The concept of sub-networks has been recently identified as an important component of 6G to enable high-demanding critical services with capabilities of hyper reliable low-latency communications (HRLLC) at the local area of the sub-network. In this paper, we formulate the power control problem for sub-networks as finding the transmit power vector, corresponding to the sub-networks, to minimize the sum interference-to-signal power ratios across all the sub-networks. We propose then a heuristic sequential iterative power allocation (SIPA) and an optimal power allocation based on gradient descent-based algorithm (GDPA). Extensive system simulations validated the proposed methods. In particular, the SIPA achieves significant performance gains over the fixed transmit power setting, with limited performance loss over the GDPA. In most cases, the proposed methods outperform the max-min power allocation with much lower feedback signaling overhead and complexity. Furthermore, to keep 99.9% of all the sub-network link instances to achieve reliability of 6 nines, the SIPA can save radio resources by about 27% compared to the fixed transmit power setting.

Motivation: Lysine acetylation exists extensively in prokaryotes, and plays a vital role in function adjustment. Recent progresses in the identification of prokaryote acetylation substrates and sites provide a great opportunity to explore the difference of substrate site specificity between prokaryotic and eukaryotic acetylation. Motif analysis suggests that prokaryotic and eukaryotic acetylation sites have distinct location-specific difference, and it is necessary to develop a prokaryote-specific acetylation sites prediction tool. Results: Therefore, we collected nine species of prokaryote lysine acetylation data from various databases and literature, and developed a novel online tool named ProAcePred for predicting prokaryote lysine acetylation sites. Optimization of feature vectors via elastic net could considerably improve the prediction performance. Feature analyses demonstrated that evolutionary information played significant roles in prediction model for prokaryote acetylation. Comparison between our method and other tools suggested that our species-specific prediction outperformed other existing works. We expect that the ProAcePred could provide more instructive help for further experimental investigation of prokaryotes acetylation. Availability and implementation: http://computbiol.ncu.edu.cn/ProAcePred. Supplementary information: Supplementary data are available at Bioinformatics online.

Targeting massive Multiple Input Multiple Output (MIMO) systems, we consider an efficient multi- panel array architecture and propose hybrid beamforming techniques for millimeter wave frequencies. Taking advantages of the multi-panel array, hybrid two-stage processing including coarse and fine beamforming can be carried out in a distributed fashion. Panel-coordinated analog beamforming as well as decoupling only requires channels measured at each panel itself. Non- linear precoding is then applied on a per-panel basis. It is shown that the proposed schemes relax the antenna dimensionality constraint and exhibit a good performance even in an overloaded case, which are practically feasible in both the single-/multi-cell scenarios.

MOTIVATION: Protein malonylation is a novel post-translational modification (PTM) which orchestrates a variety of biological processes. Annotation of malonylation in proteomics is the first-crucial step to decipher its physiological roles which are implicated in the pathological processes. Comparing with the expensive and laborious experimental research, computational prediction can provide an accurate and effective approach to the identification of many types of PTMs sites. However, there is still no online predictor for lysine malonylation. RESULTS: By searching from literature and database, a well-prepared up-to-data benchmark datasets were collected in multiple organisms. Data analyses demonstrated that different organisms were preferentially involved in different biological processes and pathways. Meanwhile, unique sequence preferences were observed for each organism. Thus, a novel malonylation site online prediction tool, called MaloPred, which can predict malonylation for three species, was developed by integrating various informative features and via an enhanced feature strategy. On the independent test datasets, AUC (area under the receiver operating characteristic curves) scores are obtained as 0.755, 0.827 and 0.871 for Escherichia coli ( E.coli ), Mus musculus ( M.musculus ) and Homo sapiens ( H.sapiens ), respectively. The satisfying results suggest that MaloPred can provide more instructive guidance for further experimental investigation of protein malonylation. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.ncu.edu.cn/MaloPred.aspx . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. AVAILABILITY AND IMPLEMENTATION: The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Lysine succinylation orchestrates a variety of biological processes. Annotation of succinylation in proteomes is the first-crucial step to decipher physiological roles of succinylation implicated in the pathological processes. In this work, we developed a novel succinylation site online prediction tool, called SuccFind, which is constructed to predict the lysine succinylation sites based on two major categories of characteristics: sequence-derived features and evolutionary-derived information of sequence and via an enhanced feature strategy for further optimizations. The assessment results obtained from cross-validation suggest that SuccFind can provide more instructive guidance for further experimental investigation of protein succinylation. AVAILABILITY AND IMPLEMENTATION: A user-friendly server is freely available on the web at: http://bioinfo.ncu.edu.cn/SuccFind.aspx. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Small cells are regarded as a promising solution to achieve green communication and deal with the coverage holes within macrocells. However, close subscriber group femtocells with universal frequency reuse introduce strong cross-tier interference in both uplink and downlink and thus reducing the overall system capacity. Conventional LTE-A macrocells only apply power control in uplink while LTE-A femtocells need both downlink and uplink power control schemes to effectively deal with the new cross-tier interference scenarios. In this paper, location based autonomous downlink power setting and uplink fraction power control schemes are proposed specifically for femtocells in order to mitigate the cross-tier interference. It is demonstrated with extensive system simulations that the proposed power control schemes can significantly improve the Macro-UE outage probability in downlink and assure the Femto-UEs with increased capacity in uplink.

Under the co-channel deployment of indoor Home-eNB (HeNB) with closed-subscriber-group (CSG) access mode and Macro-eNB (MeNB), it is very important to mitigate downlink (DL) interference toward Macro user equipments (UEs) which are located in the vicinity of a HeNB. For this scenario, based on discussions in 3GPP standardization, power setting of HeNB is recommended to be the primary DL inter-cell interference cancelation (ICIC) solution, which limits the transmit power of HeNB so that the nearby Macro UE (MUE) will not be interfered severely. Because acceptable/good control channel (CCH) performance is a pre-condition for the data channel performance, in this paper, with the focus on DL ICIC for CCH, we propose an enhanced HeNB power setting scheme. In particular, the proposed scheme considers providing effective DL CCH interference mitigation for MUE while maintaining acceptable/good DL CCH performance for Home UE (HUE). Based on simulation results, even if there is no MeNB-HeNB coordination, the proposed scheme can work well and provide better performance than existing schemes.

In the multi-cell environment, the approaches that are suitable for the single cell scenario may cause excessive interference to other cells and become suboptimal. In this paper, we present a number of receiver-centric power allocation schemes that take into account the detrimental impact of allocated resources to the neighboring cells, and use it as a guideline in determining the appropriate power levels over the spectrum. Together with a user scheduler with proportional fairness, the proposed methods demonstrate a significant improvement in the cell edge performance at barely no compromise in the average user throughput.

It is well-known that wireless scheduling algorithm could exploit multi-user diversity to enhance the network capacity. With multiple transmit/receive antennas, there are additional degrees of freedom which could deliver either spatial multiplexing gain and/or spatial diversity gain. With cross layer scheduling, there is also multi-user selection diversity which contributes to both network capacity and coverage. Due to the huge complexity involved for the optimal solution and big performance gap caused by greedy schemes, in this paper, we proposed a series of genetic algorithm based scheduling schemes with different chromosome code designs for both single- and multi-cell system resource allocation. For single-cell scheduling, we mainly focused on uplink multi-user scheduling with successive interference cancellation at receivers, and joint spatial-frequency scheduling. While for multi-cell scheduling, we proposed a centralized genetic algorithm based scheduling solution that could effectively increase the multi-cell cooperation gain. Simulation results show that the proposed algorithms could fill in most of the performance gap at reasonable complexity compared with optimal solution and greedy scheduling.

In this paper, we investigate the problem of dynamic subchannel assignments in the downlink of OFDM (Orthogonal Frequency Division Multiplexing) systems. The Kuhn-Munkres algorithm can provide the maximum weighted bipartite matching for assignment problems. In this paper, we formulate the multiobjective optimization (MO) problem in bipartite matching, and propose a modified bipartite matching algorithm(MBM) for assignments with MO requirements. This algorithm can be used to solve the weighted bipartite matching problem with multiobjective optimization. We illustrate the application of MBM to subchannel assignments in wireless OFDM systems. The simulation results show that MBM enjoys low computational complexity and maximizes the system capacity, while keeping the fairness among mobile users.

In this paper, we investigate the problem of joint frequency-spatial resource allocation for OFDM-MIMO systems. Based on Hungarian algorithm, the Kuhn-Munkres algorithm can provide the maximum weight bipartite matching for assignment problems. However it can only solve one-dimension resource allocation problems. For multi-dimension problems, such as joint frequency-spatial scheduling, we propose two modified bipartite matching algorithms to optimize the resource allocation for OFDM-MIMO systems. For LTE uplink systems with particular scheduling requirements, a modified bipartite matching algorithm is proposed. The simulation results show that our proposed algorithms can effectively increase the wireless spectrum usage and achieve high system capacity.

This paper considers the multi-user scheduling for streaming services over wideband frequency-selective and time varying channel in cellular network such as E-UTRAN. First, the frequency-selective proportional fair (PF) scheduling is proposed for multi-carrier system under user's maximum transmission power capability and continuous resource constraints, which is used to reduce the peak to average power ratio (PAPR) for uplink. In addition, the proportional fair with guaranteed bit rate (PFGBR) is proposed to enhance the quality of service (QoS) of streaming service and improve the user experiences in terms of data rate and delay. The PFGBR is to optimize the concave utility function of Sigmailog Rtwhile provides guaranteed bit rate (GBR) specifically for streaming applications. The system simulation results for both frequency-selective PF and PFGBR are presented and it shows that the PFGBR can achieve better dynamics of rate and delay than PF algorithm.