Fast Changing Channel Conditions Research Articles

Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success rate of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a Contribution-based Dual-Clip Proximal Policy Optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all the subproblems are solved. Simulation results reveal that proposed algorithm can outperform the other algorithms in terms of the MOS and transmission success rate.

Energy-Efficient Mobile Data Uploading from High-Speed Trains

Recent decades have witnessed the fast development of high-speed railway systems in many countries, which have significantly shortened the travel time between distant cities. Accompanied with this convenience is the challenge for cell phone vendors to provide broadband Internet access for passengers, particularly considering the fast changing channel conditions in high-speed trains and the limited battery of cell phones, which often cannot be re-charged in trains. In this paper, inspired by the unique spatial-temporal characteristics of wireless signals along high-speed railways, we propose a novel energy-efficient scheduling approach for uploading data from cell phones, both with soft deadlines (e.g., documents) and hard deadlines (e.g., video streaming). Our solution effectively predicts the signal strength through its spatial-temporal periodicity in this new application scenario, and smartly adjusts the transmission rate to maximize the overall data transmission rate and yet conserves the energy consumption. Performance evaluation based on realistic railway scenarios and H.264 video traces demonstrate the effectiveness of our solution and its superiority as compared to the existing solutions.

Adaptive Packet Size Control Using Beta Distribution Mobility Estimation for Rapidly Changing Mobile Networks

An adaptive algorithm to optimize the packet size in wireless mobile networks with Gauss-Markov mobility is presented. The proposed control algorithm conducts adaptive packet size control for mobile terminals that experience relatively fast changing channel conditions, which could be caused by fast mobility or other rapidly changing interference conditions. Due to the fast changing channel conditions, the packet size controller uses short channel history for channel status estimation and takes advantage of a pre-calculated probability density function (PDF) of the distance of the mobile nodes in the estimation process. The packet size is adapted to maximize the communication performance through automatic repeat request (ARQ). The adaptive packet size controlling algorithm is based on an estimation of the channel error rate and the link statistics obtained from the mobility pattern. It was found that the distribution of the link distance among mobile nodes following the Markov-Gauss mobility pattern in a circular communication range well fits the Beta PDF. By adapting the Beta PDF from the mobility pattern, the results show that it is possible to estimate the channel condition more accurately and thereby improve the throughput and utilization performance in rapidly changing wireless mobile networking systems.

Single-block differential transmit scheme for broadband wireless MIMO-OFDM systems

In frequency-selective multiple-input multiple-output (MIMO) channel, differential space-time-frequency (DSTF) modulations are known as practical alternatives that are capable of exploiting the available spatial and frequency diversities without the requirement of multichannel estimation at the receiver. However, the encoding nature of the DSTF schemes that expand several OFDM symbol periods makes the DSTF schemes susceptible to fast-changing channel conditions. In this paper, we propose a differential scheme for MIMO-OFDM systems that is able to differentially encode signal within two OFDM symbol periods, and the proposed scheme transmits the differentially encoded signal within one OFDM block. The scheme not only reduces encoding and decoding delay but also relaxes the restriction on channel assumption. The successful differential decoding of the proposed scheme depends on the assumption that the fading channels keep constant over two OFDM symbol periods rather than multiple of them as required in the existing DSTF schemes. We also provide pairwise error probability analysis and quantify the performance criteria in terms of diversity and coding advantages. The design criteria reveal that the existing diagonal cyclic codes can be applied to achieve full diversity. Performance simulations under various channel conditions show that our proposed scheme yields superior performance to previously proposed differential schemes

Research on Threshold Adjustment Algorithm in Adaptive Modulation and Coding

Adaptive Modulation and Coding ( AMC) has gained a lot of attentions in the research of High Speed Downlink Packet Access ( HSDPA). The idea is to adapt the transmission to the fast changing channel conditions by the use of different Modulation and Coding Schemes ( MCS). This paper presents an adaptive AMC algorithm and introduces a theoretical analysis model in order to to investigate its through put and Frame Error Rate ( FER). Subject to the given FER target, our numerical and link level simulation results both show that our algorithm out performs other existing adaptive algorithms.