Space-division multiple access (SDMA) utilizes linear precoding to separate users in the spatial domain and relies on
fully
treating any residual multi-user interference as noise. Non-orthogonal ...multiple access (NOMA) uses linearly precoded superposition coding with successive interference cancellation (SIC) to superpose users in the power domain and relies on user grouping and ordering to enforce some users to fully decode and cancel interference created by other users.
In this paper, we argue that to efficiently cope with the high throughput, heterogeneity of quality of service (QoS), and massive connectivity requirements of future multi-antenna wireless networks, multiple access design needs to depart from those two extreme interference management strategies, namely fully treat interference as noise (as in SDMA) and fully decode interference (as in NOMA).
Considering a multiple-input single-output broadcast channel, we develop a novel multiple access framework, called rate-splitting multiple access (RSMA). RSMA is a more general and more powerful multiple access for downlink multi-antenna systems that contains SDMA and NOMA as special cases. RSMA relies on linearly precoded rate-splitting with SIC to decode part of the interference and treat the remaining part of the interference as noise. This capability of RSMA to
partially
decode interference and partially treat interference as noise enables to softly bridge the two extremes of fully decoding interference and treating interference as noise and provides room for rate and QoS enhancements and complexity reduction.
The three multiple access schemes are compared, and extensive numerical results show that RSMA provides a smooth transition between SDMA and NOMA and outperforms them both in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths, and qualities of channel state information at the transmitter). Moreover, RSMA provides rate and QoS enhancements over NOMA at a lower computational complexity for the transmit scheduler and the receivers (number of SIC layers).
In a Non-Orthogonal Unicast and Multicast (NOUM) transmission system, a multicast stream intended to all the receivers is superimposed in the power domain on the unicast streams. One layer of ...Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding its intended unicast stream. In this paper, we first show that a linearly-precoded 1-layer Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast messages into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is better reused for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference among the unicast streams. We further propose multi-layer transmission strategies based on the generalized RS and power-domain Non-Orthogonal Multiple Access (NOMA). Two different objectives are studied for the design of the precoders, namely, maximizing the Weighted Sum Rate (WSR) of the unicast messages and maximizing the system Energy Efficiency (EE), both subject to Quality of Service (QoS) rate requirements of all messages and a sum power constraint. A Weighted Minimum Mean Square Error (WMMSE)-based algorithm and a Successive Convex Approximation (SCA)-based algorithm are proposed to solve the WSR and EE problems, respectively. Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU-LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments (with a diversity of channel directions, channel strengths and qualities of channel state information at the transmitter) and network loads (underloaded and overloaded regimes). It is superior for the downlink multi-antenna NOUM transmission.
We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. ...However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches.
Post-traumatic stress disorder (PTSD) represents a global public health concern, affecting about 1 in 20 individuals. The symptoms of PTSD include intrusiveness (involuntary nightmares or ...flashbacks), avoidance of traumatic memories, negative alterations in cognition and mood (such as negative beliefs about oneself or social detachment), increased arousal and reactivity with irritable reckless behavior, concentration problems, and sleep disturbances. PTSD is also highly comorbid with anxiety, depression, and substance abuse. To advance the field from subjective, self-reported psychological measurements to objective molecular biomarkers while considering environmental influences, we examined a unique cohort of Israeli veterans who participated in the 1982 Lebanon war. Non-invasive oral 16S RNA sequencing was correlated with psychological phenotyping. Thus, a microbiota signature (i.e., decreased levels of the bacteria sp_HMT_914, 332 and 871 and Noxia) was correlated with PTSD severity, as exemplified by intrusiveness, arousal, and reactivity, as well as additional psychopathological symptoms, including anxiety, hostility, memory difficulties, and idiopathic pain. In contrast, education duration correlated with significantly increased levels of sp_HMT_871 and decreased levels of Bacteroidetes and Firmicutes, and presented an inverted correlation with adverse psychopathological measures. Air pollution was positively correlated with PTSD symptoms, psychopathological symptoms, and microbiota composition. Arousal and reactivity symptoms were correlated with reductions in transaldolase, an enzyme controlling a major cellular energy pathway, that potentially accelerates aging. In conclusion, the newly discovered bacterial signature, whether an outcome or a consequence of PTSD, could allow for objective soldier deployment and stratification according to decreases in sp_HMT_914, 332, 871, and Noxia levels, coupled with increases in Bacteroidetes levels. These findings also raise the possibility of microbiota pathway-related non-intrusive treatments for PTSD.
Due to various green initiatives, renewable energy will be massively incorporated into the future smart grid. However, the intermittency of the renewables may result in power imbalance, thus ...adversely affecting the stability of a power system. Frequency regulation may be used to maintain the power balance at all times. As electric vehicles (EVs) become popular, they may be connected to the grid to form a vehicle-to-grid (V2G) system. An aggregation of EVs can be coordinated to provide frequency regulation services. However, V2G is a dynamic system where the participating EVs come and go independently. Thus, it is not easy to estimate the regulation capacities for V2G. In a preliminary study, we modeled an aggregation of EVs with a queueing network, whose structure allows us to estimate the capacities for regulation-up and regulation-down separately. The estimated capacities from the V2G system can be used for establishing a regulation contract between an aggregator and the grid operator, and facilitating a new business model for V2G. In this paper, we extend our previous development by designing a smart charging mechanism that can adapt to given characteristics of the EVs and make the performance of the actual system follow the analytical model.
Understanding demographic difference in facial expression of happiness has crucial implications on social communication. However, prior research on facial emotion expression has mostly focused on the ...effect of a single demographic factor (typically gender, race, or age), and is limited by the small image dataset collected in laboratory settings. First, we used 30,000 (4800 after pre-processing) real-world facial images from Flickr, to analyze the facial expression of happiness as indicated by the intensity level of two distinctive facial action units, the Cheek Raiser (AU6) and the Lip Corner Puller (AU12), obtained automatically via a deep learning algorithm that we developed, after training on 75,000 images. Second, we conducted a statistical analysis on the intensity level of happiness, with both the main effect and the interaction effect of three core demographic factors on AU12 and AU6. Our results show that females generally display a higher AU12 intensity than males. African Americans tend to exhibit a higher AU6 and AU12 intensity, when compared with Caucasians and Asians. The older age groups, especially the 40-69-year-old, generally display a stronger AU12 intensity than the 0-3-year-old group. Our interdisciplinary study provides a better generalization and a deeper understanding on how different gender, race and age groups express the emotion of happiness differently.
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to ...accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets. We develop a deep learning-based 3D cell segmentation pipeline, 3DCellSeg, to address these challenges. Compared to the existing methods, our approach carries the following novelties: (1) a robust two-stage pipeline, requiring only one hyperparameter; (2) a light-weight deep convolutional neural network (3DCellSegNet) to efficiently output voxel-wise masks; (3) a custom loss function (3DCellSeg Loss) to tackle the clumped cell problem; and (4) an efficient touching area-based clustering algorithm (TASCAN) to separate 3D cells from the foreground masks. Cell segmentation experiments conducted on four different cell datasets show that 3DCellSeg outperforms the baseline models on the ATAS (plant), HMS (animal), and LRP (plant) datasets with an overall accuracy of 95.6%, 76.4%, and 74.7%, respectively, while achieving an accuracy comparable to the baselines on the Ovules (plant) dataset with an overall accuracy of 82.2%. Ablation studies show that the individual improvements in accuracy is attributable to 3DCellSegNet, 3DCellSeg Loss, and TASCAN, with the 3DCellSeg demonstrating robustness across different datasets and cell shapes. Our results suggest that 3DCellSeg can serve a powerful biomedical and clinical tool, such as histo-pathological image analysis, for cancer diagnosis and grading.
State estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and ...interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.
Cooperative Rate-Splitting (CRS) strategy, relying on linearly precoded rate-splitting at the transmitter and opportunistic transmission of the common message by the relaying user, has recently been ...shown to outperform typical Non-cooperative Rate-Splitting (NRS), Cooperative Non-Orthogonal Multiple Access (C-NOMA) and Space Division Multiple Access (SDMA) in a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC) with user relaying. In this work, the existing two-user CRS transmission strategy is generalized to the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-user case. We study the problem of jointly optimizing the precoders, message split, time slot allocation, and relaying user scheduling with the objective of maximizing the minimum rate among users subject to a transmit power constraint at the base station. As the user scheduling problem is discrete and the entire problem is non-convex, we propose a two-stage low-complexity algorithm to solve the problem. Both centralized and decentralized relaying protocols based on selecting <inline-formula> <tex-math notation="LaTeX">K_{1} </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">K_{1} < K </tex-math></inline-formula>) strongest users are first proposed followed by a Successive Convex Approximation (SCA)-based algorithm to jointly optimize the time slot, precoders and message split. Numerical results show that by applying the proposed two-stage algorithm, the worst-case achievable rate achieved by CRS is significantly increased over that of NRS and SDMA in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel strengths). Importantly, the proposed SCA-based algorithm dramatically reduces the computational complexity without any rate loss compared with the conventional algorithm in the literature of CRS. Therefore, we conclude that the proposed <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-user CRS combined with the two-stage algorithm is more powerful than the existing transmission schemes.
Caching popular contents at base stations (BSs) of a heterogeneous cellular network (HCN) avoids frequent information passage from content providers to the network edge, thereby reducing latency and ...alleviating traffic congestion in backhaul links. The potential of caching at the network edge for tackling 5G challenges has motivated recent studies on optimal content placement in large-scale HCNs. However, due to the complexity of the network performance analysis, the existing strategies were mostly based on approximation, heuristics, and intuition. In general, optimal strategies for content placement in HCNs remain largely unknown and deriving them forms the theme of this paper. To this end, we adopt the popular random HCN model, where K tiers of BSs are modeled as independent Poisson point processes distributed in the plane with different densities. Furthermore, the random caching scheme is considered, where each of a given set of M files with corresponding popularity measures is placed at each BS of a particular tier with a corresponding probability, called placement probability. The probabilities are identical for all BSs in the same tier but vary over tiers, giving the name tier-level content placement. We consider the network performance metric, hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user. Leveraging existing results on HCN performance, we maximize the hit probability over content placement probabilities, which yields the optimal tierlevel placement policies. For the case of uniform received signalto-interference (SIR) thresholds for successful transmissions for BSs in different tiers, the policy is in closed-form, where the placement probability for a particular file is proportional to the square-root of the corresponding popularity measure with an offset depending on BS caching capacities. For the general case of non-uniform SIR thresholds, the optimization problem is non-convex and a sub-optimal placement policy is designed by approximation, which has a similar structure as in the case of uniform SIR thresholds and shown by simulation to be close-tooptimal.
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