Power-Efficient Radio Resource Allocation for Low-Medium -Altitude Aerial Platform Based TD-LTE Networks

In order to provide an increased capacity, throughput and QoS guarantee for terrestrial users in emergency scenarios, a low-medium-altitude aerial platform based time-division-duplex long term evolution (TD-LTE) system referred to as Aerial LTE, is presented in this paper. Additionally a power-efficient radio resource allocation mechanism is proposed for both the Aerial LTE downlink and uplink, which is modeled as a cooperative game. Our simulation results demonstrate that the proposed algorithm imposes an attractive tradeoff between the achievable throughput and the power consumption while ensuring fairness among users

Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-antenna Dense Small Cell Networks

This paper studies the performance of cache-enabled dense small cell networks consisting of multi-antenna sub-6 GHz and millimeter-wave base stations. Different from the existing works which only consider a single antenna at each base station, the optimal content placement is unknown when the base stations have multiple antennas. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mmWave frequencies. The maximization of the successful content delivery probability is a challenging problem. To tackle it, we first propose a constrained cross-entropy algorithm which achieves the near-optimal solution with moderate complexity. We then develop another simple yet effective heuristic probabilistic content placement scheme, termed two-stair algorithm, which strikes a balance between caching the most popular contents and achieving content diversity. Numerical results demonstrate the superior performance of the constrained cross-entropy method and that the two-stair algorithm yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and mmWave systems reveal an interesting tradeoff between caching capacity and density for the mmWave system to achieve similar performance as the sub-6 GHz system.Comment: 14 pages; Accepted to appear in IEEE Transactions on Wireless Communication

What Factors Will Determine Users’ Knowledge Payment Decision? An Theoretical and Empirical Research

With the increase of peoples’ eagerness for higher quality knowledge, paid Q&A is becoming a new tendency. However, what factors are helpful to drive potential users’ payment decisions remains unknown. In this paper, we investigated the effects of expert attributes and reputation on users’ payment decisions made on an online Q&A platform in China. We developed auto-parsing crawlers to collect online observational data and used the negative binomial panel regression method to estimate the effects of expert attributes and reputation on users’ payment decision. The results show that expert attributes such as the number of paid questions, the number of times that answers are approved, whether the expert has a personal home page, whether the expert mentions his/her area of expertise, the number of followers, score of expert answers have significant effects, whereas the times that the expert shared knowledge free and whether the expert has a real name certification do not influence users’ willingness to pay for an answer. The results help experts on paid Q&A platforms to improve their performance, perfect their personal information, and enhance users’ trust, so as to promote the development of knowledge sharing economy

Effect of Online Brand Community on Customer Behavior Exploration: Reconciling Mixed Findings via Regulatory Focus Theory

This study seeks to address the mixed findings of prior studies regarding the effect of online brand community on customer behavior. Based on the regulatory focus theory, we hypothesize that participation in a brand community tends to increase both visit and purchase frequencies of customers with promotion-focus; on the contrary, the same would typically decrease visit and purchase frequencies of customers with prevention-focus. By analyzing data from an online brand community using a “propensity-score matching” technique, we found a partial validation that attendance of the community led to increases in customer visit frequency for customers with both promotion-focus and prevention-focus. Further, our results show that customers with promotion-focus tend to purchase more; while customers with prevention-focus slightly decreased their purchase volume. Both theoretical and practical implications of our findings are discussed in the paper

Double side signal splitting SWIPT for downlink CoMP transmissions with capacity limited backhaul

This letter studies power allocation for simultaneous wireless information and power transfer in downlink coordinated multipoint (CoMP) systems. A central unit (CU) conveys data and channel information to multiple radio remote units (RRUs) via a capacity-limited backhaul. We provide a dual polarized (DP) antenna-based double side signal splitting method. Specifically, signals are split up into information decoding part [user equipment (UE) data transmitted from CU] and energy harvesting part (deterministic data created at RRUs), which are transmitted and received via vertical and horizontal polarizations of DP antennas, respectively. Normal beamformers (such as zero forcing and maximum ratio transmission) are used to reduce complexity. The problem is to maximize the sum rate satisfying per-UE received power, per-backhaul-link capacity, and per-RRU power transmission constraints. The results are provided to verify the effectiveness of the proposed scheme

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.Comment: This article has been accepted for inclusion in a future issue of China Communications Journal in IEEE Xplor

Double side signal splitting SWIPT for downlink CoMP transmissions with capacity limited backhaul

This letter studies power allocation for simultaneous wireless information and power transfer in downlink coordinated multipoint (CoMP) systems. A central unit (CU) conveys data and channel information to multiple radio remote units (RRUs) via a capacity-limited backhaul. We provide a dual polarized (DP) antenna-based double side signal splitting method. Specifically, signals are split up into information decoding part [user equipment (UE) data transmitted from CU] and energy harvesting part (deterministic data created at RRUs), which are transmitted and received via vertical and horizontal polarizations of DP antennas, respectively. Normal beamformers (such as zero forcing and maximum ratio transmission) are used to reduce complexity. The problem is to maximize the sum rate satisfying per-UE received power, per-backhaul-link capacity, and per-RRU power transmission constraints. The results are provided to verify the effectiveness of the proposed scheme

Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space-air-ground integrated network (SAGIN) with diverse resource constraints. In this paper, we dynamically consider three typical classes of radio access network (RAN) slices, namely high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay and the coverage area of these three classes of RAN slices are jointly optimized in a non-scalar form by considering the distinct channel features and service advantages of the terrestrial, aerial and satellite components of SAGINs. A joint central and distributed multi-agent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the inter-slice sub-channel and power sharing by relying on a centralized unit. Then it optimizes the intra-slice sub-channel and power allocation, and the virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.Comment: 19 pages, 14 figures, journa