In semi-decentralized federated learning, devices primarily rely on device-to-device communication but occasionally interact with a central server. Periodically, a sampled subset of devices uploads their local models to the server, which computes an aggregate model. The server can then either (i) share this aggregate model only with the sampled clients (sampled-to-sampled, S2S) or (ii) broadcast it to all clients (sampled-to-all, S2A). Despite their practical significance, a rigorous theoretical and empirical comparison of these two strategies remains absent. We address this gap by analyzing S2S and S2A within a unified convergence framework that accounts for key system parameters: sampling rate, server aggregation frequency, and network connectivity. Our results, both analytical and experimental, reveal distinct regimes where one strategy outperforms the other, depending primarily on the degree of data heterogeneity across devices. These insights lead to concrete design guidelines for practical semi-decentralized FL deployments.

Motivated by International Mobile Telecommunications (IMT)-2030, sixth generation (6G) mobile networks in 3GPP (third-generation partner project) commenced with a workshop in March 2025, attracting more than 200 contributions and 700 in-person attendances. While the details of 6G study and the eventual 6G specifications in 3GPP are yet to be developed, there is strong motivation in 3GPP to focus on the fundamental values that 6G may bring, most notably in terms of improving user experience and the overall network operation, particularly with respect to reducing the total cost of ownership (TCO). It is evident that there is a strong need to streamline and simplify the 6G specifications for efficient standardization, implementation, and commercial deployments. For the services also accommodated by the fifth generation (5G) networks, 6G is expected to provide meaningful enhancements. That is, instead of simply pushing for even higher envelopes such as peak data rates, more emphasis will be on improved coverage (especially at the cell edge and for certain data rates or services), and energy efficiency (for both end devices and networks) using the existing and new spectrum. Moreover, 6G is expected to support new services, with artificial intelligence (AI) and sensing as two primary examples. In this article, we provide a tutorial on the key technologies driving fundamental enhancements for the standardization of 6G and beyond, covering the necessary co-existence between 5G and 6G for smooth migration, AI-native radio access network (RAN), multiple-input-multiple-out (MIMO), sustainable operation with increased energy efficiency, coverage enhancements involving both terrestrial and non-terrestrial networks (NTN), integrated sensing and communication (ISAC), diverse device types including the low-end back-scattering based ambient internet of things (IoT) devices, and native support of NTN. We conclude this article by pointing out several key challenges and opportunities towards standardization of 6G and beyond.

In the above article [1], this shows a corrected statement and proof of Theorem 1. The analysis in [1] incorrectly treated subsets of scheduled devices independently, overlooking that global iterates depend on all subsets. In addition, in the proof of Lemma 2 (now Lemma 3 in this document), the expectation over the randomness in the gradient sparsification was inaccurate.

We consider a cellular massive MIMO system where swarms of wireless repeaters are deployed to improve coverage. These repeaters are full-duplex relays with small form factors that receive and instantaneously retransmit signals. They can be deployed in a plug-and-play manner at low cost, while being transparent to the network—conceptually they areactive channel scattererswith amplification capabilities. Two fundamental questions need to be addressed in repeater deployments: (i) How can we prevent destructive effects of positive feedback caused by inter-repeater interaction (i.e., each repeater receives and amplifies signals from others)? (ii) How much performance improvement can be achieved given that repeaters also inject noise and may introduce more interference? To answer these questions, we first derive a generalized Nyquist stability criterion for the repeater swarm system, and provide an easy-to-check stability condition. Then, we study the uplink performance and develop an efficient iterative algorithm that jointly optimizes the repeater gains, user transmit powers, and receive combining weights to maximize the weighted sum rate while ensuring system stability. Numerical results corroborate our theoretical findings and show that the repeaters can significantly improve the system performance, both in sub-6 GHz and millimeter-wave bands. The results also warrant careful deployment to fully realize the benefits of repeaters, for example, by ensuring a high probability of line-of-sight links between repeaters and the base station.

This paper studies integrated sensing and communication (ISAC) with dynamic time division duplex (DTDD) cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. DTDD enables the CF mMIMO system to concurrently serve both uplink (UL) and downlink (DL) users with spatially separatedhalf-duplex (HD)access points (APs) using the same time-frequency resources. Further, to facilitate ISAC, the UL APs are utilized for both UL data and target echo reception, while the DL APs jointly transmit the precoded DL data streams and target signal. In this context, we present centralized and distributed generalized likelihood-ratio tests (GLRTs) for target detection treating UL users’ signals as sensing interference. We then quantify the optimality and complexity trade-off between distributed and centralized GLRTs and benchmark the respective estimators with the Bayesian Cramér-Rao lower bound for target radar-cross section (RCS). Then, we present a unified framework for joint UL users’ data detection and RCS estimation. Next, for communication, we derive the signal-to-noise-plus-interference (SINR) optimal combiner accounting for the cross-link and radar interference for UL data processing. In DL, we use regularized zero-forcing for the users and propose two types of precoders for the target: one “user-centric” that nullifies the interference caused by the target signal to the DL users and one “target-centric” based on the dominant eigenvector of the composite channel between the target and the APs. Finally, numerical studies corroborate with our theoretical findings and reveal that theGLRT is robust to inter-AP interference, and DTDD doubles the 90%-likely sum UL-DL SE compared to traditional TDD-based CF-mMIMO ISAC systems; while using HD hardware.

This paper proposes a novel Bayesian reciprocity calibration method that consistently ensures uplink and downlink channel reciprocity in repeater-assisted multiple-input multiple-output (MIMO) systems. The proposed algorithm is formulated under the minimum mean-square error (MMSE) criterion. Its Bayesian framework incorporates complete statistical knowledge of the signal model, noise, and prior distributions, enabling a coherent design that achieves both low computational complexity and high calibration accuracy. To further enhance phase alignment accuracy, which is critical for calibration tasks, we develop a von Mises denoiser that exploits the fact that the target parameters lie on the circle in the complex plane. Simulation results demonstrate that the proposed MMSE algorithm achieves substantially improved estimation accuracy compared with conventional deterministic non-linear least-squares (NLS) methods, while maintaining comparable computational complexity. Furthermore, the proposed method exhibits remarkably fast convergence, making it well suited for practical implementation.

Future Internet-of-Things networks are envisioned to use small and cheap sensor nodes with extremely low power consumption to avoid the extensive use of batteries. To provide connectivity to a massive number of these nodes, backscatter communication (BC) is emerging as an energy- and cost-efficient technology exploiting the reflection of radio frequency signals. However, challenges such as round-trip path loss and direct link interference (DLI) between the carrier emitter and the reader limit its performance. To tackle these limitations, this paper proposes a joint access point role selection and a novel beamforming technique for bistatic BC in a distributed multiple-input multiple-output setup. The proposed approach boosts the received backscattered energy while effectively mitigating DLI, thereby reducing the error probability. We also propose a channel estimation method tailored to operate under DLI conditions and propose a mismatch detector using estimated channel coefficients. Furthermore, we derive a closed-form expression for the probability of error for the detectors and model the quantization noise caused by DLI. Finally, comprehensive simulation results show that the proposed method with 1-bit analog-to-digital converters (ADCs) effectively mitigates DLI, reduces the quantization noise, and enhances backscattered signal energy, achieving performance comparable to the benchmark scenario with 8-bit ADCs.

Recent advancements in polymer microwave fiber (PMF) technology have created significant opportunities for robust, low-cost, and high-speed sub-terahertz (THz) radio-over-fiber communications. Recognizing these potential benefits, this paper explores a novel radio-over-fiber (RoF) structure that interconnects multiple radio units (RUs), booster units (BUs), and a central unit (CU) in cascade via fiber, envisioning its application in indoor scenarios. This structure creates a number of research opportunities when considering cascaded distortion effects introduced by non-linear power amplifiers (PAs) and the propagation channel over the fiber. We propose maximum-likelihood and non-linear least-squares algorithms to estimate the entry RU and the time-of-arrival between the RoF and the user equipment, where estimating the entry RU is equivalent to estimating the propagation distance along the RoF stripe. For the case of linear PAs, we derive the Cramér-Rao lower bound to benchmark the performance of the estimators. Finally, we investigate the use of the system for uplink positioning. Our simulation results demonstrate that the proposed estimators perform satisfactorily even with the cascaded effects of non-linear PAs, and that the deployment of this RoF structure can enable new cost-effective opportunities for high-resolution positioning in indoor scenarios. In the numerical evaluation, we also use measured PMF characteristics for high-density polyethylene fibers.

Reciprocity-based, joint coherent downlink beamforming from multiple access points (APs) in distributed multiple-input multiple-output (MIMO) with independent local oscillators (LOs) requires the APs to be periodically phase-calibrated (a.k.a. phase-synchronized or phase-aligned). Such phase calibration can be accomplished by bidirectional over-the-air measurements between the APs. In this paper, we show how such over-the-air measurements can be integrated into the time-division duplexing (TDD) flow by appropriately shifting the uplink/downlink switching points of the TDD slot structure, creating short time segments during which APs can measure on one another. We also show how this technique scales to large networks. Furthermore, we analytically characterize the tradeoff between the amount of resources spent on calibration measurements and the resulting spectral efficiency of the system, when conjugate beamforming or zero-forcing beamforming is used. The results demonstrate the feasibility of distributed MIMO with phase-calibration through over-the-air inter-AP measurements integrated into the TDD flow, and the advantage of this design over schemes with dedicated calibration slots.

We propose a novel resource-efficient over-the-air (OTA) computation framework to address the huge fronthaul computational and control overhead requirements in cell-free massive multiple-input multiple-output (MIMO) networks. We show that the global sufficient statistics to decode the data symbols can be computed OTA using the locally available information at the access points (APs). We provide the essential signal processing aspects at the APs and the central processing unit (CPU) to facilitate the OTA computation of sufficient statistics. The proposed framework scales effectively with an increase in the number of APs. We also make a comprehensive study of the benefits of an OTA framework compared to a conventional digital fronthaul in terms of the overhead associated in transferring the sufficient statistics from the APs to the CPU. To evaluate the performance of the OTA framework, we give closed-form expressions for the mean-square error (MSE) of the estimators of sufficient statistics and the overall data estimator. Furthermore, we assess the symbol error rate (SER) and bit error rate (BER) of the user equipment (UEs) data to demonstrate the efficacy of our method, and benchmark them against the state-of-the-art wired fronthaul networks.

This paper presents a framework for channel estimation and beamformer design to jam adversarial drones using a monostatic massive multiple-input multiple-output (MIMO) base station (BS) architecture for the legitimate party. We adopt an iterative least-square (ILS) method to estimate the channels between the adversarial drones and the BS, as well as the radar cross-sections (RCSs) associated with the drones. Unlike existing literature, the developed algorithm is generic (i.e., agnostic to the underlying channel model) and can be translated to a parametric model as a special case. Furthermore, we propose a jamming beamforming optimization problem that aims to maximize the minimum jamming signal strength. The effectiveness of the proposed schemes is demonstrated through numerical simulations, which indicate that jamming performance close to perfect channel state information can be achieved with moderate to high values of RCS variance.

Broad beam beamforming (BF) design in multiple-input multiple-output (MIMO) can be convenient for initial access, synchronization, and sensing capabilities in cellular networks by avoiding overheads of sweeping methods while making efficient use of resources. Phase-only BF is key for maximizing power efficiency across antennas. A successful method to produce broad beams is the phase-only dual-polarization BF (DPBF). However, its efficiency has not been proved in non-line-of-sight (NLoS). Therefore, this paper contributes by evaluating DPBF in collocated and distributed MIMO configurations under both line-of-sight (LoS) and NLoS channel conditions. We model the reflection coefficients for different materials in NLoS conditions and propose the use of orthogonal space-time block code to improve the coverage compared to the DPBF in collocated MIMO (C-MIMO). We further propose a DPBF method for distributed MIMO and show that it achieves better coverage than C-MIMO with DPBF.

Phase synchronization between distributed antenna arrays requires measurements that break the standard time-division duplex (TDD) operation. We present a feasibility study on implementing such synchronization and analyze its impact on the quality of service. Considering two antenna arrays with independent local oscillators (LOs), we propose a modified TDD flow to accommodate the transmission of phase synchronization signals, formulate the phase estimation and compensation problem, and derive the achievable downlink spectral efficiency (SE). Numerical results show that frequent re-estimation of the inter-array phase disparity is essential for maximizing SE in systems with low-quality LOs. Furthermore, applying a Kalman filter for phase tracking substantially improves the SE, especially if phase estimation errors are large compared to LOs phase drifts.

This paper presents a comprehensive approach to federated learning in wireless networks. We discuss communication strategies that address packet loss and bitrate limitations in both uplink and downlink transmissions, and introduce FABLE, a novel optimization algorithm designed to operate effectively under these network constraints. The algorithm also supports non-smooth regularizers and accommodates heterogeneous data distributions across clients. We provide theoretical convergence guarantees under gradient compression and asynchronous operation, and demonstrate the efficiency of our approach through numerical experiments.

This paper studies integrated sensing and communication (ISAC) within the framework of dynamic time division duplex (DTDD) cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. DTDD enables the CF-mMIMO system to cater to both uplink (UL) and downlink (DL) users with spatially separated half-duplex (HD) access points (APs) using the same time-frequency resources. Thus, in our work, the same set of UL APs is utilized for both UL data and target echo reception, while the DL APs transmit jointly precoded DL data streams and the target symbol. In UL, we present a generalized likelihood-ratio test (GLRT) at the central processing unit (CPU) for target detection and use signal-to-noise-plus-interference (SINR) optimal combiner for UL data processing. In DL, we propose two types of precoder: one “communication-centric” that nullifies the interference caused by the target signal to the DL users; and one “target-centric” based on the dominant eigenvector of the composite channel between the target and the APs. Finally, we numerically investigate the performance of the GLRT and also sum UL-DL spectral efficiency (SE) of the communication users and benchmark the results with conventional TDD-based systems. We observe the GLRT is robust to inter-AP interference, and DTDD almost doubles the 90%-likely sum UL-DL SE compared to traditional TDD-based CF-mMIMO ISAC systems.

Decentralized learning enables distributed agents to collaboratively train a shared machine learning model without a central server, through local computation and peer-to-peer communication. Although each agent retains its dataset locally, sharing local models can still expose private information about the local training datasets to adversaries. To mitigate privacy attacks, a common strategy is to inject random artificial noise at each agent before exchanging local models between neighbors. However, this often leads to utility degradation due to the negative effects of cumulated artificial noise on the learning algorithm. In this work, we introduce CorN-DSGD, a novel covariance-based framework for generating correlated privacy noise across agents, which unifies several state-of-the-art methods as special cases. By leveraging network topology and mixing weights, CorN-DSGD optimizes the noise covariance to achieve network-wide noise cancellation. Experimental results show that CorN-DSGD cancels more noise than existing pairwise correlation schemes, improving model performance under formal privacy guarantees.

In this paper, we introduce a novel amplitude and phase reciprocity calibration protocol for distributed multi-antenna systems to enable coherent transmission from multiple access points (APs). The proposed scheme significantly enhances reciprocity calibration by resolving both phase and amplitude mismatches between APs, achieving near-optimal synchronization performance at practical signal-to-noise ratio (SNR) regimes. The protocol provides a closed-form solution for reciprocity calibration performed locally at each AP, maintaining low computational complexity while ensuring scalability and high-fidelity coherent transmission in large and dense distributed networks. The numerical results validate the robustness of the proposed technique, demonstrating accurate calibration and efficient transmission across various system configurations, including variations in the number of antennas and SNR values.

The dense and distributed deployment of sub-THz radio units (RUs) alongside sub-10 GHz access point (AP) is a promising approach to provide high data rate and reliable coverage for future 6G applications. However, beam search or RU selection for the sub-THz RUs incurs significant overhead and high power consumption. To address this, we introduce a method that leverages deep learning to infer a suitable sub-THz RU candidate from a set of sub-THz RUs using the sub-10 GHz channel characteristics. A novel aspect of this work is the consideration of inter-band beam configuration (IBBC), defined as the broadside angle between the low-band and high-band antenna patterns of the user equipment (UE). Since IBBC indicates the beamforming information or UE's orientation, it is typically not shared with the network as a part of signalling. Therefore, we propose a solution strategy to infer a suitable sub-THz RU even when UEs do not share their IBBC information. Simulation results illustrate the performance of the inferred sub-THz RU and highlights the detrimental impact of neglecting UE orientation on the systems performance.

Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without disclosing user-sensitive data. Specifically, in federated edge learning (FEEL) systems, the time-varying nature of wireless channels introduces inevitable system dynamics in the communication process, thereby affecting training latency and energy consumption. In this work, we further consider a streaming data scenario where new training data samples are randomly generated over time at edge devices. Our goal is to develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource availability under long-term energy constraints. To achieve this, we formulate a stochastic network optimization problem and use the Lyapunov drift-plus-penalty framework to obtain a dynamic resource management design. Our proposed algorithm makes adaptive decisions on device scheduling, computational capacity adjustment, and allocation of bandwidth and transmit power in every round. We provide convergence analysis for the considered setting with heterogeneous data and time-varying objective functions, which supports the rationale behind our proposed scheduling design. The effectiveness of our scheme is verified through simulation results, demonstrating improved learning performance and energy efficiency as compared to baseline schemes.

We consider a MIMO communication system operating in dynamic time-division duplex, where one uplink (UL) access point (AP) detects information symbols transmitted from UL users (UEs) in the presence of AP-AP interference (AAI) caused by the signal a downlink (DL) AP transmits to a DL UE. We propose to mitigate the interference by jointly estimating the UL symbols and the AAI channel, but show that this problem is not uniquely solvable in the least-squares sense, and then present two methods for symbol detection that overcome this issue. Both methods let the UL UEs be silent in the first sample of each coherence interval. The first method performs joint symbol detection and AAI channel estimation for the remaining samples, while the second method does AAI channel estimation followed by interference subtraction. Despite the different nature of the two methods, we show that they are equivalent and that the variance of each symbol estimate is doubled compared to the genie case where the AAI is fully canceled. Numerical results verify our findings and show that our proposed methods offer a significant reduction in terms of the bit-error rate compared with the naive method where the AAI is not canceled.

This work focuses on the communication perspective of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD). Considering the actual communication cost or delay caused by in-network information exchange in every iteration, our goal is to achieve fast convergence of the algorithm measured by improvement per transmission slot. We propose BASS, an efficient communication framework for D-SGD over wireless networks with broadcast-based subgraph sampling. More explicitly, in every iteration, we activate multiple subsets of non-interfering nodes to broadcast model updates to their neighbors. These subsets are activated randomly over time with some probabilities under a given communication cost (e.g., number of transmission slots per iteration). During the consensus update step, only bi-directional links are effectively considered to preserve the communication symmetry. As compared to existing link-based scheduling methods, the broadcasting nature of wireless channels provides inherent advantages in speeding up convergence of decentralized learning by creating more communicated links under the same number of transmission slots.

Backscatter communication (BC) has emerged as a key technology to satisfy the increasing need for low-cost and green Internet-of-Things (IoT) connectivity, especially in large-scale deployments. Unlike the monostatic BC (MoBC), the bistatic BC (BiBC) has the possibility to decrease the round-trip path loss by having the carrier emitter (CE) and the reader in different locations. Therefore, this work investigates the BiBC in the context of cell-free multiple-input multiple-output (MIMO) networks by exploring the optimal selection of CE and reader among all access points, leveraging prior knowledge about the area where the backscatter device (BD) is located. First, a maximum a posteriori probability (MAP) detector to decode the BD information bits is derived. Then, the exact probability of error for this detector is obtained. In addition, an algorithm to select the best CE-reader pair for serving the specified area is proposed. Finally, simulation results show that the error performance of the BC is improved by the proposed algorithm compared to the benchmark scenario.

We propose a novel resource efficient analog over-the-air (OTA) computation framework to address the demanding requirements of the uplink (UL) fronthaul between the access points (APs) and the central processing unit (CPU) in cell-free massive multiple-input multiple-output (MIMO) systems. We discuss the drawbacks of the wired and wireless fronthaul solutions, and show that our proposed mechanism is efficient and scalable as the number of APs increases. We present the transmit precoding and two-phase power assignment strategies at the APs to coherently combine the signals OTA in a spectrally efficient manner. We derive the statistics of the APs' locally available signals which enable us to to obtain the analytical expressions for the Bayesian and classical estimators of the OTA combined signals. We empirically evaluate the normalized mean square error (NMSE), symbol error rate (SER), and the coded bit error rate (BER) of our developed solution and benchmark against the state-of-the-art wired fronthaul based system.

Ultradense cell-free massive multiple-input multiple-output (CF-MMIMO) has emerged as a promising technology expected to meet the future ubiquitous connectivity requirements and ever-growing data traffic demands in sixth generation (6G). This article provides a contemporary overview of ultradense CF-MMIMO networks and addresses important unresolved questions on their future deployment. We first present a comprehensive survey of state-of-the-art research on CF-MMIMO and ultradense networks. Then, we discuss the key challenges of CF-MMIMO under ultradense scenarios such as low-complexity architecture and processing, low-complexity/scalable resource allocation, fronthaul limitation, massive access, synchronization, and channel acquisition. Finally, we answer key open questions, considering different design comparisons and discussing suitable methods dealing with the key challenges of ultradense CF-MMIMO. The discussion aims to provide a valuable roadmap for interesting future research directions in this area, facilitating the development of CF-MMIMO for 6G.

In distributed massive multiple-input multiple-output (MIMO) systems, multiple geographically separated access points (APs) communicate simultaneously with a user, leveraging the benefits of multi-antenna coherent MIMO processing and macro-diversity gains from the distributed setups. However, time and frequency synchronization of the multiple APs is crucial to achieve good performance and enable joint precoding. In this paper, we analyze the synchronization requirement among multiple APs from a reciprocity perspective, taking into account the multiplicative impairments caused by mismatches in radio frequency (RF) hardware. We demonstrate that a phase calibration of reciprocity-calibrated APs is sufficient for the joint coherent transmission of data to the user. To achieve synchronization, we propose a novel over-the-air synchronization protocol, named BeamSync, to calibrate the geographically separated APs without sending any measurements to the central processing unit (CPU) through fronthaul. We show that sending the synchronization signal in the dominant direction of the channel between APs is optimal. Additionally, we derive the optimal phase and frequency offset estimators. Simulation results indicate that the proposed BeamSync method enhances performance by 3 dB when the number of antennas at the APs is doubled. Moreover, the method performs well compared to traditional beamforming techniques.

We consider massive multiple-input multiple-output (MIMO) systems in the presence of Cauchy noise. First, we focus on the channel estimation problem. In the standard massive MIMO setup, the users transmit orthonormal pilots during the training phase and the received signal at the base station is projected onto each pilot. This processing is optimum when the noise is Gaussian. We show that this processing is not optimal when the noise is Cauchy and as a remedy propose a channel estimation technique that operates on the raw received signal. Second, we derive uplink-downlink achievable rates in the presence of Cauchy noise for perfect and imperfect channel state information. Finally, we derive log-likelihood ratio expressions for soft bit detection for both uplink and downlink, and simulate coded bit-error-rate curves. In addition to this, we derive and compare the symbol detectors in the presence of both Gaussian and Cauchy noises. An important observation is that the detector constructed for Cauchy noise performs well with both Gaussian and Cauchy noises; on the other hand, the detector for Gaussian noise works poorly in the presence of Cauchy noise. In other words, the Cauchy detector is robust against heavy-tailed noise, whereas the Gaussian detector is not.

Backscatter communication (BC) is a promising technique for future Internet-of-Things (IoT) owing to its low complexity, low cost, and potential for energy-efficient operation in sensor networks. There are several network infrastructure setups that can be used for BC with IoT nodes. One of them is the bistatic setup where typically there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader. In this paper, we investigate a bistatic BC setup with multiple antennas. We propose a novel transmission scheme, which includes a protocol for channel estimation at the carrier emitter (CE) as well as a transmit beamformer construction that suppresses the direct link interference between the two ends of a bistatic link (namely CE and reader), and increases the detection performance of the backscatter device (BD) symbol. Further, we derive a generalized log-likelihood ratio test (GLRT) to detect the symbol/presence of the BD. We also provide an iterative algorithm to estimate the unknown parameters in the GLRT. Finally, simulation results show that the required dynamic range of the system is significantly decreased, and the detection performance of the BD symbol is increased, by the proposed algorithm compared to a system not using beamforming at the CE.

Distributed tensor decomposition (DTD) is a fundamental data-analytics technique that extracts latent important properties from multi-attribute datasets distributed over edge devices. Its conventional one-shot implementation with over-the-air computation (AirComp) is confronted with the issues of limited storage-and-computation capacities and link interruption, which motivates us to propose a framework of on-the-fly communication-and-computing (FlyCom2) in this work. The proposed framework enables streaming computation with low complexity by leveraging a random sketching technique and achieves progressive global aggregation through the integration of progressive uploading and multiple-input-multiple-output (MIMO) AirComp. To develop FlyCom2, an on-the-fly sub-space estimator is designed to take real-time sketches accumulated at the server to generate online estimates for the decomposition. Its performance is evaluated by deriving both deterministic and probabilistic error bounds, which reveal the scaling laws of the decomposition error and inspire a threshold-based scheme to select reliably received sketches. Experimental results validate the performance gain of the proposed selection algorithm and show that compared to its one-shot counterparts, FlyCom2achieves comparable (even better with large eigen-gaps) decomposition accuracy besides dramatically reducing devices' complexity costs.

We consider a robust beamforming problem where large amount of downlink (DL) channel state information (CSI) data available at a multiple antenna access point (AP) is used to improve the link quality to a user equipment (UE) for beyond-5G and 6G applications such as environment-specific initial access (IA) or wireless power transfer (WPT). As the DL CSI available at the current instant may be imperfect or outdated, we propose a novel scheme which utilizes the (unknown) correlation between the antenna domain and physical domain to localize the possible future UE positions from the historical CSI database. Then, we develop a codebook design procedure to maximize the minimum sum beamforming gain to that localized CSI neighborhood. We also incorporate a UE specific parameter to enlarge the neighborhood to robustify the link further. We adopt an indoor channel model to demonstrate the performance of our solution, and benchmark against a usually optimal (but now sub-optimal due to outdated CSI) maximum ratio transmission (MRT) and a subspace based method. We numerically show that our algorithm outperforms the other methods by a large margin. This shows that customized environment-specific solutions are important to solve many future wireless applications, and we have paved the way to develop further data-driven approaches.

Impulsive noise can appear in communication links. In the literature, it was demonstrated that when the noise is impulsive, standard Gaussian receivers perform poorly because of the outliers in the noise. Therefore, appropriate receivers must be used when the noise is impulsive. In this paper, we compare two types of massive multiple- input multiple-output (MIMO) receivers, namely those based on a Gaussian-mixture assumption and those based on a Cauchy assumption, in terms of channel estimation quality, when the noise is impulsive. Symmetric α-stable (SαS) noises are used to model impulsive noises in the paper. In the numerical results, the Gaussian-mixture receiver outperforms the Cauchy-based receiver.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is a next-generation wireless access technology that offers superior coverage and spectral efficiency compared to conventional MIMO. With many future applications in unlicensed spectrum bands, networks will likely experience and may even be limited by out-of-system (OoS) interference. The OoS interference differs from the in-system interference from other serving users in that for OoS interference, the associated pilot signals are unknown or non-existent, which makes estimation of the OoS interferer channel difficult.In this paper, we propose a novel sequential algorithm for the suppression of OoS interference for uplink CF-mMIMO with a stripe (daisy-chain) topology. The proposed method has comparable performance to that of a fully centralized interference rejection combining algorithm but has substantially less fronthaul load requirements.

The number of wireless devices is drastically increasing, resulting in many devices contending for radio resources. In this work, we present an algorithm to detect active devices for unsourced random access, i.e., the devices are uncoordinated. The devices use a unique, but non-orthogonal preamble, known to the network, prior to sending the payload data. They do not employ any carrier sensing technique and blindly transmit the preamble and data. To detect the active users, we exploit partial channel state information (CSI), which could have been obtained through a previous channel estimate. For static devices, e.g., Internet of Things nodes, it is shown that CSI is less time-variant than assumed in many theoretical works. The presented iterative algorithm uses a maximum likelihood approach to estimate both the activity and a potential phase offset of each known device. The convergence of the proposed algorithm is evaluated. The performance in terms of probability of miss detection and false alarm is assessed for different qualities of partial CSI and different signal-to-noise ratio.

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents. In this work, we propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. Considering limited wireless communication resources, we investigate the effect of different scheduling policies and aggregation designs on the convergence performance. Driven by the importance of reducing the bias and variance of the aggregated model updates, we propose a scheduling policy that jointly considers the channel quality and training data representation of user devices. The effectiveness of our channel-aware data-importance-based scheduling policy, compared with state-of-the-art methods proposed for synchronous FL, is validated through simulations. Moreover, we show that an “age-aware” aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.

We consider distributed average consensus in a wireless network with partial communication to reduce the number of transmissions in every iteration/round. Considering the broadcast nature of wireless channels, we propose a probabilistic approach that schedules a subset of nodes for broadcasting information to their neighbors in every round. We compare several heuristic methods for assigning the node broadcast probabilities under a fixed number of transmissions per round. Furthermore, we introduce a pre-compensation method to correct the bias between the consensus value and the average of the initial values, and suggest possible extensions for our design. Our results are particularly relevant for developing communication-efficient consensus protocols in a wireless environment with limited frequency/time resources.

The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.

An intelligent reflecting surface (IRS) is a cost and energy-efficient solution to improve wireless system performance. Transmit antenna selection (AS) harnesses the benefits of multiple antennas with a smaller number of radio frequency (RF) chains. We focus on joint optimization of antenna subset and transmit beamforming at the transmitter (Tx) and passive beamforming at the IRS to maximize the receive signal power. We derive a closed-form optimal AS rule for a Tx and receiver (Rx) equipped with single RF chain each and ideal IRS. We analyze its performance with a correlated channel model and then extend it to non-ideal IRS. We also propose a simpler rule that significantly reduces the number of computations and pilots. For an Rx that performs maximal ratio combining, we propose a manifold optimization algorithm and a low-complexity selection rule. For a Tx with multiple RF chains, we propose a subset selection algorithm that yields a locally optimal solution and an alternating optimization algorithm that reduces complexity. Our simulations study the impact of estimation errors, discrete phase shifts, and channel correlation on the proposed selection rules, which perform better than the existing AS rules. They also show that the proposed low-complexity rules are near-optimal.

Backscatter communication (BSC) is a promising solution for Internet-of-Things (IoT) connections due to its low-complexity, low-cost, and energy-efficient solution for sensors. There are several network infrastructure setups that can be used for BSC with IoT nodes/passive devices. One of them is a bistatic setup where there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader side. In this paper, we investigate a bistatic BSC setup with multiple antennas. We propose a novel algorithm to suppress direct link interference between the carrier emitter (CE) and the reader using beamforming into the nullspace of the CE-reader direct link to decrease the dynamic range of the system and increase the detection performance of the backscatter device (BSD). Further, we derive a Neyman-Pearson (NP) test and an exact closed-form expression for its performance in the detection of the BSD. Finally, simulation results show that the dynamic range of the system is significantly decreased and the detection performance of the BSD is increased by the proposed algorithm compared to a system not using beamforming in the CE, which could then be used in a host of different practical fields such as agriculture, transportation, factories, hospitals, smart cities, and smart homes.

In this paper, we work on channel estimation techniques for massive multiple-input multiple-output (MIMO) with Cauchy noise. In the standard massive MIMO setup, the users transmit orthonormal pilots during the training phase and the received signal in the base station is projected onto each orthonormal pilot signal. This process is optimum when the noise is Gaussian. In other words, the obtained signal after this process is the sufficient statistic and we do not lose any information. We show that this process is not optimum when the noise is Cauchy. Hence, we propose a channel estimation technique for the unprocessed received signal. The proposed channel estimation technique is compared with the channel estimates that are obtained from the projected signal.

RadioWeaves network operates a large number of distributed antennas using cell-free architecture to provide high data rates and support a large number of users. Operating this network in an energy-efficient manner in the limited available spectrum is crucial. Therefore, we consider energy efficiency (EE) maximization of a RadioWeaves network that shares spectrum with a collocated primary network in underlay mode. To simplify the problem, we lower bound the non-convex EE objective function to form a convex problem. We then propose a downlink power allocation policy that maximizes the EE of the secondary RadioWeaves network subject to power constraint at each access point and interference constraint at each primary user. Our numerical results investigate the secondary system’s performance in interference, power, and EE constrained regimes with correlated fading channels. Furthermore, they show that the proposed power allocation scheme performs significantly better than the simpler equal power allocation scheme.

Massive multiple input multiple output (MIMO) emerged as the leading technology for supporting fifth generation (5G) and beyond 5G cellular communication systems. Due to the tremendous increase in data traffic in urban areas and to meet such a significant demand, most studies consider macro/micro cell deployments in urban environments. Internet service providers (ISPs) are less interested in providing communication services in rural areas considering the relatively low profits compared to the deployment and maintenance costs. In this paper, we investigate the massive MIMO performance in rural scenarios. In particular, we investigate different aspects to consider while designing a long-range communication system. We propose to use elevated base station (BS) with sectorized antennas with unusually large aperture and implement a user scheduling algorithm at the BS to provide full digital coverage. We analyze the coverage range of a massive MIMO system to provide high-rate services. Furthermore, we also analyze the link budget requirements and the rates users can achieve in such a SuperCell massive MIMO network.

RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next-generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signal-to-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves.

We study downlink channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in time-division duplex. The users must know their effective channel gains to decode their received downlink data. Previous works have used the mean value as the estimate, motivated by channel hardening. However, this is associated with a performance loss in non-isotropic scattering environments. We propose two novel estimation methods that can be applied without downlink pilots. The first method is model-based and asymptotic arguments are utilized to identify a connection between the effective channel gain and the average received power during a coherence interval. The second method is data-driven and trains a neural network to identify a mapping between the available information and the effective channel gain. Both methods can be utilized for any channel distribution and precoding. For the model-aided method, we derive all expressions in closed form for the case when maximum ratio or zero-forcing precoding is used. We compare the proposed methods with the state-of-the-art using the normalized mean-squared error and spectral efficiency (SE). The results suggest that the two proposed methods provide better SE than the state-of-the-art when there is a low level of channel hardening, while the performance difference is relatively small with the uncorrelated channel model.

Reciprocity-based time-division duplex (TDD) Massive MIMO (multiple-input multiple-output) systems utilize channel estimates obtained in the uplink to perform precoding in the downlink. However, this method has been criticized of breaking down, in the sense that the channel estimates are not good enough to spatially separate multiple user terminals, at low uplink reference signal signal-to-noise ratios, due to insufficient channel estimation quality. Instead, codebook-based downlink precoding has been advocated for as an alternative solution in order to bypass this problem. We analyze this problem by considering a “grid-of-beams world” with a finite number of possible downlink channel realizations. Assuming that the terminal accurately can detect the downlink channel, we show that in the case where reciprocity holds, carefully designing a mapping between the downlink channel and the uplink reference signals will perform better than both the conventional TDD Massive MIMO and frequency-division duplex (FDD) Massive MIMO approach. We derive elegant metrics for designing this mapping, and further, we propose algorithms that find good sequence mappings.

The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multi-antenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a learning framework that suits beyond 5G and towards 6G systems. This work looks into a future scenario in which there are multiple groups with different learning purposes and participating in different FL processes. We give energy-efficient solutions to demonstrate that this scenario can be realistic. First, to ensure a stable operation of multiple FL processes over wireless channels, we propose to use a massive multiple-input multiple-output network to support the local and global FL training updates, and let the iterations of these FL processes be executed within the same large-scale coherence time. Then, we develop asynchronous and synchronous transmission protocols where these iterations are asynchronously and synchronously executed, respectively, using the downlink unicasting and conventional uplink transmission schemes. Zero-forcing processing is utilized for both uplink and downlink transmissions. Finally, we propose an algorithm that optimally allocates power and computation resources to save energy at both base station and user sides, while guaranteeing a given maximum execution time threshold of each FL iteration. Compared to the baseline schemes, the proposed algorithm significantly reduces the energy consumption, especially when the number of base station antennas is large.

Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.

Novel network architectures for 6G distributed massive MIMO systems rely on coherent signaling by distributed antenna panels which are coordinated by a central controller. This type of network architecture is based on reciprocity operation where antenna panels rely on uplink channel estimates for coherent downlink precoding. This paper proposes a calibration method for distributed massive MIMO systems, which overcomes hardware non-reciprocities in order to enable reciprocity-based operation. Measurements for system calibration are collected via a beam-sweep between all pairs of antenna panels. We lay out the system model for this new setup, and propose a maximum likelihood-based procedure to compute calibration coefficients based on the collected measurement set. The procedure is computationally efficient and stable, since 1) each iteration has a closed-form, and 2) the procedure is guaranteed to converge to at least a local optimum (or saddle point). Simulations indicate significant calibration improvements compared to re-using state of the art calibration schemes for the problem at hand.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors. In this letter, we look into link-based network metrics which are polynomial functions of pair-wise node attributes defined over the links in a network. Different from node-based average consensus, such link-based metrics depend on both the distribution of node attributes and the underlying network topology. We propose a general algorithm using the weighted average consensus protocol for the distributed computation of link-based network metrics and provide the convergence conditions and convergence rate analysis.

In this paper, we investigate optimal downlink power allocation in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA massive MIMO, a base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a network-wide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA massive MIMO. For some specific scenarios, DAA massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA massive MIMO.

This paper investigates the performance of limited-fronthaul cell-free massive multiple-input multiple-output (MIMO) taking account the fronthaul quantization and imperfect channel acquisition. Three cases are studied, which we refer to as Estimate & Quantize, Quantize & Estimate, and Decentralized, according to where channel estimation is performed and exploited. Maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) receivers are considered. The Max algorithm and the Bussgang decomposition are exploited to model optimum uniform quantization. Exploiting the optimal step size of the quantizer, analytical expressions for spectral and energy efficiencies are presented. Finally, an access point (AP) assignment algorithm is proposed to improve the performance of the decentralized scheme. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting relatively few quantization bits, the performance of limited-fronthaul cell-free massive MIMO closely approaches the perfect-fronthaul performance.

Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.

In this paper, we evaluate the uplink spectral efficiency (SE) of a single-cell massive multiple-input-multiple-output (MIMO) system with distributed jammers. We define four different attack scenarios and compare their impact on the massive MIMO system as well as on a conventional single-input-multiple-output (SIMO) system. More specifically, the jammers attack the base station (BS) during both the uplink training phase and data phase. The BS uses either least squares (LS) or linear minimum mean square error (LMMSE) estimators for channel estimation and utilizes either maximum-ratio-combining (MRC) or zero-forcing (ZF) decoding vectors. We show that ZF gives higher SE than MRC but, interestingly, the performance is unaffected by the choice of the estimators. The simulation results show that the performance loss percentage of massive MIMO is less than that of the SIMO system. Moreover, we consider two types of power control algorithms: jamming-aware and jamming-ignorant. In both cases, we consider the max-min and proportional fairness criteria to increase the uplink SE of massive MIMO systems. We notice numerically that max-min fairness is not a good option because if one user is strongly affected by the jamming, it will degrade the other users' SE as well. On the other hand, proportional fairness improves the sum SE of the system compared with the full power transmission scenario.

In cell-free massive multiple-input multiple-output (MIMO) the fluctuations of the channel gain from the access points to a user are large due to the distributed topology of the system. Because of these fluctuations, data decoding schemes that treat the channel as deterministic perform inefficiently. A way to reduce the channel fluctuations is to design a precoding scheme that equalizes the effective channel gain seen by the users. Conjugate beamforming (CB) poorly contributes to harden the effective channel at the users. In this work, we propose a variant of CB dubbed enhanced normalized CB (ECB), in that the precoding vector consists of the conjugate of the channel estimate normalized by its squared norm. For this scheme, we derive an exact closed-form expression for an achievable downlink spectral efficiency (SE), accounting for channel estimation errors, pilot reuse and user's lack of channel state information (CSI), assuming independent Rayleigh fading channels. We also devise an optimal max-min fairness power allocation based only on large-scale fading quantities. ECB greatly boosts the channel hardening enabling the users to reliably decode data relying only on statistical CSI. As the provided effective channel is nearly deterministic, acquiring CSI at the users does not yield a significant gain.

Cell-free massive multiple-input-multiple-output (mMIMO) is an emerging technology for beyond 5G with its promising features such as higher spectral efficiency and superior spatial diversity as compared to conventional multiple-input-multiple-output (MIMO) technology. The main working principle of cell-free mMIMO is that many distributed access points (APs) cooperate simultaneously to serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture with a sequential fronthaul between the APs. A novel uplink sequential processing algorithm is developed, which is proved to be optimal in both the maximum spectral efficiency (SE) and the minimum mean square error (MSE) sense. A detailed quantitative analysis of the fronthaul requirement or signaling of the proposed algorithm and its comparison with competing sub-optimal algorithms is provided. Key conclusions and implications are summarized in the form of corollaries. Based on the analytical and numerical simulation results, we conclude that the proposed scheme can significantly reduce the fronthaul signaling, without compromising the communication performance.

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple PU receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.

Reconfigurable Intelligent Surface (RIS) is a promising solution to reconfigure the wireless environment in a controllable way. To compensate for the double-fading attenuation in the RIS-aided link, a large number of passive reflecting elements (REs) are conventionally deployed at the RIS, resulting in large surface size and considerable circuit power consumption. In this paper, we propose a new type of RIS, called active RIS, where each RE is assisted by active loads (negative resistance), that reflect and amplify the incident signal instead of only reflecting it with the adjustable phase shift as in the case of a passive RIS. Therefore, for a given power budget at the RIS, a strengthened RIS-aided link can be achieved by increasing the number of active REs as well as amplifying the incident signal. We consider the use of an active RIS to a single input multiple output (SIMO) system. However, it would unintentionally amplify the RIS-correlated noise, and thus the proposed system has to balance the conflict between the received signal power maximization and the RIS-correlated noise minimization at the receiver. To achieve this goal, it has to optimize the reflecting coefficient matrix at the RIS and the receive beamforming at the receiver. An alternating optimization algorithm is proposed to solve the problem. Specifically, the receive beamforming is obtained with a closed-form solution based on linear minimum-mean-square-error (MMSE) criterion, while the reflecting coefficient matrix is obtained by solving a series of sequential convex approximation (SCA) problems. Simulation results show that the proposed active RIS-aided system could achieve better performance over the conventional passive RIS-aided system with the same power budget.

Conventionally, a substantial number of reflecting elements (REs) is deployed at the intelligent reflecting surface (IRS) to mitigate the effect of the double-fading attenuation in the IRS-aided link, leading to a large surface size and considerable power consumption. In this paper, a new type of IRS, called active IRS, is proposed to solve this challenge by allowing each RE to amplify the incident signal with the assistance of the active loads (negative resistances). Thus, given a power budget at the IRS, the IRS-aided link can be enhanced by increasing the number of active REs as well as amplifying the incident signal. Specifically, we consider the use of an active IRS-aided single input multiple output (SIMO) system, in which the received signal-to-noise ratio (SNR) is maximized, by optimizing not only the reflecting coefficient matrix at the IRS but also the receive beamforming at the receiver. To solve this non-convex problem, we propose an alternating optimization algorithm, that iteratively optimizes the two design variables. In particular, the receive beamforming is founded to be in the form of a linear minimum mean square error (MMSE) detector, and the reflecting coefficient matrix is obtained via the Charnes-Cooper transformation and the semi-definite programming (SDP). Simulation results show that under a practical power consumption model, the proposed active IRS-aided system achieves better performance over the conventional passive IRS-aided system with the same power budget.

Lately, Passive Intelligent Surfaces (PIS) are being recognized to play an important role in meeting the timely demand of low-cost green sustainable Internet of Things (IoT). In this paper, we focus on maximizing the sum received power among the energy harvesting IoT users by jointly optimizing the active precoder for multi-antenna power beacon and the passive constant-envelope precoding based phase shifters (PS) design for PIS. Here, a multiuser channel estimation protocol is first introduced to obtain the least-squares estimators for the underlying effective cascaded channel links involved in the PIS assisted multi-antenna wireless power transfer as desired for the optimal precoder and PS designing. Thereafter, new semi-closed-form expressions for the proposed optimal active and passive beamforming design are derived so as to meet the low-complexity requirements of IoT communications. Finally, the numerical results are presented to validate the key nontrivial analytical claims and demonstrate the significant performance enhancement in terms of sum harvested energy among IoT users over conventional designs.

A large-scale distributed antenna system that serves the users by coherent joint transmission is called Cell-free Massive MIMO (multiple input multiple output). For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the downlink, considering both the hardware and transmit powers, where APs can be turned off. Even though this is a non-convex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program. We also propose a low-complexity algorithm that exploits group-sparsity in the problem formulation. Numerical results manifest that our optimization framework can greatly reduce the power consumption compared to keeping all APs turned on and only minimizing the transmit powers.

In this paper we consider joint beamforming of data to scheduled terminals (STs) and broadcast of system information (SI) to idle terminals (ITs) on the same time-frequency resource in multi-cell multi-user massive MIMO systems. We propose two different multi-cell approaches to the broadcast of SI, i) synchronous broadcast of same SI symbols from all cells (SAME-SI), and ii) synchronous broadcast of statistically independent SI symbols from each cell (DIFF-SI). We also consider the traditional orthogonal access (OA) approach where a fraction of physical resource is reserved for broadcast of SI. Through analysis and simulations, it is observed that for both SAME-SI and DIFF-SI, joint beamforming and broadcasting (JBB) is more energy efficient than OA.

An intelligent reflecting surface (IRS), consisting of reconfigurable metamaterials, can be used to partially control the radio environment and thereby bring new features to wireless communications. Previous works on IRS have particularly studied the range extension use case and under what circumstances the new technology can beat relays. In this paper, we study another use case that might have a larger impact on the channel capacity: rank improvement. One of the classical bottlenecks of point-to-point MIMO communications is that the capacity gains provided by spatial multiplexing are only large at high SNR, and high SNR channels are mainly appearing in line-of-sight (LoS) scenarios where the channel matrix has low rank and therefore does not support spatial multiplexing. We demonstrate how an IRS can be used and optimized in such scenarios to increase the rank of the channel matrix, leading to substantial capacity gains.

In this paper, we analyze the uplink spectral efficiency (SE) of an arbitrary user in a single-cell massive multiple-input-multiple-output (MIMO) system in which there are jammers randomly distributed. We compare this SE with that of a user in a single-input-multiple-output (SIMO) system. We utilize two types of receivers that are widely used in massive MIMO literature: Maximum-ratio-combining (MRC) and zero-forcing (ZF). The jammers attack the base station (BS) during the training and data transmission phases. In order to estimate the channel vectors of the legitimate users, the BS uses either the linear minimum mean square error (LMMSE) estimator which requires information about the jamming power or the least squares (LS) estimator which does not require any knowledge about the jamming signals. We show that ZF gives higher SE than MRC, but interestingly the performance is unaffected by the choice of the estimators. Moreover, we derive the closed form signal-to-interference-noise ratio (SINR) for the MRC receiver when the jammers attack to the BS and based on this SINR expression we utilize power control algorithms that achieve max-min fairness and proportional fairness.

Cognitive radio (CR) is an effective solution to increase the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share the spectrum with primary users (PUs). On the other hand, intelligent reflecting surface (IRS) is a promising approach to enhance the energy efficiency (EE) of wireless communication systems through passively reconfiguring the channel environments. In this paper, we propose an IRS enhanced downlink multiple-input single-output (MISO) CR systems to improve both SE and EE, where a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Specifically, for the MISO-CR system, we maximize the achievable rate of SU subject to a total power constraint on an SU transmitter (SU-TX) and an interference temperature (IT) constraint on PU-RXs, by jointly optimizing the beamforming vector at SU-TX and the phase shifts at the IRS. Furthermore, both perfect channel state information (CSI) and imperfect CSI are considered in the optimization. Numerical results demonstrate that the IRS can significantly improve the achievable rate of SU-RX under both the perfect and imperfect CSI conditions.

In this work, an implementation of a pilot-hopping sequence detector for massive machine type communication is presented. The architecture is based on solution a non-negative least squares problem. The results show that the architecture supporting 1024 users can perform more than one million detections per second with a power consumption of less than 70 mW when implemented in a 28 nm FD-SOI process.

This paper studies the transmit power optimization in multi-cell Massive multiple-input multiple-output (MIMO) systems. Network-wide max-min fairness (NW-MMF) and network-wide proportional fairness (NW-PF) are two well-known power control schemes in the literature. The NW-MMF focus on maximizing the fairness among users at the cost of penalizing users with good channel conditions. On the other hand, the NW-PF focuses on maximizing the sum SE, thereby ignoring fairness, but gives some extra attention to the weakest users. However, both of these schemes suffer from a scalability issue which means that for large networks, it is highly probable that one user has a very poor channel condition, pushing the spectral efficiency (SE) of all users towards zero. To overcome the scalability issue of NW-MMF and NW-PF, we propose a novel power control scheme that is provably scalable. This scheme maximizes the geometric mean (GM) of the per-cell max-min SE. To solve this new optimization problem, we prove that it can be rewritten in a convex optimization form and then solved using standard tools. The simulation results highlight the benefits of our model which is balancing between NW-PF and NW-MMF.

We consider joint beamforming of data to scheduled terminals (STs) and broadcast of system information (SI) to idle terminals (ITs) on the same time-frequency resource in multi-cell multi-user massive MIMO systems. We consider two different types of SI broadcast, i) synchronous broadcast of common (i.e., same) SI symbols from all cells, and ii) synchronous broadcast of cell-specific SI symbols from each cell. Through analysis we derive expressions for the achievable sum rate to STs in each cell and the rate of SI transmission to an IT for both these types of SI broadcast. We also derive expressions for the sum rate to STs and the rate to an IT for traditional orthogonal access (OA) where a fraction of physical resource is reserved for broadcast of SI. Simulations reveal that, just as in the single-cell scenario, for the multi-cell scenario also, joint beamforming and broadcasting (JBB) is more energy efficient than OA.

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) enabled heterogeneous small cells network (HSCN), where the macro base station simultaneously communicates with multiple small cell base stations (SBSs) through wireless backhaul. In each small cell, users are grouped by NOMA bases and then served by their respective SBS. The proposed framework considers the realistic imperfect channel state information and quality of service requirements of users. The goal is to investigate an energy-efficient joint power, and bandwidth allocation scheme, which aims to maximize the energy efficiency (EE) of the small cells in downlink NOMA-HSCN constrained by the maximum transmit power and the minimum required data rate simultaneously. The optimization problem is non-convex due to the fractional objective function and non-convex constraint and thus challenging to obtain an exact solution efficiently. To this end, the joint optimization is first decomposed into two subproblems. Then, an iterative algorithm to solve the power optimization subproblem is proposed with guaranteed convergence. Furthermore, we derive a closed-form solution for the bandwidth allocation subproblem. Simulation results reveal that the effectiveness of the proposed schemes in terms of EE compared to the existing NOMA and the orthogonal multiple access schemes.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single-antenna systems. Owing to the many antennas, low-cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with low-resolution analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with low-resolution ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic capacity. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.

Large-scale distributed antenna systems with many access points (APs) that serve the users by coherent joint transmission is being considered for 5G-and-beyond networks. The technology is called Cell-free Massive MIMO and can provide a more uniform service level to the users than a conventional cellular topology. For a given user set, only a subset of the APs is likely needed to satisfy the users' performance demands, particularly outside the peak traffic hours. To find achieve an energy-efficient load balancing, we minimize the total downlink power consumption at the APs, considering both the transmit powers and hardware dissipation. APs can be temporarily turned off to reduce the latter part. The formulated optimization problem is non-convex but, nevertheless, a globally optimal solution is obtained by solving a mixed-integer second-order cone program. Since the computational complexity is prohibitive for real-time implementation, we also propose two low-complexity algorithms that exploit the inherent group-sparsity and the optimized transmit powers in the problem formulation. Numerical results manifest that our optimization algorithms can greatly reduce the power consumption compared to keeping all APs turned on and only minimizing the transmit powers. Moreover, the low-complexity algorithms can effectively handle the power allocation and AP activation for large-scale networks.

This paper considers the sum spectral efficiency (SE) optimization problem in multi-cell Massive MIMO systems with a varying number of active users. This is formulated as a joint pilot and data power control problem. Since the problem is non-convex, we first derive a novel iterative algorithm that obtains a stationary point in polynomial time. To enable real-time implementation, we also develop a deep learning solution. The proposed neural network, PowerNet, only uses the large-scale fading information to predict both the pilot and data powers. The main novelty is that we exploit the problem structure to design a single neural network that can handle a dynamically varying number of active users; hence, PowerNet is simultaneously approximating many different power control functions with varying number inputs and outputs. This is not the case in prior works and thus makes PowerNet an important step towards a practically useful solution. Numerical results demonstrate that PowerNet only loses 2% in sum SE, compared to the iterative algorithm, in a nine-cell system with up to 90 active users per in each coherence interval, and the runtime was only 0.03 ms on a graphics processing unit (GPU). When good data labels are selected for the training phase, PowerNet can yield better sum SE than by solving the optimization problem with one initial point.

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.

Cell-free Massive MIMO (multiple-input multiple-output) is a promising distributed network architecture for 5G-and-beyond systems. It guarantees ubiquitous coverage at high spectral efficiency (SE) by leveraging signal co-processing at multiple access points (APs), aggressive spatial user multiplexing and extraordinary macro-diversity gain. In this study, we propose two distributed precoding schemes, referred to as local partial zero-forcing (PZF) and local protective partial zero-forcing (PPZF), that further improve the spectral efficiency by providing an adaptable trade-off between interference cancelation and boosting of the desired signal, with no additional front-hauling overhead, and implementable by APs with very few antennas. We derive closed-form expressions for the achievable SE under the assumption of independent Rayleigh fading channel, channel estimation error and pilot contamination. PZF and PPZF can substantially outperform maximum ratio transmission and zero-forcing, and their performance is comparable to that achieved by regularized zero-forcing (RZF), which is a benchmark in the downlink. Importantly, these closed-form expressions can be employed to devise optimal (long-term) power control strategies that are also suitable for RZF, whose closed-form expression for the SE is not available.

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. This work focuses on the downlink of an IRS-aided multiuser multiple-input single-output (MISO) system. A practical IRS assumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. This non-convex problem is firstly decomposed via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. In addition, an efficient algorithm with closed-form solutions is proposed for the passive beamforming, which is applicable to both the discrete phase- shift IRS and the continuous phaseshift IRS. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.

In this paper, we study an active user detection problem for massive machine type communications (mMTC). The users transmit pilot-hopping sequences and detection of active users is performed based on the received energy. We utilize the channel hardening and fa-vorable propagation properties of massive multiple-input multiple-output (MIMO) to simplify the user detection. We propose and compare a number of different user detection methods and find that using non-negative least squares (NNLS) is well suited for the task at hand as it achieves good results as well as having the benefit of not having to specify further parameters.

This paper studies the transmit power optimization in a multi-cell massive multiple-input multiple-output (MIMO) system. To over-come the scalability issue of network-wide max-min fairness (NW-MMF), we propose a novel power control (PC) scheme. This scheme maximizes the geometric mean (GM) of the per-cell max-min spectral efficiency (SE). To solve this new optimization problem, we prove that it can be rewritten in a convex form and then solved using standard tools. To provide a fair comparison with the available utility functions in the literature, we solve the network-wide proportional fairness (NW-PF) PC as well. The NW-PF focuses on maximizing the sum SE, thereby ignoring fairness, but gives some extra attention to the weakest users. The simulation results highlight the benefits of our model which is balancing between NW-PF and NW-MMF.

Backscatter communication (BSC) is emerging as the core technology for pervasive sustainable internet-of-things applications. However, owing to the resource-limitations of passive tags, this work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the full-duplex multiantenna reader and backscattering coefficients (BC) at the single antenna tags. Despite this joint optimization problem being non-convex, we present low-complexity joint TRX-BC designs by exploring the asymptotically-optimal solutions in low and high signal-to-noise-ratio regimes. We discourse that with precoder and detector designs at the reader respectively targeting downlink energy beam-forming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Selected computer simulations are presented to validate the analytical claims, shed optimal-design insights, and demonstrate the throughput enhancement of around 20% over the relevant benchmark schemes.

In this paper, we study the joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with random data arrivals. The data is generated at each user according to an individual stochastic process. Using Lyapunov optimization techniques, we develop a dynamic scheduling algorithm (DSA), which decides at each time slot the amount of data to admit to the transmission queues and the transmission rates over the wireless channel. The proposed algorithm achieves nearly optimal performance on the long-term user throughput under various fairness policies. Simulation results show that the DSA can improve the time-average delay performance compared to the state-of-the-art power control schemes developed for Massive MIMO with infinite backlogs.

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

This paper investigates the joint data and pilot power optimization for maximum sum spectral efficiency (SE) in multi-cell Massive MIMO systems, which is a non-convex problem. We first propose a new optimization algorithm, inspired by the weighted minimum mean square error (MMSE) approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime, making it suitable for online optimization in real multi-cell Massive MIMO systems. The numerical result demonstrates that the solution obtained by the neural network is 1% less than the stationary point for four-cell systems, while the sum SE loss is 2% in a nine-cell system.

Ubiquitous cell-free massive MIMO (multiple-input multiple-output) combines massive MIMO technology and user-centric transmission in a distributed architecture. All the access points (APs) in the network cooperate to jointly and coherently serve a smaller number of users in the same time-frequency resource. However, this coordination needs significant amounts of control signalling which introduces additional overhead, while data co-processing increases the back/front-haul requirements. Hence, the notion that the “whole world” could constitute one network, and that all APs would act as a single base station, is not scalable. In this study, we address some system scalability aspects of cell-free massive MIMO that have been neglected in literature until now. In particular, we propose and evaluate a solution related to data processing, network topology and power control. Results indicate that our proposed framework achieves full scalability at the cost of a modest performance loss compared to the canonical form of cell-free massive MIMO.

Current outdoor mobile network infrastructure cannot support uplink intensive mobile applications such as connected vehicles that collect and upload large amount of real time data. Our investigation reveals that with maximum-ratio (MR) decoding, it is theoretically impossible to support such applications with cell-free Massive MIMO, and it requires a very large number of service antennas in single cell configuration, making it practically infeasible; but with zero-forcing (ZF) decoding, such applications can be easily supported by cell-free Massive MIMO with very moderate number of access points (AP's), and it requires a lot more service antennas in single cell configuration. Via the newly derived SINR expressions for cell-free Massive MIMO with ZF decoding we show that uplink power control is unnecessary, and that with 10 MHz effective bandwidth for uplink data transmission, in urban and suburban morphologies, on the 2 GHz band, 90/km2and 32/km2single antenna AP's are enough to support 18 autonomous vehicles respectively. In rural morphology, using 450 MHz band, only 2/km2single antenna AP's is enough.

We analyze the effect of channel aging on the achievable rate of time division duplexed massive multiple input multiple output systems serving a number of users under aging channels, using nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA). Using the recently proposed shared uplink pilot based channel estimation for NOMA, we derive bounds on the channel estimation error variance for the two schemes. We then derive the achievable spectral efficiencies of the two schemes. Using numerical results, we show that, in slowly varying channels, using NOMA with shared pilots is preferable over OMA, while the reverse is true under fast varying channels.

The capacity of the MIMO channel taking into account both a limitation on total consumed power, and per-antenna radiated power constraints is considered. The total consumed power takes into account the traditionally used sum radiated power, and also the power dissipation in the amplifiers. For a fixed channel with full CSI at both the transmitter and the receiver, maximization of the mutual information is formulated as an optimization problem. Lower and upper bounds on the capacity are provided by numerical algorithms based on partitioning of the feasible region. Both bounds are shown to converge and give the exact capacity when number of regions increases. The bounds are also used to construct a monotonic optimization algorithm based on the branch-and-bound approach. An efficient suboptimal algorithm based on successive convex approximation performing close to the capacity is also presented. Numerical results show that the performance of the solution obtained from the suboptimal algorithm is close to that of the global optimal solution. Simulation results also show that in the low SNR regime, antenna selection provides performance that is close to the optimal scheme while at high SNR, uniform power allocation performs close to the optimal scheme.

In this paper, we consider device-to-device (D2D) communication that is underlaid in a multi-cell massive multiple-input multiple-output (MIMO) system and proposes a new framework for power control and pilot allocation. In this scheme, the cellular users (CUs) in each cell get orthogonal pilots which are reused with reuse factor one across cells, while all the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the CUs with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. We provide power control algorithms to maximize the minimum spectral efficiency (SE) and to maximize the product of the signal-to-interference-plus-noise ratios in the network. Different from prior works, in our proposed power control schemes, we consider joint pilot and data transmission optimization. Finally, we provide a numerical evaluation, where we compare our proposed power control schemes 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 SE of multi-cell massive MIMO networks.

We consider transmission of system information in a cell-free massive MIMO system, when the transmitting access points do not have any channel state information and the receiving terminal has to estimate the channel based on downlink pilots. We analyze the system performance in terms of outage rate and coverage probability and use space-time block codes to increase performance. We propose a heuristic method for pilot/data power optimization that can be applied without any channel state information at the access points. We also analyze the problem of grouping the access points, which is needed when the single-antenna access points jointly transmit a space-time block code.

Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This paper targets at maximizing the achievable sum-backscattered throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BCs) at the tags. Since this joint problem is nonconvex, we first present individually optimal designs for the TRX and BC. We show that with precoder and combiner designs at the reader, respectively, targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically optimal joint-TRX-BC designs are proposed for both low- and high-signal-to-noise ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings, such as wireless powered communication networks and BSC with imperfect channel state information. Finally, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.

This paper considers multi-cell massive multiple-input multiple-output systems, where the channels are spatially correlated Rician fading. The channel model is composed of a deterministic line-of-sight path and a stochastic non-line-of-sight component describing a practical spatially correlated multipath environment. We derive the statistical properties of the minimum mean squared error (MMSE), element-wise MMSE, and least-square channel estimates for this model. Using these estimates for maximum ratio combining and precoding, rigorous closed-form uplink (UL) and downlink (DL) achievable spectral efficiency (SE) expressions are derived and analyzed. The asymptotic SE behavior, when using the different channel estimators, are also analyzed. The numerical results show that the SE is higher when using the MMSE estimator than that of the other estimators, and the performance gap increases with the number of antennas.

Cell-free Massive MIMO (multiple-input multiple-output) refers to a distributed Massive MIMO system where all the access points (APs) cooperate to coherently serve all the user equipments (UEs), suppress inter-cell interference and mitigate the multiuser interference. Recent works demonstrated that, unlike co-located Massive MIMO, the channel hardening is, in general, less pronounced in cell-free Massive MIMO, thus there is much to benefit from estimating the downlink channel. In this study, we investigate the gain introduced by the downlink beamforming training, extending the previously proposed analysis to non-orthogonal uplink and downlink pilots. Assuming single-antenna APs, conjugate beamforming and independent Rayleigh fading channel, we derive a closed-form expression for the per-user achievable downlink rate that addresses channel estimation errors and pilot contamination both at the AP and UE side. The performance evaluation includes max-min fairness power control, greedy pilot assignment methods, and a comparison between achievable rates obtained from different capacity-bounding techniques. Numerical results show that downlink beamforming training, although increases pilot overhead and introduces additional pilot contamination, improves significantly the achievable downlink rate. Even for large number of APs, it is not fully efficient for the UE relying on the statistical channel state information for data decoding.

Green low power networking with the least requirement of dedicated radio resources is need of the hour which has led to the upsurge of backscatter communication (BSC) technology. However, this inherent potential of BSC is challenged by hardware constraints of the underlying tags. We address this timely concern by investigating the practical efficacy of multiple-input-multiple-output (MIMO) technology in overcoming the fundamental limitations of BSC. Specifically, we first introduce a novel least-squares based channel estimation (CE) protocol for multi-tag BSC settings that takes care of both the unintended ambient reflections and the inability of tags in performing estimation by themselves. Then using it, a nontrivial low-complexity algorithm is proposed to obtain the optimal transceiver designs for the multiantenna reader to maximize the minimum value of the lower-bounded backscattered throughput among the single-antenna semi-passive tags. Additional analytical insights on both individually and jointly-optimal precoding vector and detector matrix at the reader are provided by exploring the asymptotically-optimal transceiver designs. Lastly detailed numerical investigation is carried out to validate the theoretical results and quantify the practically realizable throughput fairness. Specifically, more than seven-fold increase in the common-backscattered-throughput among tags as achieved by the proposed designs over the relevant benchmarks corroborates their practical significance.

In this paper, we consider the downlink of a multi-cell multiple-input multiple-output system and find the jointly optimal number of base station (BS) antennas and transmission powers that minimize the power consumption while satisfying each user's effective signal-to-interference-and-noise-ratio constraint and the BSs' power constraints. Different from prior work, we consider a power consumption model that takes both transmitted and hardware-consumed power into account. We formulate the joint optimization problem for both single-cell and multi-cell systems. The closed-form expressions for the optimal number of BS antennas and transmission powers are derived for the single-cell case. The analysis for the multi-cell case reveals that increasing the number of BS antennas in any cell always improves the performance of the overall system in terms of both feasibility and total radiated power. A key contribution of this paper is to show that the joint optimization problem can be relaxed as a geometric programming problem that can be solved efficiently. The solution can be utilized in practice to turn on and off antennas depending on the traffic load variations. The substantial power savings are demonstrated by simulation.

We consider a multi-cell Massive MIMO system in a line-of-sight (LoS) propagation environment, for which each user is served by one base station, with no cooperation among the base stations. Each base station knows the channel between its service antennas and its users, and uses these channels for precoding and decoding. Under these assumptions we derive explicit downlink and uplink effective SINR formulas for maximum-ratio (MR) processing and zero-forcing (ZF) processing. We also derive formulas for power control to meet pre-determined SINR targets. A numerical example demonstrating the usage of the derived formulas is provided.

This work introduces a new approach to solve the joint precoding and power allocation for sum rate maximization problem in the downlink multiuser MIMO by a combination of random matrix theory and optimization theory. The new approach results in a simplified problem that, though non-convex, obeys a simple separable structure. The sum rate maximization problem is decomposed into different single-variable optimization problems that can be solved in parallel. A water-filling-like solution is found, which can be applied under some mild conditions on the SNRs of the users. The proposed scheme provides large gains over heuristic solutions when the number of users in the cell is large, which suggests the applicability in massive MIMO systems.

We study joint unicast and multigroup multicast transmission in single-cell massive multiple-input-multiple-output (MIMO) systems, under maximum ratio transmission. For the unicast transmission, the objective is to maximize the weighted sum spectral efficiency (SE) of the unicast user terminals (UTs) and for the multicast transmission the objective is to maximize the minimum SE of the multicast UTs. These two problems are coupled to each other in a conflicting manner, due to their shared power resource and interference. To address this, we formulate a multiobjective optimization problem (MOOP). We derive the Pareto boundary of the MOOP analytically and determine the values of the system parameters to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource.

Future cellular networks are expected to support new communication paradigms such as machine-type communication (MTC) services along with 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 sequence per-device approaches 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 (MaMIMO) 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 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 MaMIMO can significantly enhance the performance of such a setup and allow the inclusion of MTC services into the cellular networks without requiring additional resources.

In this paper, we investigate downlink power control in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays. The base station (BS) in each cell consists of multiple antenna arrays, which are deployed in arbitrary locations within the cell. Due to the spatial separation between antenna arrays, the large-scale propagation effect is different from a user to different antenna arrays in a cell, which makes power control a challenging problem as compared to conventional massive MIMO. We assume that the BS in each cell obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum ratio transmission for the downlink. We then derive a closed-form spectral efficiency (SE) expression, where the channels are subject to correlated fading. Utilizing the derived expression, we propose a max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate that, for the network considered in this work, optimizing for max-min SE through the max-min power control improves the sum SE of the network as compared to equal power allocation.

Large-scale massive MIMO network deployments can provide higher spectral efficiency and better coverage for future communication systems like 5G. Due to the large number of antennas at the base station, the system achieves stable channel quality and spatially separable channels to the different users. In this paper, linear, planar, circular and cylindrical arrays are used in the evaluation of a large-scale multi-cell massive MIMO network. The system-level performance is predicted using two different kinds of channel models. First, a ray-based deterministic tool is utilized in a real North American city environment. Second, an independent and identically distributed (i.i.d.) Rayleigh fading channel model is considered, as often used in previously published massive MIMO studies. The analysis is conducted in a 16-macro-cell network with outdoor and randomly distributed users. It is shown that the array configuration has a large impact on the throughput statistics. Although the system-level performance with i.i.d. Rayleigh fading can be close to the deterministic prediction in some situations (e.g., with large linear arrays), significant differences are noticed when considering other types of arrays.

In this work, we consider the downlink of a multi-user multiple-input multiple-output (MIMO) system and aim to find the jointly optimal number of base station (BS) antennas and transmission powers that minimize the power consumption while satisfying each user's signal-to-interference-and-noise-ratio (SINR) constraint and the BS's power constraint. Different from prior work, we consider a power consumption model that takes both transmitted and hardware-consumed power into account. We formulate the joint optimization problem for a single-cell system and derive closed-form expressions for the optimal number of BS antennas and transmission powers. The solution can be utilized in practice to turn on and off antennas depending on the traffic load variations. Substantial power savings are demonstrated by simulation.

In massive multiple-input-multiple-output base stations, power consumption and cost of the low-noise amplifiers (LNAs) can be substantial because of the many antennas. We investigate the feasibility of inexpensive, power efficient LNAs, which inherently are less linear. A polynomial model is used to characterize the nonlinear LNAs and to derive the second-order statistics and spatial correlation of the distortion. We show that, with spatial matched filtering (maximum-ratio combining) at the receiver, some distortion terms combine coherently, and that the signal-to-interference-and-noise ratio of the symbol estimates therefore is limited by the linearity of the LNAs. Furthermore, it is studied how the power from a blocker in the adjacent frequency band leaks into the main band and creates distortion. The distortion term that scales cubically with the power received from the blocker has a spatial correlation that can be filtered out by spatial processing and only the coherent term that scales quadratically with the power remains. When the blocker is in free-space line-of-sight and the LNAs are identical, this quadratic term has the same spatial direction as the desired signal, and hence cannot be removed by linear receiver processing.

A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared with application of the original AMP approach.

We design a jamming-resistant receiver scheme to enhance the robustness of a massive MIMO uplink system against jamming. We assume that a jammer attacks the system both in the pilot and data transmission phases. The key feature of the proposed scheme is that, in the pilot phase, the base station estimates not only the legitimate channel, but also the jamming channel by exploiting a purposely unused pilot sequence. The jamming channel estimate is used to construct linear receiver filters that reject the impact of the jamming signal. The performance of the proposed scheme is analytically evaluated using the asymptotic properties of massive MIMO. The best regularized zero-forcing receiver and the optimal power allocations for the legitimate system and the jammer are also studied. Numerical results are provided to verify our analysis and show that the proposed scheme greatly improves the achievable rates, as compared with conventional receivers. Interestingly, the proposed scheme works particularly well under strong jamming attacks, since the improved estimate of the jamming channel outweighs the extra jamming power.

This paper considers the use of non-orthogonal-multiple-access (NOMA) in multiuser MIMO systems in practical scenarios where channel state information (CSI) is acquired through pilot signaling. A new NOMA scheme that uses shared pilots is proposed. Achievable rate analysis is carried out for different pilot signaling schemes, including both uplink and downlink pilots. The achievable rate performance of the proposed NOMA scheme with shared pilot within each group is compared with the traditional orthogonal access scheme with orthogonal pilots. Our proposed scheme is a generalization of the orthogonal scheme, and can be reduced to the orthogonal scheme when appropriate power allocation parameters are chosen. Numerical results show that when downlink CSI is available at the users, our proposed NOMA scheme outperforms orthogonal schemes. However with more groups of users present in the cell, it is preferable to use multi-user beamforming instead of NOMA.

This paper considers pilot design to mitigate pilot contamination and provide good service for everyone in multi-cell massive multiple-input-multiple-output systems. Instead of modeling the pilot design as a combinatorial assignment problem, as in prior works, we express the pilot signals using a pilot basis and treat the associated power coefficients as continuous optimization variables. We compute a lower bound on the uplink capacity for Rayleigh fading channels with maximum ratio detection that applies with arbitrary pilot signals. We further formulate the max-min fairness problem under power budget constraints, with the pilot signals and data powers as optimization variables. Because this optimization problem is non-deterministic polynomial-time hard due to signomial constraints, we then propose an algorithm to obtain a local optimum with polynomial complexity. Our framework serves as a benchmark for pilot design in scenarios with either ideal or non-ideal hardware. Numerical results manifest that the proposed optimization algorithms are close to the optimal solution obtained by exhaustive search for different pilot assignments and the new pilot structure and optimization bring large gains over the state-of-the-art suboptimal pilot design.

In this paper, we study the effect of channel aging on the uplink and downlink performance of an FDD massive MIMO system, as the system dimension increases. Since the training duration scales linearly with the number of transmit dimensions, channel estimates become increasingly outdated in the communication phase, leading to performance degradation. To quantify this degradation, we first derive bounds on the mean squared channel estimation error. We use the bounds to derive deterministic equivalents of the receive SINRs, which yields a lower bound on the achievable uplink and downlink spectral efficiencies. For the uplink, we consider maximal ratio combining and MMSE detectors, while for the downlink, we consider matched filter and regularized zero forcing precoders. We show that the effect of channel aging can be mitigated by optimally choosing the frame duration. It is found that using all the base station antennas can lead to negligibly small achievable rates in high user mobility scenarios. Finally, numerical results are presented to validate the accuracy of our expressions and illustrate the dependence of the performance on the system dimension and channel aging parameters.

Downlink beamforming in Massive multiple-input and multiple-output (MIMO) either relies on uplink pilot measurements-exploiting reciprocity and time-division duplexing operation, or on the use of a predetermined grid of beams with user equipments reporting their preferred beams, mostly in frequency-division duplexing operation. Massive MIMO in its originally conceived form uses the first strategy, with uplink pilots, whereas there is currently significant commercial interest in the second, grid-of-beams. It has been analytically shown that with isotropic scattering (independent Rayleigh fading) the first approach outperforms the second. Nevertheless, there remains controversy regarding their relative performance in practical channels. In this contribution, the performances of these two strategies are compared using measured channel data at 2.6 GHz.

We consider transmission of system information in massive multiple-input multiple-output (MIMO). This information needs to be reliably delivered to inactive users in the cell without any channel state information at the base station. Downlink transmission entails the use of downlink pilots and a special type of precoding that aims to reduce the dimension of the downlink channel and the pilot overhead, which would otherwise scale with the number of base station antennas. We consider a scenario in which the base station transmits over a small number of coherence intervals, providing little time/frequency diversity. The system information is transmitted with orthogonal space-time block codes to increase reliability and performance is measured using outage rates. Several different codes are compared, both for spatially correlated and uncorrelated channels and for varying amounts of time/frequency diversity. We show that a massive MIMO base station can outperform a single-antenna base station in all considered scenarios.

The distortion from massive multiple-input multiple-output base stations with nonlinear amplifiers is studied and its radiation pattern is derived. The distortion is analyzed both in-band and out-of-band. By using an orthogonal Hermite representation of the amplified signal, the spatial cross-correlation matrix of the nonlinear distortion is obtained. It shows that, if the input signal to the amplifiers has a dominant beam, the distortion is beamformed in the same way as that beam. When there are multiple beams without any one being dominant, it is shown that the distortion is practically isotropic. The derived theory is useful to predict how the nonlinear distortion will behave, to analyze the out-of-band radiation, to do reciprocity calibration, and to schedule users in the frequency plane to minimize the effect of in-band distortion.

This paper considers the downlink precoding for physical layer multicasting in massive multiple-input multiple-output (MIMO) systems. We study the max-min fairness (MMF) problem, where channel state information at the transmitter is used to design precoding vectors that maximize the minimum spectral efficiency (SE) of the system, given fixed power budgets for uplink training and downlink transmission. Our system model accounts for channel estimation, pilot contamination, arbitrary path-losses, and multi-group multicasting. We consider six scenarios with different transmission technologies (unicast and multicast), different pilot assignment strategies (dedicated or shared pilot assignments), and different precoding schemes (maximum ratio transmission and zero forcing), and derive achievable spectral efficiencies for all possible combinations. Then, we solve the MMF problem for each of these scenarios, and for any given pilot length, we find the SE maximizing uplink pilot and downlink data transmission policies, all in closed forms. We use these results to draw a general guideline for massive MIMO multicasting design, where for a given number of base station antennas, number of users, and coherence interval length, we determine the multicasting scheme that shall be used.

We study the joint unicast and multi-group multicast transmission in massive multiple-input multiple-output systems. We consider a system model that accounts for channel estimation and pilot contamination and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs) under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto optimal point describes a particular efficient tradeoff between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, and so on) to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, and hence, the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.

Optimal physical layer multicasting (PLM) is an NP-hard problem that for simplicity has been studied under idealistic assumptions, e.g., availability of perfect channel state information (CSI), both at the base station (BS) and at the user terminals (UTs). With the advent of massive multiple-input-multiple-output (MIMO), PLM has become more challenging, as the computational complexity of the precoder design is proportional to the number of BS antennas. In this paper, we address these issues by introducing computationally efficient precoders that account for practical CSI acquisition. We derive achievable spectral efficiencies for the proposed precoders. Then we introduce a novel problem formulation for the max-min fairness power control that accounts the CSI acquisition overhead, uplink training and downlink transmission powers. We solve this problem and find the optimal uplink and downlink power control policies in closed form. Using numerical simulations, we verify the effectiveness of our proposed schemes comparing them with the state of the art PLM schemes for massive MIMO systems.

We design jamming resistant receivers to enhance the robustness of a massive MIMO uplink channel against jamming. In the pilot phase, we estimate not only the desired channel, but also the jamming channel by exploiting purposely unused pilot sequences. The jamming channel estimate is used to construct the linear receive filter to reduce impact that jamming has on the achievable rates. The performance of the proposed scheme is analytically and numerically evaluated. These results show that the proposed scheme greatly improves the rates, as compared to conventional receivers. Moreover, the proposed schemes still work well with stronger jamming power.

In massive MIMO systems, which rely on uplink pilots to estimate the channel, the time interval between pilot transmissions constrains the length of the downlink. Since switching between up- and downlink takes time, longer downlink blocks increase the effective spectral efficiency. We investigate the use of low-complexity channel models and Kalman filters for channel prediction, to allow for longer intervals between the pilots. Specifically, we quantify how often uplink pilots have to be sent when the downlink rate is allowed to degrade by a certain percentage. To this end, we consider a time-correlated channel aging model, whose spectrum is rectangular, and use autoregressive moving average (ARMA) processes to approximate the time-variations of such channels. We show that ARMA-based predictors can increase the interval between pilots and the spectral efficiency in channels with high Doppler spreads. We also show that Kalman prediction is robust to mismatches in the channel statistics.

The high hardware complexity of a massive MIMO base station, which requires hundreds of radio chains, makes it challenging to build commercially. One way to reduce the hardware complexity and power consumption of the receiver is to lower the resolution of the analog-to-digital converters (ADCs). We derive an achievable rate for a massive MIMO system with arbitrary quantization and use this rate to show that ADCs with as low as 3 bits can be used without significant performance loss at spectral efficiencies around 3.5 bpcu per user, also under interference from stronger transmitters and with some imperfections in the automatic gain control.

Consider the uplink transmission of a single-cell multi-user multiple-input multiple-output (MIMO) system with K single-antenna users and a base station (BS) equipped with a very large number of antennas denoted by M. Consider a jamming device with N > M distributed antennas attempting to deteriorate the communication between the users and the BS. We propose an asymptotic condition on the jamming power under which the jamming-plus-noise subspace overlaps with the signal subspace. Under this condition, existing blind jamming rejection methods, such as the one in [1], fail. The proposed results are based on the application of results from large-dimensional random matrix theory.

This paper optimizes the pilot assignment and pilot transmit powers to mitigate pilot contamination in Massive MIMO (multiple-input multiple-output) systems. While prior works have treated pilot assignment as a combinatorial problem, we achieve a more tractable problem formulation by directly optimizing the pilot sequences. To this end, we compute a lower bound on the uplink (UL) spectral efficiency (SE), for Rayleigh fading channels with maximum ratio (MR) detection and arbitrary pilot sequences. We optimize the max-min SE with respect to the pilot sequences and pilot powers, under power budget constraints. This becomes an NP-hard signomial problem, but we propose an efficient algorithm to obtain a local optimum with polynomial complexity. Numerical results manifest the near optimality of the proposed algorithm and show significant gains over existing suboptimal algorithms.

The phenomenon of pilot contamination (PC) in multi-cell Massive MIMO systems is investigated in the presence of imperfect timing synchronization (TS). In particular, a basic setup is considered, where a base station (BS) is perfectly synchronized with the user of its cell, but there is imperfect TS between the BS and the user in another cell, possibly due to different propagation distances. A discrete-time system model is derived based on the continuous-time system model. The discrete-time system model accurately captures the phenomenon of imperfect TS in terms of the timing mismatch and the pulse shaping filter impulse responses. The derived discrete-time system model is used to study the achievable rates of a two-cell Massive MIMO uplink. It is shown that the structure imposed to the pilot contaminating signal due to the imperfect TS can be leveraged to mitigate the effect of PC. The level of PC suppression is quantified as a function of the timing mismatch and the characteristics of the transmit/receive pulse shaping filters.

We present a method for jamming a time-division duplex link using a transceiver with a large number of antennas. By utilizing beamforming, a jammer with M antennas can degrade the spectral efficiency of the primary link more than conventional omnidirectional jammers under the same power constraint, or perform equally well with approximately 1/M of the output power. The jammer operates without any prior knowledge of channels to the legitimate transmitters, or the legitimate signals by relying on channel reciprocity.

The massive multiple-input multiple-output (MIMO) technology has great potential to manage the rapid growth of wireless data traffic. Massive MIMO achieves tremendous spectral efficiency by spatial multiplexing many tens of user equipments (UEs). These gains are only achieved in practice if many more UEs can connect efficiently to the network than today. As the number of UEs increases, while each UE intermittently accesses the network, the random access functionality becomes essential to share the limited number of pilots among the UEs. In this paper, we revisit the random access problem in the Massive MIMO context and develop a reengineered protocol, termedstrongest-user collision resolution(SUCRe). An accessing UE asks for a dedicated pilot by sending an uncoordinated random access pilot, with a risk that other UEs send the same pilot. The favorable propagation of massive MIMO channels is utilized to enable distributed collision detection at each UE, thereby determining the strength of the contenders’ signals and deciding to repeat the pilot if the UE judges that its signal at the receiver is the strongest. The SUCRe protocol resolves the vast majority of all pilot collisions in crowded urban scenarios and continues to admit UEs efficiently in overloaded networks.

A massive MIMO system, represented by a base station with hundreds of antennas, is capable of spatially multiplexing many devices and thus naturally suited to serve dense crowds of wireless devices in emerging applications, such as machine-type communications. Crowd scenarios pose new challenges in the pilot-based acquisition of channel state information and call for pilot access protocols that match the intermittent pattern of device activity. A joint pilot assignment and data transmission protocol based on random access is proposed in this paper for the uplink of a massive MIMO system. The protocol relies on the averaging across multiple transmission slots of the pilot collision events that result from the random access process. We derive new uplink sum rate expressions that take pilot collisions, intermittent device activity, and interference into account. Simplified bounds are obtained and used to optimize the device activation probability and pilot length. A performance analysis indicates how performance scales as a function of the number of antennas and the transmission slot duration.

Analog-to-digital converters (ADCs) stand for a significant part of the total power consumption in a massive multiple-input multiple-output (MIMO) base station. One-bit ADCs are one way to reduce power consumption. This paper presents an analysis of the spectral efficiency of single-carrier and orthogonal-frequency-division-multiplexing (OFDM) transmission in massive MIMO systems that use one-bit ADCs. A closed-form achievable rate, i.e., a lower bound on capacity, is derived for a wideband system with a large number of channel taps that employ low-complexity linear channel estimation and symbol detection. Quantization results in two types of error in the symbol detection. The circularly symmetric error becomes Gaussian in massive MIMO and vanishes as the number of antennas grows. The amplitude distortion, which severely degrades the performance of OFDM, is caused by variations between symbol durations in received interference energy. As the number of channel taps grows, the amplitude distortion vanishes and OFDM has the same performance as single-carrier transmission. A main conclusion of this paper is that wideband massive MIMO systems work well with one-bit ADCs.

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max-min power control algorithms. Max-min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.

We consider the Massive Multiple-Input Multiple-Output downlink with maximum-ratio and zero-forcing processing and time-division duplex operation. To decode, the users must know their instantaneous effective channel gain. Conventionally, it is assumed that by virtue of channel hardening, this instantaneous gain is close to its average and hence that users can rely on knowledge of that average (also known as statistical channel information). However, in some propagation environments, such as keyhole channels, channel hardening does not hold. We propose a blind algorithm to estimate the effective channel gain at each user, that does not require any downlink pilots. We derive a capacity lower bound of each user for our proposed scheme, applicable to any propagation channel. Compared with the case of no downlink pilots (relying on channel hardening), and compared with training-based estimation using downlink pilots, our blind algorithm performs significantly better. The difference is especially pronounced in environments that do not offer channel hardening.

In this paper, we analyze the benefits of including downlink pilots in a cell- free massive MIMO system. We derive an approximate per-user achievable downlink rate for conjugate beamforming processing, which takes into account both uplink and downlink channel estimation errors, and power control. A performance comparison is carried out, in terms of per-user net throughput, considering cell-free massive MIMO operation with and without downlink training, for different network densities. We take also into account the performance improvement provided by max-min fairness power control in the downlink. Numerical results show that, exploiting downlink pilots, the performance can be considerably improved in low density networks over the conventional scheme where the users rely on statistical channel knowledge only. In high density networks, performance improvements are moderate.

Massive MIMO systems, where the base stations are equipped with hundreds of antenna elements, are an attractive way to attain unprecedented spectral efficiency in future wireless networks. In the "classical" massive MIMO setting, the terminals are assumed fully loaded and a main impairment to the performance comes from the inter-cell pilot contamination, i.e., interference from terminals in neighboring cells using the same pilots as in the home cell. However, when the terminals are active intermittently, it is viable to avoid inter-cell contamination by pre-allocation of pilots, while same-cell terminals use random access to select the allocated pilot sequences. This leads to the problem of intra-cell pilot contamination. We propose a framework for random access in massive MIMO networks and derive new uplink sum rate expressions that take intra-cell pilot collisions, intermittent terminal activity, and interference into account. We use these expressions to optimize the terminal activation probability and pilot length.

We investigate the performance of wideband massive MIMO base stations that use one-bit ADCs for quantizing the uplink signal. Our main result is to show that the many taps of the frequency-selective channel make linear combiners asymptotically consistent and the quantization noise additive and Gaussian, which simplifies signal processing and enables the straightforward use of OFDM. We also find that single-carrier systems and OFDM systems are affected in the same way by one-bit quantizers in wideband systems because the distribution of the quantization noise becomes the same in both systems as the number of channel taps grows.

Consider a multicell multiuser MIMO (multiple-input multiple-output) system with a very large number of antennas at each base station (BS). The number of users in each cell is assumed to be fixed as the number of BS antennas grows large. Under certain conditions on the powers of the transmitting users, the signal eigenvalue spectrum is asymptotically separated from the interference-plus-noise spectrum as the number of BS antennas grows large. As it was observed in [1], this phenomenon allows to mitigate the pilot contamination problem. We provide the power limits for each user in the cell of interest above which such a separation occurs asymptotically. Unlike the approximative methods used in [1], we obtain these power limits by making use of the exact asymptotic characterizations of the interference-plus-noise spectrum. The results are based on the theory of small rank perturbations of large dimensional random matrices.

Wireless networks with many antennas at the base stations and multiplexing of many users, known as Massive MIMO systems, are key to handle the rapid growth of data traffic. As the number of users increases, the random access in contemporary networks will be flooded by user collisions. In this paper, we propose a reengineered random access protocol, coined strongest-user collision resolution (SUCR). It exploits the channel hardening feature of Massive MIMO channels to enable each user to detect collisions, determine how strong the contenders' channels are, and only keep transmitting if it has the strongest channel gain. The proposed SUCR protocol can quickly and distributively resolve the vast majority of all pilot collisions.

This paper aims to minimize the total transmit power consumption for Massive MIMO (multiple-input multiple-output) downlink cellular systems when each user is served by the optimized subset of the base stations (BSs). We derive a lower bound on the ergodic spectral efficiency (SE) for Rayleigh fading channels and maximum ratio transmission (MRT) when the BSs cooperate using non-coherent joint transmission. We solve the joint user association and downlink transmit power minimization problem optimally under fixed SE constraints. Furthermore, we solve a max-min fairness problem with user specific weights that maximizes the worst SE among the users. The optimal BS-user association rule is derived, which is different from maximum signal-to-noise-ratio (max-SNR) association. Simulation results manifest that the proposed methods can provide good SE for the users using less transmit power than in small-scale systems and that the optimal user association can effectively balance the load between BSs when needed.

In high-density low-bitrate Internet-of-Things (IoT) use case of 5G networks, the terminals and sensors are to be of extremely low-cost and low energy-consuming. Typically, the analog-to-digital converters (ADCs) dominate the power-budget of receiver chains, in particular if the quantization resolution is high. Hence, receiver architectures deploying 1-bit ADCs are of high interest towards realizing low-cost, high energy-efficiency device solutions. In this paper, we study the waveform design and optimization for a narrowband low-bitrate massive MISO downlink targeting to achieve rates higher than 1 bits/sec (per real-dimension) where the terminal receivers adopt only simple 1-bit quantization (per real-dimension) with oversampling. In this respect, first we show that for a particular precoder structure, the overall link is equivalent to that of an AWGN SISO with controlled intersymbol interference (ISI). The filter design problem for generating the desired ISI in such SISO links has been studied in previous works, however, the only known method in literature is a computationally demanding brute force search method. As a novel contribution, we develop models and tools that elaborate on the conditions to be satisfied for unique detection and existence of solution for the filter coefficients. Then, as a concrete example, the developed models and tools are utilized to show that in the absence of noise, five-times oversampling is required for unique detection of 16-QAM input alphabet. Building on these findings, we then develop novel algorithms that can efficiently design the filter coefficients. Examples and simulations are provided to elaborate on filter coefficient design and optimization, and to illustrate good SER performance of the MISO link with 1-bit receiver even at SNRs down to 5 dB.

The spatial characteristics of the out-of-band radiation that a multiuser MIMO system emits, due to its power amplifiers (modeled by a polynomial model) being nonlinear, are studied by deriving an analytical expression for the continuous-time cross-correlation of the transmit signals. It is shown that, at any spatial point and on any frequency, the received power averaged over many channel realizations from a MIMO base station is the same as from a SISO base station when the two radiate the same amount of power. For a specific channel realization however, the received power can deviate from this average. We show that the deviations from the average are small in a MIMO system with multiple users and that the deviations can be significant with only one user. Using an ergodicity argument, we conclude that out-of-band radiation is less of a problem in massive MIMO, where precoding and array gain let us reduce the total radiated power compared to SISO systems. The requirements on spectral regrowth can therefore be relaxed in MIMO systems without causing more total out-of-band radiation.

In massive multiple-input multiple-output (MIMO), most precoders result in downlink signals that suffer from high peak-to-average ratio (PAR), independently of modulation order and whether single-carrier or orthogonal frequency-division multiplexing (OFDM) transmission is used. The high PAR lowers the power efficiency of the base-station amplifiers. To increase the power efficiency, low-PAR precoders have been proposed. In this paper, we compare different transmission methods for massive MIMO in terms of the power consumed by the amplifiers. It is found that: 1) OFDM and single-carrier transmission have the same performance over a hardened massive MIMO channel and 2) when the higher amplifier power efficiency of low-PAR precoding is taken into account, conventional and low-PAR precoders lead to approximately the same power consumption. Since downlink signals with low PAR allow for simpler and cheaper hardware, than signals with high PAR, therefore, the results suggest that low-PAR precoding with either single-carrier or OFDM transmission should be used in a massive MIMO base station.

The problem of maximum likelihood (ML) detection in training-assisted single-input multiple-output (SIMO) systems with phase noise impairments is studied for two different scenarios, i.e., the case when the channel is deterministic and known (constant channel) and the case when the channel is stochastic and unknown (fading channel). Furthermore, two different operations with respect to the phase noise sources are considered, namely, the case of identical phase noise sources and the case of independent phase noise sources over the antennas. In all scenarios, the optimal detector is derived for a very general parameterization of the phase noise distribution. Furthermore, a high signal-to-noise-ratio (SNR) analysis is performed to show that symbol-error-rate (SER) floors appear in all cases. The SER floor in the case of identical phase noise sources (for both constant and fading channels) is independent of the number of antenna elements. In contrast, the SER floor in the case of independent phase noise sources is reduced when increasing the number of antenna elements (for both constant and fading channels). Finally, the system model is extended to multiple data channel uses and it is shown that the conclusion is valid for these setups, as well.

Simultaneous wireless information and power transfer techniques for multiway massive multiple-input multiple-output (MIMO) relay networks are investigated. By using two practically viable relay receiver designs, namely 1) the power splitting receiver and 2) the time switching receiver, asymptotic signal-to-interference-plus-noise ratio (SINR) expressions are derived for an unlimited number of antennas at the relay. These asymptotic SINRs are then used to derive asymptotic symmetric sum rate expressions in closed form. Notably, these asymptotic SINRs and sum rates become independent of radio frequency-to-direct current (RF-to-DC) conversion efficiency in the limit of infinitely many relay antennas. Moreover, tight average sum rate approximations are derived in closed form for finitely many relay antennas. The fundamental tradeoff between the harvested energy and the sum rate is quantified for both relay receiver structures. Notably, the detrimental impact of imperfect channel state information (CSI) on the MIMO detector/precoder is investigated, and thereby, the performance degradation caused by pilot contamination, which is the residual interference due to nonorthogonal pilot sequence usage in adjacent/cochannel systems, is quantified. The presence of cochannel interference (CCI) can be exploited to be beneficial for energy harvesting at the relay, and consequently, the asymptotic harvested energy is an increasing function of the number of cochannel interferers. Notably, in the genie-aided perfect CSI case, the detrimental impact of CCI for signal decoding can be cancelled completely whenever the number of relay antennas grows without bound. Nevertheless, the pilot contamination severely degrades the sum rate performance even for infinitely many relay antennas.

Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas M than scheduled users K because the users' channels are likely to be near-orthogonal when M/K 10. However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users K* depends on M and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of K* in the large-M regime is derived in closed form, while simulations are used to show what happens at finite M, in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal M/K is less than 10 in many cases of practical relevance. Interestingly, K* depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same K.

This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem that maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.

We illustrate potential benefits of using massive antenna arrays for wireless energy transfer (WET). Specifically, we analyze probability of outage in WET over fading channels when a base station (BS) with multiple antennas beamforms energy to a wireless sensor node (WSN). Our analytical results show that by using massive antenna arrays, the range of WET can be increased for a given target outage probability. We prove that by using multiple-antenna arrays at the BS, a lower downlink energy is required to get the same outage performance, resulting in savings of radiated energy. We show that for energy levels used in WET, the outage performance with least-squares or minimum mean-square-error channel estimates is the same as that obtained based on perfect channel estimates. We observe that a strong line-of-sight component between the BS and WSN lowers outage probability. Furthermore, by deploying more antennas at the BS, a larger energy can be transferred reliably to the WSN at a given target outage performance for the sensor to be able to perform its main tasks. In our numerical examples, the RF power received at the input of the sensor is assumed to be on the order of a mW, such that the rectenna operates at an efficiency in the order of 50%.

The downlink of a massive MIMO system is considered for the case in which the base station must concurrently serve two categories of terminals: one group to which imperfect instantaneous channel state information (CSI) is available and one group to which no CSI is available. Motivating applications include broadcasting of public channels and control information in wireless networks. A new technique is developed and analyzed: joint beamforming and broadcasting (JBB), by which the base station beamforms to the group of terminals to which CSI is available, and broadcasts to the other group of terminals, to which no CSI is available. The broadcast information does not interfere with the beamforming as it is placed in the nullspace of the channel matrix collectively seen by the terminals targeted by the beamforming. JBB is compared to orthogonal access (OA) by which the base station partitions the time-frequency resources into two disjunct parts, one for each group of terminals. It is shown that JBB can substantially outperform OA in terms of required total radiated power for given rate targets.

We consider the multiple input single output (MISO) Gaussian broadcast channel with Ntantennas at the base station (BS) and Nusingle-antenna users in the downlink. We propose a novel user grouping precoder which improves the sum rate performance of the zero-forcing (ZF) precoder specially when the channel is ill-conditioned. The proposed precoder partitions all the users into small groups of equal size. Downlink beamforming is then done in such a way that, at each user's receiver, the interference from the signal intended for users not in its group is nulled out. Intragroup interference still remains, and is cancelled through successive interference presubtraction at the BS using dirty paper coding (DPC). The proposed user grouping method is different from user selection, since it is a method for precoding of information to the selected (scheduled) users, and not for selecting which users are to be scheduled. The proposed precoder is a generalization of two special cases, one where each group has only one user (ZF precoder) and another where all users are in a single group (ZF-DP precoder). A larger group size helps improve the sum rate performance but at the cost of greater complexity. The proposed generalization, therefore, allows for tradeoff between performance and complexity.

Accurate and robust positioning in multipath environments can enable many applications, such as search-and-rescue and asset tracking. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, UWB still faces a problem with non-line-of-sight (NLOS) measurements, in which the range estimates based on time-of-arrival (TOA) will typically be positively biased. There are many techniques that address this problem, mainly based on NLOS identification and NLOS error mitigation algorithms. However, these techniques do not exploit all available information in the UWB channel impulse response. Kernel-based machine learning methods, such as Gaussian process regression (GPR), are able to make use of all information, but they may be too complex in their original form. In this paper, we propose novel ranging methods based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto a nonlinear orthogonal high-dimensional space, and a subset of these projections is then used as an input for ranging. We evaluate the proposed methods using real UWB measurements obtained in a basement tunnel, and found that one of the proposed methods is able to outperform state-of-the-art, even if little training samples are available.

Massive MIMO has been shown to greatly improve spectral and transmit-energy efficiency. When implementing a massive MIMO system, one challenge is high hardware complexity. A solution is to reduce the number of radio frequency (RF) transceiver chains by performing antenna selection. However, a full RF switch that connects the antennas and RF chains can be highly complex and incurs significant loss in output signal quality, especially when the number of antennas and RF chains are large. We therefore propose a simpler solution - binary switching architecture, which is suboptimal but provides better signal quality, as compared to the full switching network. To evaluate the proposed technique, we compare the sum-rate capacity when using several different configurations of binary switching with the performance of the full switching. Full MIMO performance obtained without antenna selection is also presented as a reference. The investigations in this paper are all based on measured channel data at 2.6 GHz, using a uniform linear array and a cylindrical array, both having 128 antenna elements. It is found that the proposed binary switching gives very competitive performance that are close to the full switching, for the measured channels. The results indicate a potential to simplify massive MIMO hardware by reducing the number of RF chains, and performing antenna selection with simple binary switching architecture.

In this paper, a new multi-cell MMSE detector is proposed for massive MIMO systems. Let K and B denote the number of users in each cell and the number of available pilot sequences in the network, respectively, with B = βK, where β ≥ 1 is called the pilot reuse factor. The novelty of the multi-cell MMSE detector is that it utilizes all B channel directions that can be estimated locally at a base station, so that intra-cell interference, parts of the inter-cell interference and the noise can all be actively suppressed, while conventional detectors only use the K intra-cell channels. Furthermore, in the large- system limit, a deterministic equivalent expression of the uplink SINR for the proposed multi-cell MMSE is derived. The expression is easy to compute and accounts for power control for the pilot and payload, imperfect channel estimation and arbitrary pilot allocation. Numerical results show that significant sum spectral efficiency gains can be obtained by the multi-cell MMSE over the conventional single-cell MMSE and the recent multi-cell ZF, and the gains become more significant as β and/or K increases. Furthermore, the deterministic equivalent is shown to be very accurate even for relatively small system dimensions.

In this paper, a new multi-cell MMSE precoder is proposed for massive MIMO systems. We consider a multi-cell network where each cell has K users and B orthogonal pilot sequences are available, with B = βK and β ≥ 1 being the pilot reuse factor over the network. In comparison with conventional single-cell precoding which only uses the K intra-cell channel estimates, the proposed multi-cell MMSE precoder utilizes all B channel directions that can be estimated locally at a base station, so that the transmission is designed spatially to suppress both parts of the inter-cell and intra-cell interference. To evaluate the performance, a large-scale approximation of the downlink SINR for the proposed multi-cell MMSE precoder is derived and the approximation is tight in the large-system limit. Power control for the pilot and payload, imperfect channel estimation and arbitrary pilot allocation are accounted for in our precoder. Numerical results show that the proposed multi-cell MMSE precoder achieves a significant sum spectral efficiency gain over the classical single-cell MMSE precoder and the gain increases as K or β grows. Compared with the recent M-ZF precoder, whose performance degrades drastically for a large K, our M-MMSE can always guarantee a high and stable performance. Moreover, the large-scale approximation is easy to compute and shown to be accurate even for small system dimensions.

In this paper we deal with spoofing detection in GNSS receivers. We derive the optimal genie detector when the true positions are perfectly known, and the observation errors are Gaussian, as a benchmark for other detectors. The system model considers three dimensional positions, and includes correlated errors. In addition, we propose several detectors that do not need any position knowledge, that outperform recently proposed detectors in many interesting cases.

We consider the massive MIMO downlink with time-division duplex (TDD) operation and conjugate beamforming transmission. To reliably decode the desired signals, the users need to know the effective channel gain. In this paper, we propose a blind channel estimation method which can be applied at the users and which does not require any downlink pilots. We show that our proposed scheme can substantially outperform the case where each user has only statistical channel knowledge, and that the difference in performance is particularly large in certain types of channel, most notably keyhole channels. Compared to schemes that rely on downlink pilots (e.g., [1]), our proposed scheme yields more accurate channel estimates for a wide range of signal-to-noise ratios and avoid spending time-frequency resources on pilots.

For both maximum ratio transmission (MRT) and zero forcing (ZF) precoding schemes and given any specific rate requirement the optimal transmit power, number of antennas to be used, number of users to be served and number of pilots spent on channel training are found with the objective to minimize the total consumed power at the base station. The optimization problem is solved by finding closed form expressions of the optimal transmit power and then search over the remaining discrete variables. The analysis consists of two parts, the first part investigates the situation when only power consumed in the RF amplifiers is considered. The second part includes both the power consumed in the RF amplifiers and in other transceiver circuits. In the former case having all antennas active while reducing the transmit power is optimal. Adaptive scheme to switch off some of the antennas at the base stations is found to be optimal in the latter case.

For time division duplexing (TDD) systems, the physical channel in the air is reciprocal for uplink (UL) and downlink (DL) within the channel coherence time. However when the transceivers' radio frequency (RF) hardware is taken into consideration, TDD channel reciprocity no longer holds because of the non-symmetric characteristics of RF transmit and receive chains. Relative calibration has been proposed to compensate this hardware impairment with a multiplicative matrix. In this paper we perform hardware measurements on this calibration matrix which gives a direct insight on the physical phenomenon of TDD transceivers. Especially, we inspect the assumption that this calibration matrix is diagonal, which is widely adopted in literature but has never been verified by experiments. This work can be regarded as an experimental base for TDD calibration or for theoretical analysis of non-perfect channel reciprocity of TDD systems.

In this paper, the problem of optimal maximum likelihood detection in a single user single-input multiple-output (SIMO) channel with phase noise at the receiver is considered. The optimal detection rules under training are derived for two operation modes, namely when the phase increments are fully correlated among the M receiver antennas (synchronous operation) and when they are independent (non-synchronous operation). The phase noise increments are parameterized by a very general distribution, which includes the Wiener phase noise model as a special case. It is proven that phase noise creates a symbol-error-rate (SER) floor for both operation modes. In the synchronous operation this error floor is independent of M, while it goes to zero exponentially with M in the non-synchronous operation.

Location awareness in wireless networks may enable many applications such as emergency services, autonomous driving and geographic routing. Although there are many available positioning techniques, none of them is adapted to work with massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G technology candidate. In this paper, we discuss possible solutions for positioning of mobile stations using a vector of signals at the base station, equipped with many antennas distributed over deployment area. Our main proposal is to use fingerprinting techniques based on a vector of received signal strengths. This kind of methods are able to work in highly-cluttered multipath environments, and require just one base station, in contrast to standard range-based and angle-based techniques. We also provide a solution for fingerprinting-based positioning based on Gaussian process regression, and discuss main applications and challenges.

Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy consumption. Here we investigate the possibilities to reduce the required number of RF chains, by performing antenna selection. While this approach is not a very effective strategy for theoretical independent Rayleigh fading channels, a substantial reduction in the number of RF chains can be achieved for real massive MIMO channels, without significant performance loss. We evaluate antenna selection performance on measured channels at 2.6 GHz, using a linear and a cylindrical array, both having 128 elements. Sum-rate maximization is used as the criterion for antenna selection. A selection scheme based on convex optimization is nearly optimal and used as a benchmark. The achieved sum-rate is compared with that of a very simple scheme that selects the antennas with the highest received power. The power-based scheme gives performance close to the convex optimization scheme, for the measured channels. This observation indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF chains and performing antenna selection using simple algorithms.

We consider a wireless network comprising a number of cochannel (hence mutually interfering) links. We study the power control problem of maximizing the rate that all links can simultaneously support under a novel setup, where receivers have interference cancelation (IC) capabilities. The problem of allocating the transmitting power is intertwined with determining the links on which receivers can perform IC and the order of cancelations. We provide and prove the theoretical results of the problem complexity and structural properties. For the problem solution, we propose a mixed-integer linear programming framework that enables jointly determining the optimal power and the IC patterns using off-the-shelf algorithms. This allows for the accurate assessment of the potential of IC for power control. Extensive numerical results are presented for performance evaluation, demonstrating the benefit of deploying IC in power control.

Multiuser multiple-input-multiple-output (MIMO) cellular systems with an excess of base station (BS) antennas (Massive MIMO) offer unprecedented multiplexing gains and radiated energy efficiency. Oscillator phase noise is introduced in the transmitter and receiver radio frequency chains and severely degrades the performance of communication systems. We study the effect of oscillator phase noise in frequency-selective Massive MIMO systems with imperfect channel state information. In particular, we consider two distinct operation modes, namely, when the phase noise processes at the M BS antennas are identical (synchronous operation) and when they are independent (nonsynchronous operation) . We analyze a linear and low-complexity time-reversal maximum-ratio combining reception strategy. For both operation modes, we derive a lower bound on the sum-capacity, and we compare their performance. Based on the derived achievable sum-rates, we show that with the proposed receive processing, an O(√M) array gain is achievable. Due to the phase noise drift, the estimated effective channel becomes progressively outdated. Therefore, phase noise effectively limits the length of the interval used for data transmission and the number of scheduled users. The derived achievable rates provide insights into the optimum choice of the data interval length and the number of scheduled users.

A robust and accurate positioning solution is required to increase the safety in GPS-denied environments. Although there is a lot of available research in this area, little has been done for confined environments such as tunnels. Therefore, we organized a measurement campaign in a basement tunnel of Linköping university, in which we obtained ultra-wideband (UWB) complex impulse responses for line-of-sight (LOS), and three non-LOS (NLOS) scenarios. This paper is focused on time-of-arrival (TOA) ranging since this technique can provide the most accurate range estimates, which are required for range-based positioning. We describe the measurement setup and procedure, select the threshold for TOA estimation, analyze the channel propagation parameters obtained from the power delay profile (PDP), and provide statistical model for ranging. According to our results, the rise-time should be used for NLOS identification, and the maximum excess delay should be used for NLOS error mitigation. However, the NLOS condition cannot be perfectly determined, so the distance likelihood has to be represented in a Gaussian mixture form. We also compared these results with measurements from a mine tunnel, and found a similar behavior.

We propose a nonlinear signal space diversity (SSD) precoding technique that produces transmit signals that have constant envelope (CE) in discrete time, resulting in low peak-to-average power ratio (PAPR) waveforms after pulse-shape filtering. We propose two methods for construction of CE signal set. While the proposed CE-SSD scheme is inferior to the conventional SSD designs in terms of coding gain performance, it performs better in terms of overall power efficiency because of the reduced back-off requirement of the power amplifier (PA).

We consider a multipair relay channel, where multiple sources communicate with multiple destinations with the help of a full-duplex (FD) relay station (RS). All sources and destinations have a single antenna, while the RS is equipped with massive arrays. We assume that the RS estimates the channels by using training sequences transmitted from sources and destinations. Then, it uses maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To significantly reduce the loop interference (LI) effect, we propose two massive MIMO processing techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the RS. We derive an exact achievable rate in closed-form and evaluate the system spectral efficiency. We show that, by doubling the number of antennas at the RS, the transmit power of each source and of the RS can be reduced by 1.5 dB if the pilot power is equal to the signal power and by 3 dB if the pilot power is kept fixed, while maintaining a given quality-of-service. Furthermore, we compare FD and half-duplex (HD) modes and show that FD improves significantly the performance when the LI level is low.

We consider a multipair decode-and-forward relay channel, where multiple sources transmit simultaneously their signals to multiple destinations with the help of a full-duplex relay station. We assume that the relay station is equipped with massive arrays, while all sources and destinations have a single antenna. The relay station uses channel estimates obtained from received pilots and zero-forcing (ZF) or maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To significantly reduce the loop interference effect, we propose two techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the relay station. We derive an exact achievable rate expression in closed-form for MRC/MRT processing and an analytical approximation of the achievable rate for ZF processing. This approximation is very tight, particularly for a large number of relay station antennas. These closed-form expressions enable us to determine the regions where the full-duplex mode outperforms the half-duplex mode, as well as to design an optimal power allocation scheme. This optimal power allocation scheme aims to maximize the energy efficiency for a given sum spectral efficiency and under peak power constraints at the relay station and sources. Numerical results verify the effectiveness of the optimal power allocation scheme. Furthermore, we show that, by doubling the number of transmit/receive antennas at the relay station, the transmit power of each source and of the relay station can be reduced by 1.5 dB if the pilot power is equal to the signal power, and by 3 dB if the pilot power is kept fixed, while maintaining a given quality of service.

We present a fast algorithm that, for a given input sequence and a linear channel code, computes the syndrome posterior probability (SPP) of the code, i.e., the probability that all parity check relations of the code are satisfied. According to this algorithm, the SPP can be computed blindly, i.e., given the soft information on a received sequence we can compute the SPP for the code without first decoding the bits. We show that the proposed scheme is efficient by investigating its computational complexity. We then consider two scenarios where our proposed SPP algorithm can be used. The first scenario is when we are interested in finding out whether a certain code was used to encode a data stream. We formulate a statistical hypothesis test and we investigate its performance. We also compare the performance of our scheme with that of an existing scheme. The second scenario deals with how we can use the algorithm for reducing the computational complexity of a blind decoding process. We propose a heuristic sequential statistical hypotheses test to use the fact that in real applications, the data arrives sequentially, and we investigate its performance using system simulations.

We investigate multiple-input single-output channel capacity taking dissipation in the power amplifiers into account. We consider the case of a fixed channel with full channel state information (CSI) at both the transmitter and receiver. The capacity expression is given on closed form, and we show that the optimal solution is antenna selection. An algorithm for finding minimum consumed power for any given mutual information is further developed, and we show that the power-mutual information pair is capacity-achieving. We also investigate the ergodic Rayleigh fading channel with full CSI at the receiver and no instantaneous CSI at the transmitter. We devise a numerical approach which finds the global optimum given a quantization of the space of possible allocated powers. We further show that down to the simulation precision, the ergodic Rayleigh fading solution again is antenna selection. It is shown that the allocation algorithms have low computational complexity and give significant rate and total consumed power gains in comparison to previous state of the art.

Far-field wireless recharging based on microwave power transfer (MPT) will free mobile devices from interruption due to finite battery lives. Integrating MPT with wireless communications to support simultaneous information-and-power transfer (SIPT) allows the same spectrum to be used for dual purposes without compromising the quality of service. In this paper, we propose the novel approach of realizing SIPT in a broadband downlink system where users are assigned orthogonal frequency sub-channels and a base station transfers information and energy to users over spatially separated channels called the data and MPT channels. Optimizing the power control for such a system results in a new class of multiuser power-control problems featuring the circuit-power constraints, namely that the wirelessly transferred power must be sufficiently large for operating receiver circuits. Solving these problems gives a set of power-control algorithms that exploit channel diversity in frequency for simultaneously enhancing the throughput and MPT efficiency. For the single-user SIPT system, the optimal power allocation is shown to perform water filling in frequency with water levels for different users depending on the corresponding MPT sub-channel gains. Next, an efficient power-control algorithm is proposed for the multiuser SIPT system based on sequential scheduling of mobiles by comparing their data rates and circuit-power constraints. This algorithm is proved to be optimal for the practical scenario of highly correlated data and MPT channels.

We consider a multi-pair relay channel where multiple sources simultaneously communicate with destinations using a relay. Each source or destination has only a single antenna, while the relay is equipped with a very large antenna array. We investigate the power efficiency of this system when maximum ratio combining/maximal ratio transmission (MRC/MRT) or zero-forcing (ZF) processing is used at the relay. Using a very large array, the transmit power of each source or relay (or both) can be made inversely proportional to the number of relay antennas while maintaining a given quality-of-service. At the same time, the achievable sum rate can be increased by a factor of the number of source-destination pairs. We show that when the number of antennas grows to infinity, the asymptotic achievable rates of MRC/MRT and ZF are the same if we scale the power at the sources. Depending on the large scale fading effect, MRC/MRT can outperform ZF or vice versa if we scale the power at the relay.

This work considers the benefits of allowing spectrum sharing among co-located wireless service providers operating in the same multicell network. Although spectrum sharing was shown to be valuable in some scenarios where the created interference can be eliminated, the benefits have not clearly shown for multicell networks with aggressive reuse. We explore this question and show that spectrum sharing is preferred for just a certain subset of the users defined by their distance from the serving bases, while beyond this distance, an orthogonal division of resources between operators gives better results. The claims are backed with theoretical analysis matching our simulations.

We consider the mapping of bits-to-superimposed constellation symbols in terms of the achievable rate on the channel for the HARQ system using superposition coding as proposed in [2]. We show that using a Gray mapping of bits-to-superimposed constellation symbols has better performance than the conventional natural mapping that results from superposition in signal space, for all the values of the superposition ratio. We also show through link-level simulations that the predicted gains in terms of achievable rate can be realized in practice using LDPC codes. Furthermore, we show that the optimal superposition ratio for the Gray mapping case results in conventional higher order constellation symbols after the superposition operation.

The papers in this special issue focus on large-scale multiple antenna wireless systems and services.

We investigate an orthogonal frequency-division multiplexing (OFDM)-based downlink transmission scheme for large-scale multi-user (MU) multiple-input multiple-output (MIMO) wireless systems. The use of OFDM causes a high peak-to-average (power) ratio (PAR), which necessitates expensive and power-inefficient radio-frequency (RF) components at the base station. In this paper, we present a novel downlink transmission scheme, which exploits the massive degrees-of-freedom available in large-scale MU-MIMO-OFDM systems to achieve low PAR. Specifically, we propose to jointly perform MU precoding, OFDM modulation, and PAR reduction by solving a convex optimization problem. We develop a corresponding fast iterative truncation algorithm (FITRA) and show numerical results to demonstrate tremendous PAR-reduction capabilities. The significantly reduced linearity requirements eventually enable the use of low-cost RF components for the large-scale MU-MIMO-OFDM downlink.

We consider the optimization of Chase combining (CC)-based hybrid-automatic repeat request (HARQ) schemes with a limit on the maximum number of retransmissions. We formulate two optimization problems: (i) minimizing the packet drop probability (PDP) under a total average transmit power constraint, and (ii) minimizing the average transmit power under a fixed PDP constraint. Towards solving these equivalent optimization problems, we provide a closed-form expression for the outage probability of a CC-HARQ scheme. We then show that solving the optimization problems using an exact expression of the outage probability becomes complex with an increase in the maximum number of retransmissions. We propose an alternative approach in which we approximate the optimization problems by using an approximate outage probability expression and formulate the two optimization problems as two equivalent geometric programming problems (GPPs), which can be solved efficiently even for a large limit on the maximum number of retransmissions. The results show that the optimal power allocation solution provides significant gains over the equal power allocation solution. For PDP values below 10-3, the optimal solution provided by the GPP approach has a performance close to that of the solution provided by solving the optimization problem exactly using nonlinear optimization techniques.

We consider the multi-user MIMO broadcast channel with M single-antenna users and N transmit antennas under the constraint that each antenna emits signals having constant envelope (CE). The motivation for this is that CE signals facilitate the use of power-efficient RF power amplifiers. Analytical and numerical results show that, under certain mild conditions on the channel gains, for a fixed M, an array gain is achievable even under the stringent per-antenna CE constraint. Essentially, for a fixed M, at sufficiently large N the total transmitted power can be reduced with increasing N while maintaining a fixed information rate to each user. Simulations for the i.i.d. Rayleigh fading channel show that the total transmit power can be reduced linearly with increasing N (i.e., an O(N) array gain). We also propose a precoding scheme which finds near-optimal CE signals to be transmitted, and has O(MN) complexity. Also, in terms of the total transmit power required to achieve a fixed desired information sum-rate, despite the stringent per-antenna CE constraint, the proposed CE precoding scheme performs close to the sum-capacity achieving scheme for an average-only total transmit power constrained channel.

A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. Lower capacity bounds for maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) detection are derived. An MRC receiver normally performs worse than ZF and MMSE. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option. The tradeoff between the energy efficiency (as measured in bits/J) and spectral efficiency (as measured in bits/channel use/terminal) is quantified for a channel model that includes small-scale fading but not large-scale fading. It is shown that the use of moderately large antenna arrays can improve the spectral and energy efficiency with orders of magnitude compared to a single-antenna system.

We consider multicell multiuser MIMO systems with a very large number of antennas at the base station (BS). We assume that the channel is estimated by using uplink training. We further consider a physical channel model where the angular domain is separated into a finite number of distinct directions. We analyze the so-called pilot contamination effect discovered in previous work, and show that this effect persists under the finite-dimensional channel model that we consider. In particular, we consider a uniform array at the BS. For this scenario, we show that when the number of BS antennas goes to infinity, the system performance under a finite-dimensional channel model with P angular bins is the same as the performance under an uncorrelated channel model with P antennas. We further derive a lower bound on the achievable rate of uplink data transmission with a linear detector at the BS. We then specialize this lower bound to the cases of maximum-ratio combining (MRC) and zero-forcing (ZF) receivers, for a finite and an infinite number of BS antennas. Numerical results corroborate our analysis and show a comparison between the performances of MRC and ZF in terms of sum-rate.

We present a scheme for reducing the part of the downlink signaling traffic in wireless multiple access systems that contains scheduling information. The theoretical basis of the scheme is that the scheduling decisions made by the base station are correlated with the CSI reports from the mobiles. This correlation can be exploited by the source coding scheme that is used to compress the scheduling maps before they are sent to the mobiles. In the proposed scheme, this idea is implemented by letting the mobiles make tentative scheduling decisions themselves, and then letting the base station transmit "agreement maps" instead of raw scheduling maps to the mobiles. The agreement maps have lower entropy and they require less resources to be transmitted than the original scheduling maps do. The improvement can be substantial. We also model the task of finding the optimal scheduling assignments according to the proposed scheme as a combinatorial optimization problem and present an efficient algorithm to find the optimal solution.

Blind decoding, used on control channels of some multi-user wireless access systems, is a technique for achieving adaptive modulation and coding. The idea is to adapt the modulation and coding scheme to the channel quality but instead of signaling the parameters used explicitly, the receiver blindly tries a number of fixed parameter combinations until a successful decoding attempt is detected, with the help of a cyclic redundancy check. In this paper we suggest a new method for reducing the complexity and energy consumption associated with such blind decoding schemes. Our idea is to use a mini-CRC injected early in the data stream to determine if the current decoding attempt is using the correct modulation and coding parameters. We analyze and exemplify the complexity gain of this approach and also investigate the impact of the rearrangement of the CRC scheme in terms of the probability of undetected error. The presented results for the complexity gain are promising and the impact on the error detection capability turns out to be small if any.

The fundamental problem of our interest here is soft MIMO detection. We propose a method that yields excellent performance, at low and at fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial step consisting of selecting columns, the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time, and it is readily and massively parallelizable.

We consider downlink cellular multi-user communication between a base station (BS) having N antennas and M single-antenna users, i.e., an N × M Gaussian Broadcast Channel (GBC). Under an average only total transmit power constraint (APC), large antenna arrays at the BS (having tens to a few hundred antennas) have been recently shown to achieve remarkable multi-user interference (MUI) suppression with simple precoding techniques. However, building large arrays in practice, would require cheap/power-efficient Radio-Frequency (RF) electronic components. The type of transmitted signal that facilitates the use of most power-efficient RF components is a constant envelope (CE) signal (i.e., the amplitude of the signal transmitted from each antenna is constant for every channel use and every channel realization). Under certain mild channel conditions (including i.i.d. fading), we analytically show that, even under the stringent per-antenna CE transmission constraint (compared to APC), MUI suppression can still be achieved with large antenna arrays. Our analysis also reveals that, with a fixed M and increasing N, the total transmitted power can be reduced while maintaining a constant signal-to-interference-noise-ratio (SINR) level at each user. We also propose a novel low-complexity CE precoding scheme, using which, we confirm our analytical observations for the i.i.d. Rayleigh fading channel, through Monte-Carlo simulations. Simulation of the information sum-rate under the per-antenna CE constraint, shows that, for a fixed M and a fixed desired sum-rate, the required total transmit power decreases linearly with increasing N, i.e., an O(N) array power gain. Also, in terms of the total transmit power required to achieve a fixed desired information sum-rate, despite the stringent per-antenna CE constraint, the proposed CE precoding scheme performs close to the GBC sum-capacity (under APC) achieving scheme.

This paper considers multicell multiuser MIMO systems with very large antenna arrays at the base station. We propose an eigenvalue-decomposition-based approach to channel estimation, that estimates the channel blindly from the received data. The approach exploits the asymptotic orthogonality of the channel vectors in very large MIMO systems. We show that the channel to each user can be estimated from the covariance matrix of the received signals, up to a remaining scalar multiplicative ambiguity. A short training sequence is required to resolve this ambiguity. Furthermore, to improve the performance of our approach, we combine it with the iterative least-square with projection (ILSP) algorithm. Numerical results verify the effectiveness of our channel estimation approach.

Large antenna arrays at the base station can facilitate power efficient single user downlink communication due to the inherent array power gain, i.e., under an average only total transmit power constraint, for a fixed desired information rate, the required total transmit power can be reduced by increasing the number of base station antennas (e.g. with i.i.d. fading, the required total transmit power can be reduced by roughly 3 dB with every doubling in the number of base station antennas, i.e., an O(N) array power gain can be achieved with N antennas). However, in practice, building power efficient large antenna arrays would require power efficient amplifiers/analog RF components. With current technology, highly linear power amplifiers generally have low power efficiency, and therefore linearity constraints on power amplifiers must be relaxed. Under such relaxed linearity constraints, the transmit signal that suffers the least distortion is a signal with constant envelope (CE). In this paper, we consider a single user Gaussian multiple-input single-output (MISO) downlink channel where the signal transmitted from each antenna is constrained to have a constant envelope (i.e., for every channel-use the amplitude of the signal transmitted from each antenna is constant, irrespective of the channel realization). We show that under such a per-antenna CE constraint, the complex noise-free received signal lies in the interior of a “doughnut” shaped region in the complex plane. The per-antenna CE constrained MISO channel is therefore equivalent to a doughnut channel, i.e., a single-input single-output (SISO) AWGN channel where the channel input is constrained to lie inside a “doughnut” shaped region. Using this equivalence, we analytically compute a closed-form expression for an achievable information rate under the per-antenna CE constraint. We then show that, for a broad class of fading channels (i.i.d. and direct-line-of-sight (DLOS)), even under the more stringent per-antenna CE constraint (compared to the average only total power constraint), an O(N) array power gain can still be achieved with N base station antennas. We also show that with N ≫1, compared to the average only total transmit power constrained channel, the extra total transmit power required under the per-antenna CE constraint, to achieve a desired information rate is small and bounded for a broad class of fading channels (i.i.d. and DLOS). We also propose novel CE precoding algorithms. The analysis and algorithms presented are general and therefore applicable to conventional systems with a small number of antennas. Analytical results are supported with numerical results for the i.i.d. Rayleigh fading channel.

Several new features are added for the High Speed Packet Access (HSPA) evolution in order to meet the requirements set by the International Mobile Telecommunications Advanced (IMT-A). The main objective of these new features is to increase the average spectral efficiency. One possible technique for improving downlink spectral efficiency would be to introduce support for four branch multiple input multiple output (MIMO), i.e. utilize up to four transmit and receive antennas to enhance the spatial multiplexing gains and to offer improved beamforming capabilities. Four branch MIMO provides up to 84 Mbps per 5 MHz carrier for high signal to noise ratio (SNR) users and improves the coverage for low SNR users. This paper discusses a few design considerations related to physical layer aspects of four branch MIMO. In the first part of the paper we outline a few design options and show that four branch MIMO can be introduced with modest impact on the existing standards and networks. In the second part of the paper we show by means of system level simulation that significant gains in average user throughput and cell edge user throughput can be achieved with four branch MIMO. Based on these evaluations we conclude that four branch MIMO is an important component in the continued evolution of the HSPA standard.

In this paper, we present an improved detector for ACK/NACK message detection in the LTE uplink control channel with imperfect channel state information at the receiver. The detector is based on the generalized likelihood-ratio test (GLRT) paradigm. We derive detection metrics for the cases when the noise variances at the receiver are known and unknown. Noise here may comprise both thermal noise and interference. Simulation results show remarkable performance gains of the GLRT-based detector with unknown noise variances compared to the training-based maximum-likelihood detector with unknown noise variances when the noise variances in two slots are different. Furthermore, the performance of the GLRT-based detector with unknown noise variances is nearly the same as that of the training-based maximum-likelihood detector with known noise variances.

The delay constraints imposed by future wireless applications require a suitable metric for assessing their impact on the overall system performance. Since the classical Shannon's ergodic capacity fails to do so, the so-called effective rate was recently established as a rigorous alternative. While prior relevant works have improved our knowledge on the effective rate characterization of communication systems, an analytical framework encompassing several fading models of interest is not yet available. In this paper, we pursue a detailed effective rate analysis of Nakagami-m, Rician and generalized-K multiple-input single-output (MISO) fading channels by deriving new, analytical expressions for their exact effective rate. Moreover, we consider the asymptotically low and high signal-to-noise regimes, for which tractable, closed-form effective rate expressions are presented. These results enable us to draw useful conclusions about the impact of system parameters on the effective rate of different MISO fading channels. All the theoretical expressions are validated via Monte-Carlo simulations.

Large antenna arrays at the transmitter (TX) have recently been shown to achieve remarkable intra-cell interference suppression at low complexity. However, building large arrays in practice, would require the use of power-efficient RF amplifiers, which generally have poor linearity characteristics and hence would require the use of input signals with a very small peak-to-average power ratio (PAPR). In this paper, we consider the single-user Multiple-Input Single-Output (MISO) channel for the case where the TX antennas are constrained to transmit signals having constant envelope (CE). We show that, with per-antenna CE transmission the effective channel seen by the receiver is a SISO AWGN channel with its input constrained to lie in a doughnut-shaped region. For a broad class of fading channels, analysis of the effective doughnut channel shows that under a per-antenna CE input constraint, i) compared to an average-only total TX power constrained MISO channel, the extra total TX power required to achieve a desired information rate is small and bounded, ii) with N TX antennas an O(N) array power gain is achievable, and iii) for a desired information rate, using power-efficient amplifiers with CE inputs would require significantly less total TX power when compared to using highly linear (power-inefficient) amplifiers with high PAPR inputs.

In this work, we consider spectrum sensing of Gaussian signals with structured covariance matrices. We show that the optimal detector based on the probability distribution of the sample covariance matrix is equivalent to the optimal detector based on the raw data, if the covariance matrices are known. However, the covariance matrices are unknown in general. Therefore, we propose to estimate the unknown parameters using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.

We consider low complexity precoding for the Multiple Input Single Output (MISO) Gaussian Broadcast channel with Ntantennas at the base station and Nusingle antenna users in the downlink. Theoretical studies have suggested high throughput communication with increasing spatial dimensions i.e., min(Nt,Nu). Nevertheless, most modern communication standards are unable to exploit the spatial dimension fully, since they are restricted to orthogonal communication techniques like TDMA/FDMA (Time/Frequency Division Multiplexed Access) which are known to be sub-optimal. This restriction is mostly due to the prohibitive complexity of optimal/near-optimal precoding schemes. On the other hand low complexity techniques like Zero Forcing (ZF) and MMSE have poor sum rate performance. In this paper, we propose a novel low-complexity user grouping based precoding scheme which schedules all users on the same time-frequency resource (i.e., optimal utilization of resources). The proposed precoder is analytically shown to achieve a sum rate performance significantly better than the ZF precoder at similar complexity. Through simulations, it is also observed to achieve a significant fraction of the sum rate achieved by the optimal schemes.

We present a fast mechanism for determining which channel code that was used on a communication link. In the proposed scheme, the receiver does not need to receive the entire data to determine the actual code. Moreover, the proposed scheme can also be used to determine the interleaving/scrambling sequence that was used at the transmitter. We investigate the performance of the scheme for some standard convolutional codes.

In this paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigen-value multiplicities. We derive the generalized likelihood-ratio test (GLRT) for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio and show that the GLRT for these applications, of which some are already known, are special cases of the general result.

The fundamental problem of our interest is soft MIMO detection for the case of block fading, i.e., when the transmitted codeword spans over several independent channel realizations. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. The new results consist of a new algorithm, a new performance measure, and a thorough complexity discussion.

We derive a Gaussian approximation of the LLR distribution conditioned on the transmitted signal and the channel matrix for the soft-output via partial marginalization MIMO detector. This detector performs exact ML as a special case. Our main results consist of discussing the operational meaning of this approximation and a proof that, in the limit of high SNR, the LLR distribution of interest converges in probability towards a Gaussian distribution.

We consider a wireless network comprising a number of mutually-interfering links. We study the transmit power control problem that determines the egalitarian signal-to-interference-plus-noise ratio under a novel setup. Namely, we assume that the receivers have multiuser detection capability, which enables decoding and cancellation of the interference, when it is strong enough. Determining the interference terms that can be cancelled is a combinatorial problem, which is intertwined with the power control problem. We propose a mixed-integer linear programming framework that jointly solves these problems optimally, using off-the-shelf algorithms. We illustrate with a simulation result the merit of the novel approach against the conventional one that precludes interference cancellation.

In this paper, we study an achievable rate region of the two-user multiple-input single-output (MISO) interference channel. We find the transmit beamforming vectors that achieve Pareto-optimal points. We do so, by deriving a sufficient condition for Pareto optimality. Given the beamforming vector of one transmitter, this condition enables us to determine the beamforming vector of the other transmitter that forms a Pareto-optimal pair. The latter can be done in closed form by solving a cubic equation. The result is validated against state-of-the-art methods via numerical illustrations.

We consider multicell multiuser MIMO systems with a very large number of antennas at the base station. We assume that the channel is estimated by using uplink training sequences, and we consider a physical channel model where the angular domain is separated into a finite number of directions. We analyze the so-called pilot contamination effect discovered in previous work, and show that this effect persists under the finite-dimensional channel model that we consider. We further derive closed-form bounds on the achievable rate of uplink data transmission with maximum-ratio combining, for a finite and an infinite number of base station antennas.

In this paper, we propose two spectrally efficient adaptive partial decode-and-forward (DF) cooperative communication schemes with quantized feedback: adaptive partial repetition DF scheme and adaptive partial coded cooperation DF scheme. We assume that the relay node only has partial channel-state information, which is obtained via an quantized feedback link. We use the so-called mutual information (MI) model to adaptively optimize the amount of message transmitted by the relay node under a given block-error-rate constraint. We develop adaptive algorithms and implementation details for both schemes. Simulation results show that with the quantized feedback, the MI model can predict well the amount of message that needs to be transmitted by the relay node, and that the two proposed schemes can substantially increase the spectral efficiency of the system.

Dynamic OFDMA has been recognized as a promising technique for improving the performance of future wireless cellular systems. However, this potential performance improvement comes at the cost of additional signaling overhead, which can have a non-negligible effect on the system efficiency. In this paper, we propose a new method for optimizing the frame length for the downlink in OFDMA systems. The method maximizes the system efficiency by taking into account both the channel conditions and the amount of signaling overhead needed to deliver scheduling maps to the users. We formulate the frame length optimization problem mathematically. By exploiting the structure of this problem, we develop an algorithm that solves a sequence of dynamic programming problems. Simulation results reveal some insight into fundamental limitations as well as provide guidelines for the design of dynamic OFDMA systems.

We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely known noise and signal powers, we set up a vector-matrix model for an OFDM signal with a cyclic prefix and derive the optimal Neyman-Pearson detector from first principles. The optimal detector exploits the inherent correlation of the OFDM signal incurred by the repetition of data in the cyclic prefix, using knowledge of the length of the cyclic prefix and the length of the OFDM symbol. We compare the optimal detector to the energy detector numerically. We show that the energy detector is near-optimal (within 1 dB SNR) when the noise variance is known. Thus, when the noise power is known, no substantial gain can be achieved by using any other detector than the energy detector. For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise power or the signal power. The GLRT detector is compared to state-of-the-art OFDM signal detectors, and shown to improve the detection performance with 5 dB SNR in relevant cases.

The computational complexity of optimum decoding for an orthogonal space-time block code GNsatisfying GNHGN= c(Σk=1K|sk|2)INwhere c is a positive integer is quantified. Four equivalent techniques of optimum decoding which have the same computational complexity are specified. Modifications to the basic formulation in special cases are calculated and illustrated by means of examples. This paper corrects and extends, and unifies them with the results from the literature. In addition, a number of results from the literature are extended to the case c >; 1.

In this article, we consider practical approaches to Costa precoding (also known as dirty paper coding). Specifically, we propose a symbol-by-symbol scheme for cancellation of interference known at the transmitter in a relay-aided downlink channel. For finite-alphabet signaling and interference, we derive the optimal (in terms of maximum mutual information) modulator under a given power constraint. A sub-optimal modulator is also proposed by formulating an optimization problem that maximizes the minimum distance of the signal constellation, and this non-convex optimization problem is approximately solved by semi-definite relaxation. For the case of binary signaling with binary interference, we obtain a closed-form solution for the sub-optimal modulator, which only suffers little performance degradation compared to the optimal modulator in the region of interest. For more general signal constellations and more general interference distributions, we propose an optimized Tomlinson-Harashima precoder (THP), which uniformly outperforms conventional THP with heuristic parameters. Bit-level simulation shows that the optimal and sub-optimal modulators can achieve significant gains over the THP benchmark as well as over non-Costa reference schemes, especially when the power of the interference is larger than the power of the noise.

We point out an error in a derivation in the recent paper, and provide a correct and much shorter calculation of the result in question. In passing, we also connect the results in to the literature on array signal processing and on principal component analysis, and show that the signal detection methods proposed in follow as special cases of standard results in these fields.

We consider the optimization of power in incremental redundancy (IR) based hybrid automatic repeat request (HARQ) schemes when the maximum number of (re)transmissions is fixed. We formulate two optimization problems: (i) minimizing the packet drop probability (PDP) under a total average transmit power constraint, and (ii) minimizing the average transmit power under a fixed PDP constraint. We consider in detail the special case of only two allowed transmissions, and we prove that the two optimization problems are equivalent. For this special case, we also provide a sub-optimal root-finding solution and compare its performance with the optimal solution obtained through an exhaustive search. The results show that the optimal power allocation can provide significant gains over the equal power solution in terms of average transmit power spent. The performance of the proposed root-finding solution is practically the same as that of the optimal solution.

We compute the random coding error exponent for linear multihop amplify-and-forward (AF) relay channels. Instead of considering only the achievable rate or the error probability as a performance measure separately, the error exponent results can give us insight into the fundamental tradeoff between the information rate and communication reliability in these channels. This measure enables us to determine what codeword length that is required to achieve a given level of communication reliability at a rate below the channel capacity. We first derive a general formula for the random coding exponent of general multihop AF relay channels. Then we present a closed-form expression of a tight upper bound on the random coding error exponent for the case of Rayleigh fading. From the exponent expression, the capacity of these channels is also deduced. The effect of the number of hops on the performance of linear multihop AF relay channels from the error exponent point of view is studied. As an application of the random coding error exponent analysis, we then find the optimal number of hops which maximizes the communication reliability (i.e., the random coding error exponent) for a given data rate. Numerical results verify our analysis, and show the tightness of the proposed bound.

We establish a general optimization framework for joint resource allocation and interference mitigation. By utilizing axiomatic interference functions, our problem formulation is very general and includes many problems as special cases. We consider the sum rate maximization problem, which is known to be NP hard. It is shown that due to the structural model of the interference function, the joint optimization of powers and adaptive receive strategies ends up with the optimization solely with respect to powers. This facilitates a reformulation of the problem under consideration as a problem of difference of convex functions (DC). Based on this DC representation, we employ a prismatic branch and bound algorithm to find a global optimum.

This paper considers the control signaling on the downlink in wireless multiple access systems, with focus on the part of the control signaling that carries information on the user's time/frequency scheduling assignments. A new idea is presented to reduce the amount of channel resources needed for this signaling. The idea is to exploit the fact that provided that only one single user is scheduled on each channel resource, then the different users' scheduling assignments are correlated. This correlation can be exploited by encoding the scheduling information differentially. In order to recover the scheduling information, a user must then decode the scheduling information of some of the others. This is possible, because on the downlink, all users can hear the transmission by the base station so that users with a high SNR may decode the control signaling sent to users with a lower SNR. We present a practical scheme to exploit this idea. Both analytical analysis and numerical examples illustrate that the proposed technique can provide a substantial reduction in signaling traffic.

We present an idea to reduce the part of the downlink signaling traffic in wireless multiple access systems that contains scheduling information. The theoretical basis of the scheme is that the scheduling decisions made by the base station are correlated with the CSI reports from the mobiles. This correlation can be exploited by the source coding scheme that is used to compress the scheduling maps before they are sent to the mobiles. In the proposed scheme, this idea is implemented by letting the mobiles make tentative scheduling decisions themselves, and then letting the base station transmit "agreement maps" instead of raw scheduling maps to the mobiles. The agreement maps have lower entropy and they require less resources to be transmitted than the original scheduling maps do. The improvement can be substantial.

We consider detection of signals encoded with orthogonal space-time block codes (OSTBC), using multiple receive antennas. Such signals contain redundancy and they have a specific structure, that can be exploited for detection. We derive the optimal detector, in the Neyman-Pearson sense, when all parameters are known. We also consider unknown noise variance, signal variance and channel coefficients. We propose a number of GLRT based detectors for the different cases, that exploit the redundancy structure of the OSTBC signal. We also propose an eigenvalue-based detector for the case when all parameters are unknown. The proposed detectors are compared to the energy detector. We show that when only the noise variance is known, there is no gain in exploiting the structure of the OSTBC. However, when the noise variance is unknown there can be a significant gain.

The computational complexity of optimum decoding for an orthogonal space-time block code is quantified. Four equivalent techniques of optimum decoding which have the same computational complexity are specified. Modifications to the basic formulation in special cases are calculated and illustrated by means of examples.

In this paper, we consider spectrum sensing of DVB-T signals in different fading environments. We compare state-of-the-art detectors including detectors based on pilot subcarriers, as well as detectors for general OFDM signals that exploit the correlation structure incurred by the cyclic prefix. Energy detection is also included for comparison. We show numerically that the choice of detector depends on the scenario, the detector requirements, and on the available prior knowledge. We also show that it is possible to obtain good detection performance by exploiting the correlation, even in a frequency selective channel.

In this paper, we study retransmission strategies for the symmetric relaying scenario when the number of retransmissions for a data packet is limited. We propose a retransmission scheme based on superposition modulation and give the exact packet drop probability expression for it when only one retransmission is allowed. We compare the performance of the proposed retransmission scheme with retransmission schemes based on classical decode-and-forward (DF) relaying and non-cooperative transmission. When only two retransmissions are allowed, the proposed scheme has a performance gain of 3 dB in SNR over the retransmission scheme based on classical DF relaying. Through simulations, we also study the effect of varying the superposition ratio for the retransmissions.

We study the uplink control signaling in 3GPP-Long Term Evolution (LTE) systems. Specifically, we propose a precoding method that uses complex-field coding (CFC) to improve the performance of the PUCCH format 2 control signaling. In the case of perfect channel state information (CSI) at the receiver and with a single receive antenna, the proposed method offers significant gains compared to the coding currently used in 3GPP-LTE. However the gains are marginal with two receive antennas. In order to examine the impact of channel estimation errors, we also derive the optimal detector for the case of imperfect receiver CSI, both for conventional coding and for the proposed CFC method.

We derive detection metrics for soft-output diversity combining for the case of imperfect channel state information at the receiver. We treat in particular the case when the noise variance at the receiver is unknown. We contrast conventional training-based methods to a detector based on the generalized likelihood-ratio (GLR) test paradigm. We study the performance of the detectors via EXIT chart analysis and via simulations of LDPC coded transmission over a fast Rayleigh fading channel. The results show that the GLR receivers can significantly outperform the conventional detectors.

Resource allocation and transmit optimization for the multiple-antenna Gaussian interference channel are important but difficult problems. The spatial degrees of freedom can be exploited to avoid, align, or utilize the interference. In recent literature, the upper boundary of the achievable rate region has been characterized. However, the resulting programming problems for finding the sum-rate, proportional fair, and minimax (egalitarian) operating points are non-linear and non-convex. In this paper, we develop a non-convex optimization framework based on monotonic optimization by outer polyblock approximation. First, the objective functions are represented in terms of differences of monotonic increasing functions. Next, the problems are reformulated as maximization of increasing functions over normal constraint sets. Finally, the idea to approximate the constraint set by outer polyblocks is explained and the corresponding algorithm is derived. Numerical examples illustrate the advantages of the proposed framework compared to an exhaustive grid search approach.

This paper proposes a new algorithm for downlink scheduling in OFDMA systems. The method maximizes the throughput, taking into account the amount of signaling needed to transmit scheduling maps to the users. A combinatorial problem is formulated and solved via a dynamic programming approach reminiscent of the Viterbi algorithm. The total computational complexity of the algorithm is upper bounded by O(K4)N) where K is the number of users that are being considered for scheduling in a frame and N is the number of resource blocks per frame.

We study the achievable rate region of the multiple-input single-output (MISO) interference channel (IFC), under the assumption that all receivers treat the interference as additive Gaussian noise. We assume the case of two users, and that the channel state information (CSI) is only partially known at the transmitters. Our main result is a characterization of Pareto-optimal transmit strategies, for channel matrices that satisfy a certain technical condition. Numerical examples are provided to illustrate the theoretical results.

This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.

Resource allocation and transmit optimization for the multiple-antenna Gaussian interference channel are important but difficult problems. Recently, there has been a large interest in algorithms that find operating points which are optimal in the sum-rate, proportional-fair, or minimax sense. Finding these points entails solving a nonlinear, non-convex optimization problem. In this paper, we develop an algorithm that solves these problems exactly, to within a prescribed level of accuracy and in a finite number of steps. The main idea is to rewrite the objective functions so that methods for monotonic optimization can be used. More precisely, we write each objective function as a difference between two functions which are strictly increasing over a normal constraint set. The so-obtained reformulated, equivalent problem can then be solved efficiently by using so-called polyblock optimization. Numerical examples illustrate the advantages of the proposed framework compared to an exhaustive grid search.

Control signaling strategies for scheduling information in cellular OFDM systems are studied. A single-cell multiuser system model is formulated that provides system capacity estimates accounting for the signaling overhead. Different scheduling granularities are considered, including the one used in the specifications for the 3G Long Term Evolution (LTE). A greedy scheduling method is assumed, where each resource is assigned to the user for which it can support the highest number of bits. The simulation results indicate that the cost of control signaling does not outweigh the scheduling gain, when compared with a simple round-robin scheme that does not need signaling of scheduling information. Furthermore, in the studied scenario, joint coding and signaling of scheduling information over all selected users is found to be superior to separate coding and signaling for each user. The results also indicate that the scheduling granularity used for LTE provides better performance than the full granularity.

This paper discusses implementation aspects of a recently proposed fixed-complexity soft-output (FCSO) symbol detector for MIMO systems (Larsson and Jalden, 2008). A further approximation to the FCSO detector is proposed which substantially reduces the complexity at the cost of a minor performance loss. With the resulting method, it is possible to carry out close-to ML detection for MIMO systems with a large number antennas (e.g. 4times4) using higher-order modulation schemes (e.g. 64-QAM) at low silicon cost in real-time. Furthermore, the parallelism inherited by the FCSO algorithm allows massive parallel processing which makes the method suitable for implementation in multi-core baseband signal processing hardware architectures.

We propose a novel half-duplex decode-and-forward relaying scheme based on partial repetition coding at the relay. In the proposed scheme, if the relay decodes the received message successfully, it re-encodes the message using the same channel code as the one used at the source, but retransmits only a fraction of the codeword. We analyze the proposed scheme and optimize the cooperation level (i.e., the fraction of the message that the relay should transmit). We compare our scheme with conventional repetition in which the relay retransmits the entire decoded message, with parallel coding, and additionally with dynamic decode-and-forward (DDF). We provide a finite-SNR analysis for all the collaborative schemes. The analysis reveals that the proposed partial repetition method can provide a gain of several dB over conventional repetition. It also shows that in general, power allocation is less important provided that one optimally allocates bandwidth. Surprisingly, the proposed scheme is able to achieve the same performance as that of parallel coding for some relay network configurations, but at a much lower complexity.

We study the MISO interference channel from a game-theoretic perspective. Recently, it was shown that the rates at the non-cooperative Nash equilibrium (NE) strategy are poor especially in the medium and high SNR regimes. A reasonable outcome of the cooperative approach, close to the Pareto boundary of the achievable rate region, was shown to be the zero-forcing (ZF) strategy. In this work, we prove that any point on the Pareto boundary can be achieved by a certain linear combination of the NE and ZF strategies. A scalar weight per user chooses between "selfish" (NE) and altruistic (ZF) behavior. Thereby, the difficult beamforming optimization is reduced to a simple weight optimization. Different optimal operating points, e.g. maximum weighted sum-rate, the Nash-bargaining solution, or the Egalitarian solution, can be obtained by a computationally efficient iterative algorithm. The results are characterized by instantaneous achievable rate regions and the corresponding operating points.

In a multi-user MIMO downlink where the base station has only estimates of the channels of the users, the sum-rate of multi-user beamforming saturates at high SNR. However, this is not the case for single-user beamforming. We propose a low-complexity user scheduling algorithm that selects the number of active users based on a closed-form approximation of the average sum-rate, and in particular does not add multiple users in the single-user optimality range. In order to develop this algorithm we derive the expected value of the rate of zero-forcing beamforming and MMSE beamforming with estimated channels and modify the greedy user selection accordingly. The gain of the proposed method is shown in numerical simulations.

We consider the problem of coordinating two competing multiple-antenna wireless systems (operators) that operate in the same spectral band. We formulate a rate region which is achievable by scalar coding followed by power allocation and beamforming. We show that all interesting points on the Pareto boundary correspond to transmit strategies where both systems use the maximum available power. We then argue that there is a fundamental need for base station cooperation when performing spectrum sharing with multiple transmit antennas. More precisely, we show that if the systems do not cooperate, there is a unique Nash equilibrium which is inefficient in the sense that the achievable rate is bounded by a constant, regardless of the available transmit power. An extension of this result to the case where the receivers use successive interference cancellation (SIC) is also provided. Next we model the problem of agreeing on beamforming vectors as a non-transferable utility (NTU) cooperative gametheoretic problem, with the two operators as players. Specifically we compute numerically the Nash bargaining solution, which is a likely resolution of the resource conflict assuming that the players are rational. Numerical experiments indicate that selfish but cooperating operators may achieve a performance which is close to the maximum-sum-rate bound.

We present a symbol-by-symbol approach to the problem of canceling known interference at the transmitter in a communication system. In the envisioned system, the modulator maps an information symbol (taken from a finite alphabet) and an interference symbol (from the complex field) onto a transmitted constellation point. Our scheme is based on joint optimization of a modulator and demodulator, subject to a constraint on the average transmit power. The demodulator picks the information symbol (as a function of the received symbol) that minimizes the average error probability. We emphasize that our focus is on transmission in a single (complex) dimension, and hence the proposed technique is a "modulation" rather than a "coding" scheme. We illustrate that the new scheme outperforms Tomlinson-Harashima precoding, which is a classical but suboptimal solution to the one-dimensional known-interference precoding problem. In our simulations, the new approach is able to perform close to the no-interference bound.

A simple structure to exploit both long-term and partial short-term channel state information at the transmitter (CSIT) over a family of correlated multiple-antenna channels is proposed. Partial short-term CSIT in the form of a weighting matrix is combined with a unitary transformation based on the long-term channel statistics. The heavily quantized feedback link is directly optimized to maximize the expected achievable rate under different power constraints, using vector quantization and convex optimization techniques on a sample channel distribution. Robustness against errors in the feedback link is also pursued with tools in channel optimized vector quantization. Simulations indicate the benefits of the proposed scheme.

The objective of this work is to assess some fundamental limits for opportunistic spectrum reuse via cognitive radio in a frequency-planned environment. We present a first-order analysis of the signal-to-noise-and-interference situation in a wireless cellular network, and analyze the impact of cognitive users starting to transmit. Two main conclusions emerge from our study. First, obtaining any substantial benefits from opportunistic spatial spectrum reuse in a frequency-planned network without causing substantial interference is going to be very challenging. Second, the cognitive users need to be more sensitive, by orders of magnitude, than the receivers in the primary system, especially if there is significant shadow fading. This latter problem can be alleviated by having cognitive users cooperate, but only if they are separated far apart so that they experience independent shadowing.

We propose a spectrally efficient transmission scheme for the half-duplex relay channel. In the proposed scheme, the relay combines N detected r-dimensional symbols and generates M new r-dimensional symbols, where M < N, using a linear transformation. The proposed linear transformation preserves the signal energy, and it facilitates decoupled symbol detection at the receiver. We also present an optimized design for the case of complex scalar modulation (r = 2) with N = 2 and M = 1. This design increases the spectral efficiency by 33 % compared to conventional decode- or amplify-and-forward relaying.

This paper presents a new approach to soft demodulation for MIMO channels. The proposed method is an approximation to the exact a posteriori probability-per-bit computer. The main idea is to marginalize the posterior density for the received data exactly over the subset of the transmitted bits that are received with the lowest signal-to-noise-ratio, and then marginalize this density approximately over the remaining bits. Unlike the exact demodulator, whose complexity is huge due to the need for enumerating all possible combinations of transmitted constellation points, the proposed method has very low complexity. Additionally, its complexity is fixed, which makes it suitable for pipelined implementation. Numerical examples illustrate its performance on slow fading 4times4 and 6times6 complex MIMO channels.

The objective of this work is to assess some fundamental limits of operation for cognitive radios in a frequency- planned environment. We present a first-order analysis of the carrier-to-noise-and-interference situation in a cellular wireless network, and analyze the impact of cognitive users starting to transmit. The main conclusion is that introducing cognitive transmitters in a frequency-planned cellular network without causing substantial interference is very challenging.

A novel method is proposed for estimation of the mutual coupling matrix of an antenna array. The method extends previous work by incorporating an unknown phase center and the element factor (antenna radiation pattern) in the model, and treating these as nuisance parameters during the estimation of coupling. To facilitate this, a parametrization of the element factor based on a truncated Fourier series is proposed. The Cramer-Rao bound (CRB) for the estimation problem is derived and used to analyze how the required amount of measurement data increases when introducing a more and more flexible model for the element factor. Finally, the performance of the proposed estimator is illustrated using data from measurements on an 8-element antenna array.

We study the maximum a posteriori probability model order selection algorithm for linear regression models, assuming Gaussian distributed noise and coefficient vectors. For the same data model, we also derive the minimum mean-square error coefficient vector estimate. The approaches are denoted BOSS (Bayesian order selection strategy) and BPM (Bayesian parameter estimation method), respectively. Both BOSS and BPM require a priori knowledge on the distribution of the coefficients. However, under the assumption that the coefficient variance profile is smooth, we derive "empirical Bayesian" versions of our algorithms, which require little or no information from the user. We show in numerical examples that the estimators can outperform several classical methods, including the well-known AIC and BIC for order selection.

We present a symbol-by-symbol approach to the problem of canceling known interference at the transmitter in a communication system. In the envisioned system, the modulator maps an information symbol (taken from a finite alphabet) and an interference symbol (from the complex field) onto a transmitted constellation point. The demodulator picks the information symbol (as a function of the received symbol) which minimizes the average error probability. We find the optimal modulator-demodulator pair, in the minimum-probability-of-symbol-error sense, via an iterative optimization procedure, for fixed average transmit power. We illustrate that the new scheme can perform close to the no-interference bound, and in particular that it outperforms Tomlinson-Harashima precoding, which is a classical but suboptimal solution to the problem under study.

We develop a general framework for design of receivers for the wireless relay channel. We derive the optimum detectors for various degrees of channel state information (CSI) at the destination. We consider both the case when the destination has access to full knowledge of the CSI and the case when it only knows the statistics of the channel. High-SNR and low-SNR approximations of the detectors are presented as well.

The paper proposed an enhancement to cooperative transmit diversity based on uncoded detect-and-forward, by using so-called constellation rearrangement (CR) at the relay. With CR, the relay uses a bit-symbol mapping that is different from the one used by the source. The authors find the optimal bit-symbol mappings for both the source and the relay and the associated optimal detectors, and show that the improvement over conventional relaying with Gray mapping at the source and the relay can amount to a power gain of several dB. This performance improvement comes at no additional power or bandwidth expense, and at virtually no increase in complexity.

We present a novel algorithm for interference suppression when the number of interferers is unknown. The key idea is to parameterize the second-order statistics of the interference via a mixture of several low-rank models. Numerical examples illustrate the performance of the method.

We derive the optimal receiver for RAKE diversity combining on channels with a sparse impulse response. The receiver is based on the Bayesian philosophy and thus it requires the knowledge of certain a priori parameters. However, we also derive an empirical Bayesian version of our receiver, which does not require any a priori knowledge, nor the choice of any user parameters. We show that both versions of our detector can outperform a classical training-based maximum-ratio-combining detector.

We design an optimum modulator for the Costa (dirty-paper) precoding problem under the constraint of a binary signaling alphabet, and assuming the interference symbols belong to a binary constellation. We evaluate the performance of our technique in terms of the mutual information between the channel input and output, and compare it to that of Tomlinson-Harashima precoding (THP) with optimized parameters. We show that our optimal modulator is always better than THP. In many relevant scenarios, the performance difference is significant

A simple structure to exploit both long-term and partial short-term channel state information at the transmitter (CSIT) over a family of correlated multiple-antenna channels is proposed. Partial short-term CSIT in the form of a weighting matrix is obtained via a resolution-constrained feedback link, combined with a unitary transformation based on the long-term channel statistics. The feedback link is optimized to maximize the expected achievable rate under different power constraints, using vector quantization techniques. Simulation indicate the benefits of the proposed scheme in all scenarios considered.

We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive a computationally efficient approximation to the minimum mean-square error (MMSE) estimate of the parameter vector. The performance of the so-obtained estimate is illustrated via numerical examples

In this paper, we study the uplink of a cellular network where reservation random access schemes such as reservation slotted ALOHA and reservation slotted nonpersistent ISMA are used for multiple access. When channel state information is not available to users when they contend for channel access, we find that capture effect inherent in wireless communications can exploit the channel variations and reserve users with favorable channels for data transmission. We investigate two channel models where in the first model, channel variations come from time-varying Nakagami-m fading and in the second model, mobile users have both time-varying fading and path loss

This paper derives new soft decision metrics for coded orthogonal signaling in symmetric /spl alpha/-stable noise, which has been used to model impulsive noise. In addition to the optimum metrics for Gaussian (/spl alpha/=2) noise and Cauchy (/spl alpha/=1) noise, a class of generalized likelihood ratio (GLR) metrics with lower side information requirements is derived. Through numerical results for a turbo code example, the Cauchy decoder is found to be robust for a wide range of /spl alpha/, and GLR metrics are found which provide performance gains relative to the Gaussian metric, but with lower complexity and less a priori information.

The problem of suppressing interference and jamming in a wireless communications receiver is a fundamental one. Many existing algorithms for this task are based on prewhitening of the received data so that the interference appears as white noise, and a conventional detector can be applied. However, such methods, in general, need to know the structure of the interference signal (for example, the rank of its autocorrelation matrix, m). The classical approach to the latter problem is either to assume that m is fixed (known), or to use rank detection techniques to estimate m first and then apply prewhitening in an adaptive fashion. This is suboptimal for two reasons: First, information is discarded when one takes a decision on m; second, no optimal rank detection methods exist. We present a new approach to the problem that circumvents the estimation of m. The key idea is to use a trained mixture model ("mixture of experts") to describe the received data, where each mixture component is associated with a particular rank, m. The optimal receiver for this model consists of a bank of detectors (one for each possible m) whose outputs are combined. One can think of this scheme as a way of taking "soft decisions" on the rank, m. Via numerical examples, we show that the new method significantly outperforms methods based on rank detection, at a modest increase in complexity.

This work studies a new concept, "constellation randomization" (CoRa), for performance improvement of transmission over quasi-static (block fading) multidimensional ("MIMO") channels with small signal constellations. The motivation behind CoRa is to reduce some of the mutual information penalty associated with the use of a finite input alphabet for signalling on a static, possibly ill-conditioned, channel. We show, both via information-theoretic considerations, and via Monte-Carlo simulation of the frame-error-rate (FER) of a coded system, that CoRa can offer a lower outage probability than a conventional spatial multiplexing system that uses a fixed signal constellation. In our examples the improvement offered by CoRa is comparable to that offered by linear (complex-field) precoding. This is interesting, because the performance gain of CoRa comes at virtually no computational expense.

This paper presents a new method for adaptive equalization of communication channels with intersymbol interference, for which the impulse response has an unknown length. Our approach is based on a parameterization of the impulse response as a Gaussian mixture model, whose parameters are estimated from a training sequence. It is shown that the new method can outperform conventional adaptive equalizers using training to estimate the channel.

Phased-array magnetic resonance imaging is an important contemporary research field in terms of the expected clinical gains in medical imaging technology. Recent research focused on heuristic coil image recombination methods as well as statistical signal processing approaches. In this paper, we investigate the performance of an adaptive signal processing approach, namely mixture of competitively trained models. The proposed method has the ability to train on a set of images and generalize its performance to previously unseen images. Performance evaluations on real data validate the effectiveness of this method.

In future applications of sensor networking technology, it is envisioned that nodes will be able to determine their geographical position by measuring the range differences between one another in a collaborative fashion. The aim of this letter is to provide quantitative expressions that can be used both to facilitate an understanding for why such distributed positioning works and to assess the ultimately achievable accuracy in practice. Specifically, we compute the Crame/spl acute/r-Rao bound on the positioning accuracy, under different assumptions on the network synchronization. Numerical examples illustrate our results.

We derive novel and compact formulas for the Cramer-Rao bound (CRB) associated with the identification of the parameters in a continuous-time autoregressive moving-average (ARMA) model, given nonuniformly sampled data. Our approach is based on a state-space formulation of the ARMA model, which facilitates a derivation of the CRB in closed form. Numerical examples illustrate our results.

We study the relation between a training-based detector and a coherent maximum-likelihood detector. We prove that under quite general conditions, the diversity gains associated with these two receivers are equal. Finally, we discuss the relation between our analysis and a related result in the literature.

We discuss new space-time codes tailored to point-to-multipoint, or broadcast, communications using layered source coding. Our codes can be encoded (and decoded) differentially, and they are based entirely on phase-shift keying. We discuss design criteria for the codes, and the design of optimal and suboptimal receiver structures. We also discuss the relation between our codes and a differentially encoded Alamouti code. Finally, we illustrate how our codes can be used to improve the throughput of a broadcasting network.

We consider signal processing methods for phased-array magnetic resonance imaging (MRI). A theoretical description of a phased-array MRI data model is presented and three image reconstruction algorithms are proposed to estimate the effective image pixels. An analysis is provided to show how the new algorithms compare to conventional reconstruction methods.

Linear space-time block coding (STBC) is a conceptually simple transmission technique for channels with multiple transmit and receive antennas. In this paper, we study a subclass of linear STBC, namely orthogonal STBC (OSTBC), with the primary goal of contributing towards a complete understanding of OSTBC. In particular, we prove that OSTBC is optimal in an important minimum mean-square error (MSE) sense. As a by-product, we also obtain a concise characterization of linear and orthogonal STBC's as well as the relationship between them. The MSE optimality of OSTBC shown herein can be used as a way of introducing this coding scheme in a formal manner.

Linear space-time block coding (STBC) is a conceptually simple transmission technique for channels with multiple transmit and receive antennas. We study a subclass of linear STBC, namely orthogonal STBC (OSTBC), with the primary goal of contributing toward a complete understanding of OSTBC. In particular, we prove that OSTBC is optimal in a minimum mean-square-error (MSE) sense, provided that a zero-forcing detector is used at the receiver. As a by-product, we also obtain a concise characterization of linear and orthogonal STBCs as well as the relationship between them. The MSE optimality of OSTBC can be used as a way of introducing this coding scheme from first principles.

We describe the so-called time-reversal space-time block coding (TR-STBC) transmission scheme for communication systems with multiple transmit antennas operating over frequency-selective channels. TR-STBC can be seen as an extension of the orthogonal space-time block codes for flat fading channels. We show that using TR-STBC, a linear filtering at the receiver can achieve an approximate decoupling of the space-time channel into scalar and independent frequency-selective channels and hence any standard maximum-likelihood sequence detector (MLSD) can be used for equalization. Numerical examples are provided to illustrate the performance of the TR-STBC transmission scheme.

The stochastic Cramer-Rao bound (CRB) plays an important role in array processing because several high-resolution direction-of-arrival (DOA) estimation methods are known to achieve! this bound asymptotically In this paper, we study the stochastic CRB on DOA estimation accuracy in the general case of arbitrary unknown noise field parametrized by a vector of unknowns. We derive explicit closed-form expressions for the CRB and examine its properties theoretically and by representative numerical examples.

Consider the problem of estimating the parameters in a continuous-time autoregressive (AR) model given measurements taken at arbitrary time instants. In this paper the Cramer-Rao bound for this problem is derived by using a technique based on the Slepian-Bang's formula and residue calculus. Furthermore, we investigate by means of numerical experiments how different sampling schemes can affect the accuracy. Interestingly enough, however, for the examples studied, the estimation accuracy is relatively insensitive to the choice of sampling strategy.

The matched-filterbank spectral estimators APES and CAPON have received considerable attention in a number of applications. Unfortunately, their computational complexity tends to limit their usage in several cases-a problem that has previously been addressed by different authors. In this paper, we introduce a novel method to the computation of the 1D and 2D APES and CAPON spectra, which is considerably faster than all existing techniques. Numerical examples are provided to demonstrate the application of APES to synthetic aperture radar (SAR) imaging, and to illustrate the reduction in computational complexity provided by our implementation.

We propose a method for estimating the amplitude spectrum of a 2D-signal from frequency domain (or phase history) data that contain gaps. This is a key problem in synthetic aperture radar (SAR) imaging with angular diversity. Our method is an extension of the well-known amplitude and phase estimation (APES) algorithm to the incomplete data case, and is called gapped-data APES (GAPES). It has recently been shown that APES minimizes a certain least-squares (LS) criterion, and our extension of APES is based on minimizing this criterion with respect to the missing data as well. We provide an example of the algorithm applied to SAR imaging from phase history data with angular diversity; and we also show the advantages of 2D data processing over 1D (row or column-wise) data processing that are enabled by our new algorithm.

The so-called orthogonal frequency division multiplexing (OFDM) technique has received considerable interest, especially in the area of wireless local area networks (WLANs). One way of meeting the demands for increased data rates in WLANs is to provide the transmitter and receiver with multiple antennas. In this letter, we consider the estimation of the channel and the design of optimal preambles (training sequences) for an OFDM system with two transmit and multiple receive antennas.

The stochastic Cramer-Rao bound (CRB) for direction estimation in array processing applications was indirectly derived some ten years ago as the (asymptotic) covariance matrix of the maximum likelihood (ML) estimator. Attempts to obtain the stochastic CRB directly via the CRB theory fell short of providing a simple derivation and consequently, no direct derivation of this useful performance bound was available in the open literature. we correct this situation by providing a textbook-like direct derivation of the stochastic CRB.

We consider the problem of estimating the direction-of-arrival (DOA) of one or more signals using an array of sensors, where some of the sensors fail to work before the measurement is completed. Methods for estimating the array output covariance matrix are discussed and used for DOA estimation together with the MUSIC algorithm and with a covariance matching technique. In contrast to MUSIC, the covariance matching technique can utilize information on the estimation accuracy of the array covariance matrix, and it is shown that this can yield a significant performance gain.