A multi-user cognitive (secondary) radio system is considered, where the spatial multiplexing mode of operation is implemented amongst the nodes, under the presence of multiple primary transmissions. The secondary receiver carries out minimum mean-squared error detection to effectively decode the secondary data streams, while it performs spectrum sensing at the remaining signal to capture the presence of primary activity or not. New analytical closed-form expressions regarding some important system measures are obtained, namely, the outage and detection probabilities, the transmission power of the secondary nodes, the probability of unexpected interference at the primary nodes, and the detection efficiency with the aid of the area under the receive operating characteristics curve. The realistic scenarios of channel fading time variation and channel estimation errors are encountered for the derived results. Finally, the enclosed numerical results verify the accuracy of the proposed framework, while some useful engineering insights are also revealed, such as the key role of the detection accuracy to the overall performance and the impact of transmission power from the secondary nodes to the primary system.

pdf/simultaneous-spectrum-sensing-and-data-reception-for-3vx1tgmm5w.pdf

Simultaneous Spectrum Sensing and Data Reception for Cognitive Spatial Multiplexing Distributed Systems

A novel algorithm for handling reducer side data skew in MapReduce based on a learning automata game

A multi-user cognitive (secondary) radio system is considered, where the spatial multiplexing mode of operation is implemented amongst the nodes, under the presence of multiple primary transmissions. The secondary receiver carries out minimum mean-squared error (MMSE) detection to effectively decode the secondary data streams, while it performs spectrum sensing at the remaining signal to capture the presence of primary activity or not. New analytical closed-form expressions regarding some important system measures are obtained, namely, the outage and detection probabilities; the transmission power of the secondary nodes; the probability of unexpected interference at the primary nodes; {\color{blue}and the detection efficiency with the aid of the area under the receive operating characteristics curve}. The realistic scenarios of channel fading time variation and channel estimation errors are encountered for the derived results. Finally, the enclosed numerical results verify the accuracy of the proposed framework, while some useful engineering insights are also revealed, such as the key role of the detection accuracy to the overall performance and the impact of transmission power from the secondary nodes to the primary system.

One of the most successful techniques in large-scale data-intensive computations is MapReduce programming. MapReduce is based on a divide and conquer approach that uses commodity computers, also known as nodes, for parallel processing. The scalability and performance of this technique are more related to the type of data distribution in map and reduce tasks. Because of many reasons such as node failure, network failure, data skewness, etc. completion time of one task could be longer than other tasks, job completion time is determined by the slowest task. One of the most important reasons for requiring more time to finish one task compared to other tasks is the skewness of data. Despite the widespread use of MapReduce because of its high flexibility and tolerability of the error, with the presence of data skewness, it cannot fully utilize the nodes for parallel processing. The objectives of this study were to review related articles and classify them based on the type of problem addressed and to determine the advantages and disadvantages of them. Open issues were also defined to present guidelines for future research on this subject. In order to achieve the aforementioned objectives, some research questions were defined and answered. In this review, it was concluded that there are important parameters have not been considered in MapReduce data skewness handling approaches.

MapReduce Data Skewness Handling: A Systematic Literature Review

To reinforce the coverage and QoS (quality of service) of on-ground cellular communication system, unmanned aerial vehicles which are carrying small cells are deployed in some emergency and disaster areas. Although ASCs (aerial small cells) can provide a higher probability of LoS (line-of-sight) transmission, the wireless backhaul link will bring extra interference to the whole system if not well designed. Therefore, in this paper, we study the backhaul scheme for UAV-assist cellular network. We first analyze the interference environments of UAV-assist cellular network considering the IBOG (In-Band to On-Ground), OBOG (Out-of-Band to On-Ground) and IBTU (In-Band to Tethered-UAV) backhauling mode, and give the descriptions of the performance metrics for each mode. Then, the considered problem is formulated as an optimization of achievable rate. We derive the optimal solutions for the involved three backhauling modes for ASCs respectively, and closed-form optimal value for each mode is acquired with proof. We also give a pseudo-code form of our proposed optimal access/backhaul spectrum allocation algorithm. The simulation results indicate that the proposed scheme can deliver a significant gain, while IBTU performs best among proposed backhauling modes.

Spectrum allocation and performance analysis for backhauling of UAV assisted cellular network

Distributed antenna systems (DAS) have been adopted in the next generation wireless networks. However, the most prior research focus on perfect channel state information (CSI), which is not always available in practice. In this letter, the outage probability of maximum ratio transmission (MRT) for DAS is derived under more realistic propagation conditions. The main contribution of this work is that the analysis accounts for pathloss and feedback delay in CSI and unequal-power cochannel interferers (CCI). The derived closed-form expressions for outage probability are verified by Monte Carlo simulations.

Outage Analysis of Distributed Antenna System with Delayed CSI and Unequal-Power Cochannel Interferers

Most of studies on Distributed Antenna System (DAS) focus on maximizing the sum capacity and perfect channel state information at transmitter (CSIT). However, CSI is inevitable imperfect in practical wireless networks. Based on the sources of error, there are two models. One assumes error lies in a bounded region, the other assumes random error. Accordingly, we propose two joint antenna selection (AS) and robust-beamforming schemes aiming to minimize the total transmit power at antenna nodes subject to quality of service (QoS) guarantee for all the mobile users (MUs) in multicell DAS. This problem is mathematically intractable. For the bounded error model, we cast it into a semidefinite program (SDP) using semidefinite relaxation (SDR) and S-procedure. For the second, we first design outage constrained robust beamforming and then formulate it as an SDP based on the Bernstein-type inequality, which we generalize it to the multi-cell DAS. Simulation results verify the effectiveness of the proposed methods.

Joint antenna selection and robust beamforming design in multi-cell Distributed Antenna System

To meet the further explosive capacity demand, network densification is a promising technology. The load imbalance between cells in an ultra-dense heterogeneous network is a major challenge, which seriously affects the performance of the system. Existing load balancing (LB) methods usually operate in reactive mode. The parameters of the cells are adjusted reactively according to the dynamic changes of network load. The inherent reactivity of existing LB schemes undermines the quality of experience (QoE) in 5G and beyond. To solve this problem, we propose a mobility management framework for load balancing, which turns the original reactive load balancing into forwardaware and active. The simulation results show that the proposed method can better optimize the network performance and realize the intelligent mobile management for the future network.

Joint Load balancing and Spatial-temporal Prediction Optimization for Ultra-Dense Network

Designing an intelligent self-organizing network (SON) architecture is challenging for future wireless networks. To meet the needs of SON, the reactive self-organizing model of the traditional network needs to be transformed into an active and interactive one. Due to the user mobility and small coverage of cells in ultra-dense networks (UDNs), the network load usually becomes unbalanced, leading to deteriorated network performance, such as low throughput, radio link failure, and poor user experience. Therefore, the technique of mobility load balancing (MLB) is critical to ensuring a seamless user experience among cells. This letter proposes an active and interactive MLB strategy for UDNs, which transforms the original reactive MLB into a forward-aware and active one. In particular, user mobility is first predicted based on the Bayesian additive regression tree (BART). Then, with the mobility predictions, the joint mobility robust optimization and MLB problem subject to users’ rate constraint is solved via safe reinforcement learning. The pertaining simulation results show that the proposed method can improve the network performance and realize intelligent mobile management for future UDNs.

Proactive Load Balancing Through Constrained Policy Optimization for Ultra-Dense Networks

A novel proactive soft load balancing framework for ultra dense network

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