This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the downlink communication between a base station (BS) and multiple user equipments (UEs) is aided by a reconfigurable intelligent surface (RIS). We study the sum rate maximization problem with the objective of finding the optimal precoding vectors and RIS configuration. Due to fronthaul limitation, each entry of the precoding vectors must be picked from a finite set of quantization labels. Furthermore, two scenarios for the RIS are investigated, one with continuous infinite-resolution reflection coefficients and another with discrete finite-resolution reflection coefficients. A novel framework is developed which, in contrast to the common literature that only offers sub-optimal solutions for optimization of discrete variables, is able to find the optimal solution to problems involving discrete constraints. Based on the classical weighted minimum mean square error (WMMSE), we transform the original problem into an equivalent weighted sum mean square error (MSE) minimization problem and solve it iteratively. We compute the optimal precoding vectors via an efficient algorithm inspired by sphere decoding (SD). For optimizing the discrete RIS configuration, two solutions based on the SD algorithm are developed: An optimal SD-based algorithm and a low-complexity heuristic method that can efficiently obtain RIS configuration without much loss in optimality. The effectiveness of the presented algorithms is corroborated via numerical simulations where it is shown that the proposed designs are remarkably superior to the commonly used benchmarks.

Multi-Gbps data rates are achievable in millimeter-wave (mmWave) bands, but a prominent issue is the tiny wavelength that results in rapid fading variations and significant pilot signaling for channel estimation. In this letter, we recognize that the angles of scattering clusters seen from the UE vary slowly compared to the small-scale fading. We characterize the beam coherence time, which quantifies how frequently the UE must update its downlink receive combining matrix. The exact beam coherence time is derived in the single-cluster case, and an achievable lower bound is proposed for the multi-cluster case. These values are determined so that at least half of the received signal gain is maintained in between the combining updates. We demonstrate how the beam coherence time can be hundreds of times larger than the channel coherence time of the small-scale fading.

In the upcoming era of communication systems, there is an anticipated shift towards using lower-grade hardware components to optimize size, cost, and power consumption. This shift is particularly beneficial for multiple-input multiple-output (MIMO) systems and internet-of-things devices, which require numerous components and extended battery lifes. However, using lower-grade components introduces impairments, including various non-linear and time-varying distortions affecting communication signals. Traditionally, these distortions have been treated as additional noise due to the lack of a rigorous theory. This thesis explores new perspective on how distortion structure can be exploited to optimize communication performance. We investigate the problem of distortion-aware beamforming in various scenarios. 

In the first part of this thesis, we focus on systems with limited fronthaul capacity. We propose an optimized linear precoding for advanced antenna systems (AAS) operating at a 5G base station (BS) within the constraints of a limited fronthaul capacity, modeled by a quantizer. The proposed novel precoding minimizes the mean-squared error (MSE) at the receiver side using a sphere decoding (SD) approach. 

After analyzing MSE minimization, a new linear precoding design is proposed to maximize the sum rate of the same system in the second part of this thesis. The latter problem is solved by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. Additionally, a heuristic quantization-aware precoding method with lower computational complexity is presented, showing that it outperforms the quantization-unaware baseline. This baseline is an optimized infinite-resolution precoding which is then quantized. This study reveals that it is possible to double the sum rate at high SNR by selecting weights and precoding matrices that are quantization-aware. 

In the third part and final part of this thesis, we focus on the signaling problem in mobile millimeter-wave (mmWave) communication. The challenge of mmWave systems is the rapid fading variations and extensive pilot signaling. We explore the frequency of updating the combining matrix in a wideband mmWave point-to-point MIMO under user equipment (UE) mobility. The concept of beam coherence time is introduced to quantify the frequency at which the UE must update its downlink receive combining matrix. The study demonstrates that the beam coherence time can be even hundreds of times larger than the channel coherence time of small-scale fading. Simulations validate that the proposed lower bound on this defined concept guarantees no more than 50 \% loss of received signal gain (SG).

I den kommande eran av kommunikationssystem finns det en förväntad förändringmot att använda hårdvarukomponenter av lägre kvalitet för att optimera storlek, kostnad och strömförbrukning. Denna förändring är särskilt fördelaktig för MIMO-system(multiple-input multiple-output) och internet-of-things-enheter, som kräver många komponenter och förlängd batteritid. Användning av komponenter av lägre kvalitet medfördock försämringar, inklusive olika icke-linjära och tidsvarierande förvrängningar sompåverkar kommunikationssignaler. Traditionellt har dessa förvrängningar behandlatssom extra brus på grund av avsaknaden av en rigorös teori. Denna avhandling utforskarett nytt perspektiv på hur distorsionsstruktur kan utnyttjas för att optimera kommunikationsprestanda. Vi undersöker problemet med distorsionsmedveten strålformning iolika scenarier.

I den första delen av detta examensarbete fokuserar vi på system med begränsadfronthaulkapacitet. Vi föreslår en optimerad linjär förkodning för avancerade antennsystem (AAS) som arbetar vid en 5G-basstation (BS) inom begränsningarna av en begränsad fronthaulkapacitet, modellerad av en kvantiserare. Den föreslagna nya förkodningen minimerar medelkvadratfelet (MSE) på mottagarsidan med användning av ensfäravkodningsmetod (SD).

Efter att ha analyserat MSE-minimering, föreslås en ny linjär förkodningsdesignför att maximera summahastigheten för samma system i den andra delen av dennaavhandling. Det senare problemet löses av en ny iterativ algoritm inspirerad av denklassiska vägda minsta medelkvadratfel (WMMSE)-metoden. Dessutom presenterasen heuristisk kvantiseringsmedveten förkodningsmetod med lägre beräkningskomplexitet, som visar att den överträffar den kvantiseringsomedvetna baslinjen. Denna baslinje är en optimerad förkodning med oändlig upplösning som sedan kvantiseras. Dennastudie avslöjar att det är möjligt att fördubbla summahastigheten vid hög SNR genomatt välja vikter och förkodningsmatriser som är kvantiseringsmedvetna.

I den tredje delen och sista delen av denna avhandling fokuserar vi på signaleringsproblemet i mobil millimetervågskommunikation (mmWave). Utmaningen medmmWave-system är de snabba blekningsvariationerna och omfattande pilotsignalering.Vi utforskar frekvensen av att uppdatera den kombinerande matrisen i en bredbandsmmWave punkt-till-punkt MIMO under användarutrustning (UE) mobilitet. Konceptet med strålkoherenstid introduceras för att kvantifiera frekvensen vid vilken UE:nmåste uppdatera sin nedlänksmottagningskombinationsmatris. Studien visar att strålkoherenstiden kan vara till och med hundratals gånger större än kanalkoherenstiden försmåskalig fädning. Simuleringar bekräftar att den föreslagna nedre gränsen för dettadefinierade koncept inte garanterar mer än 50 % förlust av mottagen signalförstärkning(SG)

In this paper, we consider a downlink multi-user multiple-input multiple-output (MU-MIMO) communication assisted by a reconfigurable intelligent surface (RIS) and study the precoding and RIS configuration design under practical system constraints. These constraints include the limited-capacity fronthaul at the transmitter side and the finite resolution of RIS elements. We investigate the sum mean squared error (MSE) minimization problem and propose an algorithm based on the block coordinate descent method to optimize the precoding, RIS configuration, and receiver gains. We compute the precoding vectors and RIS configuration using the Schnorr-Euchner sphere decoding (SESD) method which delivers the optimal MSE-minimizing solution. We numerically evaluate the performance of the proposed SESD-based methods and corroborate their effectiveness in improving the system performance.

Reconfigurable intelligent surface (RIS) is a newly-emerged technology that might fundamentally change how wireless networks are operated. Though extensively studied in recent years, the practical limitations of RIS are often neglected when assessing the performance of RIS-assisted communication networks. One of these limitations is that each RIS element is restricted to incur a controllable phase shift to the reflected signal from a predefined discrete set. This paper studies an RIS-assisted multi-user multiple-input multiple-output (MIMO) system, where an RIS with discrete phase shifts assists in simultaneous uplink data transmission from multiple user equipments (UEs) to a base station (BS). We aim to maximize the sum rate by optimizing the receive beamforming vectors and RIS phase shift configuration. To this end, we transform the original sum-rate maximization problem into a minimum mean square error (MMSE) minimization problem and employ the block coordinate descent (BCD) technique for iterative optimization of the variables until convergence. We formulate the discrete RIS phase shift optimization problem as a mixed-integer least squares problem and propose a novel method based on sphere decoding (SD) to solve it. Through numerical evaluation, we show that the proposed discrete phase shift design outperforms the conventional nearest point mapping method, which is prevalently used in previous works.

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the precoding matrix is selected in a baseband unit (BBU) and then sent over a digital fronthaul to the transmitting antenna array. The fronthaul has a limited bit resolution with a known quantization behavior. We formulate a new sum rate maximization problem where the precoding matrix elements must comply with the quantizer. We solve this non-convex mixed-integer problem to local optimality by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. The precoding optimization subproblem becomes an integer least-squares problem, which we solve with a new algorithm using a sphere decoding (SD) approach. We show numerically that the proposed precoding technique vastly outperforms the baseline of optimizing an infinite-resolution precoder and then quantizing it. We also develop a heuristic quantization-aware precoding that outperforms the baseline while having comparable complexity.

One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information (CSI).

Base stations in 5G and beyond use advanced antenna systems (AASs), where multiple passive antenna elements and radio units are integrated into a single box. A critical bottleneck of such a system is the digital fronthaul between the AAS and baseband unit (BBU), which has limited capacity. In this paper, we study an AAS used for precoded downlink transmission over a multi-user multiple-input multiple-output (MU-MIMO) channel. First, we present the baseline quantization-unaware precoding scheme created when a precoder is computed at the BBU and then quantized to be sent over the fronthaul. We propose a new precoding design that is aware of the fronthaul quantization. We formulate an optimization problem to minimize the mean squared error at the receiver side. We rewrite the problem to utilize mixed-integer programming to solve it. The numerical results manifest that our proposed precoding greatly outperforms quantization-unaware precoding in terms of sum rate.

Multi-Gbps data rates are achievable in millimeter-wave (mmWave) bands, but a prominent issue is the tiny wavelength that results in rapid fading variations and significant pilot signaling for channel estimation. In this letter, we recognize that the angles of scattering clusters seen from the user equipment (UE) vary slowly compared to the small-scale fading. We characterize the \emph{beam coherence time}, which quantifies how frequently the UE must update its downlink receive combining matrix. The exact beam coherence time is derived in the single-cluster case, and an achievable lower bound is proposed for the multi-cluster case. These values are determined so that at least half of the received signal gain is maintained in between the combining updates. We demonstrate how the beam coherence time can be hundreds of times larger than the channel coherence time of the small-scale fading.