One cornerstone in building future 5G and beyond wireless systems is to mimic the real-world environment using a simulator. The simulator needs to reflect the experienced propagation environment by the device in different scenarios. Today, the methods used to generate such an environment and finding the signal qualities at certain locations can be time-consuming for large cities with many base stations and devices. The objective of this project is speed up an existing SCM channel generator by replacing certain time-critical numerical formulas with a machine learning (ML) model that can generate the channel coefficients directly. The expectation is that this setup will provide much faster generations than any existing solution. A machine learning paradigm is suggested and implemented. The results suggests that a model can learn and generalize from the training data, and that provided solution is a possible configuration for modelling radio channels. Conclusions regarding the implementational considerations are made as guidance for future work. / En av hörnstenarna för att kunna bygga framtida trådlösa 5G system är att kunna efterlikna den verkliga miljön med hjälp av en simulator. Simulatorn måste återspegla enhetens upplevda propageringsmiljö i olika scenarier. I dagens läge kan metoderna som används för att skapa en sådan miljö, och hitta signalkvaliteterna på vissa platser vara tidskrävande för scenarier med stora städer med många basstationer och enheter. Målet med detta projekt är att påskynda en befintlig SCM-kanalgenerator genom att ersätta vissa tidskritiska numeriska formler med en maskininlärningsmodell (ML) som kan generera kanalkoefficienterna direkt. Förväntningen är att denna lösning kommer att generera data mycket snabbare än någon befintlig lösning. En sådan lösning föreslås och implementeras. Resultaten tyder på att en modell kan lära sig och generalisera av träningsdatat, och att den tillhandahållna lösningen är en möjlig konfiguration för modellering av radiokanaler. Slutsatser gällande övervägningarna vid implementeringen dras som vägledning för framtida arbete.

This thesis covers the investigation and application of continuous spatial models for multiple antenna signal processing. The use of antenna arrays for advanced sensing and communications systems has been facilitated by the rapid increase in the capabilities of digital signal processing systems. The wireless communications channel will vary across space as different signal paths from the same source combine and interfere. This creates a level of spatial diversity that can be exploited to improve the robustness and overall capacity of the wireless channel. Conventional approaches to using spatial diversity have centered on smart, adaptive antennas and spatial beam forming. Recently, the more general theory of multiple input, multiple output (MIMO) systems has been developed to utilise the independent spatial communication modes offered in a scattering environment.¶ Underlying any multiple antenna system is the basic physics of electromagnetic wave propagation. Whilst a MIMO system may present a set of discrete inputs and outputs, each antenna element must interact with the underlying continuous spatial field. Since an electromagnetic disturbance will propagate through space, the field at different positions in the space will be interrelated. In this way, each position in the field cannot assume an arbitrary independent value and the nature of wave propagation places a constraint on the allowable complexity of a wave-field over space. To take advantage of this underlying physical constraint, it is necessary to have a model that incorporates the continuous nature of the spatial wave-field. ¶This thesis investigates continuous spatial models for the wave-field. The wave equation constraint is introduced by considering a natural basis expansion for the space of physically valid wave-fields. This approach demonstrates that a wave-field over a finite spatial region has an effective finite dimensionality. The optimal basis for representing such a field is dependent on the shape of the region of interest and the angular power distribution of the incident field. By applying the continuous spatial model to the problem of direction of arrival estimation, it is shown that the spatial region occupied by the receiver places a fundamental limit on the number and accuracy with which sources can be resolved. Continuous spatial models also provide a parsimonious representation for modelling the spatial communications channel independent of specific antenna array configurations. The continuous spatial model is also applied to consider limits to the problem of wireless source direction and range localisation.

This work studies the allocation of bandwidth resources in wireless ad hoc networks. The highest-density clustering algorithm is presented to promote reuse of the spatial channel and a new slot allocation algorithm is proposed to achieve conflict-free scheduling for transmissions. Since the location-dependent contention is an important characteristic of ad hoc networks, in this paper we consider this feature of ad hoc networks to present a new cluster formation algorithm, by increasing the number of simultaneous links to enhance spatial channel reuse. Furthermore, because each cluster has its own scheduler and schedulers operate independently of each other, the transmissions may conflict among the clusters. In this paper, we classify the flows by the locations of their endpoints to prevent this problem. Finally, the proposed mechanism is implemented by simulation and the results reveal that the conflicts can be efficiently avoided without global information and the network throughput is improved without violating fairness.

Ultra-Wideband Communication is increasingly being considered as an attractive solution for high data rate short range wireless and position location applications. Knowledge of the statistical nature of the channel is necessary to design wireless systems that provide optimum performance. This thesis investigates the spatial characteristics of the channel based on measurements conducted using UWB pulses in an indoor office environment. The statistics of the received signal energy illustrate the low spatial fading of UWB signals. The distribution of the Angle of arrival (AOA) of the multipath components is obtained using a two-dimensional deconvolution algorithm called the Sensor-CLEAN algorithm. A spatial channel model that incorporates the spatial and temporal features of the channel is developed based on the AOA statistics. The performance of the Sensor-CLEAN algorithm is evaluated briefly by application to known artificial channels. UWB systems co-exist with narrowband and other wideband systems. Even though they enjoy the advantage of processing gain (the ratio of bandwidth to data rate) the low energy per pulse may cause these narrow band interferers (NBI) to severely degrade the UWB system's performance. A technique to suppress NBI using multiple antennas is presented in this thesis which exploits the spatial fading characteristics. This method exploits the vast difference in fading characteristics between UWB signals and NBI by implementing a simple selection diversity scheme. It is shown that this simple scheme can provide strong benefits in performance. / Master of Science

In large antenna systems, traditional channel estimation is costly and infeasible in some situations. Compressive sensing was proposed to estimate the channel with fewer measurements. Most of the previous work uses a predefined discrete Fourier transform matrix or overcomplete Fourier transform matrix to approximate the channel. Then, a learned dictionary trained by K-singular value decomposition (K-SVD) was proposed and was proved superiority using orthogonal matching pursuit (OMP) to reconstruct the sparse channel. However, with the development of compressive sensing, there are plenty of dictionary learning algorithms and sparse recovery algorithms. It is important to identify the effect and the performance of different algorithms when transforming the high dimensional channel vectors to low dimensional representations. In this thesis, we use a spatial channel model to generate channel vectors. Dictionaries are trained by K-SVD and method of optimal directions (MOD). Several sparse recovery algorithms are used to find the sparse approximation of the channel like OMP and gradient descent with sparsification (GraDeS). We present simulation results and discuss the performance of the various algorithms in terms of accuracy, sparsity, and complexity. We find that predefined dictionaries works with most of the algorithms in sparse recovery but learned dictionaries only work with pursuit algorithms, and only show superiority when the algorithm coincides with the algorithm in the sparse coding stage. / I stora antennsystem är traditionell kanaluppskattning kostsam och omöjlig i vissa situationer. Kompressionsavkänning föreslogs för att uppskatta kanalen med färre mätningar. Det mesta av det tidigare arbetet använder en fördefinierad diskret Fourier transformmatris eller överkompletterad Fourier -transformmatris för att approximera kanalen. Därefter föreslogs en inlärd ordbok som utbildats av K-SVD och bevisades överlägsen med hjälp av OMP för att rekonstruera den glesa kanalen. Men med utvecklingen av komprimerad avkänning finns det gott om algoritmer för inlärning av ordlistor och glesa återställningsalgoritmer. Det är viktigt att identifiera effekten och prestandan hos olika algoritmer när de högdimensionella kanalvektorerna omvandlas till lågdimensionella representationer. I denna avhandling använder vi en rumslig kanalmodell för att generera kanalvektorer. Ordböcker tränas av K-SVD och MOD. Flera glesa återställningsalgoritmer används för att hitta den glesa approximationen av kanalen som OMP och GraDeS. Vi presenterar simuleringsresultat och diskuterar prestanda för de olika algoritmerna när det gäller noggrannhet, sparsamhet och komplexitet. Vi finner att fördefinierade ordböcker fungerar med de flesta algoritmerna i gles återhämtning, men inlärda ordböcker fungerar bara med jaktalgoritmer och visar bara överlägsenhet när algoritmen sammanfaller med algoritmen i det glesa kodningsstadiet.