arXiv subjects

Fredrik Rusek

Publications and source records attributed to Fredrik Rusek.

At least 19 recordsLinked to original sources

arXiv2026

RIS-Assisted D-MIMO for Energy-Efficient 6G Indoor Networks

We propose an alternating optimization framework for maximizing energy efficiency (EE) in reconfigurable intelligent surface (RIS) assisted distributed MIMO (D-MIMO) systems under both coherent and non-coherent reception modes. The framework jointly optimizes access point (AP) power allocation and RIS phase configurations to improve EE under per-AP power and signal-to-interference-plus-noise ratio (SINR) constraints. Using majorization-minimization for power allocation together with per-element RIS adaptation, the framework achieves tractable optimization of this non-convex problem. Simulation results for indoor deployments with realistic power-consumption models show that the proposed scheme outperforms equal-power and random-scatterer baselines, with clear EE gains. We evaluate the performance of both reception modes and quantify the impact of RIS phase-shift optimization, RIS controller architectures (centralized vs. per-RIS control), and RIS size, providing design insights for practical RIS-assisted D-MIMO deployments in future 6G networks.

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arXiv2024

Spatially Selective Reconfigurable Intelligent Surfaces Through Element Permutation

A standard reconfigurable intelligent surface (RIS) can be configured to reflect signals from an arbitrary impinging direction to an arbitrary outgoing direction. However, if a signal impinges from any other direction, said signal is reflected, with full beamforming gain, to a specific direction, which is easily determined. The goal of this paper is to propose a RIS which \emph{only} reflects signals from the configured impinging direction. This can be accomplished by a RIS architecture that permutes the antenna elements in the sense that a signal is re-radiated from a different antenna than the one receiving the signal. We analytically prove this fact, and also discuss several variants and hardware implementations.

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arXiv2024

Towards 6G MIMO: Massive Spatial Multiplexing, Dense Arrays, and Interplay Between Electromagnetics and Processing

The increasing demand for wireless data transfer has been the driving force behind the widespread adoption of Massive MIMO (multiple-input multiple-output) technology in 5G. The next-generation MIMO technology is now being developed to cater to the new data traffic and performance expectations generated by new user devices and services in the next decade. The evolution towards "ultra-massive MIMO (UM-MIMO)" is not only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights from the communication, electromagnetic (EM), and circuit theory areas. This article offers a comprehensive overview of the key benefits of the UM-MIMO technology and the associated challenges. It explores massive multiplexing facilitated by radiative near-field effects, characterizes the spatial degrees-of-freedom, and practical channel estimation schemes tailored for massive arrays. Moreover, we provide a tutorial on EM theory and circuit theory, and how it is used to obtain physically consistent antenna and channel models. Subsequently, the article describes different ways to implement massive and dense antenna arrays, and how to co-design antennas with signal processing. The main open research challenges are identified at the end.

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arXiv2023

Increased Multiplexing Gain with Reconfigurable Surfaces: Simultaneous Channel Orthogonalization and Information Embedding

Reconfigurable surface (RS) has been shown to be an effective solution for improving wireless communication links in general multi-user multiple-input multiple-output (MU-MIMO) setting. Current research efforts have been largely directed towards the study of reconfigurable intelligent surface (RIS), which corresponds to an RS made of passive reconfigurable elements with only phase shifting capabilities. RIS constitutes a cost- and energy- efficient solution for increased beamforming gain since it allows to generate constructive interference towards desired directions, e.g., towards a base station (BS). However, in many situations, multiplexing gain may have greater impact on the achievable transmission rates and number of simultaneously connected devices, while RIS has only been able to achieve minor improvements in this aspect. Recent work has proposed the use of alternative RS technologies, namely amplitude-reconfigurable intelligent surface (ARIS) and fully-reconfigurable intelligent surface (FRIS), to achieve perfect orthogonalization of MU-MIMO channels, thus allowing for maximum multiplexing gain at reduced complexity. In this work we consider the use of ARIS and FRIS for simultaneously orthogonalizing a MU-MIMO channel, while embedding extra information in the orthogonalized channel. We show that the resulting achievable rates allow for full exploitation of the degrees of freedom in a MU-MIMO system with excess of BS antennas.

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arXiv2023

Trade-Offs in Decentralized Multi-Antenna Architectures: Sparse Combining Modules for WAX Decomposition

With the increase in the number of antennas at base stations (BSs), centralized multi-antenna architectures have encountered scalability problems from excessive interconnection bandwidth to the central processing unit (CPU), as well as increased processing complexity. Thus, research efforts have been directed towards finding decentralized receiver architectures where a part of the processing is performed at the antenna end (or close to it). A recent paper put forth an information-lossless trade-off between level of decentralization (inputs to CPU) and decentralized processing complexity (multiplications per antenna). This trade-off was obtained by studying a newly defined matrix decomposition--the WAX decomposition--which is directly related to the information-lossless processing that should to be applied in a general framework to exploit the trade-off. {The general framework consists of three stages: a set of decentralized filters, a linear combining module, and a processing matrix applied at the CPU; these three stages are linear transformations which can be identified with the three constituent matrices of the WAX decomposition. The previous work was unable to provide explicit constructions for linear combining modules which are valid for WAX decomposition, while it remarked the importance of these modules being sparse with 1s and 0s so they could be efficiently implemented using hardware accelerators.} In this work we present a number of constructions, as well as possible variations of them, for effectively defining linear combining modules which can be used in the WAX decomposition. Furthermore, we show how these structures facilitate decentralized calculation of the WAX decomposition for applying information-lossless processing in architectures with an arbitrary level of decentralization.

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arXiv2023

Channel Orthogonalization with Reconfigurable Surfaces

Orthogonal multi-user multiple-input multiple-output (MU-MIMO) channels allow for optimum performance with simplified precoding/equalization, and they achieve maximum multiplexing gain which is shared fairly among users. Reconfigurable intelligent surface (RIS) constitutes a promising cost-efficient solution to improve the wireless channel, since they consist of passive reflecting elements able to adjust the phases of the incoming waves. However, it is still widely unclear how these surfaces can improve spatial-multiplexing. In fact, the common RIS model cannot achieve perfect orthogonalization of MU-MIMO channels with a reasonable number of elements. Furthermore, efficient channel estimation algorithms for RIS, which are key for taking advantage of its benefits, are still a matter of research. We study two types of reconfigurable surfaces (RSs), namely amplitude-reconfigurable intelligent surface (ARIS) and fully-reconfigurable intelligent surface (FRIS), with extended capabilities over RIS. We show how these RSs allow for perfect channel orthogonalization, and, by minimizing the applied power, we show that they can potentially be implemented without the need of amplification. We also present an efficient channel estimation method for each of them that allows the base station (BS) to select the desired propagation channel.

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arXiv2021

Trade-offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition

Current research on multi-antenna architectures is trending towards increasing the amount of antennas in the base stations (BSs) so as to increase the spectral efficiency. As a result, the interconnection bandwidth and computational complexity required to process the data using centralized architectures is becoming prohibitively high. Decentralized architectures can reduce these requirements by pre-processing the data before it arrives at a central processing unit (CPU). However, performing decentralized processing introduces also cost in complexity/interconnection bandwidth at the antenna end which is in general being ignored. This paper aims at studying the interplay between level of decentralization and the associated complexity/interconnection bandwidth requirement at the antenna end. To do so, we propose a general framework for centralized/decentralized architectures that can explore said interplay by adjusting some system parameters, namely the number of connections to the CPU (level of decentralization), and the number of multiplications/outputs per antenna (complexity/interconnection bandwidth). We define a novel matrix decomposition, the WAX decomposition, that allows information-lossless processing within our proposed framework, and we use it to obtain the operational limits of the interplay under study. We also look into some of the limitations of the WAX decomposition.

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arXiv2020

Distributed and Scalable Uplink Processing for LIS: Algorithm, Architecture, and Design Trade-offs

The Large Intelligent Surface (LIS) is a promising technology in the areas of wireless communication, remote sensing and positioning. It consists of a continuous radiating surface located in the proximity of the users, with the capability to communicate by transmission and reception (replacing base stations). Despite of its potential, there are numerous challenges from implementation point of view, being the interconnection data-rate, computational complexity, and storage the most relevant ones. In order to address those challenges, hierarchical architectures with distributed processing techniques are envisioned to to be relevant for this task, while ensuring scalability. In this work we perform algorithm-architecture codesign to propose two distributed interference cancellation algorithms, and a tree-based interconnection topology for uplink processing. We also analyze the performance, hardware requirements, and architecture tradeoffs for a discrete LIS, in order to provide concrete case studies and guidelines for efficient implementation of LIS systems.

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arXiv2020

Processing Distribution and Architecture Tradeoff for Large Intelligent Surface Implementation

The Large Intelligent Surface (LIS) concept has emerged recently as a new paradigm for wireless communication, remote sensing and positioning. It consists of a continuous radiating surface placed relatively close to the users, which is able to communicate with users by independent transmission and reception (replacing base stations). Despite of its potential, there are a lot of challenges from an implementation point of view, with the interconnection data-rate and computational complexity being the most relevant. Distributed processing techniques and hierarchical architectures are expected to play a vital role addressing this while ensuring scalability. In this paper we perform algorithm-architecture codesign and analyze the hardware requirements and architecture trade-offs for a discrete LIS to perform uplink detection. By doing this, we expect to give concrete case studies and guidelines for efficient implementation of LIS systems.

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arXiv2020

Trade-offs In Quasi-Decentralized Massive MIMO

Typical massive multiple-input multiple-output (MIMO) architectures consider a centralized approach, in which all baseband data received by each antenna has to be sent to a central processing unit (CPU) to be processed. Due to the enormous amount of antennas expected in massive MIMO base stations (BSs), the number of connections to the CPU required in centralized massive MIMO is not scalable. In recent literature decentralized approaches have been proposed to reduce the number of connections between the antennas and the CPU. However, the reduction in the connections to the CPU requires more outputs per antenna to be generated. We study the trade-off between number of connections to the CPU and number of outputs per antenna. We propose a generalized architecture that allows exploitation of this trade-off, and we define a novel matrix decomposition that allows lossless linear equalization within our proposed architecture.

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arXiv2020

Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms

Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Stochastic Gradient Descent (SGD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.

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arXiv2019

An Iterative Interference Cancellation Algorithm for Large Intelligent Surfaces

The Large Intelligent Surface (LIS) concept is a promising technology aiming to revolutionize wireless communication by exploiting spatial multiplexing at its fullest. Despite of its potential, due to the size of the LIS and the large number of antenna elements involved there is a need of decentralized architectures together with distributed algorithms which can reduce the inter-connection data-rate and computational requirement in the Central Processing Unit (CPU). In this article we address the uplink detection problem in the LIS system and propose a decentralize architecture based on panels, which perform local linear processing. We also provide the sum-rate capacity for such architecture and derive an algorithm to obtain the equalizer, which aims to maximize the sum-rate capacity. A performance analysis is also presented, including a comparison to a naive approach based on a reduced form of the matched filter (MF) method. The results shows the superiority of the proposed algorithm.

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arXiv2018

Capacity Degradation with Modeling Hardware Impairment in Large Intelligent Surface

In this paper, we consider capacity degradations stemming from potential hardware impairments (HWI) of newly proposed Large Intelligent Surface (LIS) systems. Without HWI, the utility of surface-area (the first-order derivative of the capacity with respect to surface-area) is shown to be proportional to the inverse of it. With HWI, the capacity as well as the utility of surface-area are both degraded, due to a higher effective noise level caused by the HWI. After first modeling the HWI in a general form, we derive the effective noise density and the decrement of utility in closed-forms. With those the impacts of increasing the surface-area can be clearly seen. One interesting but also natural outcome is that both the capacity and utility can be decreased when increasing the surface-area in the cases with severe HWI. The turning points where the capacity and the utility start to decrease with HWI can be evaluated from the derived formulas for them. Further, we also consider distributed implementations of a LIS system by splitting it into multiple small LIS-Units, where the impacts of HWI can be significantly suppressed due to a smaller surface-area of each unit.

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arXiv2018

Fully Decentralized Massive MIMO Detection Based on Recursive Methods

Algorithms for Massive MIMO uplink detection typically rely on a centralized approach, by which baseband data from all antennas modules are routed to a central node in order to be processed. In case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, this architecture leads to a bottleneck, with critical limitations in terms of interconnection bandwidth requirements. This paper presents a fully decentralized architecture and algorithms for Massive MIMO uplink based on recursive methods, which do not require a central node for the detection process. Through a recursive approach and very low complexity operations, the proposed algorithms provide a sequence of estimates that converge asymptotically to the zero-forcing solution, without the need of specific hardware for matrix inversion. The proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.

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arXiv2018

Impact of Relay Cooperation on the Performance of Large-scale Multipair Two-way Relay Networks

We consider a multipair two-way relay communication network, where pairs of user devices exchange information via a relay system. The communication between users employs time division duplex, with all users transmitting simultaneously to relays in one time slot and relays sending the processed information to all users in the next time slot. The relay system consists of a large number of single antenna units that can form groups. Within each group, relays exchange channel state information (CSI), signals received in the uplink and signals intended for downlink transmission. On the other hand, per-group CSI and uplink/downlink signals (data) are not exchanged between groups, which perform the data processing completely independently. Assuming that the groups perform zero-forcing in both uplink and downlink, we derive a lower bound for the ergodic sumrate of the described system as a function of the relay group size. By close observation of this lower bound, it is concluded that the sumrate is essentially independent of group size when the group size is much larger than the number of user pairs. This indicates that a very large group of cooperating relays can be substituted by a number of smaller groups, without incurring any significant performance reduction. Moreover, this result implies that relay cooperation is more efficient (in terms of resources spent on cooperation) when several smaller relay groups are used in contrast to a single, large group.

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arXiv2018

Unmanned Aerial Vehicle Assisted Cellular Communication

In this paper, we consider unmanned aerial vehicles (UAVs) assisted cellular communication system, where UAVs can be used as amplify-and-forward (AF) relays. The effective channel with UAV assisted communication can be modeled as a Rayleigh product-channel, and we derive a tight lower-bound of the ergodic capacity in closed-form. With the obtained lower-bound, trade-offs between the transmit power and the equipped number of antennas of the UAVs can be analyzed. Alternatively, for a giving setting of users and the base-transceiver station (BTS), the needed transmit power and number of antennas for the UAVs can be derived in order to have a higher ergodic capacity with the UAV assisted communication than that without it.

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arXiv2018

Modulus Zero-Forcing Detection for MIMO Channels

We propose a modulus based zero-forcing (MZF) detection for multi-input multi-output (MIMO) channels. Traditionally, a ZF detector nulls out all interferences from other layers when detecting a current layer, which can yield suboptimal detection-performance due to the noise-enhancement issue. In many communication systems, finite alphabets such as M quadrature-amplitude-modulation (QAM) are widely used, which comprises \sqrt{M} pulse-amplitude-modulation (PAM) symbols for the real and imaginary parts. With finite alphabets, one feasible way to improve ZF detection is to allow controllable interferences that can be removed away by modulus operations.

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arXiv2017

On the Design of Channel Shortening Demodulators for Iterative Receivers in MIMO and ISI Channels

We consider the problem of designing demodulators for linear vector channels with memory that use reduced-size trellis descriptions for the received signal. We assume an overall iterative receiver, and for the parts of the signal not covered by the trellis description, we use interference cancelation based on the soft information provided by the outer decoder. In order to reach a trellis description, a linear filter is applied as front-end to compress the signal structure into a small trellis. This process requires three parameters to be designed: (i) the front-end filter, (ii) the feedback filter through which the interference cancelation is done, and (iii) a target response which specifies the trellis. Demodulators of this form have been studied before under then name channel shortening (CS), but the interplay between CS and interference cancelation has not been adequately addressed in the literature. In this paper, we analyze two types of CS demodulators that are based on the Forney and Ungerboeck detection models, respectively. The parameters are jointly optimized based on a generalized mutual information (GMI) function. We also introduce a third type of CS demodulator that is in general suboptimal but has closed form solutions for all parameters. Moreover, signal to noise ratio (SNR) asymptotic properties are analyzed and we show that the third CS demodulator asymptotically converges to the optimal CS demodulator in the sense of maximizing the GMI.

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