The ever-growing demand for higher wireless data rates has driven the exploration of novel methods to harness spatial degrees of freedom in multiple input multiple output (MIMO) systems. This thesis investigates the potential of both fixed and movable antenna arrays in line-of-sight (LoS) and multipath rich environments to optimize spatial multiplexing performance in narrowband and wideband settings.

First, fixed dual-polarized planar antenna arrays are considered, and their spatial configuration is optimized to maximize the MIMO channel rank and condition number. Through careful optimization of antenna spacing, it is shown that the MIMO rank can grow quadratically with the carrier frequency, enabling data rates well beyond 1 Tbps. Analytical and numerical results confirm that strategically designed sparse arrays can deliver superior spectral efficiency, even within physically compact apertures.

Building on this, arrays of movable antenna (MA) are examined for their ability to dynamically adapt antenna positions to changing channel conditions. While theoretically powerful, real time optimization of MA systems is computationally intensive and practically challenging. To address this, a pre-optimized irregular array (PIA) design is introduced, where antenna positions are fixed based on statistical knowledge of the coverage area. Using particle swarm optimization, PIAs are shown to achieve performance close to that of fully dynamic MA systems, without the need for real-time repositioning.

Finally, MA systems are analyzed in wideband MIMO scenarios, where hardware impairments and multipath richness present additional challenges. A novel wideband system model is developed that incorporates hardware non-idealities, and antenna positions are optimized to maximize the average sum rate across subcarriers. Results reveal that the performance benefits of MA systems over fixed arrays are highly dependent on factors such as transceiver quality, channel richness, and the number of subcarriers.

Overall, this work presents a unified exploration of spatial array design, spanning from optimized fixed geometries to adaptive movable configurations in both narrowband and wideband systems. The insights gained offer practical guidelines for deploying next generation high capacity MIMO communication networks.

Den ständigt växande efterfrågan på högre datahastigheter i trådlösa system har drivit på utforskandet av nya metoder för att utnyttja rumsliga frihetsgrader i MIMO-system (multiple-input multiple-output). Denna avhandling undersöker potentialen hos både fasta och rörliga gruppantenner, i såväl fri siktlinje (LOS) som flervägsrika miljöer, för att optimera prestandan för rumslig multiplexing i smalbandiga och bredbandiga system.

Först beaktas fasta, dubbelpolariserade, plana gruppantenner, och deras rumsliga konfiguration optimeras för att maximera MIMO-kanalmatrisens rang och konditionstal. Genom noggrann optimering av antennavstånden visas det att MIMO-rangen kan växa kvadratiskt med bärfrekvensen, vilket möjliggör datahastigheter långt över 1 Tbps. Analytiska och numeriska resultat bekräftar att strategiskt utformade glesa gruppantenner kan leverera överlägsen spektraleffektivitet, även när den fysiska ytan är begränsad.

Baserat på detta undersöks därefter gruppantenner bestående av rörliga antenner (MA, movable antennas) som har förmågan att dynamiskt anpassa antennpositionerna till förändrade kanalförhållanden. Även om de är teoretiskt kraftfulla, är realtidsoptimering av MA-system beräkningsintensivt och praktiskt utmanande. För att hantera detta introduceras en föroptimerad PIA-design (pre-optimized irregular array, föroptimerad oregelbunden gruppantenn), där antennpositionerna är fasta och förutbestämda baserat på statistisk kunskap om täckningsområdet för mobilmasten. Med hjälp av partikelsvärmoptimering visas att PIA-designen uppnår prestanda nära den för helt dynamiska MA-system, utan behov av realtidsoptimering av antennpositionerna.

Slutligen analyseras MA-system i bredbandiga MIMO-scenarier, där hårdvarubegränsningar och flervägsutbredning skapar ytterligare utmaningar. En ny bredbandig systemmodell härleds som inkluderar hårvarudistorsion, och antennpositionerna optimeras för att maximera den genomsnittliga datahastigheten över alla underbärvågor. Resultaten visar att de relativa prestandafördelarna med MA-system jämfört med fasta gruppantenner är starkt beroende av faktorer som sändarhårdvarans kvalitet, kanalens fädningsrikedom och antalet mängden bandbredd.

Sammantaget presenterar denna avhandling en bred utforskning av antenngruppdesign som sträcker sig från optimerade fasta geometrier till adaptiva rörliga antennkonfigurationer i både smalbandiga och bredbandiga system. De insikter som erhållits ger praktiska riktlinjer för att utformningen av nästa generations MIMO-kommunikationsnätverk med hög kapacitet.

 Movable antennas represent an emerging field in telecommunication research and a potential approach to achiev- ing higher data rates in multiple-input multiple-output (MIMO) communications when the total number of antennas is limited. Most solutions and analyses to date have been limited to narrowband setups. This work complements the prior studies by quantifying the benefit of using movable antennas in wideband MIMO communication systems. First, we derive a novel uplink wideband system model that also accounts for distortion from transceiver hardware impairments. We then formulate and solve an optimization task to maximize the average sum rate by ad- justing the antenna positions using particle swarm optimization. Finally, the performance with movable antennas is compared with fixed uniform arrays and the derived theoretical upper bound. The numerical study concludes that the data rate improvement from movable antennas over other arrays heavily depends on the level of hardware impairments, the richness of the multi- path environments, and the number of subcarriers. The present study provides vital insights into the most suitable use cases for movable antennas in future wideband systems.

Movable antennas represent an emerging field in telecommunication research and a potential approach to achieving higher data rates in multiple-input multiple-output (MIMO) communications when the total number of antennas is limited. Most solutions and analyses to date have been limited to narrowband setups. This work complements the prior studies by quantifying the benefit of using movable antennas in wideband MIMO communication systems. First, we derive a novel uplink wideband system model that also accounts for distortion from transceiver hardware impairments. We then formulate and solve an optimization task to maximize the average sum rate by adjusting the antenna positions using particle swarm optimization. Finally, the performance with movable antennas is compared with fixed uniform arrays and the derived theoretical upper bound. The numerical study concludes that the data rate improvement from movable antennas over other arrays heavily depends on the level of hardware impairments, the richness of the multipath environments, and the number of subcarriers. The present study provides vital insights into the most suitable use cases for movable antennas in future wideband systems.

Massive multiple-input multiple-output (MIMO) systems exploit the spatial diversity achieved with an array of many antennas to perform spatial multiplexing of many users. Similar performance can be achieved using fewer antennas if movable antenna (MA) elements are used instead. MA-enabled arrays can dynamically change the antenna locations, mechanically or electrically, to achieve maximum spatial diversity for the current propagation conditions. However, optimizing the antenna locations for each channel realization is computationally excessive, requires channel knowledge for all conceivable locations, and requires rapid antenna movements, thus making real-time implementation cumbersome. To overcome these challenges, we propose a pre-optimized irregular array (PIA) concept, where the antenna locations at the base station are optimized a priori for a given coverage area. The objective is to maximize the average sum rate and we take a particle swarm optimization approach to solve it. Simulation results show that PIA achieves performance comparable to MA-enabled arrays while outperforming traditional uniform arrays. Hence, PIA offers a fixed yet efficient array deployment approach without the complexities associated with MA-enabled arrays.

Future wireless networks must provide ever higher data rates. The available bandwidth increases roughly linearly as we increase the carrier frequency, but the range shrinks drastically. This paper explores if we can instead reach massive capacities using spatial multiplexing over multiple-input multiple-output (MIMO) channels. In line-of-sight (LOS) scenarios, the rank of the MIMO channel matrix depends on the polarization and antenna arrangement. We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation. The result is sparsely spaced antenna arrays that exploit radiative near-field properties. We further optimize the array geometry for minimum aperture length and aperture area, which leads to different configurations. Moreover, we prove analytically that for fixed-sized arrays, the MIMO rank grows quadratically with the carrier frequency in LOS scenarios, if the antennas are appropriately designed. Hence, MIMO technology contributes more to the capacity growth than the bandwidth. The numerical results show that massive data rates, far beyond 1 Tbps, can be reached both over fixed and mobile point-to-point links. It is also possible for a large base station to serve a practically-sized mobile device.

In this article, we present our vision for how extremely large aperture arrays, equipped with hundreds or thousands of antennas, can play a major role in future 6G networks by enabling a remarkable increase in data rates through spatial multiplexing of a massive number of data streams to both a single user and many simultaneous users. Specifically, with the quantum leap in the array aperture size, the users will be in the so-called radiative near-field region of the array, where previously negligible physical phenomena dominate the propagation conditions and give the channel matrices more favorable properties. This article presents the foundational properties of communication in the radiative near-field region and then exemplifies how these properties enable two unprecedented spatial multiplexing schemes: depth-domain multiplexing of multiple users and angular multiplexing of data streams to a single user. We also highlight research challenges and open problems that require further investigation.

Traditional point-to-point line-of-sight channels have rank 1, irrespective of the number of antennas and array geometries, due to far-field propagation conditions. By contrast, recent papers in the holographic multiple-input multiple-output (MIMO) literature characterize the maximum channel rank that can be achieved between two continuous array apertures, which is much larger than 1 under near-field propagation conditions. In this paper, we maximize the channel capacity between two dual-polarized uniform rectangular arrays (URAs) with discrete antenna elements for a given propagation distance. In particular, we derive the antenna spacings that lead to an ideal MIMO channel where all singular values are as similar as possible. We utilize this analytic result to find the two array geometries that respectively minimize the aperture area and the aperture length.