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This paper studies an integrated sensing and communication (ISAC) system for single-target detection in a cloud radio access network architecture. The system considers downlink communication and multi-static sensing approach, where ISAC transmit access points (APs) jointly serve the user equipments (UEs) and optionally steer a beam toward the target. A centralized operation of cell-free massive MIMO (multiple-input multiple-output) is considered for communication and sensing purposes. A maximum a posteriori ratio test detector is developed to detect the target in the presence of clutter, so-called target-free signals. Moreover, a power allocation algorithm is proposed to maximize the sensing signal-to-interference-plus-noise ratio (SINR) while ensuring a minimum communication SINR value for each UE and meeting per-AP power constraints. Two ISAC setups are studied: i) using only existing communication beams for sensing and ii) using additional sensing beams. The proposed algorithm's efficiency is investigated in both realistic and idealistic scenarios, corresponding to the presence and absence of the target-free channels, respectively. Although detection probability degrades in the presence of target-free channels that act as interference, the proposed algorithm significantly outperforms the interference-unaware benchmark by exploiting the statistics of the clutter. It has also been shown that the proposed algorithm outperforms the fully communication-centric algorithm, both in the presence and absence of clutter. Moreover, using an additional sensing beam improves the detection performance for a target with lower radar cross-section variances compared to the case without sensing beams., 30 pages, 6 figures

In this paper, we examine how to minimize the total energy consumption of a user equipment (UE) when it transmits a finite-sized data payload of a given length. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, but only if additional pilot signals are transmitted to configure the RIS. The challenge is that the pilot resources spent on configuring the RIS increase the energy consumption, especially when small payloads are transmitted, so it must be balanced against the energy savings during data transmission. We derive a formula for the energy consumption, taking both the pilot and data transmission power into account. It also includes the effects of imperfect channel state information, the use of phase-shifts with finite resolution at the RIS, and the passive circuit energy consumption. We also consider how dividing the RIS into subarrays consisting of multiple RIS elements using the same reflection coefficient can shorten the pilot length. In particular, the pilot power and subarray size are tuned to the payload length to minimize the energy consumption while maintaining parts of the aperture gain. Our analytical results show that, for a given geometry and transmission payload length, there exists a unique energy-minimizing subarray size and pilot power. For small payloads and when the channel conditions between the BS and UE are favorable compared to the path to the RIS, the energy consumption is minimized using subarrays with many elements and low pilot transmission power. On the other hand, when the channel conditions to the RIS are better and the data payloads are large, it is preferable to use fewer elements per subarray, potentially configuring each element individually and transmitting the pilot signals with additional power., 30 pages, 9 figures. arXiv admin note: text overlap with arXiv:2303.16625

The physical-layer foundations of cell-free massive MIMO are now well-established. The end-to-end network (from radio site to the cloud) analysis and energy-efficient operation are two critical factors to be considered for the deployment of cell-free massive MIMO functionality in a practical wireless network architecture. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. O-RAN is the key enabler for the end-to-end management of cell-free massive MIMO. In this paper, we explore the performance and power consumption of different radio technologies, i.e., cell-free massive MIMO and traditional small-cell systems, in the virtualized O-RAN architecture from an end-to-end perspective. We compare two different functional split options and different resource orchestration mechanisms, including the fully virtualized end-to-end, local cloud coordination-based, and radio-only resource allocation. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: i)``radio-only'' where radio resources are optimized independently from the cloud and ii)``local cloud coordination'' where orchestration is only allowed among a local cluster of radio units. The spectral efficiency (SE) is either considered as an optimization constraint for each user equipment or as a sum SE in the objective function. We develop several algorithms to solve the respective end-to-end optimization problems., 30 pages, 7 figures, 2 tables, submitted to IEEE journal. arXiv admin note: text overlap with arXiv:2202.09254

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., Comment: 5 pages, 4 figures, to appear at the European Signal Processing Conference, EUSIPCO 2023

In this work, we consider the deployment of reconfigurable intelligent surfaces (RISs) to extend the coverage of a millimeter-wave (mmWave) network in indoor dense spaces. We first integrate RIS into ray-tracing simulations to realistically capture the propagation characteristics, then formulate a non-convex optimization problem that minimizes the number of RISs under rate constraints. We propose a feasible point pursuit and successive convex approximation-based algorithm, which solves the problem by jointly selecting the RIS locations, optimizing the RIS phase-shifts, and allocating time resources to user equipments (UEs). The numerical results demonstrate substantial coverage extension by using at least four RISs, and a data rate of 130 Mbit/s is guaranteed for UEs in the considered area of an airplane cabin., Comment: 6 pages 8 figures. Accepted to be presented in IEEE ICC 2023

Wireless communication technology has progressed dramatically over the past 25 years, in terms of societal adoption as well as technical sophistication. In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries. There were "only" 300 million mobile subscribers in the world [1]. Cellular networks were among the first privatized telecommunication markets, and competition turned the devices into fashion accessories with attractive designs that could be individualized. The service was circumscribed to telephony and text messaging, but it was groundbreaking in that, for the first time, telecommunication was between people rather than locations. Wireless networks have changed dramatically over the past few decades, enabling this revolution in service provisioning and making it possible to accommodate the ensuing dramatic growth in traffic. There are many contributing components, including new air interfaces for faster transmission, channel coding for enhanced reliability, improved source compression to remove redundancies, and leaner protocols to reduce overheads. Signal processing is at the core of these improvements, but nowhere has it played a bigger role than in the development of multiantenna communication. This article tells the story of how major signal processing advances have transformed the early multiantenna concepts into mainstream technology over the past 25 years. The story therefore begins somewhat arbitrarily in 1998. A broad account of the state-of-the-art signal processing techniques for wireless systems by 1998 can be found in [2], and its contrast with recent textbooks such as [3]-[5] reveals the dramatic leap forward that has taken place in the interim., Comment: To appear in IEEE Signal Processing Magazine, 21 pages, 7 figures

We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual power constraint and form its transmit signal on the basis of possibly partial sharing of data bearing signals and channel state information. Our duality principle applies to ergodic achievable rates given by the popular hardening bound, and it can be interpreted as a nontrivial generalization of a previous result by Yu and Lan for deterministic channels. This generalization allows us to cover more involved information constraints, and to show that optimal joint precoders can be obtained using a variation of the recently developed team minimum mean-square error method. As particular examples, we solve the problems of optimal centralized and local precoding design in user-centric cell-free massive MIMO networks subject to per-AP power constraints.

The number of users that can be spatially multiplexed by a wireless access point depends on the aperture of its antenna array. When the aperture increases and wavelength shrinks, "new" electromagnetic phenomena can be utilized to further enhance network capacity. In this chapter, we describe how extremely large aperture arrays (ELAA) can extend the radiative near-field region to kilometer distances. We demonstrate how this affects the propagation models in line-of-sight (LoS) scenarios and enables finite-depth beamforming. In particular, it becomes possible to simultaneously serve users that are located in the same direction but at different distances., Book chapter submitted for publication in the edited book "Fundamentals of 6G Communications and Networking"

After robust connectivity, precise positioning is evolving into an innovative component of 5G service offerings for industrial use-cases and verticals with challenging indoor radio environments. In this direction, the 3GPP Rel-16 standard has been a tipping point in specifying critical innovations, followed by enhancements in Rel-17+. In this article, we follow this path to elaborate on the 5G positioning framework, measurements, and methods before shifting the focus to carrier-phase (CP) measurements as a complementary measure for time- and angular-based positioning methods toward achieving centimeter-level accuracy. As this path is not without challenges, we discuss these and outline potential solutions. As an example of solutions, we study how phase-continuous reference signaling can counter noisy phase measurements using realistic simulations in an indoor factory (InF) scenario., 5 figures, 1 table, under review for possible publication

In a recent work, we studied a novel precoding design for cell-free networks called team minimum mean-square error (TMMSE) precoding, which rigorously generalizes centralized MMSE precoding to distributed operations based on transmitter-specific channel state information (CSI). Despite its flexibility in handling different cooperation regimes at the CSI sharing level, TMMSE precoding assumes network-wide sharing of the data bearing signals, and hence it is inherently not scalable. In this work, inspired by recent advances on scalable cell-free architectures based on user-centric network clustering techniques, we address this issue by proposing a novel version of the TMMSE precoding design covering partial message sharing. The obtained framework is then successfully applied to derive a variety of novel, optimal, and efficient precoding schemes for a user-centric cell-free network deployed using multiple radio stripes. Numerical simulations of a typical industrial internet-of-things scenario corroborate the gains of TMMSE precoding over competing schemes in terms of spectral efficiency under different power constraints. Although presented in the context of downlink precoding, the results of this paper may be applied also on the uplink.

In this paper, we examine the energy consumption of a user equipment (UE) when it transmits a finite-sized data packet. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, if additional pilot signals are transmitted to configure the RIS. We derive a formula for the energy consumption taking both the pilot and data transmission powers into account. By dividing the RIS into subarrays consisting of multiple RIS elements using the same reflection coefficient, the pilot overhead can be tuned to minimize the energy consumption while maintaining parts of the aperture gain. Our analytical results show that there exists an energy-minimizing subarray size. For small data blocks and when the channel conditions between the BS and UE are favorable compared to the path to the RIS, the energy consumption is minimized using large subarrays. When the channel conditions to the RIS are better and the data blocks are large, it is preferable to use fewer elements per subarray and potentially configure the elements individually., Comment: 6 pages, 6 figures, ICC2022

A reconfigurable intelligent surface (RIS) reflects incoming signals in different ways depending on the phase-shift pattern assigned to its elements. The most promising use case is to aid the communication between a base station and a user when the user has a line-of-sight (LOS) channel to the RIS but the direct channel is blocked. The main challenge is to estimate the channel with limited resources because non-parametric estimation methods require a pilot length proportional to the large number of RIS elements. In this paper, we develop a parametric maximum likelihood (ML) channel estimation framework for estimating the LOS channel to the RIS. We demonstrate that the proposed algorithm can accurately obtain the channel and preferred RIS configuration using only a few pilots. A key novelty is that the RIS configurations used during pilot transmission are selected to progressively improve the estimation accuracy., Comment: To appear at the Asilomar Conference on Signals, Systems, and Computers 2022, 5 pages, 2 figures

This paper considers the downlink of a single-cell massive MIMO (multiple-input multiple-output) system with dual-polarized antennas at both the base station and users. We consider a channel model that takes into account several practical aspects that arise when utilizing dual polarization, such as channel cross-polar discrimination (XPD) and cross-polar receive and transmit correlations (XPC). We derive the statistical properties of the minimum mean squared error (MMSE) channel estimator for this model. Using these estimates for maximum ratio precoding, a rigorous closed-form downlink spectral efficiency (SE) expression is derived. We compare the SEs achieved in dual-polarized and uni-polarized setups numerically and evaluate the impact of XPD on the downlink SE., Comment: Published at the 25th International ITG Workshop on Smart Antennas (WSA) 2021, 6 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2202.10084

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., Comment: 5 pages, 3 figures, accepted for publication in IEEE ICASSP 2022 conference

Reconfigurable intelligent surfaces (RISs) have recently received widespread attention in the field of wireless communication. An RIS can be controlled to reflect incident waves from the transmitter towards the receiver; a feature that is believed to fundamentally contribute to beyond 5G wireless technology. The typical RIS consists of entirely passive elements, which requires the high-dimensional channel estimation to be done elsewhere. Therefore, in this paper, we present a semi-passive large-scale RIS architecture equipped with only a small fraction of simplified receiver units with only 1-bit quantization. Based on this architecture, we first propose an alternating direction method of multipliers (ADMM)-based approach to recover the training signals at the passive RIS elements, We then obtain the global channel by combining a channel sparsification step with the generalized approximate message passing (GAMP) algorithm. Our proposed scheme exploits both the sparsity and low-rankness properties of the channel in the joint spatial-frequency domain of a wideband mmWave multiple-input-multiple-output (MIMO) communication system. Simulation results show that the proposed algorithm can significantly reduce the pilot signaling needed for accurate channel estimation and outperform previous methods, even with fewer receiver units., Comment: To appear in Proc. IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, Dec.2021

This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forwards the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically., Comment: 5 pages, 2 figures, accepted in 2021 29th European Signal Processing Conference (EUSIPCO)

This paper proposes a new power control and pilot allocation scheme for device-to-device (D2D) communication underlaying a multi-cell massive MIMO system. In this scheme, the cellular users in each cell get orthogonal pilots which are reused with reuse factor one across cells, while the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the cellular users with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. Then we provide a power control algorithm that maximizes the minimum spectral efficiency (SE) of the users in the network. Finally, we provide a numerical evaluation where we compare our proposed power control algorithm with the maximum transmit power case and the case of conventional multi-cell massive MIMO without D2D communication. Based on the provided results, we conclude that our proposed scheme increases the sum spectral efficiency of multi-cell massive MIMO networks., Comment: 6 Pages, 3 Figures, WSA 2018

Future cellular networks are expected to support new communication paradigms such as machine-type communication (MTC) services along with conventional human-type communication (HTC) services. This requires base stations to serve a large number of devices in relatively short channel coherence intervals, which renders allocation of orthogonal pilot sequences per-device in each cell impractical. Furthermore, the stringent power constraints, place-and-play type connectivity and various data rate requirements of MTC devices make it impossible for the traditional cellular architecture to accommodate MTC and HTC services together. Massive multiple-input-multiple-output (mMIMO) technology has the potential to allow the coexistence of HTC and MTC services, thanks to its inherent spatial multiplexing properties and low transmission power requirements. In this work, we first tackle the optimal non-orthogonal pilot design problem and demonstrate that the optimal pilot sequences are Welch bound equality sequences. In the second part, we investigate the performance of a single cell under a shared physical channel assumption for MTC and HTC services and propose a novel scheme for sharing the time-frequency resources. The analysis reveals that mMIMO can significantly enhance the performance of such a setup and allow the inclusion of MTC services into the cellular networks without requiring additional resources., Comment: Submitted to IEEE Transactions on Communications

Transmit beamforming is a versatile technique for signal transmission from an array of $N$ antennas to one or multiple users [1]. In wireless communications, the goal is to increase the signal power at the intended user and reduce interference to non-intended users. A high signal power is achieved by transmitting the same data signal from all antennas, but with different amplitudes and phases, such that the signal components add coherently at the user. Low interference is accomplished by making the signal components add destructively at non-intended users. This corresponds mathematically to designing beamforming vectors (that describe the amplitudes and phases) to have large inner products with the vectors describing the intended channels and small inner products with non-intended user channels. While it is fairly easy to design a beamforming vector that maximizes the signal power at the intended user, it is difficult to strike a perfect balance between maximizing the signal power and minimizing the interference leakage. In fact, the optimization of multiuser transmit beamforming is generally a nondeterministic polynomial-time (NP) hard problem [2]. Nevertheless, this lecture shows that the optimal transmit beamforming has a simple structure with very intuitive properties and interpretations. This structure provides a theoretical foundation for practical low-complexity beamforming schemes. (See this lecture note for the complete abstract/introduction), Comment: Accepted for publication as lecture note in IEEE Signal Processing Magazine, 11 pages, 3 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/optimal-beamforming