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By moving to millimeter wave (mmWave) frequencies, base stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. In addition, with massive multiple-input-multiple-output (MIMO) antenna arrays employed at BSs, the beamspace channel is sparse for each user, and thus there is no need to serve all the users in a cell by all the beams therein jointly. Therefore, it is of paramount importance to develop a flexible clustered cell-free networking scheme that can decompose the whole network into a number of weakly interfered small subnetworks operating independently and in parallel. Given a per-user rate constraint for service quality guarantee, this paper aims to maximize the number of decomposed subnetworks so as to reduce the signaling overhead and system complexity as much as possible. By formulating it as a bipartite graph partitioning problem, a rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network with simple beam allocation to a fully cooperative network by increasing the required per-user rate. Simulation results demonstrate that the proposed RC-NetDecomp algorithm outperforms existing baselines in terms of average per-user rate, fairness among users and energy efficiency., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

This paper addresses the energy minimization problem in Device-to-Device (D2D) assisted Mobile Edge Computing (MEC) networks under the latency constraint of each individual task and the computing resource constraint of each computing entity. The energy minimization problem is formed as a twostage optimization problem. Specifically, in the first stage, an initial feasibility problem is formed to maximize the number of executed tasks and the global energy minimization problem is tackled in the second stage while maintaining the maximum number of executed tasks. Both of the optimization problems in two stages are NP-hard, therefore a low-complexity algorithm is developed for the initial feasibility problem with a supplementary algorithm further proposed for energy minimization. Simulation results demonstrate the near-optimal performance of the proposed algorithms and the fact that with the assistance of D2D communication, the number of executed tasks is greatly increased and the energy consumption per executed task is significantly reduced in MEC networks, especially in dense user scenario.

Moving to millimeter wave (mmWave) frequencies and deploying massive multiple input multiple output (MIMO)antenna arrays have shown great potential of supporting highdata-rate communications in the fifth-generation (5G) and beyond wireless networks, thanks to the availability of huge amounts of mmWave frequency bandwidth and massive numbers of narrow and high gain beams. A number of massive MIMObeamforming techniques have been proposed, among which the fixed-beam scheme has attracted considerable interests from both academia and industry due to its simplicity and requirement ofa a small number of radio frequency (RF) chains compared to the number of base station (BS) antennas. Moreover, a beam allocation based pure analogue fixed-beam system requires much lower complexity and less signalling overhead than the hybrid beamforming based fixed-beam system, which can, therefore, be easily implemented in the practical systems. In this paper, the sum data rate of beam allocation based multi-user massive MIMOsystems is studied where a near-optimal low complexity beam allocation algorithm is adopted. Simulation results show that our derived average sum data rate serves as a good approximation of the simulation results.

Resource allocation of cellular-assisted device-to-device (D2D) communication is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large signaling overhead for channel state information (CSI) acquisition. In this paper, a novel resource allocation framework for cellular-assisted D2D communication is developed with low signaling overhead while maintaining high system capacity. By utilizing the spatial dispersion property of the D2D pairs, a geography-based sub-cell division strategy is proposed to divide the cell into multiple sub-cells and the D2D pairs within one sub-cell are formed into one group. Then, sub-cell resource allocation is performed independently among the sub-cells without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation framework, a tractable approximation for the inter D2D interference modeling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells, which is an important parameter for maximizing the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, the system capacity can be improved significantly by adopting the proposed resource allocation framework, especially in dense D2D deployed systems.

Switched-beam systems offer a promising solution for realizing multi-user communications at millimeter wave (mmWave) frequencies. A low-complexity beam allocation (LBA) algorithm has been proposed to solve the challenging problem of maximizing sum data-rates. However, there are practical limitations in mmWave systems, such as restrictions in the number of available radio frequency transceiver chains at the base station, sensitivity to sidelobe interference and the beam generation techniques. In this paper, using generalized beam-patterns, we present the maximum sum data-rates achievable in switched-beam mmWave systems compared with fixed-beam systems by applying LBA. Then, the impact on maximum sum data rates of actual beam-patterns, obtained from a practical mmWave lens antenna, which have higher and non-uniform sidelobes compared with the theoretical beams, is assessed. Finally, as a guide for practical wireless system design, benchmarks are established for relative sidelobe levels

Conventionally, resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to obtain in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or nearoptimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of a scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive MIMO systems shows that the proposed machine-learning-based resource allocation outperforms conventional methods.

Massive multiple-input-multiple-output (MIMO) has become a promising technique to provide high-data-rate communication in fifth-generation (5G) mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed-beam based multiuser massive MIMO system where each user is served by a beam allocated to it. To maximize the sum data rate, a greedy beam allocation algorithm is proposed under the practical condition that the number of radio frequency (RF) chains is smaller than the number of users. Simulation results show that our proposed greedy algorithm achieves nearly optimal sum data rate. As only the sum data rate is optimized, there are some “worst-case” users who could suffer from strong inter-beam interference and thus experience low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users’ data rates and the max-min fairness among served users without sacrificing the sum data rate.

Massive multiple-input-multiple-output (MIMO) is a promising technique to provide high-data-rate communication in fifth-generation (5G) mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed- beam based multiuser massive MIMO system where each user is served by a beam allocated to it. As the directions of fixed beams are predetermined and the users are randomly distributed, there could be some ``worst-case'' users, located at the edge of its serving beam, suffering from strong inter-beam interference and thus experiencing low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users' data rates without sacrificing the sum data rate.

High-speed train has drawn considerable attention and become one of the most preferable conveyance mechanism. Each year the manufacture corporations reach a higher speed record which is expected to attain 1000 km/h by 2021 using hyperloop one technology. Moving at such a high speed results in a high handover (HO) rate which makes it challenging for high speed railway (HSR) mobile wireless communication to preserve steady link performance. Employing distributed antenna systems (DASs) along with the two-hop architecture, this paper proposes a fast predictive HO algorithm. In this strategy, the serving cell starts the HO preparation phase in advance by inferring the train current location. Issuing the HO preparation phase in advance reduces the HO latency and reduces the HO command failure probability as well. Lower HO command failure probability means lower HO failure probability which could greatly improve the end-users quality of services (QoS). The analytical results show that the proposed scheme performs better compared with the conventional HO scheme.

Previously, a scheme in [1] is proposed for the outdated channel state information (CSI) problem, for data transmission in time division duplex (TDD) systems. In user movement environment, the actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation. In this paper the effect of different user mobility on TDD downlink multiuser distributed antenna system is investigated. An efficient autocorrelation based feedback interval technique is proposed and updates CSI at less cost of the downlink time slots. In the proposed technique, the frequency of CSI feedback for different users is proportional to their speed. Cooperative clusters are formed to maximize sum rate where channel gain based antenna selection and user clustering based on SINR threshold is applied to reduce computational complexity. Numerical results show that sum rate superiority of the proposed scheme over the user mobility.

Resource allocation of cellular-assisted device-todevice (D2D) communication is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large signaling overhead for channel state information (CSI) acquisition. In this paper, a novel resource allocation framework for cellular-assisted D2D communication is developed with low signaling overhead while maintaining high system capacity. By utilizing the spatial dispersion property of D2D pairs, a geography-based sub-cell division strategy is proposed to divide the cell into multiple sub-cells and D2D pairs within one sub-cell are formed into one group. Then, sub-cell resource allocation is performed independently among sub-cells without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation framework, a tractable approximation for the inter D2D interference modelling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells, which is an important parameter for maximizing the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, system capacity can be improved significantly by adopting the proposed resource allocation framework, especially in dense D2D deployed systems.

PURPOSE: Vitamin K antagonists (VKAs) are the standard of care for stroke prevention in patients with atrial fibrillation (AF); therefore, there is not equipoise when comparing newer oral anticoagulants with placebo in this setting. METHODS: To explore the effect of apixaban on mortality in patients with AF, we performed a meta-analysis of apixaban versus placebo using a putative placebo analysis based on randomized controlled clinical trials that compared warfarin, aspirin, and no antithrombotic control. We used data from two prospective randomized controlled trials for our comparison of apixaban versus warfarin (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation) and apixaban versus aspirin (Apixaban Versus Acetylsalicylic Acid to Prevent Stroke in Atrial Fibrillation Patients Who Have Failed or Are Unsuitable for Vitamin K Antagonist Treatment). Using meta-analysis approaches, we indirectly compared apixaban with an imputed placebo with respect to the risk of death in patients with AF. We used results from meta-analyses of randomized trials as our reference for the comparison between warfarin and placebo/no treatment, and aspirin and placebo/no treatment. RESULTS: In these meta-analyses, a lower rate of death was seen both with warfarin (odds ratio [OR] 0.74, 95% confidence interval [CI] 0.57-0.97) and aspirin (OR 0.86, 95% CI 0.69-1.07) versus placebo/no treatment. Using data from ARISTOTLE and AVERROES, apixaban reduced the risk of death by 34% (95% CI 12-50%; p = 0.004) and 33% (95% CI 6-52%; p = 0.02), respectively, when compared with an imputed placebo. The pooled reduction in all-cause death with apixaban compared with an imputed placebo was 34% (95% CI 18-47%; p = 0.0002). CONCLUSIONS: In patients with AF, indirect comparisons suggest that apixaban reduces all-cause death by approximately one third compared with an imputed placebo.

Conventionally, the resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to be obtained in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever-increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of current scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then, the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive multiple-input-multiple-output (MIMO) systems shows that the proposed machine-learning based resource allocation outperforms conventional methods., Comment: 19 pages with 6 figures, accepted by IEEE Network Magazine

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K log N). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N/K.

A great deal of efforts have been made on the performance evaluation of distributed antenna systems (DASs). Most of them assume a regular base-station (BS) antenna layout where the number of BS antennas is usually small. With the growing interest in cellular systems with large antenna arrays at BSs, it becomes increasingly important to study how the BS antenna layout affects the rate performance when a vast number of BS antennas are employed. This paper presents a comparative study of the asymptotic rate performance of downlink multi-user systems with multiple BS antennas either co-located or uniformly distributed within a circular cell. Two representative linear precoding schemes, maximum ratio transmission (MRT), and zero-forcing beamforming (ZFBF), are considered, with which the effect of BS antenna layout on the rate performance is characterized. The analysis shows that as the number of BS antennas L and the number of users K grow infinitely while L/K → v, the asymptotic average user rates with the co-located antenna (CA) layout for both MRT and ZFBF are logarithmic functions of the ratio v. With the distributed antenna (DA) layout, in contrast, the scaling behavior of the average user rate closely depends on the precoding schemes. With ZFBF, for instance, the average user rate grows unboundedly as L, K → ∞ and L/K → v > 1, which indicates that substantial rate gains over the CA layout can be achieved when the number of BS antennas L is large. The gain, nevertheless, becomes marginal when MRT is adopted.

Despite substantial rate gains achieved by joint transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few neighboring base-station (BS) antennas to form its virtual cell, i.e, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each user's virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity.

Background In ARISTOTLE, apixaban resulted in a 21% reduction in stroke, a 31% reduction in major bleeding, and an 11% reduction in death. However, approval of apixaban was delayed to investigate a statement in the clinical study report that "7.3% of subjects in the apixaban group and 1.2% of subjects in the warfarin group received, at some point during the study, a container of the wrong type." Methods Rates of study medication dispensing error were characterized through reviews of study medication container tear-off labels in 6,520 participants from randomly selected study sites. The potential effect of dispensing errors on study outcomes was statistically simulated in sensitivity analyses in the overall population. Results The rate of medication dispensing error resulting in treatment error was 0.04%. Rates of participants receiving at least 1 incorrect container were 1.04% (34/3,273) in the apixaban group and 0.77% (25/3,247) in the warfarin group. Most of the originally reported errors were data entry errors in which the correct medication container was dispensed but the wrong container number was entered into the case report form. Sensitivity simulations in the overall trial population showed no meaningful effect of medication dispensing error on the main efficacy and safety outcomes. Conclusions Rates of medication dispensing error were low and balanced between treatment groups. The initially reported dispensing error rate was the result of data recording and data management errors and not true medication dispensing errors. These analyses confirm the previously reported results of ARISTOTLE.

A great deal of efforts have been made on the performance evaluation of distributed antenna systems (DASs). Most of them assume a regular base-station (BS) antenna layout where the number of BS antennas is usually small. With the growing interest in cellular systems with large antenna arrays at BSs, it becomes increasingly important for us to study how the BS antenna layout affects the rate performance when a massive number of BS antennas are employed. This paper presents a comparative study of the asymptotic rate performance of downlink multi-user systems with multiple BS antennas either co-located or uniformly distributed within a circular cell. Two representative linear precoding schemes, maximum ratio transmission (MRT) and zero-forcing beamforming (ZFBF), are considered, with which the effect of BS antenna layout on the rate performance is characterized. The analysis shows that as the number of BS antennas $L$ and the number of users $K$ grow infinitely while $L/K{\rightarrow}\upsilon$, the asymptotic average user rates with the co-located antenna (CA) layout for both MRT and ZFBF are logarithmic functions of the ratio $\upsilon$. With the distributed antenna (DA) layout, in contrast, the scaling behavior of the average user rate closely depends on the precoding schemes. With ZFBF, for instance, the average user rate grows unboundedly as $L, K{\rightarrow} \infty$ and $L/K{\rightarrow}\upsilon{>}1$, which indicates that substantial rate gains over the CA layout can be achieved when the number of BS antennas $L$ is large. The gain, nevertheless, becomes marginal when MRT is adopted.