270 research outputs found
Robust Beamforming and Rate-Splitting Design for Next Generation Ultra-Reliable and Low-Latency Communications
The next generation ultra-reliable and low-latency communications (xURLLC)
need novel design to provide satisfactory services to the emerging
mission-critical applications. To improve the spectrum efficiency and enhance
the robustness of xURLLC, this paper proposes a robust beamforming and
rate-splitting design in the finite blocklength (FBL) regime for downlink
multi-user multi-antenna xURLLC systems. In the design, adaptive rate-splitting
is introduced to flexibly handle the complex inter-user interference and thus
improve the spectrum efficiency. Taking the imperfection of the channel state
information at the transmitter (CSIT) into consideration, a max-min user rate
problem is formulated to optimize the common and private beamforming vectors
and the rate-splitting vector under the premise of ensuring the requirements of
transmission latency and reliability of all the users. The optimization problem
is intractable due to the non-convexity of the constraint set and the infinite
constraints caused by CSIT uncertainties. To solve it, we convert the infinite
constraints into finite ones by the S-Procedure method and transform the
original problem into a difference of convex (DC) programming. A constrained
concave convex procedure (CCCP) and the Gaussian randomization based iterative
algorithm is proposed to obtain a local minimum. Simulation results confirm the
convergence, robustness and effectiveness of the proposed robust beamforming
and rate-splitting design in the FBL regime. It is also shown that the proposed
robust design achieves considerable performance gain in the worst user rate
compared with existing transmission schemes under various blocklength and block
error rate requirements.Comment: 12 pages, 9 figure
Social-Mobility-Aware Joint Communication and Computation Resource Management in NOMA-Enabled Vehicular Networks
The existing computation and communication (2C) optimization schemes for
vehicular edge computing (VEC) networks mainly focus on the physical domain
without considering the influence from the social domain. This may greatly
limit the potential of task offloading, making it difficult to fully boom the
task offloading rate with given power, resulting in low energy efficiency (EE).
To address the issue, this letter devotes itself to investigate
social-mobility-aware VEC framework and proposes a novel EE-oriented 2C
assignment scheme. In doing so, we assume that the task vehicular user (T-VU)
can offload computation tasks to the service vehicular user (S-VU) and the road
side unit (RSU) by non-orthogonal multiple access (NOMA). An optimization
problem is formulated to jointly assign the 2C resources to maximize the system
EE, which turns out to be a mixed integer non-convex objective function. To
solve the problem, we transform it into separated computation and communication
resource allocation subproblems. Dealing with the first subproblem, we propose
a social-mobility-aware edge server selection and task splitting algorithm
(SM-SSTSA) to achieve edge server selection and task splitting. Then, by
solving the second subproblem, the power allocation and spectrum assignment
solutions are obtained utilizing a tightening lower bound method and a
Kuhn-Munkres algorithm. Finally, we solve the original problem through an
iterative method. Simulation results demonstrate the superior EE performance of
the proposed scheme
Cooperative Beamforming Design for Multiple RIS-Assisted Communication Systems
Reconfigurable intelligent surface (RIS) provides a promising way to build
programmable wireless transmission environments. Owing to the massive number of
controllable reflecting elements on the surface, RIS is capable of providing
considerable passive beamforming gains. At present, most related works mainly
consider the modeling, design, performance analysis and optimization of
single-RIS-assisted systems. Although there are a few of works that investigate
multiple RISs individually serving their associated users, the cooperation
among multiple RISs is not well considered as yet. To fill the gap, this paper
studies a cooperative beamforming design for multi-RIS-assisted communication
systems, where multiple RISs are deployed to assist the downlink communications
from a base station to its users. To do so, we first model the general channel
from the base station to the users for arbitrary number of reflection links.
Then, we formulate an optimization problem to maximize the sum rate of all
users. Analysis shows that the formulated problem is difficult to solve due to
its non-convexity and the interactions among the decision variables. To solve
it effectively, we first decouple the problem into three disjoint subproblems.
Then, by introducing appropriate auxiliary variables, we derive the closed-form
expressions for the decision variables and propose a low-complexity cooperative
beamforming algorithm. Simulation results have verified the effectiveness of
the proposed algorithm through comparison with various baseline methods.
Furthermore, these results also unveil that, for the sum rate maximization,
distributing the reflecting elements among multiple RISs is superior to
deploying them at one single RIS
Joint transmit power allocation and splitting for swipt aided OFDM-IDMA in wireless sensor networks
In this paper, we propose to combine Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) with Simultaneous Wireless Information and Power Transfer (SWIPT), resulting in SWIPT aided OFDM-IDMA scheme for power-limited sensor networks. In the proposed system, the Receive Node (RN) applies Power Splitting (PS) to coordinate the Energy Harvesting (EH) and Information Decoding (ID) process, where the harvested energy is utilized to guarantee the iterative Multi-User Detection (MUD) of IDMA to work under sufficient number of iterations. Our objective is to minimize the total transmit power of Source Node (SN), while satisfying the requirements of both minimum harvested energy and Bit Error Rate (BER) performance from individual receive nodes. We formulate such a problem as a joint power allocation and splitting one, where the iteration number of MUD is also taken into consideration as the key parameter to affect both EH and ID constraints. To solve it, a sub-optimal algorithm is proposed to determine the power profile, PS ratio and iteration number of MUD in an iterative manner. Simulation results verify that the proposed algorithm can provide significant performance improvement
Research trend of epigenetics and depression: adolescents' research needs to strengthen
ObjectiveWith its high prevalence, depression's pathogenesis remains unclear. Recent attention has turned to the interplay between depression and epigenetic modifications. However, quantitative bibliometric analyses are lacking. This study aims to visually analyze depression epigenetics trends, utilizing bibliometric tools, while comprehensively reviewing its epigenetic mechanisms.MethodsUtilizing the Web of Science core dataset, we collected depression and epigenetics-related studies. Employing VOSViewer software, we visualized data on authors, countries, journals, and keywords. A ranking table highlighted field leaders.ResultsAnalysis encompassed 3,469 depression epigenetics studies published from January 2002 to June 2023. Key findings include: (1) Gradual publication growth, peaking in 2021; (2) The United States and its research institutions leading contributions; (3) Need for enhanced collaborations, spanning international and interdisciplinary efforts; (4) Keyword clustering revealed five main themes—early-life stress, microRNA, genetics, DNA methylation, and histone acetylation—highlighting research hotspots; (5) Limited focus on adolescent depression epigenetics, warranting increased attention.ConclusionTaken together, this study revealed trends and hotspots in depression epigenetics research, underscoring global collaboration, interdisciplinary fusion, and multi-omics data's importance. It discussed in detail the potential of epigenetic mechanisms in depression diagnosis and treatment, advocating increased focus on adolescent research in this field. Insights aid researchers in shaping their investigative paths toward understanding depression's epigenetic mechanisms and antidepressant interventions
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