Search

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

6G networks have the burden to provide not only higher performance compared to 5G, but also to enable new service domains as well as to open the door over a new paradigm of mobile communication. This paper presents an overview on the role and key challenges of signal processing (SP) in future 6G systems and networks from the conditioning of the signal at transmission to MIMO precoding and detection, from channel coding to channel estimation, from multicarrier and non-orthogonal multiple access (NOMA) to optical wireless communications and physical layer security (PLS). We describe also the core future research challenges on technologies including machine learning based 6G design, integrated communications and sensing (ISAC), and the internet of bio-nano-things.

Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC). However, MTC is characterized by unprecedented traffic in cellular networks, thus posing a challenge to its successful incorporation. In this work, we propose a unified framework for amicable coexistence of MTC and HTC. We consider a heterogeneous network where machine-type devices coexist with enhanced mobile broadband (eMBB) devices and propose transceiver techniques that promote efficient signal recovery from these devices. For this, we present an eMBB pilot and MTC data generation strategy that facilitates joint coherent decoding of eMBB data and non-coherent decoding of MTC data. Furthermore, we assess the feasibility of coexistence using receiver operating characteristics, outage probability, and normalized mean square error (NMSE). Our numerical results reveal that a harmonious coexistence of the heterogeneous services can be achieved with properly configured average signal-to-noise ratios and pilot length.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a clustered user activity pattern. In particular, we address the JUICE in massive machine-type communications (mMTC) network under correlated Rayleigh fading channels with unknown channel covariance matrices. We formulate the JUICE problem as a maximum a posteriori probability (MAP) problem with properly chosen priors to incorporate the partial knowledge of the UEs’ clustered activity and the unknown covariance matrices. We derive a computationally-efficient algorithm based on alternating direction method of multipliers (ADMM) to solve the MAP problem iteratively via a sequence of closed-form updates. Numerical results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances for clustered user activity patterns.

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTC), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this paper, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces non-orthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low non-orthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Lowcomplexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.

To mitigate the computational power gap between the network core and edges, mobile edge computing (MEC) is poised to play a fundamental role in future generations of wireless networks. In this correspondence, we consider a non-orthogonal multiple access (NOMA) transmission model to maximize the worst task to be offloaded among all users to the network edge server. A provably convergent and efficient algorithm is developed to solve the considered non-convex optimization problem for maximizing the minimum number of offloaded bits in a multi-user NOMA-MEC system. Compared to the approach of optimized orthogonal multiple access (OMA), for given MEC delay, power and energy limits, the NOMA-based system considerably outperforms its OMA-based counterpart in MEC settings. Numerical results demonstrate that the proposed algorithm for NOMA-based MEC is particularly useful for delay sensitive applications.

In wideband joint communications and sensing (JCAS) systems, the waveforms are often designed and optimized over the entire bandwidth. This significantly limits the degrees of freedom in beamforming and causes severe communications performance loss, especially under a strict radar sensing constraint. In this work, we consider a downlink of a wideband multiuser (MU) multiple-input multiple-output (MIMO) system. We propose a subcarrier allocation strategy incorporated in an efficient JCAS design to improve the communications–sensing performance tradeoff of the system. The main idea is to employ a subset of subcarriers for radar sensing while enabling communications over the entire bandwidth. This creates dedicated subcarriers for communications without any interference from the radar function, whose sensing accuracy can still be guaranteed through strict design constraints. To realize the idea, we formulate the sum rate maximization problem and propose a three-step solution based on successive convex approximation and Riemannian manifold optimization. We further propose efficient initialization methods to reduce complexity and improve the convergence of the employed optimization schemes. Our simulation results for a 32 × 4 MU-MIMO system show that the proposed scheme offers 70% improvement in the system sum rate while guaranteeing the same radar beampattern mean square error and requiring less radio frequency chains compared with the conventional JCAS design over the entire bandwidth.

Ray tracing is a widely used approach for deterministic modelling of radio channels. We present our path generation pipeline, which combines environment discretization-based propagation path search with path refinement, which outputs validated paths fulfilling the Fermat’s principle of the least time. We propose a novel gradient descent-based solution for refinement. Whole pipeline is implemented as GPU computations using NVIDIA CUDA and OptiX ray tracing engine, and experimental results show efficacy of the approach.

Hybrid beamforming (HBF) is a key enabler for millimeter-wave (mmWave) communications systems, but HBF optimizations are often non-convex and of large dimension. In this paper, we propose an efficient deep unfolding-based HBF scheme, referred to as ManNet-HBF, that approximately maximizes the system spectral efficiency (SE). It first factorizes the optimal digital beamformer into analog and digital terms, and then reformulates the resultant matrix factorization problem as an equivalent maximum-likelihood problem, whose analog beamforming solution is vectorized and estimated efficiently with ManNet, a lightweight deep neural network. Numerical results verify that the proposed ManNet-HBF approach has near-optimal performance comparable to or better than conventional model-based counterparts, with very low complexity and a fast run time. For example, in a simulation with 128 transmit antennas, it attains 98.62% the SE of the Riemannian manifold scheme but 13250 times faster.

In this paper, the position control of multiple unmanned aerial vehicles, acting as cooperative jammers, is proposed to improve the security level of a legitimate ground transmission, where a precoder is designed to nullify the jamming signals at the legitimate receiver. In this scenario, the maximization of the weighted secrecy coverage, which measures the efficiency of cooperative jamming over a confined region, is addressed by following a multi armed bandit-based algorithm. The results show that our proposal converges to the one obtained by exhaustive search, and it shows significant improvement over a projected gradient-descent benchmark, while offering a shorter running-time. The results show that, for the considered system and the employed precoder, the inclusion of more than two UAVs does not lead to significant advantages in terms of secrecy performance.

This paper demonstrates a piece-wise digital predistortion (PW-DPD) for a power amplifier (PA) in 5G new radio (NR) systems. It involves modeling the digital predistorter based on the machine learning (ML) classification of the operational states. The experimental results demonstrate that by extracting some key features from 5G NR signal statistics and the PA operating point can offer better PA linearization performance/complexity tradeoff than the conventional approach based on a single pruned Volterra model. The proposed approach is validated by laboratory experiments and shows up to 3.5 dB error vector magnitude (EVM) improvement over the conventional approach for a class A PA at 28 GHz.

We propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization.

In in-band full-duplex (FD) systems, the self-interference (SI) power can be more than 100 dB higher than the power of the received data signal. In order to enable FD transmissions, several SI cancellation stages are required in an FD transceiver. By combining the cancellation at the radio frequency (RF) with a specially designed antenna and cancellation circuitry, and SI cancellation at the digital baseband, the required level of SI cancellation can be achieved. In this thesis, an FD transceiver architecture is modelled with simulation tools that allow one to use realistic antenna and analog transceiver models and at the same time enable algorithm studies. The analog SI cancellation at the RF is controlled by the baseband digital processing unit, and the tuning of the RF canceller is performed with an automatic gain control enhanced iterative algorithm. When a power amplifier is the only non-linear component at the SI channel, the digital baseband SI cancellation is based on the Hammerstein model in order to take the power amplifier non-linearity into account. When realistic values for the phase noise and imbalance between the in-phase and quadrature branches of the transceiver (IQ imbalance) are taken into account, the SI after all the cancellation stages can decrease the signal-to-interference-and-noise-ratio. In order to further enhance the SI cancellation, the Hammerstein based SI canceller is extended to cancel also the effect of the receiver IQ imbalance. With the extended baseband canceller, the cancellation performance is mainly limited by the phase noise. When the non-linear operation of a low noise amplifier at the receiver is also considered, the SI channel is modelled with the Hammerstein-Wiener model. In this case, the best SI cancellation performance at the digital baseband processing is achieved with a neural network type SI canceller., Tiivistelmä Full duplex (FD) järjestelmissä itseishäiriön teho voi olla yli 100 dB voimakkaampi kuin vastaanotetun hyötysignaalin teho. FD-lähetysten käyttö edellyttää itseishäiriön vaimentamista FD-lähetinvastaanottimessa useilla eri menetelmillä. Yhdistämällä antennisuunnittelu ja aktiivinen häiriönvaimennus radiotaajuudella sekä digitaalinen häiriönpoisto lähetinvastaanottimen kantataajuusosissa, vaadittava häiriönvaimennus voidaan saavuttaa. Väitöskirjassa FD-lähetinvastaanotin on mallinnettu sellaisilla simulaatiotyökaluilla, jotka mahdollistavat antennien ja analogisten osien tarkan mallintamisen ja samalla mahdollistavat algoritmitutkimuksen samassa ympäristössä. Analogista radiotaajuista häiriönvaimennusta kontrolloidaan kantatajuisella, digitaalisella prosessoinnilla. Analogisen häiriönvaimennuksen ohjauksessa käytetään digitaalista, iteratiivista algoritmia hyödyntäen samalla automaattista vahvistuksen säätöä vastaanottimen analogiaosissa. Kun lähetinvastaanottimen ainoa epälineaarinen osa on tehovahvistin, digitaalinen häiriönvaimennus perustuu Hammerstein-malliin. Kun realistiset vaihekohina- ja lähetinvastaanottimen kvadratuurikanavien epätasapainoarvot (IQ-epätasapaino) huomioidaan mallissa, häiriönpoiston suorituskyky heikkenee. Laajentamalla Hammersteinin mallia, IQ-epätasapainon vaikutus voidaan kuitenkin kompensoida. Tällöin suorituskykyä rajoittavaksi tekijäksi jää vaihekohina. Kun myös vastaanottimen vahvistimen epälineaarisuus huomioidaan, itseishäiriökanava mallinnetaan Hammerstein-Wiener-mallilla. Tässä tapauksessa paras häiriönpoiston suorituskyky saavutetaan käyttämällä neuroverkkotyyppistä häiriönvaimennusta.

Millimeter wave (mmWave) beamforming is a vital component of the fifth generation (5G) new radio (NR) and beyond wireless communication systems. The usage of mmWave narrow beams encounters frequent signal attenuation due to random human blockages in indoor environments. Human blockage predictions can jointly improve the signal quality as well as passively sense human activities during mmWave communication. Human sensing using wireless fidelity (WiFi) systems has earlier been studied using receiver signal strength indicator (RSSI) signal level fluctuations based on distance measurements. Other conventional approaches using cameras, lidars, radars, etc. require additional hardware deployments. Current device-free WiFi sensing approaches use vendor-specific channel state information to obtain fine-grained human blockage predictions. Our novelty in this work is to obtain fine-grained human blockage direction predictions in mmWave spectrum, using a time series of RSSI measurements and build fingerprints. We perform experiments to construct a Human Millimetre-wave Radio Blockage Detection (HuMRaBD) dataset and observe human influence in different radio beam directions during each radio initial access procedure. We design a multi layer perceptron (MLP) framework to analyze the HuMRaBD dataset over coarse-grained and fine-grained mmWave blockage directions from static and dynamic human movements. The results show that our trained MLP-trained models can simultaneously sense multiple indoor human radio-blockage directions at an average F1 score of 0.84 and area under curve (AUC) score of 0.95 during mmWave communication.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

In conventional joint communications and sensing (JCAS) designs for multi-carrier multiple-input multiple-output (MIMO) systems, the dual-functional waveforms are often optimized for the whole frequency band, resulting in limited communications–sensing performance tradeoff. To overcome the limitation, we propose employing a subset of subcarriers for JCAS, while the communications function is performed over all the subcarriers. This offers more degrees of freedom to enhance the communications performance under a given sensing accuracy. We first formulate the rate maximization under the sensing accuracy constraint to optimize the beamformers and JCAS subcarriers. The problem is solved via Riemannian manifold optimization and closed-form solutions. Numerical results for an 8 × 4 MIMO system with 64 subcarriers show that compared to the conventional subcarrier sharing scheme, the proposed scheme employing 16 JCAS subcarriers offers 60% improvement in the achievable communications rate at the signal-to-noise ratio of 10 dB. Meanwhile, this scheme generates the sensing beampattern with the same quality as the conventional JCAS design.

Terahertz (THz) communications has been envisioned as an enabling and highly promising technology for the sixth generation (6G) and beyond wireless networks which aim to provide full and unlimited wireless connectivity for the ubiquitous intelligent information society of 2030 and beyond. In particular, the ultra-wide THz band from 0.1 to 10 THz offers enormous potential to alleviate the spectrum scarcity and break the capacity limitation of emerging wireless systems, such as the fifth generation (5G) wireless networks. This will undoubtedly support epoch-making wireless applications that demand ultra-high quality of service requirements and multi-terabits/s data transmission in the 6G and beyond era, such as holographic communications, immersive extended reality, ultra-fast backhaul and wireless local area networks, and wireless high-bandwidth secure transmission. Moreover, THz transceivers and antennas boast an incredibly compact size, reaching sub-millimetric dimensions. This miniaturization enables the seamless integration of extremely small radios into various environments, giving rise to ground-breaking applications, e.g., the Internet of Nano-Things and wireless networks-on-chip. Furthermore, the utilization of the THz band extends beyond traditional radar and localization, opening doors to novel wireless sensing capabilities and underpinning cutting-edge applications such as healthcare nano-bio-sensing. Due to the aforementioned advantages, an unprecedented amount of spectrum within the 0.275–0.45 THz band was opened for land mobile and fixed service in 6G after World Radio Conference 2019. Additionally, the IEEE 802.15.3 d standard has been established as the first wireless standard in the sub-THz band (specifically, 253–322 GHz) to support data rates of 100 gigabit/s and above.

Signal degradation caused by receiver in-phase/quadrature (IQ) processing branch imbalance (IQI) is known to increase bit error rates, and deteriorate both angle of arrival (AoA) and ranging estimation accuracies. In this paper, we present an IQI compensation procedure that leverages a pilot sequence to propose an IQI compensation method, which tolerates time synchronization errors. We then explore a single anchor positioning problem and show that the proposed procedure is effective in improving the position estimation accuracy. We evaluate its performance via computer simulations. The results show that the scheme outperforms an earlier method, which is blind in the sense that it does not capitalize the pilot sequence availability.

The unprecedented increase in wireless data traffic, predicted to occur within the next decade, is motivating academia and industries to look beyond contemporary wireless standards and conceptualize the sixth-generation (6G) wireless networks. Among various promising solutions, terahertz communications (THzCom) is recognized as a highly promising technology for the 6G and beyond era, due to its unique potential to support terabit-per-second transmission in emerging applications. This article delves into key areas for developing end-to-end THzCom systems, focusing on physical, link, and network layers. Specifically, we discuss the areas of THz spectrum management, THz antennas, and beamforming, as well as the integration of other 6G-enabling technologies for THzCom. For each area, we identify the challenges imposed by the unique properties of the THz band. We then present main advancements and outline perspective research directions in each area to stimulate future research efforts for realizing THzCom in 6G and beyond wireless networks.

Sample rate conversion is an essential scheme used in almost every radio design. Supporting sampling rates higher than the clock rates require parallel processing. In this paper, we propose an algorithm for a sample rate converter (SRC) with multiple parallel output phases so that the conversion ratio can be a fixed rational number. Due to the structure of the proposed algorithm, it is suitable for embedded platforms which are restricted by their clock frequency but require very high sample rates. A dual phase output variant of the proposed algorithm is simulated with a 400 MHz input signal to perform a 15/8 conversion. The test and verification of the SRC algorithm is presented with the aid of a design example. A VLSI architecture of the dual phase output SRC is implemented on a Virtex-7 field-programmable gate array (FPGA) and results are presented.

Unmanned aerial vehicles (UAVs) are the vital components of sixth generation (6G) millimeter wave (mmWave) wireless networks. Fast and reliable beam alignment is essential for efficient beam-based mmWave communications between UAVs and the base stations (BSs). Learning-based approaches may greatly reduce the overhead by leveraging UAV data, such as position, to identify the optimal beam directions. In this paper, we propose a deep reinforcement learning (DRL)-based framework for UAV-BS beam alignment using the hierarchical deep Q-Network (hDQN) in a mmWave radio setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with three dimensional (3D) beams under diverse channel conditions. A BS serves with learnt beam-pairs in an uplink manner upon every communication request from UAV inside the multi-location environment. Compared to our prior DQN-based method, the proposed hDQN framework uses the location information and the fixed spatial arrangement of the antenna elements to reduce the beam search complexity and maximize the data rates efficiently. The results show that our proposed hDQN-based framework converges faster than the DQN-based approach with an average overall training reduction of 43% and, is generic to multi-location environments across different uniform planar array (UPA) configurations and diverse channel conditions.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

One of the main approaches for distance estimation is the received signal strength (RSS) based techniques. Their drawbacks include the requirement of accurate knowledge of the transmit power and antenna gains. Also, the traditional RSS-based techniques do not take into account the molecular absorption that occurs at terahertz frequencies. In this paper, we propose a distance estimation method for the line-of-sight case that actually takes advantage of the molecular absorption. The proposed method measures the RSS at two frequencies and does not require known transmit powers and antenna gains, which are assumed to be almost the same at the two frequencies. Therefore, the two frequencies have to be relatively close to each other. The proposed distance estimation method calculates the difference of the received powers (in the dB domain) at the two frequencies and finds the link distance based on it and the humidity level.

While intelligent reflecting surfaces (IRSs) and non-orthogonal multiple access (NOMA) techniques have shown great potential to boost the spectral and energy efficiency for future wireless networks, unmanned aerial vehicles (UAVs) are committed for enhancing the wireless connectivity with fast and flexible deployment. In this regard, we study an integration of an IRS in UAV-enabled wireless relaying system using NOMA transmissions. We also count on the impacts of residual hardware impairments (HIs) in user devices and imperfect successive interference cancellation (SIC) in NOMA, which are inevitable in practical system implementation. We analyze the system performance by deriving the closed-form expressions of outage probability (OP) and system throughput over the line-of-sight (LoS) Rician fading channels for the aerial links. We further pursue asymptotic OP analysis to reveal useful insights on the achievable diversity order. Above all, we present a deep neural network (DNN) framework for OP prediction with a short execution time under the dynamic stochastic environment. Our results validate the theoretical proposition and accentuate the performance advantages of the proposed UAV-borne IRS relaying NOMA system.

Future 6G and beyond wireless systems aim at ultra-high data rates from very large bandwidths in millimeter-wave and terahertz bands. Large bandwidth signals in antenna arrays lead to beam squint or frequency-selective array response causing losses in the beamforming gain. Recently, several hybrid analog-digital beamforming (HBF) schemes, such as the delayphase precoding employing time delay networks or wider-beam codebook design, have been proposed to tackle the beam squint. However, the severity of beam squint has not been thoroughly investigated in the literature. In this paper, by analyzing the impacts of the beam squint on the uniform planar array (UPA) and uniform linear array (ULA) structures, we analytically and numerically show that the effect of beam squint is less severe in the former. Based on this fact, we propose a simplified HBF design in which the analog beamformer is designed for the center frequency, while digital beamformers for all subcarrier frequencies are obtained based on water-filling and minimum mean square error solutions. Simulation results show that the proposed scheme for UPAs performs very close to the optimal digital beamforming scheme.

Intelligent reflecting surfaces (IRSs) have shown huge advantages in many potential use cases and thus have been considered a promising candidate for next-generation wireless systems. In this paper, we consider an IRS-assisted multigroup multicast (IRS-MGMC) system in a multiple-input single-output (MISO) scenario, for which the related existing literature is rather limited. In particular, we aim to jointly design the transmit beamformers and IRS phase shifts to maximize the sum rate of the system under consideration. In order to obtain a numerically efficient solution to the formulated non-convex optimization problem, we propose an alternating projected gradient (APG) method where each iteration admits a closed-form and is shown to be superior to a known solution that is derived from the majorization-minimization (MM) method in terms of both achievable sum rate and required complexity, i.e., run time. In particular, we show that the complexity of the proposed APG method grows linearly with the number of IRS tiles, while that of the known solution in comparison grows with the third power of the number of IRS tiles. The numerical results reported in this paper extend our understanding on the achievable rates of large-scale IRS-assisted multigroup multicast systems.

Configuration of a reconfigurable intelligent surface (RIS) as a solution to an optimization problem for spectral or energy efficiency requires the estimation of the channels between the transmitter and the RIS and between the RIS and the receiver. The channel estimation and the RIS configuration by optimization are computationally intensive processes in RIS assisted wireless links. The approach proposed in this paper simplifies and speeds up the RIS configuration process by relaxing the demand to find the optimal solution but instead aims to reach a predetermined channel quality measured with the signal-to-noise ratio. It is also shown that when a single dominant path exists between a mobile user and the RIS and also between the RIS and a base station, the RIS configuration calculated as a solution to a spectral efficiency maximization problem and configuration based on conventional antenna array beam steering give the same result.

A reconfigurable intelligent surface (RIS) can be used to control the propagation of electromagnetic waves (EM). By deploying the RIS units in radio environment allows to steer the transmitted EM waves to areas that are otherwise shadowed by buildings or other geographic formations resulting in coverage enhancement. The gain offered by the RIS is due to its ability to focus the impinging signal to the desired direction. Hence, errors in the beamforming degrade the performance of a RIS-assisted link. The effect of the errors in the beamforming at the RIS are studied in this paper. Simulations show that the errors especially in the zenith angle can have significant effect on the uplink performance. With a RIS based on a 16 × 16 element reflect array errors greater than 1° start to limit the achievable throughput. After the RIS is configured based on the direction information, the user equipment must use a proper precoder to capitalize the benefit of the path via the RIS. The usability of existing precoders from the 5G NR are also studied in this paper. In the simulated use case, utilizing a RIS with 5G NR precoders resulted in 3.6 dB gain in the uplink direction.

We consider a multi-user multiple-input single-output (MISO) communications system which is assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). Unlike conventional passive RISs, hybrid RIS is equipped with a few active elements with the ability to reflect and amplify incident signals to significantly improve the system performance. Towards a fairness-oriented design, we maximize the minimum rate among all users through jointly optimizing the transmit beamforming vectors and RIS reflecting/amplifying coefficients. Combining tools from block coordinate ascent and successive convex approximation, the challenging nonconvex problem is efficiently solved by a low-complexity iterative algorithm. The numerical results show that a hybrid RIS with 4 active elements out of a total of 50 elements with a power budget of −1 dBm offers an improvement of up to 80% to the considered system, while that achieved by a fully passive RIS is only 27%.

We consider in this work a cell-free (CF) massive multiple-input-multiple-output (mMIMO) system where multiple hybrid relay-reflecting intelligent surfaces (HR-RIS) are deployed to assist communication between access points and users. We first present the signal model and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis for the considered HR-RIS-aided CF mMIMO system, where the closed-form expression of the downlink throughput is derived. The presented analytical results are also valid for conventional CF mMIMO systems, i.e., CF mMIMO systems with and without passive reconfigurable intelligent surfaces. Finally, the analytical derivations are verified by extensive numerical results.

Serving a plethora of devices in massive machinetype communications (mMTC) can rely on spatial multiplexing enabled by massive multiple-input multiple-output (mMIMO) technology. To release the full potential, accurate channel estimation is needed. Due to the large numbers of devices it necessitates pilot reuse. We propose a pilot allocation algorithm based on multi-point channel charting (CC) to mitigate inevitable pilot contamination in a multi-cell multi-sector mMTC network with spatially correlated mMIMO channels. The generated CC represents an effective interference map from channel covariance matrices to capture the degree of pilot contamination caused by sharing the same pilot sequence among multiple users. The map is then fed into a greedy algorithm that aims at optimizing the reuse pattern of orthogonal pilot sequences to minimize the performance degradation caused by pilot contamination. The proposed CC-based method is empirically shown to obtain notable gains over a reuse-factor-aware random pilot allocation, yet leaving room for further improvements.

Massive multiple-input multiple-output (mMIMO) technology is a way to increase spectral efficiency and provide access to the Internet of things (IoT) and machine-type communication (MTC) devices. To exploit the benefits of large antenna arrays, accurate channel estimation through pilot signals is needed. Massive IoT and MTC systems cannot avoid pilot reuse because of the enormous numbers of connected devices. We propose a pilot reuse algorithm based on channel charting (CC) to mitigate pilot contamination in a multi-sector single-cell mMIMO system having spatially correlated channels. We show that after creating an interference map via CC, a simple strategy to allocate the pilot sequences can be implemented. The simulation results show that the CC-based pilot reuse strategy improves channel estimation accuracy, which subsequently improves the symbol detection performance and increases the spectral efficiency compared to other existing schemes. Moreover, the performance of the CC pilot assignment method approaches that of exhaustive search pilot assignment for small network setups.

The growth of companies is critical in the renewal of any nation. Research has shown that companies collaborating with higher education are more innovative and development oriented, and more likely to grow in staffing and turnover and to penetrate to international markets. The new public management ideology, especially wrapped up in a funding method, has gradually forced the university of applied sciences (UAS) research, development, and innovation (RDI) services to enhance the performance in the industry. By productization, UAS are to adopt the design thinking ideology, to introduce service-dominant logic, and enhance public-private partnership. With well-designed services, the social and environmental impacts of UAS are intensified. It is the outmost target of new public management. With its constructive research approach, this dissertation was aimed at generating a new contribution to the conduct of RDI activities in the UAS context. It demonstrates the dimensions that alter the UAS RDI service encountering conditions. Furthermore, the study considers the challenges and cornerstones of the prevailing customer-oriented business model and reframes the uniform, systematic model to the UAS RDI service encountering process. This research is unique, as the academic service encountering process from the perspective of productization has seldom been the key issue investigated in research. The practical significance of the dissertation is to contribute to the exchange and experience of the UAS-industry collaboration., Tiivistelmä Korkeakouluyhteistyötä tekevät yritykset ovat innovatiivisempia ja kehittämisorientoituneempia, useammin voimakkaan kasvuhakuisia, henkilöstömäärältään ja liikevaihdoltaan suurempia ja toimivat useammin valtakunnallisilla ja kansainvälisillä markkinoilla. Suomalaiset yritykset osaavat hyödyntää ammattikorkeakouluopiskelijoiden harjoitteluja ja opinnäytteitä kehittämistyössään. Sen sijaan tutkimus-, kehittämis-, ja innovaatiopalvelut (TKI-palvelut) ovat toimintoja, mitkä yritykset tunnistavat edellistä heikommin voimavarakseen. Tämän väitöstutkimuksen kirjallisuuskatsaus osoittaa, miten korkeakoulupolitiikan muutos on ohjannut akateemista intressiä yritysyhteistyön suuntaan (uusi julkisjohtamisen oppi), ja miten julkisen sektorin suhde yksityisiin toimijoihin on muuttunut (julkisen ja yksityisen sektorin kumppanuus). Tutkimuskysymyksenä on, miten asiakaslähtöisyys (palvelumuotoilu, suunnitteluajattelu) tukevat näitä tavoitteita, ja mitä haasteita ja mahdollisuuksia palveluprosessin systematiikassa on. Tämä konstruktiivinen tutkimus on ainutlaatuinen, sillä AMK:n TKI-palveluita on tuotteistamisen näkökulmasta tutkittu varsin vähän. Tutkimuksen tulokset osoittavat, että systemaattinen palvelumalli lisää osaamisen uudistumista, parantaa kehittämistyötä ja palveluvastetta. Se myös parantaa yritysten luottamusta AMK:n palvelutarjontaan. Väitöskirjan konstruktio, tarjoaa yhtenäisen menettelytavan yrityslähtöisten palveluiden tuottamiseksi ja vastaa kysymykseen, miten AMK:n TKI-palveluita voidaan tuotteistaa. Tulosten avulla on mahdollista uudistaa ammattikorkeakoulujen TKI-toiminnan menettelytapoja.

A cell-free (CF) massive multiple-input-multiple-output (mMIMO) system can provide uniform spectral efficiency (SE) with simple signal processing. On the other hand, a recently introduced technology called hybrid relay-reflecting intelligent surface (HR-RIS) can customize the physical propagation environment by simultaneously reflecting and amplifying radio waves in preferred directions. Thus, it is natural that incorporating HR-RIS into CF mMIMO can be a symbiotic convergence of these two technologies for future wireless communications. This motivates us to consider an HR-RIS-aided CF mMIMO system to utilize their combined benefits. We first model the uplink/downlink channels and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis of SE performance of the considered system. Specifically, we derive closed-form expressions for the uplink and downlink SE. The results reveal important observations on the performance gains achieved by HR-RISs compared to conventional systems. The presented analytical results are also valid for conventional CF mMIMO systems and those aided by passive reconfigurable intelligent surfaces. Such results play an important role in designing new transmission strategies and optimizing HR-RIS-aided CF mMIMO systems. Finally, we provide extensive numerical results to verify the analytical derivations and the effectiveness of the proposed system design under various settings.

A need for measurement data for frequencies above 100 GHz has increased. In this paper, we present the measurement system for millimeter wave frequencies (30–300 GHz). It is based on Keysight PNA-X vector network analyser with frequency extenders mounted on custom built 3-axis rotation stages (both the transmitter and receiver sides). We present some initial results from measurements conducted in an office corridor. Both line-of-sight (LOS) and non-LOS (NLOS) cases are included in the measurement set-ups. The results show that in all cases the channels are sparse with clear clusters from large reflecting surfaces, such as walls and floors. The measured power levels are rather strong indicating relatively low reflection losses. While the channels were sparse, there were still typically relatively large numbers of distinguishable paths. The numbers of observable paths as well as the delay spread of the channel increase with increasing beamwidth.

With massive deployment, multiple-input–multiple-output (MIMO) systems continue to take mobile communications to new heights, but the ever-increasing demands mean that there is a need to look beyond MIMO and pursue the next disruptive wireless technologies. Reconfigurable intelligent surface (RIS) is widely considered a key candidate technology block to provide the next generational leap. The first part of this article provides an updated overview of the conventional reflection-based RIS technology, which complements the existing literature to include active and semiactive RIS, and the synergies with cell-free massive MIMO (CF mMIMO). Then, we widen the scope to discuss the surface-wave-assisted RIS that represents a different design dimension in utilizing metasurface technologies. This goes beyond being a passive reflector and can use the surface as an intelligent propagation medium for superb radio propagation efficiency. The third part of this article turns the attention to the fluid antenna, a novel antenna technology that enables a diverse form of reconfigurability that can combine with RIS for ultrahigh capacity, power efficiency, and scalability. This article concludes with a discussion of the potential synergies that can be exploited between MIMO, RIS, and fluid antennas.

Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy for mitigating blockages in millimeter wave (mmWave) and terahertz (THz) communications networks. However, perfect or nearly perfect channel state information (CSI) is fundamental in order to achieve their full potential. Tra-ditionally, an RIS is fully passive without any baseband processing capabilities, which poses great challenges for CSI acquisition. Thus, we focus on the hybrid RIS architecture, where a small portion of RIS elements are active and able to processing the received pilot signals for estimating the corresponding channel. The channel estimation (CE) is done by resorting to off-the-grid compressive sensing technique, i.e., atomic norm minimization, for extracting channel parameters through two stages. Simulation results show that the proposed scheme outperforms the passive RIS CE under the same training overhead.

The nonlinearity of the fifth-generation (5G) millimeter-wave (mmWave) phased array transmitter (TX) depends on the variations in the beamforming coefficients, e.g., due to unwanted amplitude errors of the phase shifters or purposely introduced amplitude variations to shape the beam, as well as other analog component variations. Therefore, also the digital predistortion (DPD) coefficients depend on the beamforming coefficients and require a continuous update for different beamsteering directions in a conventional DPD. We propose a robust DPD approach that uses the average array response for DPD training. The average array response is estimated using the known experimental histogram of power amplifier (PA) input power variation resulting from the amplitude variations due to beamforming. The DPD training based on the average array response makes the DPD coefficients insensitive to beamforming variations. The proposed DPD strategy requires a shared feedback path for training and only a single set of DPD coefficients to linearize the array response in all beamsteering directions. The performance of the proposed DPD strategy is validated by over-the-air (OTA) measurements of a 28-GHz phased array TX over different steering angles. Experimental results show that the proposed DPD method using one set of DPD coefficients provides similar linearization performance across the steering angles compared with the DPD trained to all steering angles through the OTA reference antenna.

The hybrid relay-reflecting intelligent surface (HRRIS) has been recently introduced as an efficient solution to overcome the double path loss and limited beamforming diversity of the conventional fully passive reflecting surface. This motivates us to investigate the application of the HR-RIS in improving the secrecy capacity of millimeter wave multiple-input-multiple-output (MIMO) systems with the presence of multi-antenna eavesdropper. The joint optimization of the transmit beamformer and the relay-reflecting coefficients at RIS is tackled via alternating optimization. In the proposed solution, a closed-form expression for the optimal transmit beamformer at the transmitter is derived, and a metaheuristic solution based on particle swarm optimization is proposed to optimize the active and passive elements at the HR-RIS. The simulation results verify that under various scenarios, the HR-RIS provides significant improvement in the secrecy capacity with respect to the conventional passive reflecting surface.

Digital linearization of a phased array transmitters is usually based on feedback measurements. Various arrangements can be used to measure multiple transmit paths by using conducted or radiated methods. We propose a technique for measuring any pair of transmit paths simultaneously by using a common feedback connected to two receivers, and a cancellation method that can make the measurements independent. Together with the feedback cancellation, a single feedback line with two output ports can be used to measure different transmit chain pairs coherently under varying load conditions present in phased arrays. The cancellation filters are trained by using a fifth-generation (5G) new radio waveform and verified by over-the-air (OTA) measurements along with the predistortion performance evaluation of a 28 GHz 8-element phased array transmitter. The method achieved 30 dB cancellation between the paths and the DPD trained with the feedback cancellation achieved 4.2 % EVM and -40.8 dBc ACPR.