Design and analysis of LTE and wi-fi schemes for communications of massive machine devices

Existing communication technologies are designed with speciÿc use cases in mind, however, ex-tending these use cases usually throw up interesting challenges. For example, extending the use of existing cellular networks to emerging applications such as Internet of Things (IoT) devices throws up the challenge of handling massive number of devices. In this thesis, we are motivated to investigate existing schemes used in LTE and Wi-Fi for supporting massive machine devices and improve on observed performance gaps by designing new ones that outperform the former. This thesis investigates the existing random access protocol in LTE and proposes three schemes to combat massive device access challenge. The ÿrst is a root index reuse and allocation scheme which uses link budget calculations in extracting a safe distance for preamble reuse under vari-able cell size and also proposes an index allocation algorithm. Secondly, a dynamic subframe optimization scheme that combats the challenge from an optimisation solution perspective. Thirdly, the use of small cells for random access. Simulation and numerical analysis shows performance improvements against existing schemes in terms of throughput, access delay and probability of collision. In some cases, over 20% increase in performance was observed. The proposed schemes provide quicker and more guaranteed opportunities for machine devices to communicate. Also, in Wi-Fi networks, adaptation of the transmission rates to the dynamic channel condi-tions is a major challenge. Two algorithms were proposed to combat this. The ÿrst makes use of contextual information to determine the network state and respond appropriately whilst the second samples candidate transmission modes and uses the e˛ective throughput to make a deci-sion. The proposed algorithms were compared to several existing rate adaptation algorithms by simulations and under various system and channel conÿgurations. They show signiÿcant per-formance improvements, in terms of throughput, thus, conÿrming their suitability for dynamic channel conditions

Throughput-based rate adaptation algorithm for IEEE 802.11 vehicle networks

A key problem with IEEE 802.11 technology is adaptation of the transmission rates to the changing channel conditions, which is more challenging in vehicular networks. Although rate adaptation problem has been extensively studied for static residential and enterprise network scenarios, there is little work dedicated to the IEEE 802.11 rate adaptation in vehicular networks. Here, the authors are motivated to study the IEEE 802.11 rate adaptation problem in infrastructure-based vehicular networks. First of all, the performances of several existing rate adaptation algorithms under vehicle network scenarios, which have been widely used for static network scenarios, are evaluated. Then, a new rate adaptation algorithm is proposed to improve the network performance. In the new rate adaptation algorithm, the technique of sampling candidate transmission modes is used, and the effective throughput associated with a transmission mode is the metric used to choose among the possible transmission modes. The proposed algorithm is compared to several existing rate adaptation algorithms by simulations, which shows significant performance improvement under various system and channel configurations. An ideal signal-to-noise ratio (SNR)-based rate adaptation algorithm in which accurate channel SNR is assumed to be always available is also implemented for benchmark performance comparison

Phytochemical and anti‐plasmodial screening of three selected tropical plants used for the treatment of malaria in Oshogbo, south-western Nigeria

The use of herbal remedy is featuring prominently as alternative to orthodox medicine but little is known on scientific validation of their efficacies in malaria treatment. Questionnaire survey was conducted in Osogbo metropolis to identify the frequently used antiplasmodial herbal remedies. The aqueous extracts of the three frequently used antimalaria herbs, Mangifera indica leaves, Lawsonia inermis leaves and Enanthia chlorantha stem bark were prepared as described by herbal vendors and subsequently analyzed for phytochemical constituents and antiplasmodial efficiencies using mice model. The qualititave phytochemical analysis of the extracts showed differences in the phytochemical constituents of the three plants. The comparison of the parasite load before and after treatment showed that the parasitamia level reduced significantly (p < 0.05) in the mice treated with E. chlorantha and M. indica but increased significantly (p = 0.012; p < 0.05) in the group treated with L. inermis while no parasite was detected in the group treated with chloroquine (antimalaria drug) after treatment. The treated groups had higher concentrations of creatinine, urea, bilirubin, Aspartate aminotransferase and Alkaline phosphate in comparison with the control, an indication of the plant extracts cyto‐toxicity. The results therefore showed that the extracts of E. chlorantha and M. indica only possess chemosupressive not curative antimalaria potential while L. inermis did not show any antiplasmodial effect. Further screening on antimalaria herbal remedies therefore becomes imperative so as to guide the policy on malaria treatment regime in Nigeria.Key words: Phytochemistry, antiplasmodial, plant extracts, biochemical marker