Enhancing throughput for limited channel capacity in wireless LANs is an important subject due to limited wireless channel bandwidth. A great deal of research has been carried out and some of proposed schemes are effective. Specifically, considerable effort has been devoted to improving the IEEE 802.11 standard which is utilized widely. Previous theoretical analysis gave the upper bound of IEEE 802.11 DCF throughput which is far below the channel capacity and corresponding algorithm was proposed, which can achieve the throughput close to the upper bound. It seems that we cannot expect to enhance the throughput much more in a usual way. In the meantime, besides throughput, there are some other issues for DCF such as fairness and QoS support. However, except for several hybrid protocols, most proposals were either based on contention mode or schedule mode and neither of the two modes has possessed the good characters of the other. In this paper, we propose a new MAC scheme used for DCF (with no control node) that dynamically adapts to traffic changes without degradation of delay in the case of low traffic load and achieves high throughput which is close to transmission capacity in saturated case. The key idea is to divide the virtual frame into two parts, i.e., schedule part and contention part, and to enable each node to reserve a slot in schedule part. Unlike conventional hybrid protocols, every node does not have to intentionally reset any parameter according to the changing traffic load except its queue length. A distinguishing feature of this scheme is the novel way of allowing WLANs to work with low delay as in the contention-based mode and achieve high throughput as in the schedule-based mode without complicated on-line estimation required in previous schemes. This makes our scheme simpler and more reliable. According to analysis, we show that our scheme can greatly improve the throughput no matter whether the network is in saturated or unsaturated case.
Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in a city. This problem directly leads to the difficulty in learning latent representations of users and locations. In order to tackle the data sparsity problem and make better recommendations, users’ app usage records in different locations are introduced to compensated for both users’ interests and locations’ characteristics in this paper. An attributed graph-based representation model is proposed to dig out user–app–location associations with high-order features aggregated. Extensive experiments prove that better representations of users and locations are obtained by our proposed model, thus it greatly improves location recommendation performances compared with the state-of-art methods. For example, our model achieves 13.20%, 10.1%, and 9.44% higher performance than the state-of-art (SOTA) models in Top3Hitrate, Top3Accuracy, and nDCG3, respectively, in the Telecom dataset. In the TalkingData dataset, our model achieves 9.34%, 13.35%, and 8.56% better performance than the SOTA models in Top2Hitrate, Top2Accuracy, and nDCG2, respectively. Furthermore, numerical results demonstrate that our model can effectively alleviate the data sparsity problem in recommendation systems.
TC Pi sa transport protocol that guarantees reliable ordered delivery of data packets over wired networks. Although it is well tuned for wired networks, TC Pp erforms poorly in mobile ad hoc networks (MANETs). This is because TCP’s implicit assumption that any packet loss is due to congestion is invalid in mobile ad hoc networks where wireless channel errors, link contention, mobility and multipath routing may significantly corrupt or disorder packet delivery. If TCP misinterprets such losses as congestion and consequently invokes congestion control procedures, it will suffer from performance degradation and unfairness. To understand TCP behaviour and improve the TCP performance over multi-hop ad hoc networks, considerable research has been carried out .A st he research in this area is still active and many problems are still wide open, an in-depth and timely survey is needed. In this paper, the challenges imposed on the standard TCP in the wireless ad hoc network environment are first identified. Then some existing solutions are discussed according to their design philosophy. Finally, some suggestions regarding future research issues are presented.
This study investigated the effects of high-hydrostatic-pressure (HHP) treatment of varying intensity (100-600 MPa) and duration (10-30 min) on polyphenols and volatile aromatic compounds in Marselan red wine. The types and concentrations of polyphenols and volatile aromatic compounds were compared before and after HHP treatment; the results indicated that HHP treatment at 300 MPa for 20 min significantly increased the total polyphenol content to 369.70 mg/L, a rise of 35.82%. The contents of key polyphenols, such as resveratrol and protocatechuic acid, were significantly enhanced. Furthermore, while the total content of volatile aromatic compounds did not change significantly under this condition compared to the untreated samples, the concentration of ester compounds significantly increased to 1.81 times that of the untreated group, thereby enriching the floral and fruity aromas of the wine and effectively improving its aromatic profile and sensory quality. Principal component analysis (PCA) further validated the positive impact of HHP treatment on the flavor characteristics of Marselan red wine. These findings provide technical support for the use of HHP in improving wine quality.
Communication has been known to be one of the primary bottlenecks of federated learning (FL), and yet existing studies have not addressed the efficient communication design, particularly in wireless FL where both uplink and downlink communications have to be considered. In this paper, we focus on the design and analysis of physical layer quantization and transmission methods for wireless FL. We answer the question of what and how to communicate between clients and the parameter server and evaluate the impact of the various quantization and transmission options of the updated model on the learning performance. We provide new convergence analysis of the well-known FedAvg under non-i.i.d. dataset distributions, partial clients participation, and finite-precision quantization in uplink and downlink communications. These analyses reveal that, in order to achieve an O(1/T) convergence rate with quantization, transmitting the weight requires increasing the quantization level at a logarithmic rate, while transmitting the weight differential can keep a constant quantization level. Comprehensive numerical evaluation on various real-world datasets reveals that the benefit of a FL-tailored uplink and downlink communication design is enormous - a carefully designed quantization and transmission achieves more than 98% of the floating-point baseline accuracy with fewer than 10% of the baseline bandwidth, for majority of the experiments on both i.i.d. and non-i.i.d. datasets. In particular, 1-bit quantization (3.1% of the floating-point baseline bandwidth) achieves 99.8% of the floating-point baseline accuracy at almost the same convergence rate on MNIST, representing the best known bandwidth-accuracy tradeoff to the best of the authors' knowledge.
The freshness of status updates is imperative in mission-critical Internet of things (IoT) applications. Recently, Age of Information (AoI) has been proposed to measure the freshness of updates at the receiver. However, AoI only characterizes the freshness over time, but ignores the freshness in the content. In this paper, we introduce a new performance metric, Age of Changed Information (AoCI), which captures both the passage of time and the change of information content. Also, we examine the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets with a cost. We formulate a Markov Decision Process (MDP) to find the optimal updating policy that minimizes the weighted sum of the AoCI and the update cost. Particularly, in a special case that the physical process is modeled by a two-state discrete time Markov chain with equal transition probability, we show that the optimal policy is of threshold type with respect to the AoCI and derive the closed-form of the threshold. Finally, simulations are conducted to exhibit the performance of the threshold policy and its superiority over the zero-wait baseline policy.
Spectral Clustering (SC) is an effective clustering method for its excellent performance in partitioning non-linearly distributed data. On the other hand, Ensemble Clustering (EC), a different clustering technology, can promote cluster quality by ensembling the results of base clusterings. In this work, we concentrate on an EC framework that utilizes SC as the base method. Nevertheless, SC suffers from scalability due to its high computational complexity in constructing the Laplacian graph and computing the corresponding eigendecomposition. In the past decades, many efforts have been made to it. However, SC suffers from the scalability issue in processing extensive data, especially in web-scale scenarios. Additionally, EC requires multiple clustering results as the ensemble bases, which further aggravates resource consumption. To address this issue, LiteWSEC, a simple yet efficient Lightweight Framework for Web-scale Spectral Ensemble Clustering, is proposed to cluster web-scale data with limited resource requirements. It adopts the Web-scale Spectral Clustering (WSC) as the base method, which has minimal space overhead without computing overall embedding explicitly. LiteWSEC is highly flexible in the memory requirement, which is adaptive to the available resource. It can partition web-scale data (e.g., $n $ = 8,000 k) in an resource-limited host (e.g., memory is restricted to 1 GB). Experiments on real-world, large-scale, and web-scale datasets demonstrate both the efficiency and effectiveness of LiteWSEC over state-of-the-art SC and EC methods.
In large-scale wireless networks, severe interference may incur that leads to the age of information (AoI) degradation. It is therefore important to study how to optimize the AoI performance. This paper focuses on the average AoI minimization in random access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the average AoI is derived, based on which the optimal average AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. We further compare the average AoI optimization with the peak AoI optimization. The comparison reveals that the optimal channel access probability for the average AoI optimization and the peak AoI optimization are the same. Yet, the optimal packet arrival rate for the average AoI optimization is smaller than that for the peak AoI optimization. The gap enlarges when the node deployment density becomes small.
Relative to the Triple Modular Redundancy (TMR) scheme, the arithmetic residue codes based fault-tolerant DSP design consumes much less resources. However, the price for the low resource consumption is the fault missing problem. The basic tradeoff is that, smaller modulus used for the fault checking consumes fewer resources, but the fault missing rate is higher. The relationship between the value of modulus and the fault missing rate is analyzed theoretically in this paper for fault-tolerant FIR filter design, and the results are verified by FPGA implemented fault injections.
Next generation wireless networks target to provide quality of service (QoS) for multimedia applications. In this paper, the system supports two QoS criteria, i.e., the system should keep the handoff dropping probability always less than a predefined QoS bound, while maintaining the relative priorities of different traffic classes in terms of blocking probability. To achieve this goal, a dynamic multiple-threshold bandwidth reservation scheme is proposed, which is capable of granting differential priorities to different traffic class and to new ad handoff traffic for each class by dynamically adjusting bandwidth reservation thresholds. Moreover, in times of network congestion, a preventive measure by use of throttling new connection acceptance is taken. Another contribution of this paper is to generalize the concept of relative priority, hence giving the network operator more flexibility to adjust admission control policy by incorporating some dynamic factors such as offered load. The elaborate simulation is conducted to verify the performance of the scheme.
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