Reconfigurable Intelligent Surfaces (RIS) have been recognized as a promising technology to enhance both communication and sensing performance in integrated sensing and communication (ISAC) systems for future 6G networks. However, existing RIS optimization methods for improving ISAC performance are mainly based on semidefinite relaxation (SDR) or iterative algorithms. The former suffers from high computational complexity and limited scalability, especially when the number of RIS elements becomes large, while the latter yields suboptimal solutions whose performance depends on initialization. In this work, we introduce a lightweight RIS phase design framework that provides a closed-form solution and explicitly accounts for the trade-off between communication and sensing, as well as proportional beam gain distribution toward multiple sensing targets. The key idea is to partition the RIS configuration into two parts: the first part is designed to maximize the communication performance, while the second introduces small perturbations to generate multiple beams for multi-target sensing. Simulation results validate the effectiveness of the proposed approach and demonstrate that it achieves performance comparable to SDR but with significantly lower computational complexity.

As users in small cell networks increasingly rely on computation-intensive services, cloud-based access often results in high latency. Multi-access edge computing (MEC) mitigates this by bringing computational resources closer to end users, with small base stations (SBSs) serving as edge servers to enable low-latency service delivery. However, limited edge capacity makes it challenging to decide which services to deploy locally versus in the cloud, especially under unknown service demand and dynamic network conditions. To tackle this problem, we model service demand as a linear function of service attributes and formulate the service placement task as a linear bandit problem, where SBSs act as agents and services as arms. The goal is to identify the service that, when placed at the edge, offers the greatest reduction in total user delay compared to cloud deployment. We propose a distributed and adaptive multi-agent best-arm identification (BAI) algorithm under a fixed-confidence setting, where SBSs collaborate to accelerate learning. Simulations show that our algorithm identifies the optimal service with the desired confidence and achieves near-optimal speedup, as the number of learning rounds decreases proportionally with the number of SBSs. We also provide theoretical analysis of the algorithm's sample complexity and communication overhead.

Movable-antenna (MA) arrays are envisioned as a promising technique for enhancing secrecy performance in wireless communications by leveraging additional spatial degrees of freedom. However, when the eavesdropper is located in the same direction as the legitimate user, particularly in mmWave/THz bands where line-of-sight (LOS) propagation dominates, the secrecy performance of MA arrays becomes significantly limited, thus directionally insecure. To address this challenge, we employ a joint design that combines an MA array with a frequency-diverse array (FDA) at the transmitter to secure the transmission across both range and direction. Specifically, we derive closed-form expressions for the optimal antenna positions and frequency shifts, assuming small perturbations in both parameters from a linear frequency-diverse MA configuration. Furthermore, we compare the worst-case secrecy rate under this minor perturbation assumption with that obtained under a general constraint, where simulated annealing is employed to numerically determine the optimal parameters. Simulation results confirm that the proposed optimized frequency diverse MA approach significantly enhances secrecy performance in the presence of an eavesdropper aligned with the direction of the legitimate receiver.

The transition to Terahertz (THz) frequencies, providing an ultra-wide bandwidth, is a key driver for future wireless communication networks. However, the specific properties of the THz channel, such as severe path loss and vulnerability to blockage, pose a significant challenge in balancing data rate and reliability. This work considers reconfigurable intelligent surface (RIS)-aided THz communication, where the effective exploitation of a strong, but intermittent line-of-sight (LoS) path versus a reliable, yet weaker RIS-path is studied. We introduce a mixed-criticality superposition coding scheme that addresses this trade-off from a data significance perspective. The results show that the proposed scheme enables reliable transmission for a portion of highly critical data without significantly impacting the overall achievable sum rate and queuing delay. Additionally, we gain insights into how the LoS blockage probability and the channel gain of the RIS-link influence the rate performance of our scheme.

Reconfigurable intelligent surfaces (RIS) are a technology foreseen to play an important role in sixth-generation (6G) wireless networks. By manipulating and reflecting electromagnetic waves using adjustable reflecting elements, RIS can actively reshape the wireless environment in real time. This capability proves most effective when the RIS is positioned in line-of-sight and proximity to the transmitter and receiver due to multiplicative path loss. To meet these criteria in real-world applications, the RIS is mounted on an unmanned aerial vehicle (UAV). Combining UAV with RIS is particularly advantageous due to the lightweight and energy-efficient nature of the RIS. However, a UAV is exposed to many disturbances during flight, which can compromise the performance benefits of the RIS-enabled link. We show the UAV and RIS orientation can be estimated with an extended Kalman filter (EKF). By conducting experimental measurements and recording the UAV-mounted RIS orientation during flight, we quantify the uncertainties regarding the orientation estimation of the UAV and show their impact on the performance of the RIS link through numerical simulations.

Extended reality (XR) applications often perform resource-intensive tasks, which are computed remotely, a process that prioritizes the latency criticality aspect. To this end, this paper shows that through leveraging the power of the central cloud (CC), the close proximity of edge computers (ECs), and the flexibility of uncrewed aerial vehicles (UAVs), a UAV-aided hybrid cloud/mobile-edge computing architecture promises to handle the intricate requirements of future XR applications. In this context, this paper distinguishes between two types of XR devices, namely, strong and weak devices. The paper then introduces a cooperative non-orthogonal multiple access (Co-NOMA) scheme, pairing strong and weak devices, so as to aid the XR devices quality-of-user experience by intelligently selecting either the direct or the relay links toward the weak XR devices. A sum logarithmic-rate maximization problem is, thus, formulated so as to jointly determine the computation and communication resources, and link-selection strategy as a means to strike a trade-off between the system throughput and fairness. Subject to realistic network constraints, e.g., power consumption and delay, the optimization problem is then solved iteratively via discrete relaxations, successive-convex approximation, and fractional programming, an approach which can be implemented in a distributed fashion across the network. Simulation results validate the proposed algorithms performance in terms of log-rate maximization, delay-sensitivity, scalability, and runtime performance. The practical distributed Co-NOMA implementation is particularly shown to offer appreciable benefits over traditional multiple access and NOMA methods, highlighting its applicability in decentralized XR systems.

Joint communication and sensing (JCAS) systems aim to perform communication and radar duties simultaneously, through a single unified transmit waveform. They are expected to exploit mutually beneficial information between the JCAS functionalities through sharing hardware, spectrum and other network resources. In this paper, we consider a JCAS base station (BS) serving a single multi-antenna communication user with perfect channel state information (CSI), while simultaneously sensing an extended target without any prior knowledge. We formulate a beamforming optimisation problem to jointly max-imise the sum-rate at the communication user and the inverse Cramer-Rao bound (CRB) on the sensing target response matrix at the JCAS receiver under a total transmit power constraint. We are able to characterize the optimal beamforming direction in closed form, while the remaining scalar power allocation is solved numerically. We present a generalised low-complexity convex formulation of the JCAS beamforming optimisation and discuss it's validity for both, full rank and rank deficient beamformers. Numerical results indicate a performance gain of upto 37% in sum-rate at the same CRB with optimal antenna selection for the JCAS design compared to random selection. Further, the results illustrate JCAS functionality trade-offs and demonstrate flexibility in joint design for choosing the optimal CRB-rate pairs as a function of a CRB trade-off parameter.

The enormous quality of service (QoS) demands posed by mission-critical use-cases of future 5G/6G wireless communication raise the need for resource-efficient highly reliable and low latency connectivity solutions. Multi-connectivity is considered a promising yet resource demanding approach to enhance reliability. In this work, we study the potential of the rate-splitting multiple access (RSMA) framework as an efficient way to enable uplink multi-connectivity for data transmissions with particularly high reliability requirements. Mapping high-criticality data onto the common stream allows it to be decoded at multiple access points (APs), which enhances reliability, while the private stream is utilized to serve applications with less stringent requirements. We propose a criticality-aware RSMA-based transmission scheme with short blocklength coding and derive an iterative power allocation algorithm by means of successive convex approximation (SCA). The proposed scheme is shown to achieve an expanded stability rate region compared to two baseline schemes. Moreover, it turns out to be less impacted by short blocklength while leading to substantial rate gains, particularly in the high SNR regime.

In this work, we investigate the secrecy performance in an intelligent reflecting surface (IRS)-assisted downlink system. In particular, we consider a base station (BS)-side IRS and as such, the BS-IRS channel is assumed to be known perfectly. Of more importance, we consider the case, in which only outdated channel state information (CSI) of the IRS-user channel is available. We study the impact of outdated CSI on the secrecy performance numerically and analytically. Furthermore, we propose an element subset selection (ESS) method in order to improve the secrecy performance. A key observation is that minimal secrecy outage probability (SOP) can be achieved using a subset of the IRS, and the optimal number of selected reflecting elements can be effectively found by closed-form expressions.

Beyond fifth generation wireless communication networks (B5G) are applied in many use-cases, such as industrial control systems, smart public transport, and power grids. Those applications require innovative techniques for timely transmission and increased wireless network capacities. Hence, this paper proposes optimizing the data freshness measured by the age of information (AoI) in dense internet of things (IoT) sensor-actuator networks. Given different priorities of data-streams, i.e., different sensitivities to outdated information, mixed-criticality is introduced by analyzing different functions of the age, i.e., we consider linear and exponential aging functions. An intricate non-convex optimization problem managing the physical transmission time and packet outage probability is derived. Such problem is tackled using stochastic reformulations, successive convex approximations, and fractional programming, resulting in an efficient iterative algorithm for AoI optimization. Simulation results validate the proposed scheme's performance in terms of AoI, mixed-criticality, and scalability. The proposed non-orthogonal transmission is shown to outperform an orthogonal access scheme in various deployment cases. Results emphasize the potential gains for dense B5G empowered IoT networks in minimizing the AoI.

This paper considers an application of deep learning for channel estimation with imperfect frame synchronization in mobile communication systems. Without prior knowledge of the channel model and its characteristics, the proposed method can dynamically estimate and track channel transfer function variations based on received pilot symbols. Furthermore, this method is applicable in practical scenarios, as it considers imperfect frame synchronization and channel estimation for high-speed wireless communication scenarios. The performance and practical feasibility of the deep learning (DL)-based models are assessed by taking into account realistic frequency-selective fading scenarios. Numerical results demonstrate that the proposed method performs better for practical signal-to-noise ratios than the state-of-the-art approaches. In addition, the fine frame offsets are estimated and compensated in the synchronization block with a DL-based algorithm, which outperforms the traditional fine frame synchronization algorithms.

In this paper, we address the problem of direction of arrival (DOA) estimation for multiple targets in the presence of sensor failures in a sparse array. Generally, sparse arrays are known with very high-resolution capabilities, where N physical sensors can resolve up to ${\mathcal{O}}\left({{N^2}}\right)$ uncorrelated sources. However, among the many configurations introduced in the literature, the arrays that provide the largest hole-free co-array are the most susceptible to sensor failures. We propose here two machine learning (ML) methods to mitigate the effect of sensor failures and maintain the DOA estimation performance and resolution. The first method enhances the conventional spatial smoothing using deep neural network (DNN), while the second one is an end-to-end data-driven method. Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors. The data-driven method can maintain the performance of the array with no failures at high signal-to-noise ratio (SNR). Moreover, both approaches can even perform better than the original array at low SNR thanks to the denoising effect of the proposed DNN.

Reconfigurable intelligent surfaces (RIS) are a core component considered in sixth generation (6G) communications. By utilizing an RIS prototype system in the 5 GHz band, this paper provides a comprehensive dataset of channel measurements of various geometric arrangements of antennas and RIS. The dataset and the in-detail documentation is provided on IEEE DataPort and GitHub, respectively. Since only a few datasets with measurements of RIS prototypes are currently available, we try to fill an important gap in current research by providing this systematically measured dataset. Due to the consistent quality of the measurements that is ensured by the measurement chamber, the provided dataset is also well suited to be used as a basis for machine learning concepts.In order to provide a solid baseline for comparing and validating a wide range of applications in different scenarios, the measurements are taken in stationary scenarios. This includes different RIS-to-antenna distances with specular reflection angles, non-specular reflection angles and with the RIS placed on a rotary stage. The selected switching states of the RIS for each scenario are determined by two iterative algorithms, namely a greedy algorithm and a single-element optimization approach, as well as multiple model-based approaches. The evaluation of the data in this paper compares the overall performance for the maximization and minimization of an RIS-assisted communication link, which shows an increase in received power of up to 20 dB compared to a reference plate.

Reconfigurable intelligent surfaces (RIS) are a technology expected to meet the demanding objectives of future wireless networks by manipulating and reflecting electromagnetic waves using real-time adjustable elements to improve communication performance. However, due to multiplicative path loss, RIS are most effective when used in line-of-sight and proximity to the transmitter or receiver. Moreover, as mobility is a common feature in wireless communication networks, guaranteeing these requirements can be challenging — unless the RIS has the ability to reposition itself.To investigate the efficacy of RIS for improving communication quality in such networks, we mounted a RIS prototype on an unmanned aerial vehicle (UAV). By conducting experimental measurements and recording the UAV-mounted RIS trajectory during its flights, the measured data was reconstructed through numerical simulations to extract the key factors. To further bridge simulations and measurements, we extract the gain characterizing the improvement in communication performance resulting from real-time adjustments of the RIS configuration, compared to a fixed configuration. Numerical evaluations indicate that a real-time adjusted RIS may result in a gain of up to 20dB.

Intelligent reflecting surfaces (IRSs) are a key enabler of various new applications in 6G smart radio environments. This article aims to enhance self-interference (SI) cancellation for breath tracking with commodity WiFi devices by utilizing an IRS prototype system. SI suppression is a crucial requirement for breath tracking with a single antenna site, as the SI severely limits the radio sensing range by shadowing the received signal with its own transmit signal. To this end, we propose to assist SI cancellation by exploiting an IRS to form a suitable cancellation signal in the analog domain. Building upon a 256-element IRS prototype, we present results of breath tracking with IRS-assisted SI cancellation from a practical testbed. We use inexpensive WiFi hardware to extract the channel state information (CSI) in the 5-GHz band and analyze the phase change between a colocated transmitter and receiver with added local oscillator (LO) synchronization. As a result, we can track the breath of a test subject regardless of position in an indoor environment with a room-level range. The presented case study achieves promising performance in capturing the breath frequency and breathing patterns.

This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users’ messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm’s capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

Terahertz (THz) communication will be a key enabler for next-generation wireless systems. While THz frequency bands provide abundant bandwidth and extremely high data rates, their effective operation is inhibited by short communication ranges and narrow beams, thus, leading to major challenges pertaining to user mobility, beam alignment, and handover. In particular, there is a strong need for novel beam tracking methods that consider the tradeoff between enhancing the received signal strength via increasing beam directivity, and increasing the coverage probability by widening the beam. In this paper, a multi-objective optimization problem is formulated with the goal of jointly maximizing the expected rate and minimizing the outage probability subject to transmit power and overhead constraints. Subsequently, a novel parameterized beamformer with dynamic beamwidth adaptation is proposed. In addition to the precoder, an event-based beam tracking approach is introduced that efficiently prevents outages caused by beam misalignment and dynamic blockage while maintaining a low pilot overhead. Simulation results show that the proposed beamforming scheme improves average rate performance and reduces the amount of outages caused by the brittle THz misalignment process and the particularly severe path loss in the THz band. Moreover, the proposed event-triggered THz channel estimation approach enables connectivity with minimal overhead and reliable communication at THz bands.

Recent studies have shown that intelligent reflecting surfaces (IRS) can significantly improve the spectral and energy efficiency of wireless communication links. However, most works assume perfect transceivers and IRS, which is impractical in real communication systems. In this work, we study the effect of the hardware impairments in IRS-assisted MISO systems with single user, where we consider both phase noise caused by the imperfect transceivers and IRS. To this end, we propose a linear minimum mean square error (LMMSE) channel estimation algorithm that takes the phase noise into account. Furthermore, we study the impact of phase noise on the downlink performance of an IRS-assisted system. Both analytical and numerical results are presented, where we prove that the transceiver phase noise can be compensated with the optimized IRS, while the IRS phase noise remains but is not exacerbated. As the number of reflective elements$N$approaches infinity, the IRS phase noise results in a constant loss in terms of the ergodic rate. Moreover, we find that the phase noise has no impact on the scaling laws. If the direct channel is blocked and as$N \rightarrow \infty $, the transmit power can be scaled down by$\frac {1}{N}$and$\frac {1}{N^{2}}$, respectively, for random and optimized IRS, without compromising the received signal to noise ratio (SNR).

Reconfigurable intelligent surfaces (RIS) are a key enabler of various new applications in sixth generation (6G) smart radio environments. By utilizing an RIS prototype system, this paper aims to enhance self-interference cancellation (SIC) for in-band full-duplex (FD) communication systems. SI suppression is a crucial requirement for FD communication as the SI severely limits the performance of a node by shadowing the received signal from a distant node with its own transmit signal. To this end, we propose to assist SI cancellation by exploiting an RIS to form a suitable cancellation signal in the analog domain.Building upon a 256-element RIS prototype, we present results of RIS-assisted SIC from a practical testbed. Given an initial analog isolation of 44dB provided by the antenna design, we are able to cancel the leaked signal by an additional 59dB in the narrowband case, resulting in an overall SI suppression of 103dB without additional digital cancellation. The presented case study shows promising performance to build an FD communication system on this foundation.

Robust transceiver design against unresolvable system uncertainties is of crucial importance for reliable communication. We consider a MIMO two-hop system, where the source, the relay, and the destination are equipped with multiple antennas. Further, an intelligent reconfigurable surface (IRS) is established to cancel the residual self-interference (RSI) as much as possible. The optimization problem turns out to be non-convex and computationally extensive. We propose a new mathematical method to find a lower bound on the performance of the IRS. This method does not require performing any optimizations and provides an analytical bound that can be used as a benchmark. Further, we use this method to find achievable rates for the worst case RSI, IRS assisted full-duplex MIMO systems.

Wireless radio channels are known to contain information about the surrounding propagation environment, which can be extracted using established wireless sensing methods. Thus, today’s ubiquitous wireless devices are attractive targets for passive eavesdroppers to launch reconnaissance attacks. In particular, by overhearing standard communication signals, eavesdroppers obtain estimations of wireless channels which can give away sensitive information about indoor environments. For instance, by applying simple statistical methods, adversaries can infer human motion from wireless channel observations, allowing to remotely monitor premises of victims. In this work, building on the advent of intelligent reflecting surfaces (IRSs), we propose IRShield as a novel countermeasure against adversarial wireless sensing. IRShield is designed as a plug-and-play privacy-preserving extension to existing wireless networks. At the core of IRShield, we design an IRS configuration algorithm to obfuscate wireless channels. We validate the effectiveness with extensive experimental evaluations. In a state-of-the-art human motion detection attack using off-the-shelf Wi-Fi devices, IRShield lowered detection rates to 5% or less.

The tremendous growth of data traffic in mobile communication networks (MCNs) and the associated exponential increase in mobile devices’ numbers necessitate the use of multi-cloud radio access networks (MC-RANs) as a viable solution to cope with the requirements of next-generation MCNs (6G). In MC-RANs, each central processor (CP) manages the signal processing of its own set of base stations (BSs), and so the system performance becomes a function of the joint intra-cloud and inter-cloud interference mitigation techniques. To this end, this paper considers the problem of maximizing the network-wide energy efficiency (EE) subject to user-to-cloud association, fronthaul capacity, maximum transmit power, and achievable rate constraints, so as to determine the joint beamforming vector of each user and the user-to-cloud association strategy. The paper tackles the non-convex and mixed discrete-continuous nature of the problem formulation using fractional programming (FP) and inner-convex approximation (ICA) techniques, as well as l0-norm relaxation heuristics, and shows how the proposed approach can be implemented in a distributed fashion via a reasonable amount of information exchange across the CPs. The paper simulations highlight the appreciable algorithmic efficiency of the proposed approach over state-of-the-art schemes.

In cyber-physical systems (CPSs) and internet-of-things applications, various sensor-actuator pairs are deployed for control purposes which require timely online communication. The sensors are measuring information about the CPS, e.g., process systems, whereas the actuators are using the information to take control actions. These sensor-actuator pairs usually communicate via the same wireless medium and thus their transmissions need to be scheduled in time. When transmitting the process data, ashort blocklength source-channelcoding approach is employed to reduce data errors. We investigate the influence of the decision policy consisting of communication parameters and scheduling design on data freshness and accuracy of process monitoring systems. An age-of-information (AoI) metric is used to assess data timeliness, while the mean square error (MSE) is used to assess the precision of the predicted process values. We characterize the AoI and MSE with closed-form expressions for the blocklengths and accuracy levels, for special types of scheduling strategies, namely, round-robin and maximum-age scheduling. We optimize the coding strategies by showing anachievability regionof AoI and MSE. Other priority-based scheduling policies are also investigated. It is shown that the maximum-age policy provides excellent results in terms of AoI, while priority-based scheduling performs better in terms of MSE.

The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, not only increases the signal quality but also the interference at the users. To counteract this negative side effect, we employ rate splitting (RS), which inherently is able to mitigate the impact of interference. We facilitate practical implementation by considering a Cloud Radio Access Network (C-RAN) at the cost of finite fronthaul-link capacities, which necessitate the allocation of sensible user-centric clusters to ensure energy-efficient transmissions. Dynamic methods for RS and the user clustering are proposed to account for the interdependencies of the individual techniques. Numerical results show that the dynamic RS method establishes synergistic benefits between RS and the IRS. Additionally, the dynamic user clustering and the IRS cooperate synergistically, reflected by increased individual gains for the dynamic clustering. Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases. Around the resulting intersection, both methods affect the system concurrently, improving the energy efficiency drastically.

Recently channel state information (CSI) measurements from commercial multi-input multi-output (MIMO) WiFi systems have been ubiquitously used for different wireless sensing applications. However, the phase of the CSI realizations is usually distorted severely by phase errors due to the hardware impairments, which significantly reduce the sensing performance. In this paper, we directly utilize the modeling of the phase distortions caused by the hardware impairments and propose an adaptive CSI estimation approach based on Kalman filter (KF) with maximum-a-posteriori (MAP) estimation that considers the CSI from the previous time. The performance of the proposed algorithm is compared against the Cramér–Rao lower bound (CRLB). Simulation and experimental results demonstrate that our approach can track the channel variations while eliminating the phase errors accurately.

The concept of intelligent reflecting surfaces (IRSs) is considered as a promising technology for increasing the efficiency of mobile wireless networks. This is achieved by employing a vast amount of low-cost individually adjustable passive reflect elements, that are able to apply changes to the reflected signal. To this end, the IRS makes the environment real-time controllable and can be adjusted to significantly increase the received signal quality at the users by passive beamsteering. However, the changes to the reflected signals have an effect on all users near the IRS, which makes it impossible to optimize the changes to positively influence every transmission affected by the reflections. This results in some users not only experiencing better signal quality, but also an increase in received interference. To mitigate this negative side effect of the IRS, this paper utilizes the rate splitting (RS) technique, which enables the mitigation of interference within the network in such a way that it also mitigates the increased interference caused by the IRS. To investigate the effects on the overall power savings, that can be achieved by combining both techniques, we minimize the required transmit power, needed to satisfy per-user quality-of-service (QoS) constraints. Numerical results show the improved power savings, that can be gained by utilizing the IRS and the RS technique simultaneously. In fact, the concurrent use of both techniques yields power savings, which are beyond the cumulative power savings of using each technique separately.

In this paper, we investigate physical-layer security (PLS) methods for proximity-based group-key establishment and proof of location. Fields of application include secure car-to-car communication, privacy-preserving and secure distance evidence for healthcare, or location-based feature activation. Existing technologies do not solve the problem satisfactorily due to communication restrictions, e.g., ultra-wideband (UWB) based time of flight measurements, or trusted hardware, e.g., using global navigation satellite system (GNSS) positioning data.We introduce PLS as a possible solution candidate. It is information-theoretically secure, thereby also post-quantum resistant, and has the potential to run on resource-constrained devices with low latency. We use wireless channel properties of satellite-to-earth links, demonstrate the first feasibility study using off-the-shelf hardware testbeds, and present first evaluation results and future directions for research.

WiFi channel state information (CSI) is a key enabler of precise radio sensing applications for commercially available off-the-shelf hardware. By utilizing colocated WiFi devices, this article aims to perform Angle-of-Arrival (AoA) estimation as one instance of radio sensing. However, unsynchronized local oscillators (LOs) in a WiFi network can cause carrier frequency offsets and sampling frequency offsets, which can lead to imperfect CSI. To this end, we propose a wired LO clocking scheme to eliminate residual impairments on regular WiFi transceivers. By utilizing a common oscillator, we transform a standard WiFi setup to a frequency-aligned transceiver architecture across several colocated devices. Building upon the proposed scheme, we present a detailed investigation of hardware-related impairments and environmental conditions. In particular, we are the first to investigate the temperature-dependent behavior of the CSI phase across multiple devices. We validate the performance in a lab setup as well as in a real-world application assembly, namely, a nine-antenna uniform circular array for AoA estimation. Furthermore, we show that with only a single received packet and minor hardware adjustments, we are able to obtain the AoA of a WiFi transmitter with a mean absolute error of 1.5° in line-of-sight conditions and 19.9° in strong multipath living room conditions.

Cloud radio access networks (C-RAN) enable a network platform for beyond the fifth generation of communication networks (B5G), which incorporates the advances in cloud computing technologies to modern radio access networks. Recently, rate splitting multiple access (RSMA), relying on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been shown to manage the interference in multi-antenna communication networks efficiently. This paper considers applying RSMA in C-RAN. We address the practical challenge of a transmitter that only knows the statistical channel state information (CSI) of the users. To this end, the paper investigates the problem of stochastic coordinated beamforming (SCB) optimization to maximize the ergodic sum-rate (ESR) in the network. Furthermore, we propose a scalable and robust RS scheme where the number of the common streams to be decoded at each user scales linearly with the number of users, and the common stream selection only depends on the statistical CSI. The setup leads to a challenging stochastic and non-convex optimization problem. A sample average approximation (SAA) and weighted minimum mean square error (WMMSE) based algorithm is adopted to tackle the intractable stochastic non-convex optimization and guarantee convergence to a stationary point asymptotically. The numerical simulations demonstrate the efficiency of the proposed RS strategy and show a gain up to 27% in the achievable ESR compared with state-of-the-art schemes, namely treating interference as noise (TIN) and non-orthogonal multiple access (NOMA) schemes.

In secure distributed matrix multiplication (SDMM) the multiplication$\boldsymbol A \boldsymbol B$from two private matrices$\boldsymbol A$and$\boldsymbol B$is outsourced to$N$distributed servers. In$\ell $-SDMM, the goal is to design a joint communication-computation procedure that optimally balances conflicting communication and computation metrics without leaking any information on both$\boldsymbol A$and$\boldsymbol B$to any set of$\ell {< }N$servers. To this end, the user applies coding with$\tilde { \boldsymbol A}_{i}$and$\tilde { \boldsymbol B}_{i}$representing encoded versions of$\boldsymbol A$and$\boldsymbol B$destined to the$i$-th server. Now, SDMM involves multiple tradeoffs. One such tradeoff is the tradeoff between upload (UL) and download (DL) costs. To find a good balance between these two metrics, we propose two schemes which we term USCSA and GSCSA that are based on secure cross subspace alignment (SCSA). We show that there are various scenarios where they outperform existing SDMM schemes from the literature with respect to UL-DL efficiency. Next, we implement schemes from the literature, including USCSA and GSCSA, and test their performance on Amazon EC2. Our numerical results show that USCSA and GSCSA establish a good balance between the time spent on the communication and computation in SDMMs. This is because they combine advantages of polynomial codes, namely low time for the upload of$\tilde { \boldsymbol A}_{i}$and$\tilde { \boldsymbol B}_{i}$,$i\in [1:N]$, and the computation of$\boldsymbol O_{i}=\tilde { \boldsymbol A}_{i}\tilde { \boldsymbol B}_{i}$, with those of SCSA, being a low timing overhead for the download of$\boldsymbol O_{i}$,$i\in [1:N]$, and the decoding of$\boldsymbol A \boldsymbol B$.

In fifth-generation (5G) cellular networks, users feed back to the base station the index of the precoder (from a codebook) to be used for downlink transmission. The precoder is strongly related to the user channel and in turn to the user position within the cell. We propose a method by which an external attacker determines the user position by passively overhearing this unencrypted layer-2 feedback signal. The attacker first builds a map of fed back precoder indices in the cell. Then, by overhearing the precoder index fed back by the victim user, the attacker finds its position on the map. We focus on the type-I single-panel codebook, which today is the only mandatory solution in the 3GPP standard. We analyze the attack and assess the obtained localization accuracy against various parameters. We analyze the localization error of a simplified precoder feedback model and describe its asymptotic localization precision. We also propose a mitigation against our attack, wherein the user randomly selects the precoder among those providing the highest rate. Simulations confirm that the attack can achieve a high localization accuracy, which is significantly reduced when the mitigation solution is adopted, at the cost of a negligible rate degradation.

In cyber-physical systems, as in 5G and beyond, multiple physical processes require timely online monitoring at a remote device. There, the received information is used to estimate current and future process values. When transmitting the process data over a communication channel, source-channel coding is used in order to reduce data errors. During transmission, a high data resolution is helpful to capture the value of the process variables precisely. However, this typically comes with long transmission delays reducing the utilizability of the data, since the estimation quality gets reduced over time. In this paper, the trade-off between having recent data and precise measurements is captured for a Gauss-Markov process. An Age-of-Information (AoI) metric is used to assess data timeliness, while mean square error (MSE) is used to assess the precision of the predicted process values. AoI appears inherently within the MSE expressions, yet it can be relatively easier to optimize. Our goal is to minimize a time-averaged version of both metrics. We follow a short blocklength source-channel coding approach, and optimize the parameters of the codes being used in order to describe an achievability region between MSE and AoI.

In secure distributed matrix multiplication (SDMM) the multiplication AB from two private matrices A and B is outsourced by a user to N distributed servers. In ℓ-SDMM, the goal is to design a joint communication-computation procedure that optimally balances conflicting communication and computation metrics without leaking any information on both A and B to any set of ℓ ≤ N servers. To this end, the user applies coding with Ãiand B̃irepresenting encoded versions of A and B destined to the i-th server. Now, SDMM involves multiple tradeoffs. One such tradeoff is the tradeoff between uplink (UL) and downlink (DL) costs. To find a good balance between these two metrics, we propose two schemes which we term USCSA and GSCSA that are based on secure cross subspace alignment (SCSA). We implement schemes from the literature, in addition to USCSA and GSCSA, and test their performance on Amazon EC2. Our numerical results show that USCSA and GSCSA establish a good balance between the time spend on the communication and computation in SDMMs. This is because they combine advantages of polynomial codes, namely low time for the upload of (Ãi, B̃i)i=1Nand the computation of Oi= Ãi, B̃i, with those of SCSA, being a low timing overhead for the download of (Oi)i=1Nand the decoding of AB.

Rate-splitting multiple access (RSMA), recognized as a promising technique for future communication systems to generalize and outperform existing multiple access techniques, has been shown to enhance the spectral and energy efficiencies of multi-user multi-antenna broadcast channels (BCs). In this work, motivated by the benefits of RSMA discovered in multi-antenna BCs, we investigate the performance of RSMA in cloud radio access networks (C-RANs). Specifically, the beamforming vectors, message splits, and stream-to-base stations (BSs) allocation are jointly designed with the aim to maximize the sum rate subject to per-BS power constraints and fronthaul capacity constraints. Numerical results demonstrate that RSMA boosts the sum rate in C-RAN especially in strong interference regimes. Therefore, RSMA is a more promising transmission technique for C-RAN than other conventional transmission schemes such as treating interference as noise (TIN) or orthogonal multiple access schemes.

In this paper, we propose a novel algorithm based on supervised learning for antenna array extrapolation for the purpose of super resolution DoA estimation. We use multiple signal classification (MUSIC) as a DoA estimation technique to estimate the DoA. In order to reduce the computational burden, existing approaches focus on interpolating the missing elements in a virtual array using a sparse array (or non-uniform linear arrays) or employ antenna selection within the same aperture. In contrast, here we utilize an uniform linear array (ULA) using a low number of antennas (within a small array aperture) to extrapolate another ULA with a higher number of antennas (within a bigger aperture). This approach is not restricted to any specific antenna array configuration, in fact it can be generalized to any array configuration. We propose an algorithm which utilizes the advances of supervised learning (dictionary learning) to find a mapping between the receive signal of the small to the bigger ULA using multiple training scenarios. Each scenario is trained using the received signal of both apertures from multiple targets within different angle ranges in a multiple input multiple output (MIMO) radar setup. In the testing phase, however, we can only use the small ULA to approach the performance of the bigger ULA to a certain extent. For example, if the small ULA is a 10 × 10 antenna array and the bigger ULA is a 16 × 16 antenna array, then by means of dictionary learning, we predict the receive signal of the bigger ULA using only the small ULA. Simulations show that using our approach, the training based small ULA can resolve more targets, especially in low SNR environments when compared to the untrained ULA.

In this paper we study the spectral efficiency (SE) of a point-to-point massive multiple-input multiple-output system (P2P-massive MIMO) with limited radio frequency (RF) chains, i.e., analog-to-digital/digital-to-analog (D2A/A2D) modules, at the transceivers. The resulting architecture is known as hybrid beamforming, where the joint analog and digital beamforming optimization maximizes the SE. We analyze the SE of the system by keeping the number of RF-chains low, but placing analog amplifiers at different paths. Conventional hybrid beamforming architecture uses the amplifiers right after the D2A modules. However, placing them at the phase shifters or at the antennas, can effect the SE of hybrid beamforming. We study the optimal placement of the analog amplifiers and pinpoint the amount of loss in case of misplaced amplifiers.

In this paper we study the impact of unmanned aerial vehicles (UAVs) trajectories on terrestrial users' spectral efficiency (SE). Assuming a strong line of sight path to the users, the distance from all users to all UAVs influence the outcome of an online trajectory optimization. The trajectory should be designed in a way that the fairness rate is maximized over time. That means, the UAVs travel in the directions that maximize the minimum of the users' SE. From the free-space path-loss channel model, a data-rate gradient is calculated and used to direct the UAVs in a long-term perspective towards the local optimal solution on the two-dimensional spatial grid. Therefore, a control system implementation is designed. Thereby, the UAVs follow the data-rate gradient direction while having a more smooth trajectory compared with a gradient method. The system can react to changes of the user locations online; this system design captures the interaction between multiple UAV trajectories by joint processing at the central unit, e.g., a ground base station. Because of the wide spread of user locations, the UAVs end up in optimal locations widely apart from each other. Besides, the SE expectancy is enhancing continuously while moving along this trajectory.

Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of a large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typically of concern. In this paper, we study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product AB of two finite field private matrices A and B. In this problem, the user exploits the computational resources of N servers to compute the matrix product, but simultaneously tries to conceal the private matrices from the servers. Our goal is to maximize the communication rate while preserving security, where we allow for up to ℓ ≤ N servers to collude. To this end, we propose a general aligned secret sharing and matrix partition scheme for which we optimize the partition of matrices A and B as a function of N and ℓ in order to maximize the achievable rate. A proposed inductive approach gives us an analytical close-to-optimal solution which significantly outperforms the existing scheme of Chang and Tandon in terms of (i) communication rate, (ii) maximum tolerable number of colluding servers and (iii) computational complexity.

In this paper, we investigate a multiple-input multiple-output (MIMO) system with simultaneous information detection (ID) and energy harvesting (EH) receiver. This point-to-point system operates in the vicinity of active interfering nodes. The receiver performs power splitting where a portion of received signal undergoes analog energy harvesting circuitry. Further, the information content of the other portion is extracted after performing digital beamforming. In this MIMO system, information carrier eigen-modes are not necessarily the eigen-modes with the strongest energy level. Hence, it is beneficial to perform independent beamforming at the receiver of MIMO-P2P channel. Here, we utilize a hybrid analog/digital beam-forming for the purpose of simultaneous ID and EH in such scenarios. This design, provides extra design degrees-of-freedom in eigen-mode selection for ID and EH purposes independently. Worst-case performance of this receiver structure is discussed. Finally, its benefits are compared to the classical receiver structure and the gains are highlighted.

In this paper, we are concerned with indoor localization based on multiple-antenna channel measurements. Indoor localization is an active area of research due to its great importance in many applications. We propose a hybrid algorithm which combines the benefits of two techniques, namely signal processing and machine learning. We validate our algorithm based on real measurements acquired from two practical setups. Our approach shows a very promising performance in the IEEE CTW 2019 - Positioning Algorithm Competition where the algorithm achieves an accuracy within RMSE values below 10 cm. We further build a setup in another indoor environment, where the algorithm still proves a very good performance compared to state-of-the art techniques used in indoor localization tasks.

We jointly design an information-theoretic transmit and receive radar beamformers for spatially near multiple extended targets. We maximize the mutual information (MI) between the received signals and the targets signatures that allows the extraction of the unknown features, which may include shape, dimensions, and material. However, high interference caused by spatially near targets might obstruct the information extraction, and directing the beamformers toward the steering vector as done in conventional beamformers does not solve this problem, especially for extended targets. In this letter, an iterative algorithm is presented to solve this problem using alternative minimization, dividing it into two blocks. The first block is solving for the transmit beamformers successively using block coordinate descent, and the second one is solving for the receiver beamformers using the minimum variance distortionless response. We also show the effect of using our beamformers on the waveform design problem. Numerical results indicate that this algorithm can achieve substantially higher MI than the existing conventional methods. Thus, except for some degenerate cases, having fixed beamformers instead of optimized ones lead to significant performance degradation.

A secret-key agreement for in-band full-duplex (FD) technology can be regarded as a valuable method for establishing security at the physical layer level. In this paper, we study an FD system model in conjunction with a secret-key agreement. The legitimate users interact through two-way channel probing and one-way reconciliation. We first introduce two complementary key generation models for FD and half-duplex (HD) settings, and compare their performances by introducing the key-reconciliation function. Furthermore, we study the impact of probing-reconciliation tradeoff and the role of a strong eavesdropper, and analyze the system performance in the high-signal-to-noise ratio regime. We show that under certain conditions, the FD mode negatively affects the capabilities of the eavesdropper and offers several advantages in terms of secret-key rate over the conventional HD setups. Our analysis reveals that the perfect self-interference cancellation is not necessary to obtain performance gains over the HD mode.

The characterization of fundamental performance bounds of many-to-many communication systems in which participating nodes are active in an intermittent way is one of the major challenges in communication theory. In order to address this issue, we introduce the multiple-input multiple-output (MIMO) three-way channel (3WC) with an intermittent node and study its degrees-of-freedom (DoF) region and sum-DoF. We devise a non-adaptive encoding scheme based on zero-forcing, interference alignment, and erasure coding, and show its DoF region (and thus sum-DoF) optimality for non-intermittent 3WCs and its sum-DoF optimality for (node-) intermittent 3WCs. However, we show by example that in general some DoF tuples in the intermittent 3WC can only be achieved by adaptive schemes such as decode-forward relaying. This shows that non-adaptive encoding is sufficient for the non-intermittent 3WC and for the sum-DoF of intermittent 3WCs but adaptive encoding is necessary for the DoF region of intermittent 3WCs. This paper contributes to a better understanding of the fundamental limits of multi-way communication systems with intermittency and the impact of adaptation therein.

Wireless nodes in future communication systems need to overcome three barriers when compared to their transitional counterparts, namely, to support significantly higher data rates, have long-lasting energy supplies, and remain fully operational in interference-limited heterogeneous networks. This could be partially achieved by providing three promising features, which are radio frequency (RF) energy harvesting, improper Gaussian signaling, and operating in full-duplex communication mode, i.e., transmit and receive at the same time within the same frequency band. In this paper, we consider these aspects jointly in a multi-antenna heterogeneous two-tier network. In this network, the users in the femtocell share the scarce resources with the cellular users in the macro-cell and have to cope with the interference from the macro-cell base station as well as the transmitter noise and residual self-interference due to imperfect full-duplex operation. Interestingly enough, while these impairments are detrimental from the achievable rate perspective, they are beneficial from the energy harvesting aspect, as they carry RF energy. In this paper, we consider this natural tradeoff jointly and propose appropriate optimization problems for beamforming and optimal resource allocation. Moreover, various receiver structures are employed for both information detection and energy harvesting (EH) capabilities. This paper aims at characterizing the tradeoff between the achievable rates and harvested energies. Rate and energy maximization problems are thoroughly investigated. Interestingly, with sufficiently high EH demands, we observe the convergence of the rate region obtained by non-linear precoding to the rate region achieved by widely linear precoding at the base station.

An emerging trend of next generation communication systems is to provide network edges with additional capabilities such as additional storage resources in the form of caches to reduce file delivery latency. To investigate this aspect, we study the fundamental limits of a cache-aided wireless network consisting of one central base station, M transceivers and K receivers from a latency-centric perspective. We use the normalized delivery time (NDT) to capture the per-bit latency for the worst-case file request pattern at high signal-to-noise ratios (SNR), normalized with respect to a reference interference-free system with unlimited transceiver cache capabilities. For various special cases that satisfy K+M≤4, we establish the optimal tradeoff between cache storage and latency. This is facilitated through establishing a novel converse (for arbitrary M and K) and an achievability scheme on the NDT. Our achievability scheme is a synergistic combination of multicasting, zero-forcing beamforming and interference alignment.

Multi-way and device-to-device (D2D) communications are currently considered for the design of future communication systems. Unmanned aerial vehicles (UAVs) can be effectively deployed to extend the communication range of D2D networks. To model the UAV-D2D interaction, we study a multi-antenna multi-way channel with two D2D users and an intermittently available UAV node. The performance in terms of sum-rate of various transmission schemes is compared. Numerical results show that for different ground environments, the scheme based on a combination of interference alignment, zero-forcing and erasure-channel treatment outperforms other schemes at low, medium and high SNRs and thus represents a viable transmission strategy for UAV-aided multi-way D2D networks.

We investigate Gaussian widely linear precoding known as improper Gaussian signaling for the cellular uplink with inter-cell interference, known as interference multiple access channel (IMAC). This transmission scheme provides extra degrees of freedom by treating the real and imaginary components of the complex Gaussian signal differently. Since current standards mainly utilize linear beamforming for waveform generation, we highlight the benefits of widely linear beamforming over multiple temporal dimensions (symbol extension in time) in the IMAC. This scheme achieves significantly higher information rates compared to conventional proper Gaussian signaling at the expense of extra complexity at the transmission phase. We study the sum-power minimization problem under rate constraints. This problem is a difference of concave functions (DC) program, hence, a non-convex problem. By numerical simulations, we observe the benefits of improper Gaussian signaling alongside symbol extension in power consumption for both single-antenna and multi-antenna base stations. Interestingly, we observe that at strong interference scenarios, the efficiency of improper Gaussian signaling outperforms conventional proper Gaussian signaling at low rate demands. Moreover, in such scenarios the sum-power required for achieving particular rate demands is significantly reduced.

Hybrid architectures are generally composed of a cyber cloud with additional support of edge caching. By utilizing the benefits associated with cloud computing and caching, powerful enhanced interference management techniques can be readily utilized - that among others - also results in low-latency transmission. In this paper, we study the impact of cloud and edge processing on the latency for a heterogenous network (HetNet) consisting of two users and two transmitters. We define an information-theoretic metric, the delivery time per bit (DTB), that captures the delivery latency. We establish bounds on the DTB as a function of cache size, backhaul capacity and wireless channel parameters. We show the optimality on the DTB for various channel regimes.

Multi-hop communication is a spectral-efficient approach for connecting multiple pairs when direct links are absent or have insufficient strength. In this paper, we highlight the benefits of improper Gaussian signaling for a multi-pair full-duplex MIMO relay network in terms of power efficiency. By employing improper Gaussian transmission, the power minimization problem under rate constraints is intrinsically a non-convex problem due to non-convex rate expressions in the constraint set. We utilize Fenchel's inequality to linearize the non-convex part of the constraint, which results in feasible solutions of the problem in polynomial time. This approximation results in an upper-bound for the original problem which gets tighter in the number of iterations performed. The solution is compared with analytical beamforming solutions, namely zero-forcing (ZF) and maximum-ratio transmission/combining (MRT/MRC). Due to the feasibility of single rank real-valued transmission in improper Gaussian signaling, the optimal solution switches to single rank transmission depending on the constraints defined by the requests of the users, which can not be captured by proper Gaussian signaling.

The degrees-of-freedom (DoF) of the multi-antenna three-way channel (3WC) with an intermittent node is studied. Special attention is given to the impact of adaptation when the intermittent node has the largest number of antennas. A non-adaptive transmission scheme based on interference alignment, zero-forcing, and erasure-channel treatment is proposed, and its corresponding DoF region is derived. Then, it is shown that this scheme achieves the sum-DoF of the intermittent channel, in addition to the DoF region of the nonintermittent one. Thus, adaptation is not necessary from those perspectives. To the contrary, it is shown that adaptation is necessary for achieving the DoF region of the intermittent case. This is shown by deriving an outer bound for the intermittent channel with nonadaptive encoding, and proposing an adaptive scheme which achieves DoF tuples outside this bound. This highlights the importance of cooperation in this intermittent network.

The optimality of the simple scheme of treating interference as noise (TIN) is studied in this paper for the 2 × M linear deterministic (LD) X-channel. A new capacity upper bound is derived. In the considered scheme (denoted as 2-IC-TIN), the setup is reduced to a 2-user interference channel while the receivers employ TIN. It is shown that as long as 2-IC-TIN is optimal in a M × 2 X-channel, it is also capacity-optimal in the 2 × M X-channel which is generated by changing the role of transmitters and receivers. The result of this paper expands the capacity optimal regime of TIN for the 2 × M LD X-channel compared to the state of the art.

Stringent mobile usage characteristics force wireless networks to undergo a paradigm shift from conventional connection-centric to content-centric deployment. With respect to 5G, caching and heterogenous networks (HetNet) are key technologies that will facilitate the evolution of highly content-centric networks by facilitating unified quality of service in terms of low-latency communication. In this paper, we study the impact of transceiver caching on the latency for a HetNet consisting of a single user, a receiver and one cache-assisted transceiver. We define an information-theoretic metric, the delivery time per bit (DTB), that captures the delivery latency. We establish coinciding lower and upper bounds on the DTB as a function of cache size and wireless channel parameters; thus, enabling a complete characterization of the DTB optimality of the network under study. As a result, we identify cache beneficial and non-beneficial channel regimes.

Cloud radio access networks (C-RAN) are expected to be the backbone of next generation communication networks. In order to reduce the backhaul cost, which is a bottleneck in C-RAN, popular files are cached in local memories at remote radio heads (RRH's) and thus in close proximity to the users demanding it. In this paper we investigate different caching strategies with the objective to reduce backhaul and transmit power cost. It turns out that the problem of jointly minimizing the transmit power and backhaul costs constitutes a mixed integer non linear program (MINLP). First, we introduce slack variables to formulate the problem as a standard mixed integer second order cone program (MI-SOCP). With this formulation we can get the global optimal solution with reduced computational costs. However, in large-scale networks with large number of users and RRH's, using MI-SOCP is either inefficient or even intractable in some cases. Therefore, we introduce an inflation based polynomial time algorithm. We show, with numerical simulations, that our approach provides close-to-optimal solutions with much smaller amount of time compared to that needed for getting the global optimal. We also show that our approach is more efficient than the other state of the art algorithms, besides it always yields an integer feasible solution as opposed to other relaxation techniques. We also point out the essential impact of caching scheme on the trade-off between transmit power and backhaul costs. It turns out that content redundancy at the local caches, enables the RRH's to cooperate for transmit power cost reduction, but at the expense of increased backhaul cost.

This letter deals with the joint information and energy processing at the receiver of a point-to-point communication channel. In particular, the tradeoff between the achievable information rate and harvested energy for a multiple-antenna power splitting receiver is investigated. Here, the rate-energy region characterization is of particular interest, which is intrinsically a nonconvex problem. In this letter, an efficient algorithm is proposed for obtaining an approximate solution to the problem in polynomial time. This algorithm is mainly based on the Taylor approximation in conjunction with semidefinite relaxation, which is solved by interior-point methods. Moreover, we utilize the Gaussian randomization procedure to obtain a feasible solution for the original problem. It is shown that by proper receiver design the rate-energy region can be significantly enlarged compared to the state of the art, while at the same time the receiver hardware costs is reduced by utilizing less number of energy harvesting circuitry.

This paper studies the problem of secure communication over the broadcast channel with receiver-side information under the lens of individual secrecy constraints, that is, the transmitter wants to send two independent messages to two receivers, which have, respectively, the desired message of the other receiver as side information, while keeping the eavesdropper ignorant of each message (i.e., the information leakage rate from each message to the eavesdropper is made vanishing). Building upon one-time pad, secrecy coding, and broadcasting schemes, achievable rate regions are investigated, and the capacity region for special cases of either a weak or strong eavesdropper (compared to both legitimate receivers) is characterized. Interestingly, the capacity region for the former corresponds to a line and the latter corresponds to a rectangle with missing corners; a phenomenon occurring due to the coupling between user's rates. Moreover, the individual secrecy capacity region is also fully characterized for the case where the eavesdropper's channel is deterministic. In addition to discrete memoryless setup, Gaussian scenarios are studied. For the Gaussian model, in addition to the strong and weak eavesdropper cases, the capacity region is characterized for the low and high SNR regimes when the eavesdropper's channel is stronger than one receiver but weaker than the other. Remarkably, positive secure transmission rates are always guaranteed under the individual secrecy constraint, unlike the case of the joint secrecy constraint (i.e., the information leakage rate from both messages to the eavesdropper is made vanishing). Thus, this notion of secrecy serves as an appropriate candidate for trading off secrecy level and transmission rate, making secrecy more affordable but still acceptable to the end user.

This paper studies the problem of secure communications over broadcast channels under theindividualsecrecy constraints. That is, the transmitter wants to send two independent messages to two legitimate receivers in the presence of an eavesdropper, while keeping the eavesdropper ignorant ofeachmessage (i.e., the information leakage rate fromeachmessage to the eavesdropper is made vanishing). Building upon Carleial–Hellman’s secrecy coding, Wyner’s secrecy coding, and the framework of Marton’s coding together with techniques, such as rate splitting and indirect decoding, an achievable individual secrecy rate region is established with the characterization of capacity regions for some special cases. In particular, the individual secrecy capacity region for the linear deterministic model is fully characterized, and for the Gaussian model, a constant gap (i.e., 0.5 b within the individual secrecy capacity region) result is obtained. To illustrate the impact of different secrecy constraints on the corresponding capacity regions, comparisons are made with those satisfying joint secrecy and without secrecy constraints. Overall, when compared with the joint secrecy constraint, the results allow for trading off secrecy level and throughput in the system.

Treating interference as noise (TIN) as the most appropriate approach in dealing with interference and the conditions on its optimality has attracted the interest of researchers recently. However, our knowledge on necessary and sufficient conditions of TIN is restricted to a few setups with limited number of users. In this paper, we study the optimality of TIN in terms of the generalized degrees of freedom (GDoF) for a fundamental network, namely, the M × 2 X-channel. To this end, the achievable GDoF of TIN with power allocations at the transmitters is studied. It turns out that the transmit power allocation maximizing the achievable GDOF is given by ON-OFF signaling as long as the receivers use TIN. This leads to two variants of TIN, namely, P2P-TIN and 2-IC-TIN. While in the first variant the M × 2 X-channel is reduced to a point-topoint (P2P) channel, in the second variant, the setup is reduced to a two-user interference channel in which the receivers use TIN. The optimality of these two variants is studied separately. To this end, novel genie-aided upper bounds on the capacity of the X-channel are established. The conditions on the optimality of P2P-TIN can be summarized as follows. P2P-TIN is GDoF-optimal if there exists a dominant multiple access channel or a dominant broadcast channel embedded in the X channel. Furthermore, the necessary and sufficient conditions on the GDoF-optimality of 2-IC-TIN are presented. Interestingly, it turns out that operating the M × 2 X-channel in the 2-IC-TIN mode might be still GDOF optimal, although the conditions given by Geng et al. are violated. However, 2-IC-TIN is sub-optimal if there exists a single interferer which causes sufficiently strong interference at both receivers. The comparison of the results with the state of the art shows that the GDOF optimality of TIN is expanded significantly.

Decreasing communication latency is one of the main challenges for many applications in future communication systems. As a solution, we study in this work the benefits of using some cache-equipped helpers in a wireless communication scenario. More precisely, a wireless network with a transmitter, two cache-equipped helpers, and two receivers is studied from the latency point of view. While the transmitter has access to the whole data, the helpers store some data in their cache in order to support the transmitter in fulfilling the demands of the receivers. The main challenge is to develop a joint caching placement and the transmission strategy, which minimizes the latency of the communication. To this end, a general encoded caching is considered, in which each message is split into four types of sub-messages. The sub-messages of the first and second types are stored only in the cache of the first and second helper, respectively. The sub-messages of the third type are cached at both helpers, while the sub-messages of the fourth type are not cached at any helper. The size of the sub-messages is optimized in a way such that the delivery time of one bit for the worst-case demand is minimized. Moreover, a general lower bound on the delivery time is provided. Using this lower bound as a benchmark, the performance of the proposed caching policy is evaluated. It is shown that facilitating the cache-equipped helpers decreases the latency of broadcasting. Furthermore, it is shown that for the proposed caching policy, storing identical sub-messages in the cache of different helpers cannot be optimal if the cache size of the helpers is insufficiently large.

We study the problem of secure transmission over a Gaussian multi-input single-output (MISO) two receiver channel with an external eavesdropper, under the assumption that the state of the channel which is available to each receiver is conveyed either perfectly (P) or with delay (D) to the transmitter. Denoting by S1, S2, and S3the channel state information at the transmitter (CSIT) of user 1, user 2, and eavesdropper, respectively, the overall CSIT can then alternate between eight possible states, i.e., (S1, S2, S3) ∈ {P,D}3. We denote by λS1S2S3the fraction of time during which the state S1S2S3occurs. Under these assumptions, we consider the multi-receiver setup and characterize the SDoF region of fixed hybrid states PPD, PDP, and DDP. We then focus our attention on the symmetric case in which λPDD= λDPD. For this case, we establish bounds on the SDoF region. The analysis reveals that alternating CSIT allows synergistic gains in terms of SDoF; and shows that, by opposition to encoding separately over different states, joint encoding across the states enables strictly better secure rates.

A network of three nodes mutually communicating with each other is studied. This multi-way network is a suitable model for three-user device-to-device communications. The main goal of this paper is to characterize the capacity region of the underlying Gaussian three-way channel (3WC) within a constant gap. To this end, a capacity outer bound is derived using cut-set bounds and genie-aided bounds. For achievability, the 3WC is first transformed into an equivalent star channel. This latter is then decomposed into a set of “successive” sub-channels, leading to a sub-channel allocation problem. Using backward decoding, interference neutralization, and known results on the capacity of the star-channel relying of physical-layer network coding, an achievable rate region for the 3WC is obtained. It is then shown that the achievable rate region is within a constant gap of the developed outer bound, leading to the desired capacity approximation. Interestingly, in contrast to the Gaussian two-way channel (TWC), adaptation is necessary in the 3WC. Furthermore, message splitting is another ingredient of the developed scheme for the 3WC, which is not required in the TWC. The two setups are, however, similar in terms of their sum-capacity pre-log, which is equal to 2. Finally, some interesting networks and their approximate capacities are recovered as special cases of the 3WC, such as the cooperative broadcast channel and multiple access channel.

The symmetric K-user interference channel (IC) is studied with the goal of characterizing its capacity region in the strong interference regime within a constant gap. The achievable rate region of a scheme combining rate splitting at the transmitters and interference alignment and successive decoding/computation at the receivers is derived. Next, it is shown that this scheme achieves the so-called greedy-max corner points of the capacity region within a constant gap. By combining this result with previous results by Ordentlich et al. on the sum-capacity of the symmetric IC, a constant gap characterization of the capacity region for the strong interference regime is obtained. This leads to the first approximate characterization of the capacity region of the symmetric K-user IC. Furthermore, a new scheme that achieves the sum-capacity of the channel in the strong interference regime within a constant gap is also proposed, and the corresponding gap is calculated. The advantage of the new scheme is that it leads to a characterization within a constant gap without leaving an outage set contrary to the scheme by Ordentlich et al.

Despite the simplicity of the scheme of treating interference as noise (TIN), it was shown to be sum-capacity optimal in the Gaussian interference channel (IC) with very-weak (noisy) interference. In this paper, the two-user IC is altered by introducing an additional transmitter that wants to communicate with one of the receivers of the IC. The resulting network thus consists of a point-to-point channel interfering with a multiple access channel (MAC) and is denoted by PIMAC. The sum-capacity of the PIMAC is studied with main focus on the optimality of TIN. It turns out that TIN in its naive variant, where all transmitters are active and both receivers use TIN for decoding, is not the best choice for the PIMAC. In fact, a scheme that combines both time division multiple access and TIN (TDMA-TIN) strictly outperforms the naive-TIN scheme. Furthermore, it is shown that in some regimes, TDMA-TIN achieves the sum-capacity for the deterministic PIMAC and the sum-capacity within a constant gap for the Gaussian PIMAC. In addition, it is shown that, even for very-weak interference, there are some regimes where a combination of interference alignment with power control and TIN at the receiver side outperforms TDMA-TIN. As a consequence, on the one hand, TIN in a cellular uplink is approximately optimal in certain regimes. On the other hand, those regimes cannot be simply described by the strength of interference.

The impact of cooperation on interference management is investigated by studying an elemental wireless network, the so-called symmetric interference relay channel (IRC), from a generalized degrees of freedom (GDoF) perspective. This is motivated by the fact that the deployment of relays is considered as a remedy to overcome the bottleneck of current systems in terms of achievable rates. The focus of this paper is on the regime in which the interference link is weaker than the source-relay link in the IRC. Our approach toward studying the GDoF goes through the capacity analysis of the linear deterministic IRC (LD-IRC). New upper bounds on the sum capacity of the LD-IRC based on genie-aided approaches are established. These upper bounds together with some existing upper bounds are achieved by using four novel transmission schemes. Extending the upper bounds and the transmission schemes to the Gaussian case, the GDoF of the Gaussian IRC is characterized for the aforementioned regime. This completes the GDoF results available in the literature for the symmetric GDoF. It turns out that even if the incoming and outgoing links of the relay are both weaker than the desired channel, involving a relay can increase the GDoF. Interestingly, utilizing the relay in this case can increase the slope of the GDoF from -2 [in the interference channel (IC)] to -1 or 0. This shrinks the regime where ignoring the interference by treating it as noise is optimal. Furthermore, the analysis shows that if the relay ingoing and outgoing links are sufficiently strong, the relay is able to neutralize the interference completely. In this case, the bottleneck of the transmission will be the interference links, and hence, the GDoF increases if the interference link gets stronger. It is shown that in the strong interference regime, in contrast to the IC, the GDoF can be a monotonically decreasing function of the interference level.

The impact of multi-hopping schemes on the communication latency in a relay channel is studied. The main aim is to characterize conditions under which such schemes decrease the communication latency given a reliability requirement. Both decode-forward (DF) and amplify-forward (AF) with block coding are considered, and are compared with the point-to-point (P2P) scheme which ignores the relay. Latency expressions for the three schemes are derived, and conditions under which DF and AF reduce latency are obtained for high signal-to-noise ratio (SNR). Interestingly, these conditions are more strict when compared to the conditions under which the same multi-hopping schemes achieve higher long-term (information-theoretic) rates than P2P. It turns out that the relation between the source-destination SNR and the harmonic mean of the SNR's of the channels to and from the relay dictates whether multi-hopping reduces latency or not.

This paper studies the problem of secure communication over broadcast channels under the lens of individual secrecy constraints (i.e., information leakage from each message to an eavesdropper is made vanishing). It is known that, for the communication over the degraded broadcast channels, the stronger receiver is able to decode the message of the weaker receiver. In the individual secrecy setting, the message for the weaker receiver can be further utilized to secure the partial message that is intended to the stronger receiver. With such a coding spirit, it is shown that more secret bits can be conveyed to the stronger receiver. In particular, for the corresponding Gaussian model, a constant gap (i.e., 0.5 bits within the individual secrecy capacity region) result is obtained. Overall, when compared with the joint secrecy constraint, the results allow for trading-off secrecy level and throughput in the system.

The wireless medium offers many opportunities for broadcast communications. However, it also opens the possibility for attackers to eavesdrop the broadcast data or to pretend to be another node or device. These two attacks define the protection goals, namely, confidentiality and authenticity. Traditionally, both are solved by cryptographic approaches exploiting knowledge available in the surrounding infrastructure. The novel communication paradigms for the Internet of Things or cyber-physical systems do not scale with the standard cryptographic approach. Instead it is possible to exploit properties of the underlying physical channel to provide countermeasures against eavesdropping and impersonation attacks. Thereby, the random fading channel induces uncertainty which is detrimental but at the same time also helpful. In this paper, we review and describe a generalized model for physical-layer-based confidential data transmission and wireless authentication. A key role is played by the channel uncertainty and available design dimensions such as time, frequency, and space. We show that wireless authentication and secret-key generation can work in multicarrier and multiple-antenna systems and explain how even outdated channel state information can help to increase the available secure degrees of freedom. This survey focuses on the system design of wireless physical-layer confidentiality and authenticity under channel uncertainty. The insights could lead to a design of practical systems which are preparing the ground for confidentiality and authenticity already on the physical layer of the communication protocol stack.

A full-duplex wireless network with three users that want to establish full message exchange via a relay is considered. Thus, this network which is known as the Y-channel has a total of six messages, two outgoing, and two incoming at each user. The users are not physically connected, and thus the relay is essential for their communication. The deterministic Y-channel is considered first, its capacity region is characterized, and shown not to be given by the cut-set bounds. The capacity achieving scheme has three different components (strategies): 1) a bidirectional; 2) a cyclic; and 3) a unidirectional strategy. Network coding is used to realize the bidirectional and the cyclic strategies, and thus to prove the achievability of the capacity region. The result is then extended to the Gaussian Y-channel where the capacity region is characterized within a constant gap independent of the channel parameters.

The K-user multiple-input multiple-output (MIMO) multi-way relay channel (Y-channel) consisting of K users with M antennas each and a common relay node with N antennas is studied in this paper. Each user wants to exchange messages with all the other users through the relay. A transmission strategy is proposed for this channel. The proposed strategy is based on two steps: 1) channel diagonalization and 2) cyclic communication. The channel diagonalization is applied by using zero-forcing beam-forming. After the channel diagonalization, the channel is decomposed into parallel sub-channels. Cyclic communication is then applied, where signal-space alignment for network-coding is used over each sub-channel. The proposed strategy achieves the optimal degree-of-freedom (DoF) region of the channel if N ≤ M. To prove this, a new DoFs outer bound is derived. As a by-product, we conclude that the MIMO Y-channel is not separable, i.e., independent coding on separate sub-channels is not enough, and one has to code jointly over several sub-channels.

This paper studies the problem of secret-message transmission over a wiretap channel with correlated sources in the presence of an eavesdropper who has no source observation. A coding scheme is proposed based on a careful combination of 1) Wyner-Ziv's source coding to generate secret key from correlated sources based on a certain cost on the channel, 2) one-time pad to secure messages without additional cost, and 3) Wyner's secrecy coding to achieve secrecy based on the advantage of legitimate receiver's channel over the eavesdropper's. The work sheds light on optimal strategies for practical code design for secure communication/storage systems.

We study the problem of secure transmission over a two-user Gaussian multi-input single-output (MISO) broadcast channel under the assumption that the channel to each receiver is conveyed either perfectly (P) or with delay (D) to the transmitter. Denoting S1and S2to be the channel state information at the transmitter (CSIT) of user 1 and user 2, respectively; the overall CSIT can then alternate between four states, i.e., (S1, S2) ∈ {P,D}2. We denote λS1S2be the fraction of time the state S1S2occurs, and focus on the symmetric case such that λS1S2= λS2S1. Under these assumptions, we first consider the Gaussian MISO wiretap channel and characterize the secure degrees of freedom (SDoF). Next, we generalize this model to the two-user Gaussian MISO broadcast channel and establish an inner bound on the SDoF region. This result shows the synergistic SDoF gains of alternating CSIT and illustrates that, as opposed to encoding separately over different states, an improved SDoF region is achievable by joint encoding across these states.

A 3-user device-to-device (D2D) communications scenario is studied where each user wants to send and receive a message from each other user. This scenario resembles a 3-way communication channel. The capacity of this channel is unknown in general. In this paper, a sum-capacity upper bound that characterizes the degrees-of-freedom of the channel is derived by using genie-aided arguments. It is further shown that the derived upper bound is achievable within a gap of 2 bits, thus leading to an approximate sum-capacity characterization for the 3-way channel. As a by-product, interesting analogies between multi-way communications and multi-way relay communications are concluded.

In this paper, we study the problem of secure communication over the broadcast channel with receiver side information, under the lens of individual secrecy constraints (i.e., information leakage from each message to an eavesdropper is made vanishing). Several coding schemes are proposed by extending known results in broadcast channels to this secrecy setting. In particular, individual secrecy provided via one-time pad signal is utilized in the coding schemes. As a result, we obtain an achievable rate region together with a characterization of the capacity region for special cases of either a weak or strong eavesdropper (compared to both legitimate receivers). Interestingly, the capacity region for the former corresponds to a line and the latter corresponds to a square with missing corners; a phenomenon occurring due to the coupling between user's rates. At the expense of having a weaker notion of security, positive secure transmission rates are always guaranteed, unlike the case of the joint secrecy constraint.

The sum-capacity of a three user interference wired network for time-varying channels is considered. Due to the channel variations, it is assumed that the transmitters are only able to track the connectivity between the individual nodes, thus only the (alternating) state of the network is known. By considering a special subset of all possible states, we show that state splitting combined with joint encoding over the alternating states is required to achieve the sum-capacity. Regarding upper bounds, we use a genie aided approach to show the optimality of this scheme. This highlights that more involved transmit strategies are required for characterizing the degrees of freedom even if the transmitters have heavily restricted channel state information.

The degrees of freedom (DoF) of an X-network with M transmit and N receive nodes utilizing interference alignment with the support of J relays each equipped with Ljantennas operating in a half-duplex non-regenerative mode is investigated. Conditions on the feasibility of interference alignment are derived using a proper transmit strategy and a structured approach based on a Kronecker-product representation. The advantages of this approach are twofold: First, it extends existing results on the achievable DoF to generalized antenna configurations. Second, it unifies the analysis for time-varying and constant channels and provides valuable insights and interconnections between the two channel models. It turns out that a DoF ofNM/M+N-1 is feasible whenever the sum of the L2j≥ [N - 1][M - 1].

The degrees of freedom (DoF) of the MIMO Y-channel, a multi-way communication network consisting of 3 users and a relay, are characterized for arbitrary number of antennas. The converse is provided by cut-set bounds and novel genie-aided bounds. The achievability is shown by a scheme that uses beamforming to establish network coding on-the-fly at the relay in the uplink, and zero-forcing pre-coding in the downlink. It is shown that the network has min{2M2+2M3, M1+ M2+ M3,2N} DoF, where Mjand N represent the number of antennas at user j and the relay, respectively. Thus, in the extreme case where M1+M2+M3dominates the DoF expression and is smaller than N, the network has the same DoF as the MAC between the 3 users and the relay. In this case, a decode and forward strategy is optimal. In the other extreme where 2N dominates, the DoF of the network is twice that of the aforementioned MAC, and hence network coding is necessary. As a byproduct of this work, it is shown that channel output feedback from the relay to the users has no impact on the DoF of this channel.

A network where three users want to establish multiple unicasts between each other via a relay is considered. This network is called the Y-channel and resembles an elemental ingredient of future wireless networks. The sum-capacity of this network is studied. A characterization of the sum-capacity within an additive gap of 2 bits, and a multiplicative gap of 4, for all values of channel gains and transmit powers is obtained. Contrary to similar setups where the cut-set bounds can be achieved within a constant gap, they cannot be achieved in our case, where they are dominated by our new genie-aided bounds. Furthermore, it is shown that a time-sharing strategy, in which at each time two users exchange information using coding strategies of the bidirectional relay channel, achieves the upper bounds to within a constant gap. This result is further extended to the${\rm K}$-user case, where it is shown that the same scheme achieves the sum-capacity within$2\log (K-1)$bits.

It was shown recently that the 2-user interference channel with a cognitive relay (IC-CR) has full degrees of freedom (DoF) almost surely, that is, 2 DoF. The purpose of this work is to check whether the DoF of the K-user IC-CR, consisting of K user pairs and a cognitive relay, follow as a straight forward extension of the 2-user case. As it turns out, this is not the case. The k-user IC-CR is shown to have 2K/3 DoF if K >; 2 for the when the channel is time varying, achievable using interference alignment. Thus, while the basic k-user IC with time varying channel coefficients has 1/2 DoF per user for all K, the k-user IC-CR with varying channels has 1 DoF per user if K = 2 and 2/3 DoF per user if K >; 2. Furthermore, the DoF region of the 3-user IC-CR with constant channels is characterized using interference neutralization, and a new upper bound on the sum-capacity of the 2-user IC-CR is given.

The generalized degrees of freedom (GDoF) of the symmetric two-user Gaussian interference relay channel (IRC) is studied. While it is known that the relay does not increase the DoF of the IC, this is not known for the more general GDoF. For the characterization of the GDoF, new sum-capacity upper bounds and lower bounds are derived. The lower bounds are obtained by a new scheme, which is based on functional decode-and-forward (FDF). The GDoF is characterized for the regime in which the source-relay link is weaker than the interference link, which constitutes half the overall space of channel parameters. It is shown that the relay can indeed increase the GDoF of the IRC and that it is achieved by FDF.

A multi-way communication network with three nodes and a relay is considered. The three nodes in this so-called Y-channel, communicate with each other in a bi-directional manner via the relay. Studying this setup is important due to its being an important milestone for characterizing the capacity of larger networks. A transmit strategy for the Gaussian Y-channel is proposed, which mimics a previously considered scheme for the deterministic approximation of the Y-channel. Namely, a scheme which uses nested-lattice codes and lattice alignment is used, to perform network coding. A new mode of operation is introduced, named `cyclic communication', which interestingly turns out to be an important component for achieving the capacity region of the Gaussian Y-channel within a constant gap.

The symmetric two-user Gaussian interference relay channel (IRC) is studied from a generalized degrees of freedom (GDoF) perspective. While it is known that the relay does not in crease the DoF of the IRC, such a characterization has not been reported for the GDoF yet. The focus of this paper is on all cases where the interference link is stronger than the link from the source to the relay. This regime basically covers half the space of all possible parameters of the IRC. By using genie-aided approaches, new sum-capacity upper bounds are derived. These bounds are then compared with rates achieved with a novel transmission scheme, which is based on a functional decode-and-forward (FDF) strategy. It is shown that the GDoF of the IRC is achieved by FDF in the given regime, and that a relay can indeed increase the GDoF of IRC. Finally, the FDF scheme is compared with other schemes like decode-and-forward as well as compress-and-forward at low, moderate, and high signal-to-noise ratios.

The capacity region of multi-pair bidirectional relay networks, in which a relay node facilitates the communication between multiple pairs of users, is studied. This problem is first examined in the context of the linear shift deterministic channel model. The capacity region of this network when the relay is operating at either full-duplex mode or half-duplex mode for arbitrary number of pairs is characterized. It is shown that the cut-set upper-bound is tight and the capacity region is achieved by a so called divide-and-conquer relaying strategy. The insights gained from the deterministic network are then used for the Gaussian bidirectional relay network. The strategy in the deterministic channel translates to a specific superposition of lattice codes and random Gaussian codes at the source nodes and successive interference cancelation at the receiving nodes for the Gaussian network. The achievable rate of this scheme with two pairs is analyzed and it is shown that for all channel gains it achieves to within 3 bits/sec/Hz per user of the cut-set upper-bound. Hence, the capacity region of the two-pair bidirectional Gaussian relay network to within 3 bits/sec/Hz per user is characterized.

The interference channel achievable rate region is presented when the interference is treated as noise. The formulation starts with the 2-user channel, and then extends the results to the n-user case. The rate region is found to be the convex hull of the union of n power control rate regions, where each power control rate region is upperbounded by a (n-1)-dimensional hyper-surface characterized by having one of the transmitters transmitting at full power. The convex hull operation lends itself to a time-sharing operation depending on the convexity behavior of those hyper-surfaces. In order to know when to use time-sharing rather than power control, the paper studies the hyper-surfaces convexity behavior in details for the 2-user channel with specific results pertaining to the symmetric channel. It is observed that most of the achievable rate region can be covered by using simple On/Off binary power control in conjunction with time-sharing. The binary power control creates several corner points in the n-dimensional space. The crystallized rate region, named after its resulting crystal shape, is hence presented as the time-sharing convex hull imposed onto those corner points; thereby offering a viable new perspective of looking at the achievable rate region of the interference channel.

The capacity region of the Gaussian cognitive interference network consisting of a primary multiple access channel and a secondary point-to-point system is considered. Its capacity region with weak interference is known. In this paper, the capacity region of a sub-regime of the strong interference regime is studied. An outer bound on the capacity region of this setup with strong interference is derived. An inner bound is shown to coincide with the strong interference outer bound under some conditions, thus characterizing the capacity of this setup for a set of channel parameters.

The linear shift deterministic Y-channel is studied. That is, we have three users and one relay, where each user wishes to broadcast one message to each other user via the relay, resulting in a multi-way relaying setup. The cut-set bounds for this setup are shown to be not sufficient to characterize its capacity region. New upper bounds are derived, which when combined with the cut-set bounds provide an outer bound on the capacity region. It is shown that this outer bound is achievable, and as a result, the capacity region of the linear shift deterministic Y-channel is characterized.

Two-way multi-antenna relaying with multiple relays offers great potential to increase the reliability and performance of bidirectional data transmission in relay networks. The optimal transmit strategies at the relay and at the nodes are known for the decode and forward strategy at one relay. However, for multiple linear amplify and forward relays the optimal strategy is not known. We compare three different recent proposals, DFT relaying, dual channel matching, and ANOMAX with optimal transmit strategies at the nodes under average power constraints at the relays and with linear MMSE receivers. It turns out that for a few relays DFT relaying performs reasonably whereas for more relays channel aware relaying gains significantly in performance. For large number of relays in rich multi-path fading it is more important to adapt the relay strategy than the node strategies to the current channel state.

Rate bounds for the non-coherent two-way relaying channel under the high SNR assumption are derived. No channel knowledge is assumed at neither the terminals nor at the relays. An upper and lower bound on the achievable rates are derived and shown that they differ only by a constant in the high SNR regime. As corollary from the analysis, the degrees of freedom of the non-coherent two-way channel are derived. Additionally, it is shown that the degrees of freedom can be achieved by an AF scheme.

We investigate the low-SNR sum rate performance of QPSK for symmetric interference channels. The QPSK performance is described by the minimum energy per bit and the wideband slope pertaining to the sum capacity. Comparing this with the minimum energy per bit and wideband slope of corresponding interference channels using optimal inputs, we find that QPSK achieves optimal performance in all of the cases where exact sum capacities are known. We also show that a simplified Han-Kobayashi scheme is suboptimal in the low-SNR regime when the input alphabet is the whole set of complex numbers.

Antenna selection is a powerful method in order to reduce the complexity of transmission and reception in a multi-antenna system. Interestingly, this method has been considered mostly for point-to-point communication. In this paper, we investigate the impact of different antenna selection criteria on the performance of wireless networks using interference alignment, a transmit strategy which has attracted a lot of interest, in order to improve the performance of wireless interference networks significantly. The selection criteria we consider for antenna selection are based on the properties of the channel such as signal-to-leakage-noise ratio, chordal distance, eigenvalues of the effective channel matrices and receiver side SNR. In order to avoid the exhaustive search needed to obtain the optimal solution we introduce three greedy low complexity selection algorithms. These algorithms perform reasonably well, however there remains a gap to the exhaustive search.

The tradeoff between diversity, interference alignment and rate for a K user multiple-antenna interference network is analyzed. It is assumed that the sources employ a space-time code in combination with linear preceding, while the receiving nodes use linear detectors. We show that interference alignment is needed if the system is operating at or close to the maximum achievable rate. For low rates the preferred strategy is to utilize all antennas in order to achieve high diversity gains, rather than using some of the antennas to align the interference. For the case of K = 3 users, an exact characterization of the tradeoff is provided. We also investigate the impact of channel estimation errors on the diversity of the system. It turns out that small channel estimation errors can be tolerated, while larger errors reduce the diversity gain significantly.

We study the capacity of the Gaussian two-pair fullduplex directional (or two-way) relay network with a single-relay supporting the communication of the pairs. This network is a generalization of the well known bidirectional relay channel, where we have only one pair of users. We propose a novel transmission technique which is based on a specific superposition of lattice codes and random Gaussian codes at the source nodes. The relay attempts to decode the Gaussian codewords and the superposition of the lattice codewords of each pair. Then it forwards this information to all users. We analyze the achievable rate of this scheme and show that for all channel gains it achieves to within 2 bits/sec/Hz per user of the cut-set upper bound on the capacity region of the two-pair bidirectional relay network.

In this paper we study the capacity region of the multi-pair bidirectional (or two-way) wireless relay network, in which a relay node facilitates the communication between multiple pairs of users. This network is a generalization of the well known bidirectional relay channel, where we have only one pair of users. We examine this problem in the context of the deterministic channel interaction model, which eliminates the channel noise and allows us to focus on the interaction between signals. We characterize the capacity region of this network when the relay is operating at either full-duplex mode or half-duplex mode (with non adaptive listen-transmit scheduling). In both cases we show that the cut-set upper bound is tight and, quite interestingly, the capacity region is achieved by a simple equation-forwarding strategy.

Recently, a full rate orthogonal space-time block code achieving full diversity while preserving low decoding complexity with very limited feedback was proposed. Based on a feedback of only p-1 bits, these codes exhibit a higher coding gain as well. In this work, we propose a low complexity, though close to optimal, feedback selection scheme. Furthermore, we analyze the performance of these codes with arbitrary number of transmit and receive antennas. We show that with this feedback based codes full diversity of nTnRPis obtained. Finally, we provide closed form expressions for the ergodic and outage mutual information as well as error rates.

The performance of multiuser MIMO systems can be significantly increased by channel aware scheduling and signal processing at the transmitters based on channel state information. In the multiple-antenna uplink multi-carrier scenario, the base station decides centrally on the optimal signal processing and spectral power allocation as well as scheduling. An interesting challenge is the reduction of the overhead in order to inform the mobiles about their transmit strategies. In this work, we propose to reduce the feedback by chunk processing and quantization. We maximize the weighted sum rate of a MIMO OFDM MAC under individual power constraints and chunk size constraints. An efficient iterative algorithm is developed and convergence proved. The feedback overhead as a function of the chunk size is considered in the rate computation and the optimal chunk size is determined by numerical simulations for various channel models. Finally, the issues of finite modulation and coding schemes as well as quantization of the preceding matrices are addressed.

Current generation wireless systems employ high frequency reuse and shrinking cell sizes. Thus, there has been significant recent attention on the analysis of interference limited systems). We consider single input single output (SISO) broadcast (BC) with intercell interference. We allow the received powers from each source of interference to be different For example, for the two interferer case, a user receives average power c1> 0 from interferer 1 and c2> 0 from interferer 2 where c1and c2need not be equal. We characterize the cumulative distribution function of the resulting signal to interference ratio (SIR) which is a ratio of weighted exponential random variables. It is thus a generalization of the F-distribution which is a scaled ratio of equally weighted exponential random variables. Surprisingly, there is no simple closed form expression for the resulting cdf in the literature. We present a nontrivial calculation that yields a simple, closed form expression for the cumulative distribution function (cdf) of the SIR. We show that this function is a Schur-concave function of the vector of average received powers from the various interferers. Furthermore, we derive a simple closed form expression for the cdf of the SINR. Again, we find that this function is Schur-concave in the average received powers. As a result we conclude that for any average transmit, receive, and thermal noise powers, the probability of achieving any SINR is highest when all interference power originates from a single interferer and lowest when the power is divided equally among the interferers. Opportunistic scheduling (OS) can be an effective tool to mitigate interference. For high signal and interference transmit power, the SINR is well approximated by the SIR. We analyze the scaling of the SIR using OS as the number of users grows for an arbitrary number of interferers. For J received interference powers {cj}j=1j, the SIR is asymptotically inversely proportional to the geometric mean, (Pij=1jcj)1/j.

We study the performance of a communication link with a single-antenna transmitter and a single-antenna receiver in the presence of interferers. This is the building block of wireless systems like sensor networks or multi-cell downlink transmission networks. In more details, we analyze the impact of the location of the interferers on the outage probability of the one-to-one communication link corrupted by the interferers. In order to guarantee a fair comparison, the received average interference power level is kept constant for all scenarios. Conventionally, it is often assumed that the interferers are for simplicity located on a unit circle around the receiver. It turns out that this is a very pessimistic approach. More realistically, the interferers are distributed arbitrarily around the receiver with some interferers being closer to the receiver and some further away. Using majorization theory, we show that the outage probability is a Schur-concave function with respect to the interfererspsila location. This result basically says that having the interferers more spread out within the cell provides lower outages.

Recently, a quasi-orthogonal space-time block code (QSTBC) capable of achieving a significant fraction of the outage mutual information of a multiple-input-multiple-output (MIMO) wireless communication system for the case of nT= 4 transmit and nR = 1 receive antennas was proposed. We generalize these results to nT= 2ntransmit and an arbitrary number of receive antennas. Furthermore, we completely characterize the structure of the equivalent channel for the general case and show that for all nT= 2nand nRthe eigenvectors of the equivalent channel are fixed and independent from the channel realization. Furthermore, the eigenvalues of the equivalent channel are independent identically distributed random variables each following a noncentral chi-square distribution with 4nRdegrees of freedom. Based on these important insights into the structure of the QSTBC, we derive tight lower and upper bounds for the outage probability achieved with QSTBC. Finally, by utilizing the special structure of the QSTBC, we propose a new transmit strategy, which decouples the signals transmitted from different antennas in order to detect the symbols separately with a linear ML-detector rather than joint detection, an up to now only known advantage of orthogonal space-time block codes (OSTBC).

This paper presents a practical algorithm for antenna selection in multiple-input multiple-output wireless communication systems employing space-time block codes (STBC). It first shows that maximizing the channel Frobenius norm helps maximize the mutual information for both orthogonal STBC and quasi-orthogonal STBC. However, the computational complexity for finding the optimal antenna subset grows exponentially with the number of antennas. This paper identifies that the channel Frobenius norm maximization problem can be formulated as a quadratically constrained quadratic programming (QCQP) problem. Then, despite the fact that the problem is non-convex, a semidefinite relaxation of QCQP enables the problem to be solved approximately by semidefinite programming in polynomial time. Simulation results indicate that the loss of semidefinite relaxation is negligible. It is also shown that although the combination of STBC and antenna selection is not always beneficial, it is a robust transmission strategy in the high SNR regime when only imperfect channel information is available.

In this paper, we study a wireless multiple-input multiple-output system in a Rayleigh flat-fading environment with correlation among the transmit antennas. We assume that the receiver has perfect CSI and the transmitter only knows the correlation matrix. The transmitter employs a quasi-orthogonal space-time block code in combination with a linear precoder; the receiver uses a linear MMSE detector. We analyze the optimal transmit precoding strategy that minimizes the average sum MSE at the receiver. We show that, as expected, the optimal precoding directions coincide with the eigenvectors of the transmit correlation matrix. The optimal power allocation, however, only supports at most 2 directions at all SNRs independent of the number of transmit antennas, which correspond to the 2 largest eigenvalues of the transmit correlation matrix. We characterize this optimal power allocation by the necessary and sufficient optimality conditions. At high SNRs, the optimal allocation approaches equal power on the two supported modes. At low SNRs, the weaker mode is dropped and the precoding matrix becomes single-mode beamforming. We provide a closed-form expression characterizing this low-SNR range. Numerical simulations confirm our theoretical analysis.

Recently, the capacity region of the MIMO broadcast channel (BC) was completely characterized and duality between MIMO multiple access channel (MAC) and MIMO BC with perfect channel state information (CSI) at transmitter and receiver was established. In this work, we propose a MIMO BC approach in which only information about the channel norm is available at the base and hence no dirty paper precoding (DPC) can be applied. However, a certain set of individual performances in terms of MSE or zero-outage rates can be guaranteed at any time by applying an orthogonal space time block code (OSTBC). The guaranteed MSE region without superposition coding is characterized in closed form and the impact of diversity, fading statistics, and number of transmit antennas is analyzed. The guaranteed MSE region with superposition coding is also studied. Finally, the guaranteed sum MSE is briefly discussed.

In this paper, strategies for dynamic user grouping and shared frequency resource assignments for OFDMA systems are proposed. The users are grouped based on their modulation type and a metric for this purpose is introduced which considers tradeoffs between maximum rate and minimum allocated resources per user. The available resources are distributed through the modulation groups based on fairness criteria. Equal power allocation on the loaded subcarriers of each user is further incorporated. Simulations show that under equal power assumptions the algorithm proposed in the following can bring a better exploitation of the available bandwidth and can provide good QoS to an increased number of users simultaneously.

In order to obtain full spatial diversity available in a multiple-input multiple-output (MIMO) system, orthogonal space-time block codes (OSTBC) are employed due to their low decoding complexity. However, the drawback of OSTBC is that the code rate decreases with increasing number of transmit antennas. Using a recent result on the properties of Gaussian quadratic forms, we show that an OSTBC for an odd number of transmit antennas is always outperformed in terms of different performance measures, namely, the outage probability, the outage mean-squared error (MSE), and the raw bit-error rate (BER), by an OSTBC for an even number of transmit antennas, which has a strong impact on the design and application of OSTBC

It is well known, that the Alamouti scheme is the only space-time code from orthogonal design achieving the capacity of multiple-input multiple-output (MIMO) wireless communication systems with nT= 2 transmit antennas and nR= 1 receive antenna. In this work, we propose the n-times stacked Alamouti scheme for nT= 2n transmit antennas and show that this scheme achieves the capacity in the case of nR= 1 receive antenna. For the more general case of more than one receive antenna, we show that if the number of transmit antennas is higher than the number of receive antennas we achieve a high portion of the capacity with this scheme. Further, we show that the MIMO capacity is at most twice the rate achieved with the proposed scheme for all SNR. We derive lower and upper bounds for the rate achieved with this scheme and compare it with upper and lower bounds for the capacity. Finally, we illustrate the theoretical results by numerical simulations.

Othogonal space-time block codes (OSTBC) are an efficient mean in order to exploit the diversity offered by the wireless multiple-input multiple-output (MIMO) channel. It was shown that the Alamouti scheme, an OSTBC for nT= 2 transmit antennas, achieves the capacity of such MIMO channels with nR= 1 receive antenna. However, by increasing the number of transmit antennas, the transmission rate of the OSTBC is monotonically decreasing. Recently, this rate reduction was characterized completely. Using a recent result on the properties of Gaussian quadratic forms, we show two key results. Assume the transmitter has not channel state information. We show that for given rate and SNR it is optimal to use a subset of all available antennas with equal power allocation. Further on, we show that an OSTBC for an odd number of transmit antennas is always outperformed by an OSTBC for either the next lower or the next higher even number of transmit antennas. Finally, the impact of spatial correlation with uninformed transmitter is characterized. We illustrate the theoretical results by numerical simulations. The theoretical and simulation results show the suboptimality of the OSTBC for an odd number of antennas with respect to the outage probability minimization

In this work, we study a MIMO system with a transmitter using a linear dispersion code (LDC) and a linear MMSE detector at the receiver in a Ricean flat-fading environment. The focus of our work is the analysis of the optimal transmit strategy using different types of LDC assuming that the transmitter knows only the mean channel matrix. On the one hand we consider spatial multiplexing (SM) schemes achieving high data rates, however sacrificing diversity. On the other hand, we have schemes achieving full diversity like quasi-orthogonal space-time block codes (QSTBC) or orthogonal space-time block codes (OSTBC). Depending on the LDC in use, the optimization problem is either convex or non-convex. For both these classes of LDC we derive the properties of the average normalized MSE and analyze the impact of the mean component on the MSE, the optimal transmit strategy and the optimal power allocations

We consider a multi-input multi-output (MIMO) multiple access channel (MAC) where the base station has perfect channel state information (CSI), while the mobiles have only partial CSI. The statistical CSI at the mobiles can be obtained either by implicit (during downlink transmission) or explicit (via a low rate control channel) feedback. The base applies a linear multiuser MMSE receiver and thus the optimization criteria is the average sum MSE. We optimize the average sum MSE with implicit and explicit mean- or covariance-feedback under individual power constraints. The optimal transmit directions of a user corresponds to the eigenvectors of his channel mean or correlation matrix. The optimal power allocation is characterized in terms of its beamforming range. Finally, the gain due to covariance feedback and all theoretical results are illustrated by numerical simulation.

The demand for wireless broadband access systems supporting mobility of the individual users has dramatically increased in recent years. To this end, we analyze the impact of user-mobility in the uplink of an OFDMA system on the performance of pilot-aided channel estimation. We analyze the mean square error (MSE) performance of two pilot-aided channel estimation schemes, the simple Gauss-Markov estimator and the optimal LMMSE estimator. We derive closed-form expressions for the MSE taking into account the impact of intercarrier-interference and time-variations of the channel. Different pilot allocation strategies are analyzed and their performances are compared. Finally, the results are illustrated by numerical simulations based on the WiMax 802.16e specifications

In this work, we study a MIMO system employing quasi-orthogonal space-time block codes (QSTBC) at the transmitter with an MMSE receiver in a Ricean flat-fading environment. We assume that the receiver has perfect CSI and the transmitter knows only the mean channel matrix either by feedback or channel estimation. We analyze the impact of the mean component on the MSE, the optimal transmit strategy and the optimal power allocation. Finally, we derive some bounds on the error rate performance for different scenarios with the MMSE receiver.

The delay-limited capacity is defined as the transmission rate that can be guaranteed in all fading states under finite long-term power constraints. For the single-input single-output Rayleigh fading channel it is zero. In contrast it is greater than zero in multiple antenna channels but depends on the properties of the fading channel, e.g. on the spatial correlation. In this work, we prove that the delay-limited capacity is Schur-concave with respect to the spatial correlation. In addition to the average power constraint, we apply a peak-power constraint which limits the kurtosis of the input signal. We derive the delay-limited capacity for this general class of multiple antenna channels with correlation under peak-power and long-term power constraint. Without the stringent delay constraint, the maximum throughput is defined as the transmission rate times successful transmission probability. When the transmitter is uninformed, the maximum throughput is achieved for small SNR by using only one transmit antenna and for high SNR by using all available transmit antennas. When the transmitter has perfect channel knowledge, the optimal power allocation under long-term power constraint is analyzed and the impact of correlation is discussed by numerical simulations.

Recently, a quasi-orthogonal space-time block code (QSTBC) capable of achieving a significant fraction of the outage mutual information of a multiple-input-multiple output (MIMO) wireless communication system for the case of four transmit and one receive antennas was proposed. We generalize these results to 2/sup n/ transmit and an arbitrary number of receive antennas. Furthermore, we derive an analytical lower bound for the fraction of outage probability achieved with QSTBC and show that this bound is tight for low signal-to-noise-ratios (SNR) values and also for increasing number of receive antennas. We present also an upper bound, which is tight for high SNR values and derive analytical expressions for the case of four transmit antennas. Furthermore, by utilizing the special structure of the QSTBC we propose a new transmit strategy, which decouples the signals transmitted from different antennas in order to detect the symbols separately with a linear ML-detector rather than joint detection, an up to now only known advantage of orthogonal space-time codes (OSTBC).

Space-time block codes (STBC) exploit multiple-input multiple-output (MIMO) communication systems in order to obtain diversity for high link reliability. Unfortunately, it is not possible to construct STBC with transmission rate equal to one for more than two transmit antennas. A way to achieve higher transmission rates is to use quasi-orthogonal STBC (QSTBC). We can improve the performance of QSTBC through a special constellation rotation. We derive the optimal rotation analytically. Numerical simulations show that our analytical approach outperforms the regular QSTBC as well as approaches where a simulative method is used in order to obtain better performance in comparison to regular QSTBC. In addition, we study the impact of the rotation on the achievable portion of the outage capacity.

We propose a "turbo" coding scheme for the multiple-input multiple-output (MIMO) Rayleigh fading channel consisting of the serial concatenation of a block code as the outer code and different orthogonal space-time block codes (STBC) for more than two transmit antennas as the inner code. Here, we consider the orthogonal STBC as a mapping scheme in space and time. At the receiver, we apply iterative space-time detection and decoding. We analyze the impact of different mapping strategies on the information transfer of the soft-input-soft-output (SISO) space-time detector. Moreover, we analytically show that additional performance gains over Gray mapping can be obtained by different mapping strategies. Furthermore, we use extrinsic information transfer characteristics (EXIT-charts) in order to predict the performance and the behavior of the system.

In this work, we study the iterative decoding of a low-complexity space-time architecture proposed by [G. Caire and G. Colavolpe, September 2001; G. Caire and G. Colavolpe, April 2001] employing per-survivor-processing at the receiver with the soft-output Viterbi-algorithm (SOVA, [J. Hagenauer and P. Hoether]). At the transmitter, the space-time architecture is serially concatenated with an outer code. With the availability of extrinsic information at the receiver delivered by the SOVA, it is now possible to use a novel receiver scheme performing iterative (turbo) decoding in order to improve the performance of the architecture. According to the ZF or to the MMSE criterion, linear feedforward and -backward filters of the decision-feedback space-time decoder are derived. Furthermore, the decision metric of the SOVA employing per-survivor-processing is developed and the performance of the scheme is analyzed and compared with the scheme in [G. Caire and G. Colavolpe, April 2001].