Efficient 3D Placement of a UAV Using Particle Swarm Optimization

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.Comment: 6 pages, 7 figure

Wireless speech and audio communications

The limited applicability of Shannon’s separation theorem in practical speech/audio systems motivates the employment of joint source and channel coding techniques. Thus, considerable efforts have been invested in designing various types of joint source and channel coding schemes. This thesis discusses two different types of Joint Source and Channel Coding (JSCC) schemes, namely Unequal Error Protection (UEP) aided turbo transceivers as well as Iterative Source and Channel Decoding (ISCD) exploiting the residual redundancy inherent in the source encoded parameters.More specifically, in Chapter 2, two different UEP JSCC philosophies were designed for wireless audio and speech transmissions, namely a turbo-detected UEP scheme using twin-class convolutional codes and another turbo detector using more sophisticated Irregular Convolutional Codes (IRCC). In our investigations, the MPEG-4 Advanced Audio Coding (AAC), the MPEG-4 Transform-Domain Weighted Interleaved Vector Quantization (TwinVQ) and the Adaptive MultiRate WideBand (AMR-WB) audio/speech codecs were incorporated in the sophisticated UEP turbo transceiver, which consisted of a three-stage serially concatenated scheme constituted by Space-Time Trellis Coding (STTC), Trellis Coded Modulation (TCM) and two different-rate Non-Systematic Convolutional codes (NSCs) used for UEP. Explicitly, both the twin-class UEP turbo transceiver assisted MPEG-4 TwinVQ and the AMR-WB audio/speech schemes outperformed their corresponding single-class audio/speech benchmarkers by approximately 0.5 dB, in terms of the required Eb/N0, when communicating over uncorrelated Rayleigh fading channels. By contrast, when employing the MPEG-4 AAC audio codec and protecting the class-1 audio bits using a 2/3-rate NSC code, a more substantial Eb/N0 gain of about 2 dB was achieved. As a further design alternative, we also proposed a turbo transceiver employing IRCCs for the sake of providing UEP for the AMR-WB speech codec. The resultant UEP schemes exhibited a better performance when compared to the corresponding Equal Error Protection (EEP) benchmark schemes, since the former protected the audio/speech bits according to their sensitivity. The proposed UEP aided system using IRCCs exhibits an Eb/N0 gain of about 0.4 dB over the EEP system employing regular convolutional codes, when communicating over AWGN channels, at the point of tolerating a SegSNR degradation of 1 dB. In Chapter 3, a novel system that invokes jointly optimised ISCD for enhancing the error resilience of the AMR-WB speech codec was proposed and investigated. The resultant AMR-WB coded speech signal is protected by a Recursive Systematic onvolutional (RSC) code and transmitted using a non-coherently detected Multiple-Input Multiple-Output (MIMO) Differential Space-Time Spreading (DSTS) scheme. To further enhance the attainable system performance and to maximise the coding advantage of the proposed transmission scheme, the system is also combined with multi-dimensional Sphere Packing (SP) modulation. The AMR-WB speech decoder was further developed for the sake of accepting the a priori information passed to it from the channel decoder as extrinsic information,where the residual redundancy inherent in the AMR-WB encoded parameters was exploited.Moreover, the convergence behaviour of the proposed scheme was evaluated with the aid of both Three-Dimensional (3D) and Two-Dimensional (2D) EXtrinsic Information Transfer (EXIT) charts. The proposed scheme benefitted from the exploitation of the residual redundancy inherent in the AMR-WB encoded parameters, where an approximately 0.5 dB Eb/N0 gain was achieved in comparison to its corresponding hard speechdecoding based counterpart. At the point of tolerating a SegSNR degradation of 1 dB, the advocated scheme exhibited an Eb/N0 gain of about 1.0 dB in comparison to the benchmark scheme carrying out joint channel decoding and DSTS aided SP-demodulation in conjunction with separate AMR-WB decoding, when communicating over narrowband temporally correlated Rayleigh fading channels.In Chapter 4, two jointly optimized ISCD schemes invoking the soft-output AMRWB speech codec using DSTS assisted SP modulation were proposed. More specifically, the soft-bit assisted iterative AMR-WB decoder’s convergence characteristics were further enhanced by using Over-Complete source-Mapping (OCM), as well as a recursive precoder. EXIT charts were used to analyse the convergence behaviour of the proposed turbo transceivers using the soft-bit assisted AMR-WB decoder. Explicitly, the OCM aided AMR-WB MIMO transceiver exhibits an Eb/N0 gain of about 3.0 dB in comparison to the benchmark scheme also using ISCD as well as DSTS aided SP-demodulation, but dispensing with the OCM scheme, when communicating over narrowband temporally correlated Rayleigh fading channels. Finally, the precoded soft-bit AMR-WB MIMO transceiver exhibits an Eb/N0 gain of about 1.5 dB in comparison to the benchmark scheme dispensing with the precoder, when communicating over narrowband temporally correlated Rayleigh fading channels

The Effect of Rainfall on the UAV Placement for 5G Spectrum in Malaysia

In this paper, the influence of rainfall on the deployment of UAV as an aerial base station in the Malaysia 5G network is studied. The outdoor-to-outdoor and outdoor-to-indoor path loss models are derived by considering the user’s antenna height, rain attenuation, and the wall penetration loss at high frequencies. The problem of finding the UAV 3D placement is formulated with the objective to minimize the total path loss between the UAV and all users. The problem is solved by invoking two algorithms, namely Particle Swarm Optimization (PSO) and Gradient Descent (GD) algorithms. The performance of the proposed algorithms is evaluated by considering two scenarios to determine the optimum location of the UAV, namely outdoor-to-outdoor and outdoor-to-indoor scenarios. The simulation results show that, for the outdoor-to-outdoor scenario, both algorithms resulted in similar UAV 3D placement unlike for the outdoor-to-indoor scenario. Additionally, in both scenarios, the proposed algorithm that invokes PSO requires less iterations to converge to the minimum transmit power compared to that of the algorithm that invokes GD. Moreover, it is also observed that the rain attenuation increases the total path loss for high operating frequencies, namely at 24.9 GHz and 28.1 GHz. Hence, this resulted in an increase of UAV required transmit power. At 28.1 GHz, the presence of rain at the rate of 250 mm/h resulted in an increase of UAV required transmit power by a factor of 4 and 15 for outdoor-to-outdoor and outdoor-to-indoor scenarios, respectively

Efficient Deployment of Multi-UAVs in Massively Crowded Events

In this paper, the efficient 3D placement of UAV as an aerial base station in providing wireless coverage for users in a small and large coverage area is investigated. In the case of providing wireless coverage for outdoor and indoor users in a small area, the Particle Swarm Optimization (PSO) and K-means with Ternary Search (KTS) algorithms are invoked to find an efficient 3D location of a single UAV with the objective of minimizing its required transmit power. It was observed that a single UAV at the 3D location found using the PSO algorithm requires less transmit power, by a factor of 1/5 compared to that when using the KTS algorithm. In the case of providing wireless coverage for users in three different shapes of a large coverage area, namely square, rectangle and circular regions, the problems of finding an efficient placement of multiple UAVs equipped with a directional antenna are formulated with the objective to maximize the coverage area and coverage density using the Circle Packing Theory (CPT). Then, the UAV efficient altitude placement is formulated with the objective of minimizing its required transmit power. It is observed that the large number of UAVs does not necessarily result in the maximum coverage density. Based on the simulation results, the deployment of 16, 19 and 26 UAVs is capable of providing the maximum coverage density of 78.5%, 82.5% and 80.3% for the case of a square region with the dimensions of 2 km × 2 km, a rectangle region with the dimensions of 6 km × 1.8 km and a circular region with the radius of 1.125 km, respectively. These observations are obtained when the UAVs are located at the optimum altitude, where the required transmit power for each UAV is reasonably small

The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq

The high speeds resulting from the use of millimeter waves (mmWave) in 5G mobile networks are accompanied by high path loss. The issue of generating a reliable propagation model of radio waves is crucial to the development of cellular networks since it reveals essential information regarding the properties of the wireless channel. The received signal strength, the coverage area, and the outage probability in certain places may all be determined through theoretical or empirical radio frequency propagation models, which offer essential valuable information regarding signal path loss and fading. This work analyzes a comprehensive three-dimensional ray-tracing method at 28 GHz for Najaf city, Iraq. The optimum path loss model for the city of Najaf is evaluated using the close-in (CI) model. On average, the values of the main parameters of CI model nn, XσCI{X}_{\sigma }^{{\rm{CI}}} accomplished, respectively, 3.461866667 and 11.13958333. The lowest achievable path loss exponent was 3.0619 across all analyzed scenarios, while the highest possible value was 4.1253. The results of this work can serve as a baseline for mmWave measurement campaigns conducted in comparable conditions, and they provide a new avenue for future research into mmWave at 28 GHz in Iraq

Power-Efficient Wireless Coverage Using Minimum Number of UAVs

Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density