Empirical evaluation of predictive channel-aware transmission for resource efficient car-to-cloud communication

Nowadays vehicles are by default equipped with communication hardware. This enables new possibilities of connected services, like vehicles serving as highly mobile sensor platforms in the Internet of Things (IoT) context. Hereby, cars need to upload and transfer their data via a mobile communication network into the cloud for further evaluation. As wireless resources are limited and shared by all users, data transfers need to be conducted efficiently. Within the scope of this work three car-to-cloud data transmission algorithms Channel-Aware Transmission (CAT), predictive CAT (pCAT) and a periodic scheme are evaluated in an empirical setup. CAT leverages channel quality measurements to start data uploads preferably when the channel quality is good. CAT's extension pCAT uses past measurements in addition to estimate future channel conditions. For the empirical evaluation, a research vehicle was equipped with a measurement platform. On test drives along a reference route vehicle sensor data was collected and subsequently uploaded to a cloud server via a Long Term Evolution (LTE) network

PAPR reduction in OFDM communications with generalized discrete Fourier transform

The main advantage of Generalized Discrete Fourier Transform (GDFT) is its ability to design a wide selection of constant modulus orthogonal code sets, based on the desired performance metrics mimicking the engineering specs of interest. One of the main drawbacks of Orthogonal Frequency Division Multiplexing (OFDM) systems is the high Peak to Average Power Ratio (PAPR) value which is directly related to power consumption of the system. Discrete Fourier Transform (DFT) spread OFDM technology, also known as Single Carrier Frequency Division Multiple Access (SCFDMA), which has a lower PAPR value, is used for uplink channel. In this thesis, the PAPR of DFT spread OFDM was further decreased by using a GDFT concept. The performance improvements of GDFT based PAPR reduction for various SCFDMA communications scenarios were evaluated by simulations. Performance simulation results showed that PAPR efficiency of SCFDMA systems for Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (16-QAM), digital modulation techniques are increased

Jointly optimal chunk and power allocation in uplink SC-FDMA

For a single carrier frequency division multiple access (SC-FDMA) system, we obtain the jointly optimal power and chunk allocation policies which maximize the sum rate. Our solution is applicable to both localized and interleaved subcarrier mapping schemes. We solve the joint optimization problem by sequentially solving two sub-problems: power allocation and chunk allocation. Primarily, we use an optimal power allocation algorithm, which we derive from Karush-Kuhn-Tucker (KKT) conditions; and then we convert the optimum chunk assignment problem into a maximum weighted matching problem on a bipartite graph, and hence solve it in polynomial time. We also propose two greedy chunk allocation algorithms with lower complexity, and demonstrate that these algorithms produce near optimal results, especially for interleaved subcarrier mapping, when used in conjunction with optimal power control.Publisher's Versio

Spatial Frequency Scheduling for Uplink SC-FDMA based Linearly Precoded LTE Multiuser MIMO Systems

This paper investigates the performance of the uplink single carrier (SC) frequency division multiple access (FDMA) based linearly precoded multiuser multiple input multiple output (MIMO) systems with frequency domain packet scheduling. A mathematical expression of the received signal to interference plus noise ratio (SINR) for the studied systems is derived and a utility function based spatial frequency packet scheduling algorithms is investigated. The schedulers are shown to be able to exploit the available multiuser diversity in time, frequency and spatial domains

Combined-order Algorithm using Promethee Method Approach and Analytic Hierarchy Decision for Chunk Allocation in LTE Uplink Systems

The problem of chunk-based resource allocation for the uplink of Long Term Evolution is investigated. In this paper, a combined order using the promethee method and analytic hierarchy decision for chunk allocation algorithm is proposed. The utility of each order is sorted based on promethee method approach so that the utility of each order could be approximated as the average of all criteria on each order. To decide the best allocation, analytic hierarchy process score is assigned to its order based on their decision criteria weighting factor to find the best allocation. Using a particular weighting factor, the proposed algorithms outperform the previous mean greedy algorithms which use user-order allocation in term of spectral efficiency and data rate fairness without increase the time complexity. It also outperform iterative swapping chunk algorithm in term of  data rate fairness

A Combined User-order and Chunk-order Algorithm to Minimize The Average BER for Chunk Allocation in SC-FDMA Systems

A Chunk by chunk-based allocation is an emerging subcarrier allocation in Single Carrier Frequency Division Multiple Access (SC-FDMA) due to its low complexity. In this paper, a combined user-order  and chunk-order allocation for solving chunk allocation problem which minimizes the average BER of all users while improving the throughput in SC-FDMA uplink is proposed. The subcarrier grouping into a chunk of all users on both-order allocations are performed by averaging the BER of a contiguous subcarriers within a chunk. The sequence of allocation is according to the average of users’ BER on user-order allocation and the average of chunks’ BER on chunk-order allocation. The best allocation is determined by choosing one of both-order allocations which provides the smaller BER systems. The simulation results showed that the proposed algorithm can outperform the previous algorithms in term of  average BER and throughput without increase the time complexity.

Dynamic resource allocation algorithms for long term evolution (LTE) wireless broadband networks

Following the successful standardization of High-Speed Packet Access (HSPA), the 3rd Generation Partnership Project (3GPP) recently specified the Long Term Evolution (LTE) as a next generation radio network technology to meet the increasing performance requirements of mobile broadband. The results include a flexible and spectrally efficient radio link protocol design with low overhead. The first release of LTE provides peak rates of 300 Mbps in downlink and 75 Mbps in uplink. It is a significant increase in spectrum efficiency compared to the previous cellular systems. Single-Carrier Frequency Division Multiple Access (SC-FDMA) has been selected as the uplink access scheme in the LTE. With SC-FDMA, the frequency spectrum resource is divided into time-frequency grids, referred to as resource blocks (RBs). Multiple-access is achieved by distributing resource blocks to users. The function of resource block allocation algorithms is to distribute resource blocks among users in a fair and efficient manner. The Modulation and coding scheme is determined adaptively according to the time-varying channel conditions. Sounding Reference Signals (SRS) are transmitted in the uplink direction to allow for the base station to estimate the uplink channel quality at different frequencies. The LTE system supports wideband SRS and narrowband SRS. We have developed an in-house simulation program in C++ to evaluate and compare the performance of CASA and ICAS algorithms in terms of packet loss ratio, delay, and throughput. Simulation results show that the proposed algorithms are able to satisfy the QoS requirements. Both of proposed algorithms support multiple CoSs simultaneously without impeding the first class (Expedited Forwarding) transmission. Also, both of the proposed algorithms achieve high throughput in a large range cell