| 1. |
- Birney, Ewan, et al.
(författare)
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Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project
- 2007
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 447:7146, s. 799-816
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Tidskriftsartikel (refereegranskat)abstract
- We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.
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| 2. |
- Zhang, Shuo, et al.
(författare)
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An Energy-Efficient Continuous Deployment Scheme for UAV-D2D Networks
- 2023
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Ingår i: ICC 2023. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 222-227
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Konferensbidrag (refereegranskat)abstract
- Unmanned aerial vehicles (UAVs) are regarded as powerful assistance for emergency communications due to their disregard for the limitations of the geographic environment. In this paper, we consider a multi-UAV-assisted wireless emergency communication system, where UAVs are applied as aerial base stations to serve terrestrial device-to-device users (DUs). Our goal is to maximize the UAVs' energy efficiency (EE) through the user grouping strategy with a joint optimization scheme regarding UAVs' trajectories and transmit power. To deal with the resultant mix-integer non-linear programming problem, we divide the optimization process into two stages. In the first stage, we discretize the trajectory into a set of stop points (SPs). Then, the grouping of DUs is achieved by pre-planning the location and optimization range of SPs. In the second stage, with the determined DU grouping strategy, we apply Dinkelbach method and successive convex approximation to convert the original problem into a solvable convex optimization problem. Finally, simulation results verify the effectiveness of our proposed algorithm, which has better performance compared with benchmark schemes in the low user-density region.
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| 3. |
- Chen, Hao, et al.
(författare)
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Federated Learning over Wireless IoT Networks with Optimized Communication and Resources
- 2022
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; 9:17, s. 16592-16605
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Tidskriftsartikel (refereegranskat)abstract
- To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two sub-problems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables including power variables, bandwidth variables and transmission indicators. Then a linear-search based power and bandwidth allocation method is developed. Given appropriate hyper-parameters, we show that the proposed communication-efficient federated learning (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.
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| 4. |
- Geng, Zhaoquan, et al.
(författare)
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Zero-Shot Recurrent Graph Neural Networks for Beam Prediction in Non-Terrestrial Networks
- 2022
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Ingår i: 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1400-1405
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Konferensbidrag (refereegranskat)abstract
- Beam management has been considered as one of the most challenging issues in mobile communications, especially in non-terrestrial networks with high-speed low-earth orbit satellites. When the user and the satellite are moving, the satellite equipped with multiple antennas needs to sweep different beam directions periodically to provide continuous service to the user. To reduce the signaling overhead in beam sweeping, we develop a recurrent graph neural network (RGNN) to predict the next beam direction that maximizes the signal strength. Compared with state-of-the-art recurrent neural networks with gated recurrent units (GRU), RGNN reduces the number of training parameters by 99.8% by exploiting a graph representation of the beams. To improve the generalization ability of RGNN in satellite communications with dynamic antenna directions, we integrate RGNN with a first-order meta-learning algorithm. After meta training, no sample is required to fine-tune the RGNN in unseen scenarios, and this approach is referred to as zero-shot meta-learning. Our simulation results show that the RGNN outperforms the GRU in terms of the convergence time and generalization ability, and the prediction accuracy with zero-shot meta-learning can be up to 97%. Even for unseen antenna directions, instead of sweeping all the neighboring beam directions, the satellite only needs to send reference signals towards few beam directions (e.g., two out of six neighboring beam directions) according to the output of the RGNN. In this way, the signaling overhead for beam sweeping can be reduced by 66%.
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| 5. |
- Gerstein, Mark B, et al.
(författare)
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What is a gene, post-ENCODE? : History and updated definition
- 2007
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Ingår i: Genome Research. - : Cold Spring Harbor Laboratory. - 1088-9051 .- 1549-5469. ; 17:6, s. 669-681
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Tidskriftsartikel (refereegranskat)abstract
- While sequencing of the human genome surprised us with how many protein-coding genes there are, it did not fundamentally change our perspective on what a gene is. In contrast, the complex patterns of dispersed regulation and pervasive transcription uncovered by the ENCODE project, together with non-genic conservation and the abundance of noncoding RNA genes, have challenged the notion of the gene. To illustrate this, we review the evolution of operational definitions of a gene over the past century--from the abstract elements of heredity of Mendel and Morgan to the present-day ORFs enumerated in the sequence databanks. We then summarize the current ENCODE findings and provide a computational metaphor for the complexity. Finally, we propose a tentative update to the definition of a gene: A gene is a union of genomic sequences encoding a coherent set of potentially overlapping functional products. Our definition side-steps the complexities of regulation and transcription by removing the former altogether from the definition and arguing that final, functional gene products (rather than intermediate transcripts) should be used to group together entities associated with a single gene. It also manifests how integral the concept of biological function is in defining genes.
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| 6. |
- Lei, Wanlu, et al.
(författare)
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Joint Beam Training and Data Transmission Control for mmWave Delay-Sensitive Communications : A Parallel Reinforcement Learning Approach
- 2022
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Ingår i: IEEE Journal of Selected Topics in Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1932-4553. ; 16:3, s. 447-459
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Tidskriftsartikel (refereegranskat)abstract
- Future communication networks call for new solutions to support their capacity and delay demands by leveraging potentials of the millimeter wave (mmWave) frequency band. However, the beam training procedure in mmWave systems incurs significant overhead as well as huge energy consumption. As such, deriving an adaptive control policy is beneficial to both delay-sensitive and energy-efficient data transmission over mmWave networks. To this end, we investigate the problem of joint beam training and data transmission control for mmWave delay-sensitive communications in this paper. Specifically, the considered problem is firstly formulated as a constrained Markov Decision Process (MDP), which aims to minimize the cumulative energy consumption over the whole considered period of time under delay constraint. By introducing a Lagrange multiplier, we transform the constrained MDP into an unconstrained one, which is then solved via a parallel-rollout-based reinforcement learning method in a data-driven manner. Our numerical results demonstrate that the optimized policy via parallel-rollout significantly outperforms other baseline policies in both energy consumption and delay performance.
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| 7. |
- Ma, Xinying, et al.
(författare)
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Cooperative Beamforming for RIS-Aided Cell-Free Massive MIMO Networks
- 2023
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.
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| 8. |
- Wang, Meng, et al.
(författare)
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Joint Computation Offloading and Resource Allocation for MIMO-NOMA Assisted Multi-User MEC Systems
- 2023
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Ingår i: IEEE Transactions on Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0090-6778 .- 1558-0857. ; 71:7, s. 4360-4376
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs' channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs' channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs' decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms.
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| 9. |
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| 10. |
- Zhang, Deyou, Postdoc, et al.
(författare)
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Broadband Over-the-Air Computation for Federated Learning in Industrial IoT
- 2022
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Ingår i: IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- We consider a broadband over-the-air computation empowered model aggregation scheme for federated learning (FL) in Industrial Internet of Things systems. Due to fading and communication noise, the received global gradient parameters inevitably become inaccurate, leading to a notable decrease of the learning performance. Instead of discarding any edge nodes to reduce the aggregation error, we propose to assign each of them a proper weight coefficient in the model aggregation procedures, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required in this paper. We derive an upper bound on the performance loss of the proposed FL scheme, which is shown to be related to the weight coefficients of edge nodes and the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones. Then, we derive a closed-form expression for MSE and use it as the objective function to formulate an optimization problem with respect to the edge nodes’ transmit equalization coefficients, their weight coefficients, and the receive scalars of the cloud server. We transform the formulated optimization problem into a convex one and solve it optimally using CVX. Last, we leverage the popular MNIST dataset and conduct experiments to evaluate the prediction accuracy of the proposed FL scheme. Simulation results demonstrate its superior performances.
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| 11. |
- Zhang, Deyou, et al.
(författare)
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Fluid Antenna Array Enhanced Over-the-Air Computation
- 2024
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Ingår i: IEEE Wireless Communications Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2337 .- 2162-2345. ; 13:6, s. 1541-1545
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Tidskriftsartikel (refereegranskat)abstract
- Over-the-air computation (AirComp) has emerged as a promising technology for fast wireless data aggregation by harnessing the superposition property of wireless multiple-access channels. This letter investigates a fluid antenna (FA) array-enhanced AirComp system, employing the new degrees of freedom introduced by antenna movements. Specifically, we jointly optimize the transceiver design and antenna position vector (APV) to minimize the mean squared error (MSE) between target and estimated function values. To tackle the resulting highly non-convex problem, we adopt an alternating optimization technique to decompose it into three subproblems. These subproblems are then iteratively solved until convergence, leading to a locally optimal solution. Numerical results show that FA arrays with the proposed transceiver and APV design significantly outperform the traditional fixed-position antenna arrays in terms of MSE.
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| 12. |
- Zhang, Deyou, Postdoc, 1991-, et al.
(författare)
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IRS Assisted Federated Learning : A Broadband Over-the-Air Aggregation Approach
- 2024
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 23:5, s. 4069-4082
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Tidskriftsartikel (refereegranskat)abstract
- We consider a broadband over-the-air computation empowered model aggregation approach for wireless federated learning (FL) systems and propose to leverage an intelligent reflecting surface (IRS) to combat wireless fading and noise. We first investigate the conventional node-selection based framework, where a few edge nodes are dropped in model aggregation to control the aggregation error. We analyze the performance of this node-selection based framework and derive an upper bound on its performance loss, which is shown to be related to the selected edge nodes. Then, we seek to minimize the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones by optimizing the selected edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. By resorting to the matrix lifting technique and difference-of-convex programming, we successfully transform the formulated optimization problem into a convex one and solve it using off-the-shelf solvers. To improve learning performance, we further propose a weight-selection based FL framework. In such a framework, we assign each edge node a proper weight coefficient in model aggregation instead of discarding any of them to reduce the aggregation error, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required. We also analyze the performance of this weight-selection based framework and derive an upper bound on its performance loss, followed by minimizing the MSE via optimizing the weight coefficients of the edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. Furthermore, we use the MNIST dataset for simulations to evaluate the performance of both node-selection and weight-selection based FL frameworks.
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| 13. |
- Zhang, Deyou, Postdoc, 1991-, et al.
(författare)
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IRS Assisted Federated Learning: A Broadband Over-the-Air Aggregation Approach
- 2023
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Ingår i: IEEE Transactions on Wireless Communications. - 1536-1276 .- 1558-2248.
-
Tidskriftsartikel (refereegranskat)abstract
- We consider a broadband over-the-air computation empowered model aggregation approach for wireless federated learning (FL) systems and propose to leverage an intelligent reflecting surface (IRS) to combat wireless fading and noise. We first investigate the conventional node-selection based framework, where a few edge nodes are dropped in model aggregation to control the aggregation error. We analyze the performance of this node-selection based framework and derive an upper bound on its performance loss, which is shown to be related to the selected edge nodes. Then, we seek to minimize the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones by optimizing the selected edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. By resorting to the matrix lifting technique and difference-of-convex programming, we successfully transform the formulated optimization problem into a convex one and solve it using off-the-shelf solvers. To improve learning performance, we further propose a weight-selection based FL framework. In such a framework, we assign each edge node a proper weight coefficient in model aggregation instead of discarding any of them to reduce the aggregation error, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required. We also analyze the performance of this weight-selection based framework and derive an upper bound on its performance loss, followed by minimizing the MSE via optimizing the weight coefficients of the edge nodes, their transmit equalization coefficients, the IRS phase shifts, and the receive factors of the cloud server. Furthermore, we use the MNIST dataset for simulations to evaluate the performance of both node-selection and weight-selection based FL frameworks.
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| 14. |
- Zhang, Deyou, et al.
(författare)
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Mobile User Trajectory Tracking for IRS Enabled Wireless Networks
- 2021
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Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 70:8, s. 8331-8336
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we consider an intelligent reflecting surface (IRS) enabled mobile network, where a fixed access point (AP) communicates with a mobile user (MU) via the aid of an IRS. We assume that the MU moves from one elementary square to another following a Markov random walk within a grid, and propose a maximum a posteriori (MAP) criterion to track the movement of the MU by leveraging the line-of-sight component of the IRS-MU link. Since it is infeasible to derive an explicit expression for the average probability of estimation error (APEE) for the proposed MAP criterion, we derive a closed-form upper bound for the APEE, which is used as the cost function to optimize the phase shifts of the IRS units. Considering the unit modulus constraints incurred by the IRS units, a manifold optimization (MO) method is firstly employed to gain a favorable solution to the formulated optimization problem, followed by a low-complexity codebook based solution to circumvent the high computational cost of the MO method. Our numerical results demonstrate the superior performance of the proposed IRS phase shift designs over the benchmark method.
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| 15. |
- Zhang, Deyou, Postdoc, 1991-, et al.
(författare)
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Over-the-Air Computation Empowered Federated Learning : A Joint Uplink-Downlink Design
- 2023
-
Ingår i: 98th IEEE Vehicular Technology Conference, VTC 2023-Fal. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we investigate the communication designs of over-the-air computation (AirComp) empowered federated learning (FL) systems considering uplink model aggregation and downlink model dissemination jointly. We first derive an upper bound on the expected difference between the training loss and the optimal loss, which reveals that optimizing the FL performance is equivalent to minimizing the distortion in the received global gradient vector at each edge node. As such, we jointly optimize each edge node transmit and receive equalization coefficients along with the edge server forwarding matrix to minimize the maximum gradient distortion across all edge nodes. We further utilize the MNIST dataset to evaluate the performance of the considered FL system in the context of the handwritten digit recognition task. Experiment results show that deploying multiple antennas at the edge server significantly reduces the distortion in the received global gradient vector, leading to a notable improvement in recognition accuracy compared to the single antenna case.
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| 16. |
- Zhang, Deyou, Postdoc, 1991-, et al.
(författare)
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Training Beam Sequence Design for mmWave Tracking Systems With and Without Environmental Knowledge
- 2022
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 21:12, s. 10780-10795
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we consider a millimeter wave multiple-input single-output tracking system, where the time-varying angle of departure (AoD) is assumed to change following a discrete state Markov process. Depending on whether the associated AoD transition function is available or not, we propose two different training beam sequence design approaches. Specifically, in the case when the AoD transition function is available, we leverage the maximum a posteriori criterion to estimate the updated AoD in each beam tracking period. Since it is infeasible to derive an explicit expression for the resultant estimation error rate, we turn to its upper bound, which possesses a closed-form expression and is therefore used as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by the analog phase shifters, we resort to a particle swarm algorithm to solve the formulated optimization problem. In the case when the AoD transition function is unavailable, we turn to the maximum likelihood criterion for AoD estimation. To cope with the unknown AoD transition function, we reformulate the beam tracking problem as a partially observable Markov decision process problem and develop an actor-critic reinforcement learning framework to obtain an efficient training beam sequence design. Numerical results demonstrate superiorities of the proposed training beam sequence design approaches for both two cases.
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| 17. |
- Zhang, Deyou, et al.
(författare)
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Training Beam Sequence Design for Multiuser Millimeter Wave Tracking Systems
- 2021
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Ingår i: IEEE Transactions on Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0090-6778 .- 1558-0857. ; 69:10, s. 6939-6955
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a novel training beam sequence design for multiuser millimeter wave tracking systems is proposed. For each receiver, a single-path channel model is firstly investigated, where we introduce a maximum a posteriori (MAP) criterion to estimate the time-varying angle of departure (AoD), followed by an extended Kalman filter to update the stale complex path gain. We then employ training beam sequence design to minimize the estimated AoD's average mean squared error (AMSE), which however has no explicit expression. We firstly derive a closed-form upper bound for the AMSE and then simplify this upper bound into a tractable form, based on which a nonlinear optimization problem (NLP) is formulated. By solving this NLP optimally using its corresponding Karush-Kuhn-Tucker conditions, we obtain an efficient training beam sequence. The proposed MAP criterion and its associated training beam sequence design are further extended to multi-path scenarios, where a joint estimation of the multiple paths is firstly discussed, followed by a sequential estimation as a low-complexity alternative. Numerical results demonstrate the superiority of our proposed scheme over the existing benchmark methods, especially in the case when the receivers' channels change rapidly.
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