguishing these users effectively. To cooperate with the location-based channel estimation, a pilot assignment algorithm is also proposed to ensure that the users in different cells using the same pilot sequence have differentAOAs at base station. The simulation results demonstrate that the proposed scheme can reduce the inter-cell interference caused by the reuse of the pilot sequence and thus improves the overall system performance significantly