On the Application of the Baum-Welch Algorithm for Modeling the Land Mobile Satellite Channel

Accurate channel models are of high importance for the design of upcoming mobile satellite systems. Nowadays most of the models for the LMSC are based on Markov chains and rely on measurement data, rather than on pure theoretical considerations. A key problem lies in the determination of the model parameters out of the observed data. In this work we face the issue of state identification of the underlying Markov model whose model parameters are a priori unknown. This can be seen as a HMM problem. For finding the ML estimates of such model parameters the BW algorithm is adapted to the context of channel modeling. Numerical results on test data sequences reveal the capabilities of the proposed algorithm. Results on real measurement data are finally presented.Comment: IEEE Globecom 201

Ku-Band AG Channel Modeling

With the rise in use of Unmanned Aerial Systems (UAS), there is a need for safe and reliable integration into existing infrastructure. A proposed system for beyond line of sight control links for UAS is a Ku-band air-to-satellite communication system. To ensure this proposed system does not interfere with existing terrestrial infrastructure that operates in the Ku band, an examination of the Ku-band air-to-ground channel is required. The focus of this thesis is the modeling of the Ku-band AG channel. Tests consisting of transmitting a single tone continuous wave signal were conducted with a signal generator onboard NASA’s Viking S-3 aircraft. Transmission was via a custom Ku-band monopole antenna mounted on the bottom of the aircraft’s fuselage. A ground site mimicking existing terrestrial point-to-point infrastructure was used to collect received power measurements. Also included at the ground site were two wider-beamwidth antennas, to enable some assessment of multipath and polarization effects. Measurements were conducted using three inclination (elevation) angles, 0°, 2.5°, and 5°, and the aircraft was flown at three approximately constant-altitudes. The channel characteristic that was measured was attenuation, also termed path loss. Path loss estimates were made using free-space and two- ray models, and these models were refined by the inclusion of atmospheric refraction, attenuation due to fog, and curved earth models for the various flight geometries. These models were further refined by assessing angular offsets to the antenna gain patterns, as the aircraft transmitter gain patterns had deep nulls that affected measured power as the aircraft pitch varied both up and down during test flights. Measured data was then also fit to a log-distance model for each flight test. These log-distance models are commonly used for terrestrial settings and provide a measure of goodness of fit. Overall, path loss exponents are close to the value of 2, as expected; this is the value for a free-space channel. Log-distance models yielded standard deviations in the range of 1.68 to 5.13 dB. When the inclination angle of the receiver was 5°, the measured fit equation for co-polarized antennas was found to have a path loss exponent very close to that of free-space, but as inclination angles decreased (closer to the horizon boresight), path loss exponents increased

Physical layer forward error correcetion in DVB-S2 networks.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.The rapid growth of wireless systems has shown little sign of ceasing, due to increased consumer demand for reliable interactive services. A key component of the development has centered on satellite networks, which allows provision of services in scenarios where terrestrial systems are not viable. The Digital Video Broadcasting-Satellite Second Generation (DVB-S2) standard was developed for use in satellite broadcast applications, the foremost being video broadcasting. Inherent to DVB-S2 is a powerful forward error correction (FEC) module, present in both the Physical and Data Link Layer. Improving the error correcting capability of the FEC is a natural advent in improving the quality of service of the protocol. This is more crucial in real time satellite video broadcast where retransmission of data is not viable, due to high latency. The Physical Layer error correcting capability is implemented in the form of a concatenated BCH-LDPC code. The DVB-S2 standard does not define the decoding structure for the receiver system however many powerful decoding systems have been presented in the literature; the Belief Propagation-Chase concatenated decoder being chief amongst them. The decoder utilizes the concept of soft information transfer between the Chase and Belief Propagation (BP) decoders to provide improved error correcting capability above that of the component decoders. The following dissertation is motivated by the physical layer (PL) FEC scheme, focused on the concatenated Chase-BP decoder. The aim is to generate results based on the BP-Chase decoder in a satellite channel as well as improve the error correcting capability. The BP-Chase decoder has shown to be very powerful however the current literature provides performance results only in AWGN channels. The AWGN channel however is not an accurate representation of a land-mobile satellite (LMS) channel; it does not consider the effect of shadowing, which is prevalent in satellite systems. The development of Markov chain models have allowed for better description of the characteristics of the LMS channel. The outcome being the selection of a Ku band LMS channel model. The selected LMS channel model is composed of 3 states, each generating a different degree of shadowing. The PL system has been simulated using the LMS channel and BP-Chase receiver to provide a more accurate representation of performance of a DVB-S2 network. The effect of shadowing has shown to reduce coding performance by approximately 4dB, measured over several code lengths and decoders, when compared with AWGN performance results. The second body of work aims to improve the error correcting capability of the BP-Chase decoder, concentrating on improving the LDPC decoding module performance. The LDPC system is the basis for the powerful error correcting ability of the concatenated scheme. In attempting to improve the LDPC decoder a reciprocal improvement is expected in the overall decoding performance of the concatenated decoder. There have been several schemes presented which improve BP performance. The BP-Ordered statistics decoder (OSD) was selected through a process of literary review; a novel decoding structure is presented incorporating the BP-OSD decoder into the BP-Chase structure. The result of which is the BP-OSD-Chase decoder. The decoder contains two stages of concatenation; the first stage implements the BPOSD algorithm which decodes the LDPC code and the second stage decodes the BCH code using the Chase algorithm. Simulation results of the novel decoder implementation in the DVBS2 PL show a coding gain of 0.45dB and 0.15dB versus the BP and BP-Chase decoders respectively, across both the AWGN and LMS channel

Networking And Security Solutions For Vanet Initial Deployment Stage

Vehicular ad hoc network (VANET) is a special case of mobile networks, where vehicles equipped with computing/communicating devices (called smart vehicles ) are the mobile wireless nodes. However, the movement pattern of these mobile wireless nodes is no more random, as in case of mobile networks, rather it is restricted to roads and streets. Vehicular networks have hybrid architecture; it is a combination of both infrastructure and infrastructure-less architectures. The direct vehicle to vehicle (V2V) communication is infrastructure-less or ad hoc in nature. Here the vehicles traveling within communication range of each other form an ad hoc network. On the other hand, the vehicle to infrastructure (V2I) communication has infrastructure architecture where vehicles connect to access points deployed along roads. These access points are known as road side units (RSUs) and vehicles communicate with other vehicles/wired nodes through these RSUs. To provide various services to vehicles, RSUs are generally connected to each other and to the Internet. The direct RSU to RSU communication is also referred as I2I communication. The success of VANET depends on the existence of pervasive roadside infrastructure and sufficient number of smart vehicles. Most VANET applications and services are based on either one or both of these requirements. A fully matured VANET will have pervasive roadside network and enough vehicle density to enable VANET applications. However, the initial deployment stage of VANET will be characterized by the lack of pervasive roadside infrastructure and low market penetration of smart vehicles. It will be economically infeasible to initially install a pervasive and fully networked iv roadside infrastructure, which could result in the failure of applications and services that depend on V2I or I2I communications. Further, low market penetration means there are insufficient number of smart vehicles to enable V2V communication, which could result in failure of services and applications that depend on V2V communications. Non-availability of pervasive connectivity to certification authorities and dynamic locations of each vehicle will make it difficult and expensive to implement security solutions that are based on some central certificate management authority. Nonavailability of pervasive connectivity will also affect the backend connectivity of vehicles to the Internet or the rest of the world. Due to economic considerations, the installation of roadside infrastructure will take a long time and will be incremental thus resulting in a heterogeneous infrastructure with non-consistent capabilities. Similarly, smart vehicles will also have varying degree of capabilities. This will result in failure of applications and services that have very strict requirements on V2I or V2V communications. We have proposed several solutions to overcome the challenges described above that will be faced during the initial deployment stage of VANET. Specifically, we have proposed: A VANET architecture that can provide services with limited number of heterogeneous roadside units and smart vehicles with varying capabilities. A backend connectivity solution that provides connectivity between the Internet and smart vehicles without requiring pervasive roadside infrastructure or large number of smart vehicles. A security architecture that does not depend on pervasive roadside infrastructure or a fully connected V2V network and fulfills all the security requirements. v Optimization solutions for placement of a limited number of RSUs within a given area to provide best possible service to smart vehicles. The optimal placement solutions cover both urban areas and highways environment