This work investigates a full-duplex (FD)-enhanced Rate-Splitting Multiple Access (RSMA) system under practical constraints, including imperfect channel state information (CSI) and successive interference cancellation (SIC). We derive closed-form expressions for key performance metrics, such as outage probability and throughput, for both uplink and downlink users. The analysis considers co-channel interference (CCI) from uplink to downlink users and models the self-interference (SI) channel as a random variable. Monte Carlo simulations validate the analytical results and highlight the impact of system imperfections on RSMA-FD performance. At low transmit power, imperfect CSI significantly affects the system, though this effect weakens as power increases. In contrast, imperfect SIC becomes more detrimental at high transmit power, causing severe degradation. Additionally, neglecting CCI and assuming perfect SI cancellation leads to substantial overestimation of performance. Lastly, we demonstrate that the SI cancellation factor must be carefully selected to suppress interference effectively. Otherwise, a poor choice limits the full potential of FD technology.

Radio frequency (RF) wireless power transfer (WPT) is an appealing technology to provide sustainable and cost-efficent power supply to low-power devices in future wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is a serious challenge for practical applications. The key contributing factors include channel losses, transceivers’ power consumption, and losses from components such as the digital-to-analog converter (DAC), high-power amplifier (HPA), and rectenna. Careful consideration of these factors is essential for optimizing PTE, which is the focus of this research. Herein, we consider a fully connected hybrid multi-antenna transmit architecture that aims to charge non-linear energy harvesters. First, we present a mathematical framework to determine the harvested power from multi-tone signal transmissions and the system’s power consumption. Then, we formulate a joint waveform and analog beamforming design problem to minimize system’s power consumption and fulfill user’s charging needs. With this in place, and due to the problem high-complexity, we propose a particle swarm optimization (PSO)-based solution. Moreover, we also model the problem as a Markov decision process and propose a solution based on deep deterministic policy gradient (DDPG). Numerical results demonstrate that the proposed algorithms converge to suboptimal solutions. Moreover, simulation results show that system power consumption reduces with lower DAC and phase shifter resolution, as well as increased antenna length. Conversely, power consumption rises with the number of users and RF chains. Notably, across all these scenarios, PSO-JWB outperforms DDPG-JWB, requiring lower overall system power consumption.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology to seamlessly charge low-power devices, but its low end-to-end power transfer efficiency remains a critical challenge. To address the latter, low-cost transmit/radiating architectures, e.g., based on reconfigurable intelligent surfaces (RISs), have shown great potential. Beyond diagonal (BD) RIS is a novel branch of RIS offering enhanced performance over traditional diagonal RIS (D-RIS) in wireless communications, but its potential gains in RF-WPT remain unexplored. Motivated by this, we analyze a BD-RIS-assisted single-antenna RF-WPT system to charge a single rectifier, and formulate a joint beamforming and multi-carrier waveform optimization problem aiming to maximize the harvested power. We propose two solutions relying on semi-definite programming for fully connected BD-RIS, a successive convex approximation (SCA)-based beamforming approach, and an efficient low-complexity iterative method relying on SCA. Numerical results show that the proposed algorithms converge and that adding transmit sub-carriers or RIS elements improves the harvesting performance. We show that the transmit power budget impacts the relative power allocation among different sub-carriers depending on the rectifier’s operating regime, while BD-RIS shapes the cascade channel differently for frequency-selective and flat scenarios. Finally, we verify by simulation that BD-RIS and D-RIS achieve the same performance under pure far-field line-of-sight conditions (in the absence of mutual coupling). Meanwhile, BD-RIS outperforms D-RIS as the non-line-of-sight components of the channel become dominant.

Communication reliability, as defined by 3GPP, is the probability of achieving a desired quality of service (QoS). Traditionally, this metric is evaluated by averaging the QoS success indicator over spatiotemporal random variables. Recently, the meta distribution (MD) has emerged as a two-level analysis tool that characterizes system-level reliability as a function of link-level reliability thresholds. However, existing MD studies have two limitations. First, they focus exclusively on spatial and temporal randomness corresponding to node distribution and fading channels, respectively, leaving stochastic behaviors in other domains largely unexplored. Second, they are restricted to first-order MDs with two randomness levels, restricting applicability to scenarios requiring higher-order MD characterization. To address these gaps, we propose a hierarchical framework for higher-order MD reliability in wireless networks, where each layer’s success probability is formulated and fed into the next layer, yielding overall MD reliability at the highest level. We apply this framework to wireless networks by capturing three levels of temporal dynamics representing fast, slow, and static random elements, and provide a comprehensive second-order MD reliability analysis for two application scenarios. The effectiveness of the proposed approach is demonstrated via these representative scenarios, supported by detailed analytical and numerical evaluations. Our results highlight the value of hierarchical MD representations across multiple domains and reveal the significant influence of inner-layer target reliabilities on overall performance.

In dynamic metasurface antennas (DMAs) architecture, multiple radiating metamaterial elements are embedded onto a microstrip, with each microstrip connected to a dedicated radio frequency (RF) chain. Therefore, this architecture results in reduced cost and power consumption. This paper proposes a dynamic hybrid beamforming architecture that utilizes a switching network connected to the DMA structure to select efficient microstrips, thereby maximizing the achievable rate while minimizing RF power consumption in near-field communication systems. To model the RF power consumption, a binary diagonal matrix—referred to as the microstrip selection matrix—is defined, where the number of non-zero diagonal elements indicates the number of microstrips required to serve the user. Specifically, we jointly optimize the microstrip selection matrix, the transmit beamforming vectors, and the configurable weights of the DMA’s metamaterial elements, subject to the maximum power budget constraint, the integer constraints of the microstrip selection, and the Lorentzian circle constraint associated with the DMA elements. The optimization problem is non-convex due to the coupling between continuous and discrete decision variables, which makes it challenging to solve. In this regard, we employ a combination of the alternating optimization method, the quadratic transform, and the augmented Lagrangian technique to address these challenges. Simulation results demonstrate that the proposed algorithm outperforms conventional hybrid beamforming approaches in DMA architectures with respect to both achievable rate and RF power consumption.

Communication reliability is typically assessed by averaging the QoS success indicator over spatial and temporal variables. The meta distribution (MD) has recently emerged as a powerful two-level analysis framework, providing insights into system-level (outer) reliability relative to link-level (inner) thresholds. While prior studies focus on first-order spatiotemporal MD reliability, applications beyond this structure remain unexplored. This work introduces a second-order MD reliability analysis framework and applies it to spatial-spectral-temporal MD analysis for frequency-hopping THz communication. Numerical results show how inner-layer target reliabilities in temporal/spectral domains affect overall spatial MD reliability. It is also shown that adopting a non-uniform frequency-hopping pattern enhances spatial MD reliability but reduces resiliency and increases jamming risk.

Radio frequency (RF) wireless power transfer (WPT) is promising for promoting sustainability in future wireless systems, but its low end-to-end power transfer efficiency is a critical challenge. For this, reconfigurable intelligent surfaces (RISs) can be leveraged to enhance efficiency by providing nearly passive beamforming gains. Beyond diagonal (BD) RIS is a new RIS variant offering greater performance benefits than traditional diagonal RIS (D-RIS), though its potential for RF-WPT remains unexplored. Motivated by this, we consider a single-input single-output BD-RIS-aided RF-WPT system and we formulate a joint beamforming and waveform optimization problem aiming to maximize the harvested power at the receiver. We propose an optimization framework relying on successive convex approximation, alternating optimization, and semi-definite relaxation. Numerical results show that increasing the number of transmit sub-carriers or RIS elements improves the harvested power. We verify by simulation that BD-RIS leads to the same performance as D-RIS under far-field line-of-sight conditions (in the absence of mutual coupling), while it outper-forms D-RIS as the non-line-of-sight components dominate.

Integrating extremely large antenna arrays (ELAAs) with extremely large reconfigurable intelligent surfaces (XLRISs) in millimeter wave (mmWave) communications places devices in the near-field (NF) region, significantly boosting spectral efficiency (SE). This paper investigates beam focusing for SE maximization by determining the optimal placement of XL-RIS in a multi-input single-output (MISO) system. Specifically, to maximize SE, the transmit beamforming vector at the base station (BS) and the phase-shifting vector at the XL-RIS are jointly optimized. To explore the optimal placement of XL-RIS, we consider three scenarios where the positions of the BS and user are fixed, but the XL-RIS is placed in either the NF or farfield (FF) of both. Since the SE maximization problem is nonconvex with highly coupled variables, we propose an alternating optimization algorithm that decouples the problem into two subproblems: transmit beamforming optimization and phase shift optimization. Both sub-problems are reformulated as convex problems using the semi-definite programming (SDP) technique and are then solved with standard convex optimization tools. Simulation results show that placing the XL-RIS in the NF region of the BS achieves higher SE compared to other configurations.

The advent of 6G wireless networks promises to rev-olutionize communication with ultra-reliable, high-throughput, and low-latency services for applications like autonomous systems and immersive media. A critical aspect of 6G is network resiliency, which ensures reliable performance under challenging conditions such as extreme weather, congestion, and interference. The outage probability, which quantifies the likelihood of a communication link failing to meet its required Quality of Service (QoS), is a key metric for assessing resiliency. One strategy for improving resiliency is adding redundancy, such as introducing new transmitters. However, the impact of redundancy on outage probability is complex. Traditional models often assume independent fading channels, which oversimplify the real-world dependencies between transmitters. To address this, we use copulas, a mathematical tool that models joint distributions while accounting for various dependence structures between fading channels. This approach is particularly useful in complex environments where correlations between links vary. Our results show that less dependency between links leads to a lower outage probability, while more dependency results in higher outage probabilities. Additionally, we apply Fréchet- Hoeffding bounds to derive bounds for the outage probability that show how adding redundancy, such as additional link, can affect outage probability, enhancing our analysis of network resiliency.

Interference prediction and resource allocation are critical challenges in mission-critical applications where stringent latency and reliability constraints must be met. This paper proposes a novel Gaussian process regression (GPR)-based framework for predictive interference management and resource allocation in future 6G networks. Firstly, the received interference power is modeled as a Gaussian process, enabling both the prediction of future interference values and their corresponding estimation of uncertainty bounds. Differently from conventional machine learning methods that extract patterns from a given set of data without any prior belief, a Gaussian process assigns probability distributions to different functions that possibly represent the data set which can be further updates using Bayes' rule as more data points are observed. For instance, unlike deep neural networks, the GPR model requires only a few sample points to update its prior beliefs in real-time. Furthermore, we propose a proactive resource allocation scheme that dynamically adjusts resources according to predicted interference. The performance of the proposed approach is evaluated against two benchmarks prediction schemes, a moving average-based estimator and the ideal genie-aided estimator. The GPR-based method outperforms the moving average-based estimator and achieves near-optimal performance, closely matching the genie-aided benchmark.

Emerging massive machine-type communications service class needs to support many devices while ensuring that scarce radio resources are utilized efficiently. Nonorthogonal multiple access is proposed to minimize the signaling overhead and optimize resource allocation. However, during the initial access, the base station (BS) is presented with the challenge of identifying sparsely active devices in the absence of knowledge about the sparsity and channel state information. The user channels in most practical scenarios have common reflection paths, making them partially correlated, which can be exploited to improve the detection performance at the BS. In this context, we formulate a novel multiuser detection (MUD) problem in spatially correlated Rician channels, which we reformulate as a multilabel classification problem utilizing deep learning techniques. We propose two diverse approaches to tackle this problem: 1) ViT-Net, a vision transformer-based architecture, and 2) FAR-Net, a fully activated deep neural network featuring residual connections. Our analysis highlights the significance of spatial correlation for MUD, which can accord around 13% higher overloading ratio compared to the noncorrelated scenario. Numerical evaluations demonstrate the effectiveness of the proposed model in addressing spatial correlation compared to the existing deep-learning models.

We characterize three near-field sub-regions for phased array antennas by elaborating on the boundaries Fraunhofer, radial-focal, and non-radiating distances. The Fraunhofer distance which is the boundary between near and far field has been well studied in the literature on the principal axis (PA) of single-element center-fed antennas, where PA denotes the axis perpendicular to the antenna surface passing from the antenna center. The results are also valid for phased arrays if the PA coincides with the boresight, which is not commonly the case in practice. In this work, we completely characterize the Fraunhofer distance by considering various angles between the PA and the boresight. For the radial-focal distance, below which beamfocusing is feasible in the radial domain, a formal characterization of the corresponding region based on the general model of near-field channels (GNC) is missing in the literature. We investigate this and elaborate that the maximum-ratio-transmission (MRT) beamforming based on the simple uniform spherical wave (USW) channel model results in a radial gap between the achieved and the desired focal points. While the gap vanishes when the array size N becomes sufficiently large, we propose a practical algorithm to remove this gap in the non-asymptotic case when N is not very large. Finally, the non-radiating distance, below which the reactive power dominates active power, has been studied in the literature for single-element antennas. We analytically explore this for phased arrays and show how different excitation phases of the antenna array impact it. We also clarify some misconceptions about the non-radiating and Fresnel distances prevailing in the literature.

Discontinuous reception (DRX) is a key technology for reducing the energy consumption of industrial Internet of Things (IIoT) devices. Specifically, DRX allows the devices to operate in a low-power mode when no data reception is scheduled, and its effectiveness depends on the proper configuration of the DRX parameters. In this paper, we characterize the DRX process departing from a semi-Markov chain modeling and detail two ways to set DRX parameters to minimize the device power consumption while meeting a mean delay constraint. The first method exhaustively searches for the optimal configuration, while the second method uses a low-complexity metaheuristic to find a sub-optimal configuration, thus considering ideal and practical DRX configurations. Notably, within the DRX parameters, the inactivity timer (IT) is a caution time that specifies how long a device remains active after the last information exchange as a precedent to a low-power mode. Traditionally, the IT is restarted whenever new data is received, which might sometimes needlessly extend the active time. Herein, we propose a more efficient method in which the transmit base station (BS) explicitly indicates restarting the timer through the control channel only when appropriate. The decision is based on the BS's knowledge about its buffer status. We consider Poisson and bursty traffic models, which are typical in IIoT setups, and verify our proposal's suitability for reducing the devices' energy consumption without significantly compromising the communication latency. Specifically, energy saving gains up to 30% can be obtained regardless of the arrivals rate and delay constraints.

Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely large-scale programmable metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the desired focal point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSI-independent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the phase distribution image (PDI) of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing quasi-liquid layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.

We study the problem of resource reservation and allocation (RRA), aiming to enable network operators (NOs) to effectively develop the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency (URLLC) services by acquiring resources–such as spectrum, base stations (BSs), and transmit power of the BSs–from the infrastructure provider (InP). To do this, we quantitatively analyze the impact of spatial-temporal aspects of the network, including the spatial distributions of users and BSs, wireless channel conditions, and user traffic statistics, on resources required to meet users’ requirements using the meta distribution. Since spatial-temporal aspects operate on different time and space scales, we investigate a multi-timescale RRA (MT-RRA) scheme that facilitates collaboration between the InP and NOs to acquire resources through long-timescale RR (LT-RR) over the spatial distributions of users and user traffic statistics and medium-timescale RR (MT-RR) over wireless channel fading. Subsequently, NOs allocate these resources to their eMBB and URLLC users over shorter timescales. To address these problems, an iterative decomposition method is proposed. The MT-RRA scheme leverages the flexibility of the MT-RR, the cost-effectiveness of the LT-RR, and the benefits of spatial-temporal considerations. The simulation results are presented to demonstrate the performance of the proposed MT-RRA scheme and highlight its superiority over single-timescale RRA schemes.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for future wireless systems. However, the low power transfer efficiency (PTE) is a critical challenge for practical implementations. One of the main inefficiency sources is the power consumption and loss introduced by key components such as high-power amplifier (HPA) and rectenna, thus they must be carefully considered for PTE optimization. Herein, we consider a near-field RF-WPT system with a dynamic metasurface antenna (DMA) at the transmitter and non-linear energy harvesters. We provide a mathematical framework to calculate the power consumption and harvested power from multi-tone signal transmissions. Based on this, we propose an approach relying on alternating optimization and successive convex approximation for waveform optimization and beam focusing to minimize power consumption while meeting energy harvesting requirements. Numerical results show that increasing the number of transmit tones reduces the power consumption by leveraging the rectifier’s non-linearity more efficiently. Moreover, they demonstrate that increasing the antenna length improves the performance, while DMA outperforms fully-digital architecture in terms of power consumption. Finally, our results verify that the transmitter focuses the energy on receivers located in the near-field, while energy beams are formed in the receivers’ direction in the far-field region.

Efficient connectivity for energy constrained massive Internet of Things (IoT) nodes is among the key design challenges for future wireless networks. In this work, we analyze the downlink performance of a massive IoT network considering simultaneous wireless information and power transfer (SWIPT). We consider the rate-splitting multiple access (RSMA) scheme in the finite blocklength (FBL) regime under realistic assumptions such imperfect channel state information (CSI), imperfect successive interference cancellation (SIC), and hardware impairments in the energy harvesting circuitry. The system performance is assessed by evaluating closed-form expressions for the block-error rate (BLER) and goodput. We also derive analytical expressions for the average harvested energy considering linear and non-linear characteristics of the energy-constrained IoT nodes. The effect of the power splitting (PS) factor under linear and non-linear regimes on the BLER is also discussed. Monte Carlo simulations corroborate the accuracy of the derived expressions, which highlight the impact of increasing the blocklength and demonstrate the performance degradation generated by imperfect CSI, imperfect SIC, and hardware impairment. The results reveal that the integration of PS-SWIPT in RSMA networks can offer around 28% ~ 33% performance improvement in terms of BLER over SWIPT-enabled non-orthogonal multiple access networks.

A reconfigurable intelligent surface (RIS) tames the wireless propagation environment and emerges as a key enabler for beyond fifth-generation communication systems. In this paper, the performance of a wireless-powered Internet-of-Things (IoT) network is studied under the assistance of passive and active RIS. In particular, a nonlinear energy harvesting (EH) model is employed at the IoT devices, which harvest radio frequency energy from a dedicated energy station. The analytical expression of outage probability (OP) is derived in terms of the Meijer-G function over-generalized Nakagami-m fading channels. The asymptotic (high signal-to-noise ratio) OP is also analytically characterized, and the diversity order of the considered network is obtained. Further, sum throughput and energy efficiency expressions are derived for delay-limited and delay-tolerant transmission modes, while the ergodic capacity is derived analytically by employing the Gaussian Chebyshev quadrature approximation. The performance attained under the considered nonlinear EH model is compared to that attained with a traditional linear EH model, which is impractical. Finally, the derived analytical expressions are verified via Monte-Carlo simulations.

This paper delves into the unexplored frequency-dependent characteristics of beyond diagonal reconfigurable intelligent surfaces (BD-RISs). A generalized practical frequency-dependent reflection model is proposed as a fundamental framework for configuring fully-connected and group-connected RISs in a multi-band multi-base station (BS) multiple-input multiple-output (MIMO) network. Leveraging this practical model, multi-objective optimization strategies are formulated to maximize the received power at multiple users connected to different BSs, each operating under a distinct carrier frequency. By relying on matrix theory and exploiting the symmetric structure of the reflection matrices inherent to BD-RISs, relaxed tractable versions of the challenging problems are achieved for scenarios with obstructed and unobstructed direct channel links. The relaxed solutions are then combined with codebook-based approaches to configure the practical capacitance values for the BD-RISs. Simulation results reveal the frequency-dependent behaviors of different RIS architectures and demonstrate the effectiveness of the proposed schemes. Notably, BD-RISs exhibit high reflection performance across the intended frequency range, remarkably outperforming conventional single-connected RISs. Moreover, the proposed optimization approaches prove effective in enabling the targeted operation of BD-RISs across one or more carrier frequencies. The results also shed light on the potential for harmful interference in the absence of synchronization between RISs and adjacent BSs.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for sustainable support of massive Internet of Things (IoT). However, RF-WPT systems are characterized by low efficiency due to channel attenuation, which can be mitigated by precoders that adjust the transmission directivity. This work considers a multi-antenna RF-WPT system with multiple non-linear energy harvesting (EH) nodes with energy demands changing over discrete time slots. This leads to the charging scheduling problem, which involves choosing the precoders at each slot to minimize the total energy consumption and meet the EH requirements. We model the problem as a Markov decision process and propose a solution relying on a low-complexity beamforming and deep deterministic policy gradient (DDPG). The results show that the proposed beamforming achieves near-optimal performance with low computational complexity, and the DDPG-based approach converges with the number of episodes and reduces the system’s power consumption, while the outage probability and the power consumption increase with the number of devices.

Beam management procedures needed to support highly directional transmission links in wireless systems have been shown to be susceptible to replay attacks that can induce beam alignment failure. This paper proposes a new replay attack against beam management procedures using passive reconfigurable intelligent surfaces (RISs). For launching the proposed attack, we propose a combinatorial multi-armed bandit (CMAB)-based adversarial RIS that smartly controls which reference signals enter a certain indoor network service area to manipulate indoor user equipments (UEs) into poor beam selection. Our results indicate that our adversarial RIS can degrade outdoor-to-indoor communication by several orders of magnitude, potentially disrupting indoor communication.

Wireless communications systems must increasingly support a multitude of machine-type communications devices, thus calling for advanced strategies for active user detection (AUD). Recent literature has investigated AUD techniques based on compressed sensing, highlighting the critical role of signal sparsity. This study examines the relationship between frequency diversity and signal sparsity in the AUD problem. Single-antenna users transmit multiple copies of non-orthogonal pilots across multiple frequency channels and the base station independently performs AUD in each channel using the orthogonal matching pursuit algorithm. We note that, although frequency diversity may improve the likelihood of successful reception of the signals, it may also damage the channel sparsity level, leading to important trade-offs. We show that a sparser signal significantly benefits AUD, surpassing the advantages brought by frequency diversity in scenarios with limited temporal resources and/or high numbers of receive antennas. Conversely, with longer pilots and fewer receive antennas, investing in frequency diversity becomes more impactful, resulting in a tenfold AUD performance improvement.

This paper presents the characterization of near-field propagation regions for phased array antennas, with a particular focus on the propagation boundaries defined by Fraunhofer and Fresnel distances. These distances, which serve as critical boundaries for understanding signal propagation behavior, have been extensively studied and characterized in the literature for single-element antennas. However, the direct application of these results to phased arrays, a common practice in the field, is argued to be invalid and non-exact. This work calls for a deeper understanding of near-field propagation to accurately characterize such boundaries around phased array antennas. More specifically, for a single-element antenna, the Fraunhofer distance is dF= 2D2sin2(0)/λ where D represents the largest dimension of the antenna, λ is the wavelength and θ denotes the observation angle. We show that for phased arrays, dFexperiences a fourfold increase (i.e., dF= 8D2sin2(θ)/λ) provided that $\left| {\theta - \frac{\pi }{2}} \right| > {\theta ^F}$ (which holds for most practical scenarios), where θFis a small angle whose value depends on the number of array elements, and for the case $\left| {\theta - \frac{\pi }{2}} \right| \leq {\theta ^F}$, we have dF∈ [2D2/λ, 8D2cos2(θF)/λ], where the precise value is obtained according to some square polynomial function ${{\tilde F}}\left( \theta \right)$. Besides, we also prove that the Fresnel distance for phased array antennas is given by ${d^{\text{N}}} = 1.75\sqrt {{D^3}/\lambda }$ which is $\sqrt 8$ times greater than the corresponding distance for a conventional single-element antenna with the same dimension.

While the robustness of rate-splitting multiple access (RSMA) to imperfect channel state information (CSI) is well-documented, its susceptibility to attacks launched with malicious reconfigurable intelligent surfaces (RISs) remains unexplored. This paper fills this gap by investigating three potential RIS-induced attacks against RSMA in a multi-user multiple-input multiple-output (MIMO) network: random interference, aligned interference, and mitigation attack. The random interference attack employs random RIS coefficients to disrupt RSMA. The other two attacks are triggered by optimizing the RIS through weighted-sum strategies based on the projected gradient method. Simulation results reveal significant degradation caused by all the attacks under perfect CSI conditions. Remarkably, when imperfect CSI is considered, RSMA, owing to its flexible power allocation strategy designed to counter CSI-related interference, can be robust to the attacks even when the base station is blind to them. It is also shown that RSMA can significantly outperform conventional space-division multiple access (SDMA).

Reconfigurable intelligent surfaces (RIS)-based communications with reflection modulation (RM) is a novel area of research that opens up a range of unconventional modulation techniques. Existing literature primarily focuses on specific applications where the RIS encodes its own information onto its reflection pattern. Quadrature reflection modulation (QRM) and reflection pattern modulation (RPM) are two promising reflection pattern designs that effectively deliver local data available at the RIS. This paper explores a more general application of RIS-based information transfer for a single-user downlink system via jointly mapped RM (JRM), where the RIS and the access point (AP) jointly deliver the information available at the AP. The data symbols are mapped to a constellation of tuples, each tuple containing a transmit signal and a reflection pattern. Two JRM constellation designs are proposed, namely jointly-mapped QRM (JQRM) and jointly-mapped RPM (JRPM). The proposed constellation design employs a smaller transmit signal set size compared to a generic modulation scheme, increasing the separation among adjacent constellation points. A jointly active and passive beamforming design is adopted for a multiple-input-single-output (MISO) downlink system. The simulation results analyze and compare the bit-error-rate (BER) performance of the proposed JQRM and JRPM schemes, with their respective separately-mapped counterparts and theoretical upper bounds as benchmarks.

As 6G goes toward applying extremely large-scale antenna arrays (ELAAs) as well as very high operating frequencies, the near-field propagation region expands to even hundreds of meters. This provides conventional communication systems with new challenges as well as opportunities. In this context, effectively managing the complexities of near-field communication, and harnessing its unique features require a deep understanding of near-field signal behavior, particularly when initiated from ELAAs. The radial beamfocusing through ELAAs is one of the important unique near-field features which has been less analytically explored and characterized in the literature so far. In this paper, we formally define and characterize the radial focal region and establish the conditions for achieving a near-field focal point in the radial domain. We analytically reveal that employing a maximum ratio beamformer leads to a radial focal gap between the desired and achievable focal points. We analytically prove that when the number of arrays becomes extremely large through ELAAs, the gap is eliminated. In addition, for non-ELAA scenarios where the number of array elements is not very high, we present a beamforming algorithm to remove the gap and precisely create a radial focal point at the desired location. The achieved outcomes are verified through numerical results for single-focal and multi-focal scenarios.

A deep learning (DL)-based joint pilot and data power control algorithm that solves the sum rate maximization problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. The sum rate optimization problem for the uplink is formulated subject to per-user total transmit energy budget constraints, where user pilot and data power allocations are optimized to maximize the system sum rate. Instead of solving the non-convex problem using mathematical optimization theory, we utilize a data-driven solution approach to learn the optimal solutions. Specifically, we model a deep neural network (DNN) and train it via unsupervised learning using a custom loss function that captures the sum rate optimization objective and transmit energy constraints in the optimization problem. This unsupervised learning approach has a simpler and more flexible model training stage since it does not require labeled data for model training as in supervised learning. Simulation results show that the proposed DNN-based joint pilot and data power control algorithm improves the system sum rate compared to equal power and equal energy allocation heuristics and data power control-only approach. Furthermore, the joint power allocation results in significant energy savings (around 60 %) compared to fixed power allocation schemes.

The terahertz (THz) communication is a key enabler for 6G applications such as ultra-reliable and low-latency communications (URLLCs), as it can provide sufficient spectrum resources for a high data rate and low latency. However, THz communications have poor penetrability with limited coverage. To address this issue, we consider a dual connectivity THz and millimeter-wave (mmWave) heterogeneous network, where mmWave base stations (BSs) and THz BSs are distributed based on a Poisson point process and a Thomas cluster process, respectively, which results in the THz BSs being clustered around the mmWave BSs. This captures the inter-tier spatial dependency and leverages the benefits of both mm Wave and THz links. Employing stochastic geometry, we derive expressions for the reliability probability of URLLC users and the link selection probability for a dual connectivity THz and mm Wave heteroge-neous network. The numerical results validate our analysis with simulation and demonstrate the impact of the maximum delay threshold requirement for URLLC users, density of THz BSs, and molecular absorption noise on the network performance.

One of the primary goals of future wireless systems is to foster sustainability, for which, radio frequency (RF) wireless power transfer (WPT) is considered a key technology enabler. The key challenge of RF-WPT systems is the extremely low end-to-end efficiency, mainly due to the losses introduced by the wireless channel. Distributed antenna systems are undoubtedly appealing as they can significantly shorten the charging distances, thus, reducing channel losses. Interestingly, radio stripe systems provide a cost-efficient and scalable way to deploy a distributed multi-antenna system, and thus have received a lot of attention recently. Herein, we consider an RF-WPT system with a transmit radio stripe network to charge multiple indoor energy hotspots, i.e., spatial regions where the energy harvesting devices are expected to be located, including near-field locations. We formulate the optimal radio stripe deployment problem aimed to maximize the minimum power received by the users and explore two specific predefined shapes, namely the straight line and polygon-shaped configurations. Then, we provide efficient solutions relying on geometric programming to optimize the location of the radio stripe elements. The results demonstrate that the proposed radio stripe deployments outperform a central fully-digital square array with the same number of elements and utilizing larger radio stripe lengths can enhance the performance, while increasing the system frequency may degrade it.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. The associated complexity further escalates when the channel coefficients are fast-varying as in scenarios with user mobility. In this paper, we propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor. The implemented framework is designed for identifying the aging pattern and predicting enhanced estimates of the wireless channels in correlated fast-fading environments. Insightful simulation results demonstrate that our proposed CNN-AR approach is robust to channel aging, exhibiting a high-precision estimation accuracy. The results also show that our approach can achieve high spectral efficiency and low pilot overhead compared to traditional methods.

Rate splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) are capable of offering low latency, high bandwidth efficiency and superior multi-user connectivity whereas simultaneous wireless information and power transfer (SWIPT) has the potential to improve energy efficiency and sustainability for future-generation networks. In this article, we study a SWIPT-enabled RSMA-aided downlink system with imperfect channel state information and imperfect successive interference cancellation. In particular, we evaluate the system performance by deriving closed-form expressions for key performance metrics such as outage probability, average power harvested at users, and throughput. Moreover, we validate the accuracy of the derived closed-form expressions using Monte Carlo simulations. Our results confirm RSMA can result in around 30% reduction of the per-user outage probability over NOMA.

Generative AI has received significant attention among a spectrum of diverse industrial and academic domains, thanks to the magnificent results achieved from deep generative models such as generative pre-trained transformers (GPT) and diffusion models. In this paper, we explore the applications of denoising diffusion probabilistic models (DDPMs) in wireless communication systems under practical assumptions such as hardware impairments (HWI), low-SNR regime, and quantization error. Diffusion models are a new class of state-of-the-art generative models that have already showcased notable success with some of the popular examples by OpenAI and Google Brain. The intuition behind DDPM is to decompose the data generation process over small “denoising” steps. Inspired by this, we propose using denoising diffusion model-based receiver for a practical wireless communication scheme, while providing network resilience in low-SNR regimes, non-Gaussian noise, different HWI levels, and quantization error. We evaluate the reconstruction performance of our scheme in terms of mean-squared error (MSE) metric. Our results show that more than 25 dB improvement in MSE is achieved compared to deep neural network (DNN)-based receivers. We also highlight robust out-of-distribution performance under non-Gaussian noise.

Grant-free protocols exploiting compressed sensing multi-user detection (MUD) are appealing for solving the random access problem in massive Internet of Things (IoT) networks with sporadic device activity. Such protocols would greatly benefit from prior deterministic knowledge of the sparsity level, i.e., the instantaneous number of simultaneously active devicesK. Aiming at this, herein we introduce a framework relying on coordinated pilot transmissions (CPTs) for detectingK. Specifically, the proposed CPT mechanism includes a downlink (DL) phase for channel state information acquisition that resolves fading uncertainty in the uplink (UL) transmission phase using shared UL pilot symbols for channel compensation. We propose a signal sparsity level detector and analytically assess its accuracy when network channels are subject to Rayleigh fading. We show that the variance of the estimator increases withK, and its distribution approximates that of the sum of a Student’stand Gaussian random variable. The numerical results evince the need for carefully configuring the duration of the DL and UL phases. Indeed, we show that relatively short DL phases are preferable in highly sparse networks given the total CPT duration is fixed. Finally, we discuss and exemplify with some early results the potential of the proposed CPT framework for MUD, and highlight relevant research directions.

We propose a novel approach to smart spot-beamforming (SBF) in the Fresnel zone leveraging extremely large-scale programmable metasurfaces (ELPMs). A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including ultra-high-speed wireless communication, location-based multiple access (LDMA), efficient wireless power transfer (WPT), interference mitigation, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by developing a novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structure's benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.

Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central units (vCUs) and distributed units (vDUs), their resources, and the routing for each BS data flow. The objective is to minimize the long-term total network operation cost while adapting to the varying traffic demands and resource availability. In the first step, testbed measurements are performed to study the relationship between the traffic demands and computing resources, which reveals high variance and depends on the platform and its load. Consequently, finding the perfect model of the underlying system is non-trivial. Therefore, to solve the proposed problem, a deep reinforcement learning (RL)-based framework is proposed and developed using model-free RL approaches. Moreover, the problem consists of multiple BSs sharing the same resources, which results in a multi-dimensional discrete action space and leads to a combinatorial number of possible actions. To overcome this curse of dimensionality, action branching architecture, which is an action decomposition method with a shared decision module followed by neural network is combined with Dueling Double Deep Q-network (D3QN) algorithm. Simulations are carried out using an O-RAN compliant model and real traces of the testbed. Our numerical results show that the proposed framework successfully learns the optimal policy that adaptively selects the vRAN configurations, where its learning convergence can be further expedited through transfer learning even in different vRAN systems. It also offers significant cost savings by up to 59% of a static benchmark, 35% of Deep Deterministic Policy Gradient with discretization, and 76% of non-branching D3QN.

Over the past decade, polar codes have received significant attraction and have been selected as the coding method for the control channel in fifth-generation (5G) wireless communication systems. However, conventional polar codes are reliant solely on binary ($2 \times 2$) kernels, which restricts their block length to being only powers of 2. In response, multi-kernel (MK) polar codes have been proposed as a viable solution to achieve increased flexibility in code length. This article proposes unrolled and pipelined architectures for encoding both systematic and nonsystematic MK polar codes, capable of high-throughput encoding of codes constructed with binary, ternary ($3 \times 3$), or binary-ternary mixed kernels. Furthermore, two novel nonsystematic stage-folded encoders, designed to minimize resource usage, have been introduced for the encoding of pure-ternary and MK codes. The proposed MK encoders additionally provide the functionality of dynamic kernel assignment. The proposed architectures exhibit an unprecedented level of flexibility by supporting 83 different codes and offering various architectures that provide tradeoffs between throughput and resource consumption. The FPGA implementation results demonstrate that a partially pipelined polar encoder of size$N=4096$operating at a frequency of 270 MHz gives a throughput of 1080 Gb/s. In addition, a new compiler scripted in Python is introduced to automatically generate HDL modules for the desired encoders. By inserting the desired parameters, a designer can simply obtain all the necessary VHDL files for FPGA implementation.

We study the joint active/passive beamforming and channel blocklength (CBL) allocation in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency communication (URLLC) system. The considered scenario is a finite blocklength (FBL) regime and the problem is solved by leveraging a deep reinforcement learning (DRL) algorithm named twin-delayed deep deterministic policy gradient (TD3). First, assuming an industrial automation system, the signal-to-interference-plus-noise ratio and achievable rate in the FBL regime are identified for each actuator. Next, the joint active/passive beamforming and CBL optimization problem is formulated where the objective is to maximize the total achievable FBL rate in all actuators, subject to non-linear amplitude response at the RIS elements, BS transmit power budget and total available CBL. Since the formulated problem is highly non-convex and non-linear, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3. The considered method relies on interacting RIS with the industrial automation environment by taking actions which are the phase shifts at the RIS elements, CBL variables, and BS beamforming to maximize the expected observed reward, i.e., the total FBL rate. We assess the performance loss of the system when the RIS is non-ideal, i.e., with non-linear amplitude response, and compare it with ideal RIS without impairments. The numerical results show that optimizing the RIS phase shifts, BS beamforming, and CBL variables via the TD3 method with deterministic policy outperforms conventional methods and it is highly beneficial for improving the network total FBL rate considering finite CBL size.

Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multilayer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This article contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to the following: 1) reliability theory; 2) short packet communications; 3) inequalities, distribution bounds, and tail approximations; 4) rare-events simulation; 5) queuing theory and information freshness; and 6) large-scale tools, such as stochastic geometry, clustering, compressed sensing, and mean-field (MF) games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout this article, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.

Polar codes have received growing attention in the past decade and have been selected as the coding scheme for the control channel in the fifth generation (5G) wireless communication systems. However, the conventional polar codes have only been constructed by binary ($2\times 2$) kernel, which poses block length limitation to powers of 2. To attain more flexible block lengths, multi-kernel polar codes are proposed. In this paper, a combinational architecture for multi-kernel polar codes with high throughput is proposed based on successive cancellation decoding algorithm. The proposed scheme can decode pure-binary, pure-ternary ($3\times 3$), and binary-ternary mixed polar codes. The decoder’s architecture is rate-flexible, meaning that a new code rate can be assigned to the decoder at every clock cycle. The proposed architecture is validated by FPGA implementation, and the results reveal that a code of size$N=81$achieves the coded throughput of 1664.5 Mbps. A Python-based polar compiler is also proposed to automatically generate the HDL modules for target decoders. A designer can input the target block length and kernel ordering of a polar code and get the required VHDL files automatically.

Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators, such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of aspecial-purpose Industrial-Internet-of-Things (IIoT) networkcomprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose sixth generation (6G) IIoT network incorporating seven functional building blocks categorized intospecial-purpose functionalitiesandenabling technologies. The former consists ofWireless Environment Control,Traffic/Channel Prediction,Proactive Resource Management,andEnd-to-End Optimizationfunctions, whereas the latter includesSynchronization and Coordination,Machine Learning and Artificial Intelligence Algorithms, andAuxiliary Functions. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network.

Proactive and intelligent management of network resource utilization (RU) using deep learning (DL) can significantly improve the efficiency and performance of the next generation of wireless networks. However, variations in wireless RU are often affected by uncertain events and change points due to the deviations of real data distribution from that of the original training data. Such deviations, which are known as dataset drifts, can subsequently lead to a shift in the corresponding decision boundary degrading the DL model prediction performance. To address these challenges, we present hardware-accelerated real-time radio frequency (RF) analytics and drift-awareness modules for robust DL predictions. We have prototyped the proposed design on a Zynq-7000 System-on-Chip that contains an FPGA and an embedded ARM processor. We have used Xilinx Vivado design suite for synthesis and analysis of the HDL design for the proposed solution. To detect dataset drifts, the proposed solution adopts a distance-based technique on FPGA to quantify in real-time the change between the prediction distribution obtained from DL predictions and data distribution of input streaming samples. Using various performance metrics, we have extensively evaluated the performance of the proposed solution and shown that it can significantly improve the DL model robustness in the presence of dataset drifts.

We propose a novel scheme that allows MIMO system to modulate a set of permutation matrices to send more information bits, extending our initial work on the topic. This system is called Permutation Matrix Modulation (PMM). The basic idea is to employ a permutation matrix as a precoder and treat it as a modulated symbol. We continue the evolution of index modulation in MIMO by adopting all-antenna activation and obtaining a set of unique symbols from altering the positions of the antenna transmit power. We provide the analysis of the achievable rate of PMM under Gaussian Mixture Model (GMM) distribution and finite cardinality input (FCI). Numerical results are evaluated by comparing PMM with the other existing systems. We also present a way to attain the optimal achievable rate of PMM by solving a maximization problem via interior-point method. A low complexity detection scheme based on zero-forcing (ZF) is proposed, and maximum likelihood (ML) detection is discussed. We demonstrate the trade-off between simulation of the symbol error rate (SER) and the computational complexity where ZF performs worse in the SER simulation but requires much less computational complexity than ML.

An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formu-late this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.

This paper introduces a new interference-aware user association (UA) scheme for a multi-base station (BS) wireless network in which intelligent reflecting surfaces (IRSs) are leveraged to improve each multi-antenna BS’s coverage region and mitigate the vulnerability to non-line of sight paths. We aim to maximize the total network downlink achievable rate by jointly optimizing the reflective phase shifters at IRSs while associating mobile users (MUs) to BSs, which is an intractable non-convex problem. An alternating optimization-based algorithm based on solving two sub-problems, i.e., one for UA and one for reflective phase shift optimization, is proposed to tackle the non-convex problem. The proposed algorithm optimizes phase shifters at IRS through fractional programming techniques, and the UA is solved by successive convex approximation (SCA). Simulation results show that the proposed algorithm significantly improves the total network achievable rate compared to heuristic methods, e.g., matching game.

Radio-frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multiantenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this article, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario, where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as$0.85\sqrt {M}$, where$M$is the number of PB’s antenna elements. Moreover, a rotation-specific power control mechanism was proposed to: 1) fairly optimize the WET process if devices’ positioning information is available and/or 2) avoid hazards to human health in terms of specific absorption rate (SAR), which is an RF exposure metric that quantifies the absorbed power in a unit mass of human tissue. We show that RAB performance even approaches quickly (or surpasses, for scenarios with a sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI-based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight (LOS) conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology.

In this article, we evaluate the effective energy efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited Internet of Things (IoT) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model, measured by the ratio of effective capacity to the total consumed power. To maximize the EEE, there is a need to optimize transmission parameters, such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power, or rate alone, the complexity is aggravated by the simultaneous consideration of both variables. Hence, we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore, we investigate the performance of the EEE under constant and random arrivals, where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories, we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that, we compare the performance of different iterative algorithms, such as Dinkelbach’s and cross entropy, which guarantee the convergence for the optimal solution. By numerical analysis, the influence of source characteristics, fixed transmission rate, error probability, coding blocklength, and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally, the results illustrate that modified Dinkelbach’s algorithm has high performance and low complexity compared to others.

Various industrial Internet of Things applications demand execution periods throughout which no communication failure is tolerated. However, the classical understanding of reliability in the context of ultra-reliable low-latency communication (URLLC) does not reflect on the time-varying characteristics of the wireless channel. In this article, we introduce a novel mission reliability and mission effective capacity metric that takes these phenomena medium into account, while specifically studying multiconnectivity (MC)-enabled industrial radio systems. We assume uplink short packet transmission with no channel state information at URLLC user (the transmitter) and sporadic traffic arrival. Moreover, we leverage the existing framework of dependability theory and provide closed-form expressions (CFEs) for the mission reliability of the MC system using the maximal-ratio combining scheme. We do so by utilizing the mean time to first failure, which is the expected time of failure occurring for the first time. Moreover, we also derive exact CFEs for second-order statistics, such as level crossing rate and average fade duration, showing how fades are distributed in fading channels with respect to time. Furthermore, the design throughput maximization problem under the mission reliability constraint is solved numerically through the cross-entropy method.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous Internet of Things (IoT) deployments. In this work, we leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor distributed massive multiple-input multiple-output system. Based on semi-definite programming, successive convex approximation (SCA), and maximum ratio transmission (MRT) techniques, we derive optimal and sub-optimal precoders aimed at minimizing the radio stripes’ transmit power while exploiting information of the power transfer efficiency of the EH circuits at the UEs. Moreover, we propose an analytical framework to assess and control the electromagnetic field (EMF) radiation exposure in the considered indoor scenario. Numerical results show that i) the EMF radiation exposure can be more easily controlled at higher frequencies at the cost of a higher transmit power consumption, ii) training is not a very critical factor for the considered indoor system, iii) MRT/SCA-based precoders are particularly appealing when serving a small number of UEs, thus, especially suitable for implementation in a time domain multiple access (TDMA) scheduling framework, and iv) TDMA is more efficient than spatial domain multiple access (SDMA) when serving a relatively small number of UEs. Results suggest that additional boosting performance strategies are needed to increase the overall system efficiency, thus making the technology viable in practice.

In virtualized radio access network (vRAN), the base station (BS) functions are decomposed into virtualized components that can be hosted at the centralized unit or distributed units through functional splits. Such flexibility has many benefits; however, it also requires solving the problem of finding the optimal splits of functions of the BSs in such a way that minimizes the total network cost. The underlying vRAN system is complex and precise modelling of it is not trivial. Formulating the functional split problem to minimize the cost results in a combinatorial problem that is provably NP-hard, and solving it is computationally expensive. In this paper, a constrained deep reinforcement learning (RL) approach is proposed to solve the problem with minimal assumptions about the underlying system. Since in deep RL, the action selection is the outcome of inference of a neural network, it can be done in real-time while training to update the neural networks can be done in the background. However, since the problem is combinatorial, even for a small number of functions, the action space of the RL problem becomes large. Therefore, to deal with such a large action space, a chain rule-based stochastic policy is exploited in which a long short-term memory (LSTM) network-based sequence-to-sequence model is applied to estimate the policy that is selecting the functional split actions. However, the utilized policy is still limited to an unconstrained problem, and each split decision is bounded by vRAN’s constraint requirements. Hence, a constrained policy gradient method is leveraged to train and guide the policy toward constraint satisfaction. Further, a search strategy by greedy decoding or temperature sampling is utilized to improve the optimality performance at the test time. Simulations are performed to evaluate the performance of the proposed solution using synthetic and real network datasets. Our numerical results show that the proposed RL solution architecture successfully learns to make optimal functional split decisions with the accuracy of the solution is up to 0.05% of the optimality gap. Moreover, our solution can achieve considerable cost savings compared to C-RAN or D-RAN systems and a faster computational time than the optimal baseline.

A graph neural network (GNN) based access point (AP) selection algorithm for cell-free mas-sive multiple-input multiple-output (MIMO) systems is proposed. Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network. A GNN based on the inductive graph learning framework GraphSAGE is used to obtain the embed-dings which are then used to predict the links between the nodes. The numerical results show that compared to the proximity-based AP selection algorithms, the proposed GNN based algorithm predicts the potential APs with more accuracy. Compared to the large scale fading coefficient based AP selection algorithms, the proposed algorithm does not require measured and sorted signal strengths of all the neighbouring APs. Furthermore, the proposed algorithm is scalable in terms of the number of users in the cell-free system.

A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Max-min rate optimization problem in a cell-free massive MIMO uplink setup is formulated, where user power allocations are optimized in order to maximize the minimum user rate. Instead of modeling the problem using mathematical optimization theory, and solving it with iterative algorithms, our proposed solution approach is using DL. Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate. This novel unsupervised learning-based approach does not require optimal power allocations to be known during model training as in previously used supervised learning techniques, hence it has a simpler and flexible model training stage. Numerical results show that the proposed DNN achieves a performance-complexity trade-off with around 400 times faster implementation and comparable performance to the optimization-based algorithm. An online learning stage is also introduced, which results in near-optimal performance with 4-6 times faster processing.

In this work, we investigate the impact of two cooperative unmanned aerial vehicle (UAV)-based jammers on the secrecy performance of a ground wireless network in the presence of an eavesdropper. For that purpose, we investigate the secrecy-area related metrics, Jamming Coverage and Jamming Efficiency. Moreover, we propose a hybrid metric, the so-called Weighted Secrecy Coverage (WSC) and a virtual distributed multiple-input-multiple-output (MIMO)-based zero-forcing precoding scheme to avoid the jamming effects on the legitimate receiver. For evaluating these metrics, we derive a closed-form position-based metric, the secrecy improvement. Our mathematical derivations and comparative simulations show that the proposed zero-forcing scheme leads to an improvement on the secrecy performance in terms of the WSC, and provides conditions for improvement of Jamming Efficiency. They also show positioning trends on the UAVs over a fixed orbit around the legitimate transmitter as well as power allocation trends for optimal secrecy.

Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.

In this paper, a novel method is proposed to study the tradeoff between energy efficiency (EE) of small-cell users and unmanned aerial vehicles (UAV) users in multi-cell orthogonal frequency division multiple access (OFDMA)-based networks. Contrary to the prior works that only maximize the EE of the UAV network subject to some constraints on transmit power of UAV users, we formulate a multi-objective optimization problem (MOOP) that jointly maximize the EE of small-cell and UAV users while guaranteeing the minimum rate for UAV users as well as maximum transmit powers for the corresponding small-cell and UAV BSs. The proposed MOOP is transformed into a single optimization problem (SOOP) by the weighted Tchebycheff approach. Then, an iterative technique is used to optimize alternatively subchannels and transmission powers of small-cell and UAV networks at each step. Numerical results show that a substantial performance gain can be obtained over the existing solutions.

This paper addresses the secrecy performance of the downlink of a non-orthogonal multiple access network in the presence of multiple randomly located eavesdroppers. The network consists of a base station and a near receiver that are located inside a protected zone, free of eavesdroppers, while a far user is located outside. Herein, it is considered that the source transmits a superposed jamming signal to enhance the secrecy performance. In this sense, imperfections on the removal of the jamming signal by the legitimate receivers are also investigated. Integral-form exact and closed-form approximate expressions for the secrecy outage probability are derived by employing stochastic geometry tools. The expressions are corroborated via Monte Carlo simulations.

Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.

Grant-free random access and uplink non- orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.

Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel model-based simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks.

Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. We use the concept of meta distribution (MD) of the signal-to-interference ratio (SIR) to gain a complete understanding of the per-link reliability and describe the performance of two scheduling methods for data aggregation of machine type communication (MTC): random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements.

In this paper, we propose a novel index modulation mechanism called permutation channel modulation (PCM) by exploiting spatial resource of multiple input multiple output (MIMO). A set of permutation matrices is treated as indices to convey information bits by modulating a block of bits to a permutation matrix. Assuming that channel state information at transmitter (CSIT) is known, modulated permutation matrix is multiplied to the singular values of MIMO channel matrix obtained from decomposing the channel matrix using singular values decomposition (SVD). Besides to a permutation matrix, information bits are also modulated to constellation symbols. Therefore, the transmitted signals contain two sources of information: a permutation matrix and constellation symbols. At the receiver, transmitted symbols and a permutation matrix are detected using our proposed detection scheme. We derive the capacity expression and define the optimal capacity by finding the optimal power allocation at each transmission. As performance measure, capacity of PCM is compared with existing techniques. Using 4 × 4, we find that the capacity of PCM is doubled and remains superior with higher antenna settings. Our work creates a new paradigm of index modulation for multiple antenna transmissions by exploiting its spatial resource and significantly improves the capacity and outage probability performances.

In this paper, we introduce a new load balancing user association scheme for millimeter wave (mmWave) cellular networks in which intelligent reflecting surface (IRS) is applied in the cellular network to improve the coverage region of each cell and mitigate mmWave vulnerability to non-line of sight (N-LoS) paths. The user association scheme improves network performance significantly by adjusting the interference according to the association. We study the IRS-assisted mmWave cellular network where one IRS is deployed to assist in the communication from the base station (BS) to mobile users (MUs) in each cell. We balance BS loads and maximize a network utility by optimizing the user association with a matching game. Simulation results show that the proposed scheme significantly improves the throughput compared to conventional user association techniques.

This work proposes and evaluates a hybrid Bayesian-based localization method to estimate the position of a target node using received signal strength and time of flight measurements. In our investigations, we consider these measurements are acquired through a distributed antenna system which is connected to a common master anchor node. The baseline non-hybrid scenarios use only received signal strength measurements to estimate the position of interest, while the hybrid implementation combines time of arrival measurements as well. Both Bayesian-based (non) hierarchical approaches approximates the posterior distribution of the target's location coordinates using Markov Chain Monte Carlo methods. The hierarchical method introduces conditional interdependencies to the model parameters, resulting in less model variance. Herein, the root mean square error is used to evaluate the performance of the indoor test scenarios. Our results show that both hybrid and hierarchical approaches outperform the baseline Bayesian model, while the former significantly increase the accuracy the target position estimate.

In this work, we study the coexistence in the same Radio Access Network (RAN) of two use cases present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC). eMBB services are requested for applications that demand extremely high data rates and moderate requirements on latency and reliability, whereas mMTC enables applications for connecting a massive number of low-power and low-complexity devices. The coexistence of both services is enabled by means of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under the orthogonal slicing, the radio resources are exclusively allocated to each service, while in the non-orthogonal slicing the traffics from both services overlap in the same radio resources. We evaluate the uplink performance of both services in a scenario with a multi-antenna Base Station (BS). Our simulation results show that the performance gains obtained through multiple receive antennas are more accentuated for the non-orthogonal slicing than for the orthogonal allocation of resources, such that the non-orthogonal slicing outperforms its orthogonal counterpart in terms of achievable data rates or number of connected devices as the number of receive antennas increases.

The 5G systems feature three generic services: enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC) and Ultra-Reliable and Low-Latency Communications (URLLC). The diverse requirements of these services in terms of data-rates, number of connected devices, latency and reliability can lead to a sub-optimal use of the 5G network, thus network slicing is proposed as a solution that creates customized slices of the network specifically designed to meet the requirements of each service. Under the network slicing, the radio resources can be shared in orthogonal and non-orthogonal schemes. Motivated by Industrial Internet of Things (IIoT) scenarios where a large number of sensors may require connectivity with stringent requirements of latency and reliability, we propose the use of Non-Orthogonal Multiple Access (NOMA) to improve the number of URLLC devices that are connected in the uplink to the same base station (BS), for both orthogonal and non-orthogonal network slicing with eMBB devices. The multiple URLLC devices transmit simultaneously and across multiple frequency channels. We set the reliability requirements for the two services and evaluate the pairs of achievable sum rates. We show that, even with overlapping transmissions from multiple eMBB and URLLC devices, the use of NOMA techniques allows us to guarantee the reliability requirements for both services.

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet-of-Things (IoT) devices enabled by the upcoming sixth-generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this article. Candidates enablers, such as energy beamforming (EB), distributed antenna systems (DASs), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling, and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultralow power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions toward realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet-of-Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio-frequency transmitters called power beacons (PBs). In this article, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

The rapidly increasing demand in indoor small cell networks has given rise to the concept of local 5G operator (OP) for local service delivery. In this regard, we develop a novel game-theoretic framework with geometric programming to model and analyze cache-enabled small cell base stations (SBSs) with infrastructure sharing for local 5G OP networks. In such a network, the local 5G OP provides wireless network in indoor area and rent out the infrastructure which are RAN and cache storage to multiple mobile network operators (MNOs) while guarantee the quality-of-experience (QoE) at the users (UEs) of MNOs. We formulate a Stackelberg game model where the local 5G OP is the leader and the MNOs are the followers. The local 5G OP aims to maximize its profit by optimizing its infrastructure rental fee, and the MNOs aim to minimize their renting cost of infrastructure by minimizing the cache intensity subject to latency constraint at each UE. Here, the cache intensity is defined as the product of the number of SBSs per unit area and the number of popular files stored in each SBS. The optimization problems of the local 5G OP and the MNOs are transformed into geometric programming. Accordingly, the subgame perfect equilibrium of Stackelberg game is obtained through the succesive geometric programming (SGP) method. Since the MNOs share their rented infrastructure, for cost sharing, we apply the concept of Shapley value to divide the cost among the MNOs. We show that the cost sharing problem can be mapped into a simplified “airport runway cost sharing problem”, in which the Shapley value can be computed efficiently. Finally, we present an extensive performance evaluation that reveals interesting insights into designing resource sharing with edge caching in local 5G OP networks.

Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in a setup with WET in the downlink, and periodic or Poisson-traffic Wireless Information Transfer (WIT) in the uplink. When CSI is available, we show that a maximum ratio transmission beamformer is close to optimum whenever the farthest node experiences at least 3 dB of power attenuation more than the remaining devices. On the other hand, although the adopted CSI-free mechanism is not capable of providing average harvesting gains, it does provide greater WET/WIT diversity with lower energy requirements when compared with the CSI-based scheme. Our numerical results evidence that the CSI-free scheme performs favorably under periodic traffic conditions, but it may be deficient in case of Poisson traffic, specially if the setup is not optimally configured. Finally, we show the prominent performance results when the uplink transmissions are periodic, while highlighting the need of a minimum mean square error equalizer rather than zero-forcing for information decoding.

Belief propagation (BP) is an iterative decoding algorithm for polar codes which can be parallelized effectively to achieve higher throughput. However, because of the presence of error floor due to cycles and stopping sets in the factor graph, the performance of the BP decoder is far from the performance of state of the art cyclic redundancy check (CRC) aided successive cancellation list (CA-SCL) decoders. It has been shown that successive BP decoding on multiple permuted factor graphs, which is called the multi-trellis BP decoder, can improve the error performance. However, when permuting the entire factor graph, since the decoder dismisses the information from the previous permutation, the number of iterations required is significantly larger than that of the standard BP decoder. In this work, we propose a new variant of the multi-trellis BP decoder which permutes only a subgraph of the original factor graph. This enables the decoder to retain information of variable nodes in the subgraphs, which are not permuted, reducing the required number of iterations needed in-between the permutations. As a result, the proposed decoder can perform permutations more frequently, hence being more effective in mitigating the effect of cycles which cause oscillation errors. Experimental results show that for a polar code with block length 1024 and rate 0.5 the error performance gain of the proposed decoder at the frame error rate of $10^{-6}$ is 0.25 dB compared to multi-trellis decoder based on full permutations. This performance gain is achieved along with reduced latency in terms of the number of iterations.

In this paper a proposal to improve the reliability of a downlink multiuser (MU) MIMO transmission scheme is investigated with the use of a new approach in orthogonal space-time block codes (OSTBC) and network coding with a superposition modulated system and side information. With the new encoded OSTBC approach, diversity is offered where each user receives all other users' symbols, which allows the recovery of symbols in several ways. In addition, multiple users can be accommodated with the same resource, which is quite useful in a wireless system where resources are always restricted. By employing superposition modulation, the side information needed for error recovery can be transmitted over the same resource used for the normal information frame. In addition, the proposed system exploits diversity through a novel technique of sub-constellation alignment-based signal combining for efficient side information dissemination. A detailed analysis of the new OSTBC approach is carried out. It is shown that the performance of the MU-MIMO system can be improved significantly in terms of block and frame error rates (BLER, FER) considered as reliability measures. By accommodating a reasonable number of multiple users, high reliability is achieved at the expense of the rate. To compensate for the low rate, conventional OSTBC can be considered and simulation results are shown, where, as a penalty to pay, multiple orthogonal resources are required.

In this paper, we consider the problem of admission control in 5G networks where enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users are coexisting. URLLC users require low latency and high reliability while eMBB users require high data rates. Thus, it is essential to control the admission of eMBB users while giving priority to all URLLC users in a network where both types of users are coexisting. Our aim is to maximize the number of admitted eMBB users to the system with a guaranteed data rate while allocating resources to all URLLC users. We formulated this as an l0minimization problem. Since it is an NP-hard problem we have used approximation methods and sequential convex programming to obtain a suboptimal solution. Numerically we have shown that the proposed algorithm achieves near-optimal performance. Our algorithm is able to maximize the number of admitted eMBB users with an optimal allocation of resources while giving priority to all URLLC users.

We propose and evaluate an iterative localization mechanism employing Bayesian inference to estimate the position of a target using received signal strength measurements. The probability density functions of the target's coordinates are estimated through a Bayesian network. Herein, we consider an iterative procedure whereby our predictor (posterior distribution) is updated in a sequential order whenever new measurements are made available. The performance of the mechanism is assessed in terms of the respective root mean square error and kernel density estimation of the target coordinates. Our numerical results showed the proposed iterative mechanism achieves increasingly better estimation of the target node position each updating round of the Bayesian network with new input measurements.

The millimeter wave (mmWave) communication uses directional antennas. Hence, achieving fine alignment of transmit and receive beams at the initial access phase is quite challenging and time-consuming. In this paper, we provide a dynamic-weight based beam sweeping direction and synchronization signal block (SSB) allocation algorithm to optimize the cell search of the initial access in mmWave 5G NR networks. The number of SSBs transmitted in each beam sweeping direction depends on previously learned experience which is based on the number of detected UEs (user equipment) per SSB for each sweeping direction. Overall, numerical simulation results indicate that the proposed algorithm is shown to be capable of detecting more users with a lower misdetection probability. Furthermore, it is possible to achieve the same performance with a smaller number of dynamic resource (i.e., SSB) allocation, compared to constant resource allocation.

End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems and conventional channel coded systems with convolutional coding (CC), in order to understand the potential of deep learning-based systems as alternatives to conventional systems. From the simulations, autoencoder implementation was observed to have a better BER in 0-5 dB Eb/N0range than its equivalent half-rate convolutional coded BPSK with hard decision decoding, and to have only less than 1 dB gap at a BER of 10-5. Furthermore, we have also proposed a novel low complexity autoencoder architecture to implement end-to-end learning of coded systems in which we have shown better BER performance than the baseline implementation. The newly proposed low complexity autoencoder was capable of achieving a better BER performance than half-rate 16-QAM with hard decision decoding over the full 0-10 dB Eb/N0range and a better BER performance than the soft decision decoding in 0-4 dB Eb/N0range.

Unmanned aerial vehicle (UAV) base stations (BSs) are reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide such requirements due to disasters. In this paper, we consider optimal UAV-deployment problem in 3D space for a mmWave network. The objective is to deploy multiple aerial BSs simultaneously to completely serve the ground users. We develop a novel algorithm to find the feasible positions for a set of UAV-BSs from a predefined set of locations, subject to a signal-to-interference-plus-noise ratio (SINR) constraint of every associated user, UAV-BS's limited hovering altitude constraint and restricted operating zone constraint. We cast this 3D positioning problem as an ℓ0minimization problem. This is a combinatorial, NP-hard problem. We approximate the ℓ0minimization problem as non-combinatorial ℓ1-norm problem. Therefore, we provide a suboptimal algorithm to find a set of feasible locations for the UAV-BSs to operate. The analysis shows that the proposed algorithm achieves a set of the location to deploy multiple UVABSs simultaneously while satisfying the constraints.

Integrated access and backhaul (IAB) networks consist of IAB-donor, IAB-nodes, and user-equipments (UEs). Both IAB-donor and IAB-node provide access to UEs while IAB-donor and IAB-nodes exchange UEs data via wireless in-band backhaul using the same frequency-time resources shared with access links. Multi-antenna beamformer techniques can be used to mitigate the complicated cross-link interference scenarios arising from IAB systems. In this paper, an iterative beamformer design with the weighted queue minimization (WQM) objective is proposed for the time-division-duplexed (TDD) based IAB system. In the considered TDD based IAB model, in a given timeslot, IAB-nodes and IAB-donor are assumed to be different uplink (UL)/downlink (DL) transmission modes to mitigate conventional half-duplex loss. Also, the beamformer design is carried out over two timeslots, considering both UL and DL transmission at each node. Specifically, user-specific UL/DL queues are introduced at the IAB-nodes to guarantee the BS to/from UE data delivery. The proposed beamformer solution is based on the iterative evaluation of Karush-Kuhn-Tucker (KKT) conditions of the optimization problem, which can practically be implemented in both centralized and decentralized manner. The numerical examples illustrate the superior system performance of the proposed method in comparison to the conventional half-duplex relaying system.

The current autonomous driving architecture places a heavy burden in signal processing for the graphics processing units (GPUs) in the car. This directly translates into battery drain and lower energy efficiency, crucial factors in electric vehicles. This is due to the high bit rate of the captured video and other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor at the top of the car which is an essential feature in autonomous vehicles. LiDAR is needed to obtain a high precision map for the vehicle AI to make relevant decisions. However, this is still a quite restricted view from the car. This is the same even in the case of cars without a LiDAR such as Tesla. The existing LiDARs and the cameras have limited horizontal and vertical fields of visions. In all cases it can be argued that precision is lower, given the smaller map generated. This also results in the accumulation of a large amount of data in the order of several TBs in a day, the storage of which becomes challenging. If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud. However, the required data rates in the order of several Gbps are difficult to be met even with the advent of 5G. Therefore, we propose to have a coordinated set of LiDAR's outside at an elevation which can provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency. The calculations we have based on industry standard equipment from several manufacturers show that this is not just a concept but a feasible system which can be implemented.The proposed system can play a supportive role with existing autonomous vehicle architecture and it is easily applicable in an urban area.

In URLLC the performance of short message communications highly depends on the training sequence length due to the stringent latency and reliability requirements. In this paper, we study the performance of cooperative and non-cooperative transmissions under imperfect channel estimation and Rayleigh fading for URLLC. We assume a peak power constraint on pilot symbols in addition to the average power constraint which is used for comparison purposes. We obtain the optimal training length as a function of blocklength and power constraint factor to meet the URLLC requirements. Moreover, the simulation results show the impact of pilot overhead on reliability, latency, and goodput of cooperative communications compared to point-to-point transmission.

Future 5G networks will increasingly target local small cell deployments complementing the coverage of the existing outdoor networks. Moreover, the establishment of building-specific high-quality 5G networks by different stakeholders through local spectrum availability has gained increasing attention and new concepts, such as the micro operator, have emerged. To make these kinds of local 5G networks a reality, feasible spectrum sharing models are needed, which calls for a thorough understanding of the impact of the inter-operator interference on the performance of the victim micro operator. This paper presents system simulation results evaluating the feasibility of the local 5G micro operator concept for a scenario where two uncoordinated micro operators, sharing the same channel in the 26 GHz band, have deployed their networks on the same floor. The obtained results indicate that such highly local deployment is feasible in the given band if the penetration loss of the wall separating the micro operators is at least equal to 57 dB, corresponding approximately to a 17 cm thick concrete wall. The required isolation between the operators depends highly on the deployment scenario and therefore, feasible operation is possible in some deployments even with lower wall penetration losses. This highlights the fact that the worst case inter-operator interference levels alone do not properly model the specifics of the co-existence scenario, and can lead to overly protective requirements regarding the channel assignments.

We consider the problem of channel estimation in hybrid transceiver architectures operating in millimeter wave (mmWave) band. Due to the dynamic features of the environment and the sensitivity of mmWave bands to blockage and deafness, it is important to estimate mmWave channels with a low complexity and high performance algorithm. In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. In order to solve the estimation problem, we propose a multi-stage based low complexity algorithm. Simulation results show that the proposed algorithm significantly reduces the computational complexity while preserving the quality of the estimation.

In this paper, we present a finite-block-length comparison between the orthogonal multiple access (OMA) scheme and the non-orthogonal multiple access (NOMA) for the uplink channel. First, we consider the Gaussian channel, and derive the closed form expressions for the rate and outage probability. Then, we extend our results to the quasi-static Rayleigh fading channel. Our analysis is based on the recent results on the characterization of the maximum coding rate at finite block-length and finite block-error probability. The overall system throughput is evaluated as a function of the number of information bits, channel uses and power. We find what would be the respective values of these different parameters that would enable throughput maximization. Furthermore, we analyze the system performance in terms of reliability and throughput when applying the type-I ARQ protocol with limited number of retransmissions. The throughput and outage probability are evaluated for different blocklengths and number of information bits. Our analysis reveals that there is a trade-off between reliability and throughput in the ARQ. While increasing the number of retransmissions boosts reliability by minimizing the probability of reception error, it results in more delay which decreases the throughput. Nevertheless, the results show that NOMA always outperforms OMA in terms of throughput, reliability and latency regardless of the users priority or the number of retransmissions in both Gaussian and fading channels.

Coming cellular systems are envisioned to open up to new services with stringent reliability and energy efficiency requirements. In this paper, we focus on the joint power control and rate allocation problem in single-input multiple-output (SIMO) wireless systems with Rayleigh fading and stringent reliability constraints. We propose an allocation scheme that maximizes the energy efficiency of the system while making use only of average statistics of the signal and interference, and the number of antennas M that are available at the receiver side. We show the superiority of the maximum ratio combining (MRC) scheme over selection combining (SC) in terms of energy efficiency, and prove that the gap between the optimum allocated resources converges to (M!)1/(2M)as the reliability constraint becomes more stringent. Meanwhile, in most cases, MRC was also shown to be more energy efficient than the switch and stay combining (SSC) scheme, although this does not hold only when operating with extremely large M, extremely high/small average signal/interference power and/or highly power consuming receiving circuitry. Numerical results show the feasibility of the ultra-reliable operation when M increases, while greater the fixed power consumption and/or drain efficiency of the transmit amplifier is, the greater the optimum transmit power and rate.

Non-orthogonal multiple access (NOMA) has been identified as a promising technology for future wireless systems due to its performance gains in spectral efficiency when compared to conventional orthogonal schemes (OMA). This gain can be easily translated to an increasing number of served users, but imposes a challenge in the system reliability which is of vital importance for new services and applications of coming cellular systems. To cope with these issues we propose a NOMA rate control strategy that makes use only of topological characteristics of the scenario and the reliability constraint. We attain the necessary conditions so that NOMA overcomes the OMA alternative, while we discuss the optimum allocation strategies for the 2-user NOMA setup when operating with equal rate or maximum sum-rate goals. In such scenario we show that the user with the largest target error probability times the ratio between the average receive signal power and the average interference power, should be scheduled to be decoded first for optimum performance. We compare numerically the performance of our allocation scheme with its ideal counterpart requiring full CSI at the BSs and infinitely long blocklength, and show how the gap increases as the reliability constraint becomes more stringent. Results also evidence the benefits of NOMA when the co-interference can be efficiently canceled, specially when the goal is to maximize the sum-rate.

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter-wave (mm-wave) networks. Enabling multi-hop mm-wave transmissions raises a potential issue of increased latency, and thus, this paper aims at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, a new system design, which exploits multiple antenna diversity, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution. The numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99.9999% and reduces latency by 50.64% and 92.9% as compared to baseline models. Furthermore, the results showcase the key tradeoff between latency and network arrival rate.

In order to reduce the latency of data delivery, one of techniques is to cache the popular contents at the base stations (BSs) i.e. edge caching. However, the technique of caching at edge can only reduce the backhaul delay, other techniques such as BS densification will also need to be considered to reduce the fronthaul delay. In this work, we study the trade-offs between BS densification and cache size under delay constraint at a typical user (UE). For this, we use the downlink SINR coverage probability and throughput obtained based on stochastic geometrical analysis. The network deployment of BS and cache storage is introduced as a minimization problem of the product of the BS intensity and cache size which we refer to the product of “cache intensity” under probabilistic delay constraint. We examine the cases when (i) either BS intensity or the cache size is held fixed, and (ii) when both BS intensity and the cache size are vary. For the case when both BS intensity and the cache size are variable, the problem become nonconvex and we convert into a geometric programing which we solve it analytically.

Wireless networks are becoming extremely pervasive while traffic demand is ever increasing. In order to cope with the forecast increase in traffic volume for the upcoming years, as well as the number of connected devices, new technologies, practices and spectrum rearrangements are required. In this context, a key question arises: how to provide extensive backhaul connectivity and capacity for pervasive ultra dense networks? The answer is rather complex, if feasible. To shed some light into this issue we overview potential technologies, either wired or wireless, and identify technical challenges. Moreover, we evaluate an illustrative scenario of a ultra-dense network that operates with hybrid wired-wireless backhaul. We assume multiple radio access technologies at small and macro base stations (BSs), and we discuss optimal traffic splitting and routing solutions for different topologies and traffic profiles.

5G emerges with ultra-dense deployments of small cell networks to address the location specific service demand of various vertical sectors. While the development of technical solutions for network densification is progressing, less attention has been paid to local operator models and location specific service offerings. Efficient local service delivery promoting innovation and competition calls for opening of the mobile market for new entrants to operate local radio access networks (RAN) and 3rd parties to integrate their services into the networks. This paper applies the recently proposed micro operator concept to local ultra-dense networks where different stakeholders can become micro operators and deploy their small cell RAN for tailored service delivery. We depict the resulting high-level architecture and enabling techniques focusing on network slicing and spectrum micro licensing. While network slicing is well addressed in 5G research from the mobile network operator (MNO) view for providing an end-to-end connection for 3rd parties, there is little work on how the ultra-dense small cell RAN could be operated by another stakeholder. Micro operators could offer the RAN or end-to-end network slice that comprises of network elements belonging to different stakeholders with the help of local spectrum micro licensing with quality guarantees.

This paper presents a novel opportunistic spectrum sharing scheme that applies ARQ protocol to achieve ultra reliability in the finite blocklength regime. A primary user shares its licensed spectrum to a secondary user, where both communicate to the same base station. The base station applies ARQ with the secondary user, which possess a limited number of trials to transmit each packet. We resort to the interweave model in which the secondary user senses the primary user activity and accesses the channel with access probabilities which depend on the primary user arrival rate and the number of available trials. We characterize the secondary user access probabilities and transmit power in order to achieve target error constraints for both users. Furthermore, we analyze the primary user performance in terms of outage probability and delay. The results show that our proposed scheme outperforms the open loop and non-opportunistic scenarios in terms of secondary user transmit power saving and primary user reliability.

We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks. Owing to the high path loss and blockage of mmWave links, multi-hop paths/routes between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: “how to select the best paths and how to allocate rates over these paths subject to latency constraints?” To answer this question, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path/route selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.

The conflicting problems of growing mobile service demand and underutilization of dedicated spectrum has given rise to a paradigm where mobile network operators (MNOs) share their infrastructure among themselves in order to lower their operational costs, while at the same time increase the usage of their existing network resources. We model and analyze such an infrastructure sharing system considering a single buyer MNO and multiple seller MNOs. Assuming that the locations of the BSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability for a user served by the buyer MNO in an infrastructure sharing environment. We analyze the trade-off between increasing the transmit power of a base station (BS) and the intensity of BSs owned by the buyer MNO required to achieve a given quality-of-service (QoS) in terms of the SINR coverage probability. Also, for a seller MNO, we analyze the power consumption of the network per unit area (i.e., areal power consumption) which is shown to be a piecewise continuous function of BS intensity, composed of a linear and a convex function. Accordingly, the BS intensity of the seller MNO can be optimized to minimize the areal power consumption while achieving a minimum QoS for the buyer MNO. We then use these results to formulate a single-buyer multiple-seller BS infrastructure market. The buyer MNO is concerned with finding which seller MNO to purchase from and what fraction of BSs to purchase. On the sellers' side, the problem of pricing and determining the fraction of infrastructure to be sold is formulated as a Cournot oligopoly market. We prove that the iterative update of each seller's best response always converges to the Nash Equilibrium.

Cooperative content delivery using multiple air interfaces (CCDMI) is a powerful solution to mitigate congestion in cellular networks. In CCDMI, the operator distributes content to selected users that further distribute it locally among its nearby users. However, a user that is capable of contributing to CCDMI might act selfishly and refuse to participate. Although the operator can encourage user participation by offering incentives, it has incomplete information about the users' willingness to participate. In order to overcome this problem of adverse selection in CCDMI, we propose two contract-based methods under information asymmetry. In both methods, the operator designs a performance-based contract set for the users that are capable of local content distribution. Using a mathematical analysis, we show that the optimal contract under information asymmetry achieves close to optimal utility for the users and the operator, compared with the information symmetry case. Moreover, the users with high willingness to participate get positive utility and the users with low willingness get zero utility. Hence, by assigning contracts, the operator can motivate user participation, despite the information asymmetry between them. Our results verify that the proposed methods improve the system performance in terms of the utility of the operator and the users.

Data aggregation is a promising approach to enable massive machine-type communication. This paper focuses on the aggregation phase where a massive number of machine-type devices (MTDs) transmit to aggregators. By using non-orthogonal multiple access (NOMA) principles, we allow several MTDs to share the same orthogonal channel in our proposed hybrid access scheme. We develop an analytical framework based on stochastic geometry to investigate the system performance in terms of average success probability and average number of simultaneously served MTDs, under imperfect successive interference cancellation (SIC) at the aggregators, for two scheduling schemes: random resource scheduling and channel-aware resource scheduling (CRS). We identify the power constraints on the MTDs sharing the same channel to attain a fair coexistence with purely orthogonal multiple access (OMA) setups. Then, power control coefficients are found, so that these MTDs perform with similar reliability. We show that under high access demand, the hybrid scheme with CRS outperforms the OMA setup by simultaneously serving more MTDs with reduced power consumption.

The problem of admission control in a multicell downlink multiple-input single-output system is considered. The objective is to maximize the number of admitted users subject to the signal-to-interference-plus-noise ratio constraint for each admitted user and a transmit power constraint at each base station. We cast the admission control problem as an l0minimization problem. This problem is known to be combinatorial NP-hard. Hence, we have to rely on suboptimal algorithms to solve it. We first approximate the l0minimization problem via a non-combinatorial one. Then, we propose centralized and distributed algorithms to solve the non-combinatorial problem. To develop the centralized algorithm, we use the sequential convex programming method. The distributed algorithm is derived by using the alternating direction method of multipliers in conjunction with sequential convex programming. We show numerically that the proposed admission control algorithms achieve a near-to-optimal performance. Next, we extend the admission control problem to provide fairness, where a long term fairness among the users is guaranteed. We focus on proportional and max-min fairness and propose dynamic control algorithms via Lyapunov optimization. It is shown numerically that the proposed fair admission control algorithms guarantee fairness among the users.

5G system design is a complex process due to a great variety of applications and their diverse requirements. This article describes our experiences in developing a centimeter waves mobile broadband concept satisfying future capacity requirements. The first step in the process was the radio channel measurement campaign and statistical modeling. Then the link level design was performed tightly together with the radio frequency (RF) implementation requirements to allow as large scalability of the air interface as possible. We started the concept development at 10 GHz frequency band and during the project World Radiocommunication Conference 2015 selected somewhat higher frequencies as new candidates for 5G. Thus, the main learning was to gain insight of interdependencies of different phenomena and find feasible combinations of techniques and parameter combinations that might actually work in practice, not only in theory.

Compressive sensing theory asserts that, under certain conditions, a high dimensional but compressible signal can be recovered from a small number of random linear projections by utilizing computationally efficient algorithms. The a priori knowledge of the basis in which the signal of interest is sparse is the key assumption utilized by such algorithms. However, the basis in which the signal is the sparsest is unknown for many natural signals of interest. Instead there may exist multiple bases which lead to a compressible representation of the signal: e.g., an image is compressible in different wavelet transforms. We show that a significant performance improvement can be achieved by utilizing multiple estimates of the signal using sparsifying bases in the context of signal reconstruction from compressive samples. Further, we derive a customized interior-point method to jointly obtain multiple estimates of a 2-D signal (image) from compressive measurements utilizing multiple sparsifying bases as well as the fact that the images usually have a sparse gradient.

We analyze the performance of the type-I automatic repeat request (ARQ) protocol with ultra-reliability constraints. First, we show that achieving a very low packet outage probability by using an open loop setup is a difficult task. Thus, we introduce the ARQ protocol as a solution for achieving the required low outage probabilities for ultra reliable communication. For this protocol, we present an optimal power allocation scheme that would allow us to reach any outage probability target in the finite block-length regime. We formulate the power allocation problem as minimization of the average transmitted power under a given outage probability and maximum transmit power constraint. By utilizing the Karush-Kuhn-Tucker (KKT) conditions, we solve the optimal power allocation problem and provide a closed form solution. Next, we analyze the effect of implementing the ARQ protocol on the throughput. We show that by using the proposed power allocation scheme we can minimize the loss of throughput that is caused from the retransmissions. Furthermore, we analyze the effect of the feedback delay length in our scheme.

Hybrid beamforming (HBF) is a promising approach to be employed in millimeter-wave massive MIMO systems. In this paper, four HBF algorithms with partially connected radio frequency architecture are proposed for large antenna arrays in multi-user MISO systems. The first two algorithms aim at minimizing the difference between either the fully digital zero forcing (ZF) or maximum ratio transmission (MRT) beamformer, and the hybrid beamformer of each user. The other two algorithms apply either ZF or MRT HBF solution to each subarray. The average sum rate performance of the proposed algorithms are evaluated using a realistic geometry-based stochastic channel model, and compared with digital ZF and MRT approaches. Numerical results demonstrate that the subarray-based ZF algorithm is superior to other proposed hybrid methods in all simulation cases.

The performance of a single carrier system employing a large number of antennas is explored in this paper, considering the realizability in the millimeter-Wave (mmWave) range in 5G standardization. A significant disadvantage of Orthogonal Frequency division Multiplexing (OFDM) systems, which are currently being used in 4G LTE downlink, is its large peak-to-average power patio (PAPR). In addition, considering massive multiple-input-multiple-output (MIMO) scenario, implementing Inverse fast Fourier transform(IFFT)/fast Fourier transform(FFT) blocks per antenna branch makes the transceivers susceptible to higher complexity in implementation. A single carrier system has therefore the advantage of being relatively simple at the receiver side. However, due to the precoding needed in the downlink, the PAPR value increases with the number of channel taps. Performance of the system is investigated through simulations for single and multiuser cases with different large antenna configurations. Various channel configurations were considered including channel correlation, a measured channel model, and errors in the channel estimate. It can be seen from the results that single carrier scheme with sufficiently higher number of antennas at the base station side provides good bit error rate (BER) performance.

In this paper, the problem of uplink (UL) and downlink (DL) resource optimization, mode selection, and power allocation is studied for wireless cellular networks under the assumption of in-band full duplex base stations, non-orthogonal multiple access (NOMA) operation, and queue stability constraints. The problem is formulated as a network utility maximization problem for which a Lyapunov framework is used to decompose it into two disjoint subproblems of auxiliary variable selection and rate maximization. The latter is further decoupled into a user association and mode selection (UAMS) problem and a UL/DL power optimization (UDPO) problem that are solved concurrently. The UAMS problem is modeled as a many-to-one matching problem whose goal is to associate users to small cell base stations and select transmission mode (half-/full-duplex and orthogonal/NOMA). Then, an algorithm is proposed to solve the problem by finding a pairwise stable matching. Subsequently, the UDPO problem is formulated as a sequence of convex problems and is solved using the concave-convex procedure. Simulation results demonstrate that the proposed scheme is effective in allocating UL and DL power levels after dynamically selecting the operating mode and the served users, under different traffic intensity conditions, network density, and self-interference cancellation capability. The proposed scheme is shown to achieve up to 63% and 73% of gains in UL and DL packet throughput, and 21% and 17% in UL and DL cell edge throughput, respectively, compared with the existing baseline schemes.

The total variation (TV) minimization can be utilized in a compressive sensing framework to recover a signal from a small number of measurements by searching for a signal with a sparse gradient. However, many natural signals of interest, such as natural images, generally have sparse representations in known transforms. Hence, the performance of the signal reconstruction procedure can be improved by also taking into account this transform-domain sparsity of the signal. Thus, the TV minimization problem can be modified by introducing an $l_1$-norm penalty term. The $l_1$-regularized TV minimization problem searches for a signal with a sparse gradient and a sparse representation in the given transform. The main contribution of this paper is the derivation of a customized interior-point method for solving the $l_1$-regularized TV minimization problem that computes the search direction of the Newton method efficiently by exploiting the specific structure of the Hessian.

In this paper, an adaptive scheme for amplify-and-forward relaying networks is proposed, which selects a certain transmission mode for each communication process. Depending on the instantaneous channel conditions, one of the following modes is selected: direct transmission with no cooperation, cooperative transmission with half-duplex relaying and maximal-ratio combining at the destination, or cooperative transmission with full-duplex relaying and maximal-ratio combining at the destination. A three-node network is considered, containing a single-antenna source, a two-antenna relay that is able to implement full-duplex communication, and a single-antenna destination. Energy normalization per block is assumed, so that in those modes using cooperation, the system’s transmission power is shared between source and relay. The performance analysis is provided in terms of outage probability and energy efficiency. We derive a tight approximate expression in closed form for the outage probability and an approximate expression in integral form for the mean energy consumption. The results show that our scheme outperforms all of transmission modes separately in terms of outage probability, while being more energy efficient than the cooperative transmission modes. In addition, the asymptotic analysis proves that the proposed scheme achieves full diversity order equal to 2, thus outperforming those schemes with direct transmission or full-duplex cooperation only.

We consider a multi-operator small cell network where mobile network operators are sharing a common pool of radio resources. The goal is to ensure long term fairness of spectrum sharing without coordination among small cell base stations. It is assumed that spectral allocation of the small cells is orthogonal to the macro network layer, and thus, only the small cell traffic is modeled. We develop a decentralized control mechanism for base stations using the Gibbs sampling based learning technique, which allocates a suitable amount of spectrum for each base station. Five algorithms are compared addressing co-primary multi-operator resource sharing under heterogeneous traffic requirements and the performance is assessed through extensive system-level simulations. The main performance metrics are user throughput and fairness between operators. The numerical results demonstrate that the proposed Gibbs sampling based learning algorithm provides about tenfold cell edge throughput gains compared to state-of-the-art algorithms, while ensuring fairness between operators.

The problem of spectrum sharing between two operators in a dynamic network is considered. We allow both operators to share (a fraction of) their licensed spectrum band with each other by forming a common spectrum band. The objective is to maximize the gain in profits of both operators by sharing their licensed spectrum bands rather than using them exclusively, while considering the fairness among the operators. This is modeled as a two-person bargaining problem, and cast as a stochastic optimization. To solve this problem, we propose centralized and distributed dynamic control algorithms. At each time slot, the proposed algorithms perform the following tasks: 1) determine spectrum price for the operators; 2) make flow control decisions of users data; and 3) jointly allocate spectrum band to the operators and design transmit beamformers, which is known as resource allocation (RA). Since the RA problem is NP-hard, we have to rely on sequential convex programming to approximate its solution. To derive the distributed algorithm, we use alternating direction method of multipliers for solving the RA problem. Numerically, we show that the proposed distributed algorithm achieves almost the same performance as the centralized one. Furthermore, the results show that there is a trade-off between the achieved profits of the operators and the network congestion.

One of the many problems faced by current cellular network technology is the underutilization of the dedicated licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each other's spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. To obtain the maxima of social welfare, the computation of the game theoretical solution using the generic Markov Chain Monte Carlo method is proposed. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system.

We study the problem of joint load balancing and interference mitigation in heterogeneous networks in which massive multiple-input multiple-output macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs), is assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of SCs density, number of BS antennas, and transmit power levels at low and high frequency bands. It is shown that our proposed approach achieves a 5.6 times gain in terms of cell-edge performance as compared with the closed-access baseline in ultra-dense networks with 350 SC BSs per km2.

With increasing popularity of high speed trains and traffic forecast for future cellular networks, the need to provide improved data rates using higher frequency bands (HFBs) for train passengers is becoming crucial. In this paper, we modify the OFDM frame structure for HST, taking into account the increasing sensitivity to speed at HFBs. A lower bound on the SNR/SINR for a given rate for reliable communication was derived considering the physical layer parameters from the OFDM frame. We also analyze different pathloss models in the context of examining the required gain needed to achieve the same performance as with microwave bands. Finally, we present a time-based analogue beamforming selection approach for HST. We observed that, irrespective of the pathloss models used, the required gains are within the same range. For the same SNR/SINR at different frequency bands, the achievable data rate varies with respect to the frequency bands. Our results show the potential of the use of HFBs. However, due to the increased sensitivity of some channel parameters, a maximum frequency band of 38 GHz is suggested. Evaluation of our proposed beamforming scheme indicates a close performance to the optimal SVD scheme with a marginal rate gap of less than 2 b/s/Hz.

In this paper, we study a problem where multiple operators (OPs) need to share a common pool of spectrum with each other. Our objective is to maximize the social welfare, defined as the overall weighted sum rate of the OPs. The problem is decomposed into two parts: the first part is to allocate RBs to OPs, which we do so by extending the framework of many-to-one matching game with externalities. The second part is to allocate power of small cell base stations (SBSs) belonging to each OP, which is accomplished using reinforcement learning. Assuming that the SBSs associated with each OPs are spatially distributed according to Poisson point process (PPP), we show that pairwise stable matchings achieve local maximas of the social welfare function. We propose two algorithms to search for the stable matchings. Simulation results show that these algorithms are well behaved in terms of convergence and efficiency of the solutions.

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables capturing the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS, which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network's outage probabilities compared to a baseline model, which focuses on improving EE while attempting to satisfy the users' instantaneous quality-of-service requirements.

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a twostate machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.

In a cooperative content distribution (CCD) using multiple interfaces, a smart wireless device receives content from a base station (BS) on its cellular interface, and it broadcasts the same content through another wireless interface, such as WiFi. However, different users can experience different link qualities, and users with slow wireless links can be a bottleneck in terms of CCD performance. To address this problem, we propose a device selection method, which leverages multiple interfaces of the selected devices to perform CCD. Our proposed method takes into account the link quality of both primary (cellular) and secondary (WiFi/short-range) interfaces of the devices, and selects the devices with the best link quality for CCD. To analyze the stability of the proposed CCD method against selfish deviators, we model the problem as a repeated CCD game. We show that although the proposed method yields significant gains in terms of energy and frequency carrier savings, it is vulnerable to selfish deviating users. To address this challenge, we propose a carrier aggregation-based incentive mechanism. The analytical and simulation results show that the proposed mechanism maximizes individual and network payoffs, and is an equilibrium against unilateral selfish deviations.

We propose an adaptive transmission scheme for cognitive decode-and-forward relaying networks, whereby, before each communication process, one out of three transmission modes is dynamically selected in order to maximize the instantaneous capacity of the system, namely, half-duplex (HD) relaying, full-duplex (FD) relaying, or direct transmission with no cooperation. The following key issues, relevant to underlay spectrum sharing and cooperative relaying, are considered: 1) the overall transmit power at the secondary network is constrained by both the maximum tolerable interference at the primary receiver and the maximum transmit power available at the secondary nodes; 2) under FD operation, the secondary relay is subject to residual self-interference, which is modeled as a fading channel; and 3) the signals coming from the secondary source and relay are handled at the secondary destination via maximal-ratio combining, in the HD relaying mode, and via a joint-decoding technique, in the FD relaying mode. We derive an exact analytical expression for the outage probability of the proposed scheme. Then, an approximate closed-form expression is proposed, and a corresponding asymptotic expression is derived. Monte Carlo simulations are run to validate the accuracy of the presented mathematical analysis and to showcase the tightness of the proposed approximation.

In this paper, a novel cluster-based approach for maximizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to locally group coupled small cell base stations (SBSs) into clusters based on location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function, which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users' quality-of-service requirements. Due to the lack of intercluster communications, clusters compete with one another to improve the overall network's energy efficiency. This intercluster competition is formulated as a noncooperative game between clusters that seek to minimize their respective cost functions. To solve this game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes an epsilon-coarse correlated equilibrium for the studied game. Simulation results show that the proposed approach yields significant performance gains reaching up to 36% of reduced energy expenditures and upto 41% of reduced fractional transfer time compared to conventional approaches.

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit power, in ultra dense small cell networks (UDNs). To address this problem, a dynamic stochastic game (DSG) is formulated between small cell base stations (SBSs). This game enables to capture the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable two partial differential equations. User scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean field theory, the proposed solution yields an equilibrium control policy per SBS which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 18.1% gains in EE and 98.2% reductions in the network's outage probabilities compared to a baseline model.

In this paper, we address the proportional fair (PF) issue of a K link full-duplex (FD) multiple-input multiple-output (MIMO) interference channel, where each link consists of two FD nodes exchanging information simultaneously. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from the nodes in other links due to simultaneous transmission from each link. The PF issue is important for networks with asymmetric topology and/or asymmetric traffic demands. We demonstrate that the proposed algorithm provides a good trade-off between sum achievable rate and rate distribution for asymmetric links, and moreover we show that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline half-duplex (HD) schemes.

The problem of robust weighted sum-rate maximization (WSRMax) in multicell downlink multi-input single-output systems is considered. We assume that the channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NP-hard even in the case of perfect CSI. Assuming a bounded ellipsoidal model for the CSI errors, we maximize the worst-case weighted sum-rate and proposed a fast but possibly suboptimal algorithm. The proposed algorithm is based on alternating optimization technique and sequential convex programming. Numerical results show that the convergence speed of the proposed algorithm is fast, and it finds a close-to-optimal solution in only a few iterations.

This paper investigates the performance of dual-hop full-duplex relaying schemes subject to co-channel interference (CCI) and noise. In our analysis, two main scenarios are considered: in the first scenario, the link between the source and destination is seen as interference and, in the second one, it is seen as useful information. In both schemes, the effect of self-interference at the relay is taken into account. Accurate, closed- form expressions for the outage probability are derived for the general case, in which CCI and noise are assumed at both the relay and destination. The derived expressions allow for independent non-identically distributed Nakagami-m fading. Based on these expressions, special cases (but, yet new) assuming CCI only at the relay and assuming CCI only at the destination are examined. It is shown that CCI at the relay is more harmful for the system performance than CCI at the destination.

In this paper, we focus on the joint subcarrier and power allocation for an orthogonal frequency division multiple access (OFDMA) full-duplex (FD) system with the goal of maximizing the sum-rate subject to power constraints at the base station (BS) and uplink users, and subcarrier constraints. A greedy subcarrier allocation algorithm based on the necessary conditions of the optimization problem and a power allocation algorithm based on the iterative water-filling (IWF) are proposed. A hybrid scheduler that can switch between FD, half-duplex (HD) uplink and HD downlink mode at each time-slot to maximize the sum-rate is presented. Simulation results reveal that the proposed hybrid scheduling switches to FD scheduling at high self-interference cancellation values, and to HD-time-division- duplexing (TDD) scheduling at low self-interference cancellation values, and thus improves the sum-rate over the traditional HD-TDD scheduling.

This paper investigates a hybrid network configuration in which full-duplex base stations serve half-duplex users on both Uplink and Downlink simultaneously. Users are modeled as a homogeneous Poisson point process while the channel is modeled as a composite fading with correlated Log-normal shadowing and Nakagami-m fading. We characterize the signal-to-interference-ratio at an user of interest, and then evaluate how the network performs in terms of outage probability. We account for the cross-correlation between the user of interest and a random co-site interferer within range. Thus, we provide a valuable insight on on how the hybrid network performs under the assumption of correlated shadowing. We show that when the aforesaid correlation is low, the user of interest can achieve higher data rate at expense of high outage; however, if the distance to the serving base station is short and the cross correlation is high, a satisfactory data rate can be sustained at low outage.

We assume a full-duplex (FD) cooperative network subject to hostile attacks and undergoing composite fading channels. We focus on two scenarios: a) the transmitter has full CSI, for which we derive closed-form expressions for the average secrecy rate; and b) the transmitter only knows the CSI of the legitimate nodes, for which we obtain closed-form expressions for the secrecy outage probability. We show that secure FD relaying is feasible, even under strong self-interference and in the presence of sophisticated multiple antenna eavesdropper.

This letter deals with the secrecy capacity of the radio channel in interference-limited regime. We assume that interferers are uniformly scattered over the network area according to a Point Poisson Process and the channel model consists of path-loss, log-normal shadowing and Nakagami-m fading. Both the probability of non-zero secrecy capacity and the secrecy outage probability are then derived in closed-form expressions using tools of stochastic geometry and higher-order statistics. Our numerical results show how the secrecy metrics are affected by the disposition of the desired receiver, the eavesdropper and the legitimate transmitter.

This letter investigates the outage probability of a variable-gain amplify-and-forward full-duplex relaying network in which not only the relaying link but also the direct link is used to convey information, thus improving the system reliability. We consider a basic three-node relaying system composed of a source, a destination, and a two-antenna relay—one antenna for transmission, one antenna for reception. Also, we consider that the full-duplex relay undergoes some residual self interference, modeled as a fading channel, and that maximal-ratio combining is employed at the destination to merge the signals from the source and relay. We derive an exact integral-form expression for the outage probability and validate this via Monte Carlo simulation. In addition, we propose a highly-accurate closed-form approximation to the outage probability, as well as a simple asymptotic expression at high signal-to-noise ratio. The use of the direct link is shown to overcome the zero diversity order inherent to full-duplex relaying.

In this paper we investigate the physical layer security in a cooperative scenario where the source and the eavesdropper are equipped with multiple antennas, while the relay and the destination are single-antenna devices. We consider that no channel state information (CSI) is available at the transmitter, besides the index of the selected transmit antenna. Closed-form expressions for the secrecy outage probability of the selective decode-and-forward (SDF) and incremental decode-and-forward (IDF) schemes are derived. In addition, we also conduct an asymptotic analysis to get further insights on the performance of each scheme, which predicts a performance floor inherent to the scenario without CSI at the transmitter. Moreover, we also analyze the schemes in terms of the secrecy throughput, which shows that the performance floor that appear in the outage analysis only affects the secrecy throughput if the floor is too high, as is the case of the non-cooperative transmission. Our analysis show that IDF can considerably outperform the SDF and the non-cooperative transmission both in terms of secrecy outage probability and secrecy throughput.

We consider a K link multiple-input multiple-output (MIMO) interference channel, where each link consists of two full-duplex (FD) nodes exchanging information simultaneously in a bi-directional communication fashion. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from other links due to simultaneous transmission at each link. We consider the transmit and receive filter design for weighted sum-rate (WSR) maximization problem subject to sum-power constraint of the system or individual power constraints at each node of the system. Based on the relationship between WSR and weighted minimum-mean-squared-error (WMMSE) problems for FD MIMO interference channels, we propose a low complexity alternating algorithm which converges to a local WSR optimum point. Moreover, we show that the proposed algorithm is not only applicable to FD MIMO interference channels, but also applicable to FD cellular systems in which a base station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) users operating in half-duplex (HD) mode, simultaneously. It is shown in simulations that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline HD schemes.

The problem of robust weighted sum-rate maximization (WSRMax) in multicell downlink multi-input single-output systems is considered. We assume that channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NP-hard even in the case of perfect CSI. We propose optimal and suboptimal but fast-converging algorithms for WSRMax problem with CSI errors. Assuming bounded ellipsoidal model for the CSI errors, we optimize the worst-case weighted sum-rate. The proposed optimal algorithm is based on branch and bound (BB) technique, and it globally solves the worst-case WSRMax problem with an optimality certificate. As the convergence speed of the BB method can be slow for large networks, we also provide a fast but possibly suboptimal algorithm based on alternating optimization technique and sequential convex programming. The optimal BB based algorithm can be used to provide performance benchmarks for any suboptimal algorithm. Numerical results show that the convergence speed of the suboptimal algorithm is fast, and it finds a close-to-optimal solution in only a few iterations.

To tackle the challenge of providing higher data rates within limited spectral resources we consider the case of multiple operators sharing a common pool of radio resources. Four algorithms are proposed to address co-primary multi-operator radio resource sharing under heterogeneous traffic in both centralized and distributed scenarios. The performance of these algorithms is assessed through extensive system-level simulations for two indoor small cell layouts. It is assumed that the spectral allocations of the small cells are orthogonal to the macro network layer and thus, only the small cell traffic is modeled. The main performance metrics are user throughput and the relative amount of shared spectral resources. The numerical results demonstrate the importance of coordination among co-primary operators for an optimal resource sharing. Also, maximizing the spectrum sharing percentage generally improves the achievable throughput gains over non-sharing.

Thanks to the smart device revolution, modern wireless devices have increased computational/storage capabilities and can also support for multiple network interfaces such as cellular and WiFi interfaces. Intelligent utilization of multiple network interfaces can address the problem of cellular traffic congestion and it can also increase the frequency resources of cellular networks. Cooperative content distribution (CCD) is one such technique that can be performed by using multiple wireless interfaces. In CCD, a device receives content from a base station on its cellular interface and distributes it to other devices in its vicinity through another wireless interface such as WiFi. However, due to the broadcast nature of the secondary links such as WiFi, even a single bad link can serve as a bottleneck in terms of the CCD performance. To address this problem, in this paper, we propose a device selection method for CCD that takes into account both the primary (cellular) and secondary link (WiFi/short-range) network interfaces. The proposed method incurs little overhead as it utilizes information such as acknowledgement of data packets, that already exists in the network. We evaluate and compare (with the other content delivery methods) the performance of the proposed method in terms of: number of carriers utilized by a cellular base station (BS); average bits-per-Joule performance; and the average time required to deliver a content file. Moreover, we also take into account the impact of the presence of independent competing/interfering links (such as competing users in the unlicensed band) on the performance of the proposed method.

In this paper we investigate the performance of a cooperative network in the presence of an eavesdropper (Eve). Alice and Bob communicate with the help of a relay, which can operate either in half-duplex (HD) or full-duplex (FD) mode. We account for the self interference at the relay when operating under FD mode. Our analysis focus in the case that the CSI of Eve is not available at Alice. Thus, we derive closed-form expressions for secrecy outage probability. Our results allow us to compare the performance of FD and HD cooperative scenarios under secrecy constraints and, despite the additional interference at the relay, show the advantages of FD relaying over HD. In addition, we also show that a cooperative network is more vulnerable if Eve is closer to Alice than to the relay.

This paper considers an underlay cognitive radio network where primary and secondary networks coexist. The optimization target is to minimize the sum power of secondary transmitters while satisfying the worst case minimum SINR constraint for each secondary user (SU) and maximum aggregate interference constraint for each primary user (PU). Imperfect channel state information (CSI) is assumed, and the corresponding CSI errors are bounded by ellipsoids. We propose an alternating direction method of multipliers-based decentralized beamforming algorithm which relies only on local imperfect CSI and limited backhaul signaling. The convergence behavior of the proposed algorithm is studied via numerical examples.

The design of energy-efficient mechanisms is one of the key challenges in emerging wireless small cell networks. In this paper, a novel approach for opportunistically switching ON/OFF base stations to improve the energy efficiency in wireless small cell networks is proposed. The proposed approach enables the small cell base stations to optimize their downlink performance while balancing the load among each another, while satisfying their users' quality-of-service requirements. The problem is formulated as a noncooperative game among the base stations that seek to minimize a cost function which captures the tradeoff between energy expenditure and load. To solve this game, a distributed learning algorithm is proposed using which the base stations autonomously choose their optimal transmission strategies. Simulation results show that the proposed approach yields significant performance gains in terms of reduced energy expenditures up to 23% and reduced load up to 40% compared to conventional approaches.

Filter Bank Multicarrier (FBMC) systems has drawn interest as an alternative to orthogonal frequency division multiplexing (OFDM), as FBMC offers higher spectral efficiency and less susceptibility to synchronization errors. One drawback in FBMC systems is the presence of inter-carrier-interference (ICI) and inter-symbol-interference (ISI), which degrades the system performance when operating under fading channels. In this work we consider the bit error rate (BER) performance of a multiple input multiple output (MIMO) FBMC system. We evaluate the performance of linear and non-linear transceiver processing techniques, which attempt to mitigate the effect of ICI and ISI in MIMO FBMC systems. Simulation results are presented to evaluate and compare the transceiver processing techniques discussed.

A lattice code construction that employs two separate lattices, a high dimension lattice for coding gain and a low-dimension lattice for shaping gain, is described. Systematic lattice encoding is a method to encode an integer sequence to a lattice point that is nearby that integer sequence. We describe the “Voronoi integers” ℤm/Λs, the set of integers inside the fundamental Voronoi region of a shaping lattice Λs, and a concrete scheme to label these integers. By first shaping the information using the Voronoi integers in low dimension, and then performing systematic lattice encoding using a high-dimension lattice, good shaping and coding gains can be simultaneously obtained. We concentrate on the case of using the E8lattice for shaping and low-density lattice codes (LDLC) with dimension ~ 10,000 for coding. While optimal shaping provides a well-known 1.53 dB gain, previously reported shaping gains with LDLC lattices are on the order of 0.4 dB. The proposed method preserves the shaping gain of the E8lattice, that is, as much as 0.65 dB. This shaping operation can be implemented with lower complexity than previous LDLC approaches.

In this paper, we present the channel measurement results of vehicle-to-vehicle (V2V) measurement campaign carried out in Oulu city center, Finland. The measurements were conducted with EB Propsound CSTMat 2.3 GHz and 5.25 GHz center frequencies. The antennas were installed on the roof of the vehicles and the measurements were performed for single-input multiple-output (SIMO) antenna configuration. The campaign results are presented in the form of path loss, delay spread (DS), maximum excess delay, the standard deviation of slow fading (SF) and K-factor. Furthermore, we propose the method for calculating correlation distance for large scale parameters in V2V channel and present the results for correlation distances of SF, DS and K-factor. The correlation distances less than 11 meters were observed.

A theoretical framework is presented for the evaluation of sum ergodic rate of a full-duplex underlay device-to-device network, when it shares the uplink resources of a conventional cellular user. The sum-rate of the full-duplex network is compared with a half-duplex network with equivalent radio frequency hardware complexity. Closed-form approximations are derived for the sum ergodic rate of the systems. Furthermore, the sum-rate performances are investigated for the case when a transmit power constraint is imposed on the underlay network to minimize the interference on the cellular network. The analytical results presented can be used as a tool to identify when full-duplex transmissions are viable in underlay device-to-device networks.

This paper investigates optimal relay selection schemes for orthogonal space-time block coded multiple-input multiple-output system with non-coherent amplify-and-forward relays, where channel state information is not available at the source and relays. The source-relay and relay-destination channels undergo Rayleigh and Rician fading, respectively. Two possible relay selection schemes are proposed, and both are statistically characterised by deriving exact closed form expression for the cumulative distribution function and probability density function of the instantaneous signal-to-noise ratio (SNR) at the destination. In the first relay selection method, maximizing instantaneous SNR at the destination is considered to select the best relay. In the second scheme, maximizing relay-destination channel is considered. The derived statistical results are used to analyse the performance of the system with outage probability, average bit error rate and ergodic capacity. Finally, we compare both relay selection schemes with respect to the relay pool size and Rician factor.

This paper investigates the performance of dualhop multiple antenna amplify-and-forward relaying system of transmit beamforming (TB) and maximum ratio combining considering feedback delay and co-channel interference (CCI) at the relay node. Source-relay and relay-destination channels experience Rayleigh and Rician fading respectively. TB vector is calculated using outdated channel state information due to the feedback delay from relay-to-source, and the relay node experience CCI due to frequency reuse in the cellular network. We derive new closed form expressions for the exact cumulative distribution function of the end-to-end signal-to-interference-plus-noise ratio to statistically characterized the system. We also derive closed form expression for the outage probability, bit error rate and ergodic capacity. The system performance is investigated using the derived performance metrics to analyze the effect of Rician fading, CCI, feedback delay and number of antennas.

We study the joint subcarrier allocation and pre-coder design (JSAPD) problem to maximize the sum rate of downlink orthogonal frequency division multiple access (OFDMA) systems under a sum power constraint. Naturally, this problem belongs to a class of combinatorial optimization problems which are difficult to solve in general. Based on the concept of big-M formulation, and by exploiting its specific structure, we can transform the JSAPD problem into a mixed integer second order cone program (MI-SOCP), which then offers two advantages. Firstly, when the number of subcarriers/users is small, the design problem can be solved to global optimum in reasonable time by dedicated solvers. Secondly, when the number of subcarriers/users is large, near-optimal solutions of the JSAPD problem can be found by considering the continuous convex relaxation of the MI-SOCP. Numerical experiments are carried out to demonstrate the improved performance of the proposed designs compared to known solutions.

In this paper, we study the stability property of a cognitive radio system comprised of a set of source-destination pairs having different priorities. In particular, we focus attention on the effect of imperfect sensing on the stability region of the system, which has been overlooked in most of related previous work. The adopted cognitive access protocol allows the secondary user not only to exploit the idle slots of the primary user but also to transmit along with the primary user with some probability. This is aimed at achieving the full utilization of the shared channel with capture, i.e., a transmission can be correctly decoded at the destination, even in the presence of other transmissions, if the received signal-to-interference-plus-noise ratio (SINR) exceeds a certain threshold for successful decoding. The abolition of strong primacy, however, requires the secondary user to properly regulate its multi-access probability in order not to impede the primary user's stability guarantee. To this end, the maximum stability region of the system is characterized which describes the theoretical limit on rates that can be pushed into the system while maintaining the queues stable. Interestingly, we found that even with non-zero sensing error rates, there exists a condition for which we can achieve the identical stability region that is achieved with perfect sensing. This is when the destinations enjoy fairly strong capture, and if then sensing errors do not affect the stability region for the queueing system. For the case when the specified condition does not hold, we precisely quantify the loss due to the imperfect sensing in terms of the size of the stability region. Finally, we study the problem of controlling the operating point of the sensing device over its receiver operating characteristic (ROC) and summarize some key aspects observed in the control.

In this letter, we consider a multi-cell multiuser MISO network. We aim to minimize the sum power over base stations (BSs) while guaranteeing the worst case SINR for each user. We propose a decentralized robust beamforming design which relies only on local imperfect channel state information and limited backhaul signaling. First, the non-convex problem is approximated by a convex one via the semidefinite relaxation and S-Procedure methods. Then, we propose a primal decomposition method to equivalently turn the approximated problem into a network-level master problem and BS-level subproblems, which can be optimally solved using an iterative projected subgradient method and a convex optimization solver, respectively. The proposed algorithm is applicable when it yields a rank-one solution providing an optimal solution also for the original problem. Computational and backhaul signaling loads per iteration are reduced as compared with the existing algorithm.

This paper analyzes the performance of an optimal single stream beamforming scheme for a multiple-input multiple-output (MIMO) relay network with dual-hop fixed-gain amplify-and-forward (AF) relaying. The source-relay and relay-destination channels undergo Rayleigh and Rician fading respectively. Different Rician fading scenarios are considered for relay-destination channel, depending on the rank of the Rician channel matrix. The channel state information is only available at the destination, and the destination computes the optimal transmit and receive beamforming vectors to maximize the instantaneous signal-to-noise ratio (SNR). The optimal transmit beamforming vector is sent back to the transmitter via a dedicated feedback link. We derive new analytical expressions for the cumulative distribution function, probability density function, and moments to statistically characterize the properties of the instantaneous SNR. These statistical properties are used to analyze the system performance in terms of the outage probability, average bit error rate, and the ergodic capacity. The performance analysis investigates the effects of the Rician factor, rank of the line-of-sight component, and number of antennas at the nodes on the system performance. The results reveal that the optimal single stream beamforming system provides better performance than an orthogonal space-time block coded based AF MIMO system.

This paper proposes linear precoder-decoder schemes for a multiple-input multiple-output (MIMO) underlay device-to-device (D2D) communication system by considering two D2D modes: two-way relaying based D2D and direct D2D. The D2D communication takes place in the same spectrum as the cellular communication. In the two-way relaying based D2D mode, the relay uses physical layer network coding (PNC). The precoder-decoder design is based on minimizing mean square errors (MSE), which is useful to mitigate interference and to improve the performance of both D2D and cellular communications. Distributed and centralized algorithms are proposed considering bi-directional communication in both D2D and cellular communications. In the direct D2D mode, a similar MSE procedure is adopted, and exact solutions are derived for precoder-decoder matrices. In the numerical results, the optimality and convergence properties of the proposed algorithms are analyzed. Additionally, the system performances are investigated with interference thresholds and maximum available power at the nodes. Two transmit mode selection schemes are considered as dynamic and static selection schemes. Finally, these selection schemes are investigated over an XY grid by varying the position of a given device. The results reveal that the PNC two-way relaying based D2D mode extends the coverage area of D2D communication.

The problem of throughput optimization in decentralized wireless networks with spatial randomness under queue stability and packet loss constraints is investigated in this paper. Two key performance measures are analyzed, namely the effective link throughput and the network spatial throughput. Specifically, the tuple of medium access probability, coding rate, and maximum number of retransmissions that maximize each throughput metric is analytically derived for a class of Poisson networks, in which packets arrive at the transmitters following a geometrical distribution. Necessary conditions so that the effective link throughput and the network spatial throughput are stable and achievable under bounded packet loss are determined, as well as upper bounds for both cases by considering the unconstrained optimization problem. Our results show in which system configuration stable achievable throughput can be obtained as a function of the network density and the arrival rate. They also evince conditions for which the per-link throughput-maximizing operating points coincide or not with the aggregate network throughput-maximizing operating regime.

We consider a single-macrocell heterogeneous multiple-input multiple-output network, where the macrocell shares the same frequency band with the femto network. The interference power to the macro users from the femto base stations is kept below a threshold to guarantee that the performance of the macro users does not degrade due to the femto network. We consider the problem of finding the set of all achievable power-rate tuples for this setting. We first formulate a two-dimensional vector optimization problem in which we consider maximizing the sum-rate and minimizing the sum-power, subject to maximum power and interference threshold constraints. The considered problem is NP-hard. We provide a method to solve the problem by using the relationship between the weighted sum-rate maximization and weighted-sum-mean-squared-error minimization problems. Furthermore, using the proposed algorithm, we evaluate the impact of imposing interference threshold constraints and the impact of co-channel deployment in heterogeneous networks. The proposed algorithm can be used to evaluate the performance of real heterogeneous networks via off-line numerical simulations.

We investigate the spectral efficiency of full-duplex small cell wireless systems, in which a full-duplex capable base station (BS) is designed to send/receive data to/from multiple half-duplex users on the same system resources. The major hurdle for designing such systems is due to the self-interference at the BS and co-channel interference among users. Hence, we consider a joint beamformer design to maximize the spectral efficiency subject to certain power constraints. The design problem is first formulated as a rank-constrained optimization problem, and the rank relaxation method is then applied. However, the relaxed problem is still nonconvex, and thus, optimal solutions are hard to find. Herein, we propose two provably convergent algorithms to obtain suboptimal solutions. Based on the concept of the Frank-Wolfe algorithm, we approximate the design problem by a determinant maximization program in each iteration of the first algorithm. The second method is built upon the sequential parametric convex approximation method, which allows us to transform the relaxed problem into a semidefinite program in each iteration. Extensive numerical experiments under small cell setups illustrate that the full-duplex system with the proposed algorithms can achieve a large gain over the half-duplex system.

The deployment of underlay small base stations (SBSs) is expected to significantly boost the spectrum efficiency and the coverage of next-generation cellular networks. However, the coexistence of SBSs underlaid to a macro-cellular network faces important challenges, notably in terms of spectrum sharing and interference management. In this paper, we propose a novel game-theoretic model that enables the SBSs to optimize their transmission rates by making decisions on the resource occupation jointly in the frequency and spatial domains. This procedure, known as interference draining, is performed among cooperative SBSs and allows to drastically reduce the interference experienced by both macro- and small cell users. At the macrocell side, we consider a modified water-filling policy for the power allocation that allows each macrocell user (MUE) to focus the transmissions on the degrees of freedom over which the MUE experiences the best channel and interference conditions. This approach not only represents an effective way to decrease the received interference at the MUEs but also grants the SBS tier additional transmission opportunities and allows for a more agile interference management. Simulation results show that the proposed approach yields significant gains at both macrocell and small cell tiers, in terms of average achievable rate per user, reaching up to 37%, relative to the non-cooperative case, for a network with 150 MUEs and 200 SBSs.

We introduce a model for capturing the effects of beam misdirection on coverage and throughput in a directional wireless network using stochastic geometry. In networks employing ideal sector antennas without sidelobes, we find that concavity of the orientation error distribution is sufficient to prove monotonicity and quasi-concavity (both with respect to antenna beamwidth) of spatial throughput and transmission capacity, respectively. Additionally, we identify network conditions that produce opposite extremal choices in beamwidth (absolutely directed versus omni-directional) that maximize the two related throughput metrics. We conclude our paper with a numerical exploration of the relationship between mean orientation error, throughput-maximizing beamwidths, and maximum throughput, across radiation patterns of varied complexity.

Under the opportunistic spectrum access paradigm, the shared pool of spectrum bands that the multiple autonomous cognitive radios (CRs) need to compete for is not necessarily homogeneous. The non-homogeneity in channels may lead to payoff distribution conflict among autonomous CRs, as each CR would prefer the outcome in which it selects the more desirable channels. To address this challenge, we have designed an adaptive strategy that (without explicit coordination) enables the CRs to autonomously reach an outcome that maximizes the total CR network throughput and minimizes the payoff distribution conflict among the CRs. We utilize the framework of repeated games with private monitoring to: 1) study the dynamic channel selection problem; 2) analyze the stability of the proposed strategy; and 3) investigate the impact of deviations by a selfish CR on the performance of the proposed strategy. In our model, multiple autonomous CRs are not able to observe the channel selections of other competing CRs. Rather, they get a signal from which the selections must be inferred.

Antenna correlation is usually viewed as a detrimental effect in a multiple input multiple output (MIMO) system. This paper investigates how this affects the performance of an amplify-and-forward (AF) relay network. We consider multiple antennas at all nodes with a general correlation matrix structure having an arbitrary eigenvalue distribution. We derive exact closed form expression for the ergodic capacity and simplify to special case of distinct eigenvalues. Further, we investigate the system in high signal-to-noise ratio (SNR) and derive a simple asymptotic expression. Our results provide a comprehensive analysis and useful insight about the ergodic capacity of the system.

Orthogonal frequency division multiplexing with offset QAM (OFDM/OQAM) modulation scheme, more commonly known as filter bank multicarrier (FBMC), has received greater attention recently, due to its spectral efficiency compared to standard OFDM. However, intrinsic interference in FBMC causes a negative effect on the system performance depending on the channel condition. Hence, in this paper, we analyze the interference in OFDM/OQAM systems and introduce a preceder based on signal-to-leakage-plus-noise ratio (SLNR) to overcome the effect of interference on the system. First we implement the system in a more efficient way using inverse fast Fourier transform (IFFT). Then based on that model, the precoding matrix is generated. Finally through simulation and analysis, reduction of interference and improvement of performance are investigated.

Self organizing network techniques are needed to manage serious two-tier interference in an ad hoc operation of dense femtocell deployment. This paper considers downlink frequency domain inter-cell interference coordination (ICIC) in femtocell deployment. The studied resource partitioning method works such that each active femtocell may adapt, determine, and advertise a so-called binary resource partition sequence indicating which physical resource blocks (PRBs) the femtocells may occupy for their current operation. The main idea is to avoid interference from neighbor femto access points (FAPs) and try to achieve guaranteed bit-rate to femto users. The considered coordination method requires only low rate, infrequent updates in stationary phase and enables ICIC in distributed self organizing manner. The resource partitioning strategies are simulated in an LTE compliant system level simulator. The system level results show that different resource partitioning methods provide substantial gain when MRC receiver is used. When LMMSE receiver is used gains are smaller but at least one of the resource partitioning methods provided a gain over the case without ICIC. Furthermore, it is shown that proposed method works in two different femto environments.

In this work, we consider a hybrid of interweave and underlay modes of operation for cognitive radio systems with random connectivity and bursty packet arrivals. Under the designed hybrid access policy, the secondary communication system is allowed to operate in the interweave mode only when its transmission has no harm on the primary communication. This is when the primary communication system is idle or the interference link from the secondary source to the primary destination is disconnected. The secondary communication system can optionally operate in the underlay mode, although when it is inevitable to interfere with the primary communication. The underlay mode is activated with some probability, called the hybrid rate. We analyze the stability of the hybrid access policy and show that it is not always beneficial when compared against the interweave-only mode. Thus, the condition for which the hybrid access policy can outperform is specified.

This paper investigates the secrecy performance of multiple-input single-output (MISO) wiretap channels with transmit antenna selection (TAS) and subject to an interference-limited eavesdropper. By considering NAantennas at the transmitter, a single antenna at the legitimate receiver and eavesdropper, and multiple arbitrary co-channel interferers M at the eavesdropper, an exact closed-form expression for the secrecy outage probability is derived. The exact expression is simplified for two, yet novel, special cases. Based on these analytical expressions, an asymptotic secrecy outage analysis is carried out and it shows that the diversity order of the considered system equals to min(NA, M). The proposed analysis is corroborated through Monte Carlo simulation results. Illustrative numerical examples are depicted and insightful discussions are drawn. It is shown that the secrecy outage performance is largely limited by the number of interference channels and the number of transmit antennas due to diversity gain.

In this paper, a novel transmit antenna selection scheme for multiple input multiple output (MIMO) wiretap channels is proposed. This new scheme achieves higher secrecy performance by exploiting eavesdropper's channel state information. The key idea behind our proposal is to perform the antenna selection aiming to maximize the overall secrecy rate instead of maximizing the instantaneous signal-to-noise ratio of the main channel. In our analysis, we assume that the legitimate receiver and the eavesdropper employ a maximal-ratio combining technique to combine the received signals from the transmitter. Considering Nakagami-m fading channels, a closed-form expression for the secrecy outage probability is derived, which can be used as a quality of service metric. Further, an asymptotic analysis is carried out and the diversity/array gains are obtained. The ergodic secrecy rate is also investigated for the case of multiple input single output wiretap channels. Representative numerical examples are plotted and validated through Monte Carlo simulations. Insightful discussions are drawn from the proposed analysis.

This paper considers an optimal single stream beamforming for a multiple-input multiple-output (MIMO) relay network with non-coherent dual-hop amplify-and-forward (AF) relaying. The source-relay and relay-destination channels undergo Rayleigh and Rician fading respectively. The channel state information is only available at the destination, and the destination computes the optimal transmit and receive beamforming vectors to maximize the instantaneous signal-to-noise ratio (SNR) at the destination. The optimal transmit beamforming vector will be sent back to the transmitter via a dedicated feedback link. We derive closed-form expressions for the cumulative density function, probability density function, and moments to statistically characterize the properties of the instantaneous SNR. These statistical properties are used to analyze the performance of the system with outage probability, average bit error rate, and ergodic capacity. The results of the performance analysis reveal that Rician factor and number of antennas at the nodes improve the system performance, and the optimal single stream beamforming system provides better error performance than an orthogonal space-time block coded based AF MIMO system.

We propose a multiple-input multiple-output (MIMO) two-way relaying based underlay device-to-device (D2D) communication system, in which relay uses physical layer network coding (PNC). Both cellular and D2D communication take place in the same spectrum. D2D communication is based on PNC, and a joint transceiver design is required to facilitate PNC mapping with MIMO channels. A joint precoder-decoder scheme is also considered in the cellular communication to mitigate interference, and improve the error performance. Mean square errors at nodes are considered as the objective function, and interference threshold limits and power constraints are also used in joint precoder-decoder design problems. These turn out to be non-convex optimization problems, and we propose two algorithms in both phases of D2D and cellular communication. Distributed optimization methods are used in the proposed algorithms. We analyze the convergence of proposed algorithms, and the system performances are investigated with different D2D pair locations, interference constraint thresholds, and maximum available power at nodes.

European Telecommunication Standards Institute (ETSI) is standardizing the 3GPP Long Term Evolution Advanced (LTE-A) relay nodes (RNs) which are intended to be used for providing enhanced cell edge coverage and capacity. We present simulation results on the prospected LTE-A mobile relay node (MRN) applications. The future vehicles can communicate with their environment by exchanging information with surrounding sensor networks and objects. This is related to the concept of Internet of Things (IoT), in which even the smallest objects are equipped with sensors and connectivity capabilities, in order to report information on, e.g., temperature and air pressure. We consider a scenario where in-car connectivity is provided by a short range wireless link such as Wi-Fi or Bluetooth, which can be utilized by the passengers, various sensors, or the vehicle itself. We study the achievable system performance improvement when MRNs are utilized in comparison with the case, in which the users are independently connected to the LTE network. Through simulations, we show that the wireless links via MRNs can give significant advantage when compared to the direct connections from personal handsets to LTE base stations (eNBs).

In this paper, we investigate a robust joint precoder-decoder design scheme for a multiple-input multiple-output (MIMO) physical layer network coding (PNC) based two-way relay system. Precoders at the source nodes and decoder at the relay node are used to facilitate PNC operations during multiple access stage. Both channel estimation error and antenna correlations are used to formulate an optimization problem to minimize weighted mean square error (WMSE) under a total power constraint. The problem becomes non- convex, and we propose an algorithm to solve it optimally. The effect of estimation error, antenna correlation parameters, weighting parameters, and number of antennas at nodes are also considered in the numerical analysis.

he co-channel deployment of a femtocell with a macrocell is considered. We investigate the resource allocation in the downlink with co-channel interference. A novel utility function is introduced as an energy efficiency metric. Two competing objectives (energy minimization and capacity maximization) are included in the utility function in order to balance the network in possible energy savings and to maintain the quality of service of end users. Energy Efficiency = (Sum Energy consumption at the base station)/(Sum rate of the cell). In order to improve the energy efficiency of the entire network, tiers are separately addressed, so that we could avoid a centralized resource allocation scheme. The problem is then converted into a convex optimization formulation with an interference coordination scheme.

This paper presents the derivation for per-tier outage probability of a randomly deployed femtocell network over an existing macrocell network. The channel characteristics of macro user and femto user are addressed by considering different propagation modeling for outdoor and indoor links. Location based outage probability analysis and capacity of the system with outage constraints are used to analyze the system performance. To obtain the simplified expressions, approximations of ratios of Rayleigh random variables (RVs), Rayleigh to log normal RVs and their weighted summations, are derived with the verifications using simulations.

In this paper we present system level simulation results for a cooperative moving relay node (MRN) system deployed on a high speed train (HST). Recently interest has grown in using MRNs to provide enhanced cellular coverage to users in public transport, particularly HSTs, of which the modern construction materials and techniques cause high vehicle penetration loss (VPL) when signals propagate into the train. MRNs utilising antenna arrays on the exterior and interior of the train are a promising solution to overcoming this VPL in order to provide onboard users with improved service. We show that a cooperative system of 8 MRNs onboard a HST is able to provide significant improvements to achievable throughput of onboard users when compared to direct transmission, as well as indirectly improving the throughput of other users located in cells the HST is passing through.

Based on the Estimate-and-Forward (EF) relaying scheme with the Bayesian estimator proposed in our previous work, we investigate the impact of the approximation of the residual noise in this paper. A more precise model is proposed to characterize the residual noise on the symbol estimate, using a pre-calculated lookup table at the destination. Compared to the previously utilized Gaussian noise approximation, the new model introduces additional complexity, but requires less signaling overhead transmitted from the relay to the destination. In the simulations, it shows better performance, compared to the other relaying schemes.

We propose an energy-efficient method to enable the data collection of wireless sensor networks (WSNs) assisted by a cellular network. They use the same radio resources in an underlay manner resulting in bandwidth efficiency improvements. During the sensor data collection (SDC) period, the cellular user functions as a mobile sink node while keeping the normal cellular traffic, and multicarrier code-division multiple-access (MC-CDMA) transmission is utilized in the sensor-to-sink. Since the energy of sensor symbols from MC-CDMA transceiver is small and spread in the frequency domain, it eventually gives only rise to noise-level of each sub-carrier. After formulating the SDC procedure, we analyze the bit error probability of both the cellular traffic and sensor transmission. From the simulation results, the proposed algorithm is proved to be efficient.

This paper addresses energy efficiency and throughput performance of joint LTE-A macrocell and femtocell deployment. We consider two techniques discontinuous transmission (DTX) and offloading of users from macrocells to lower power femtocells. When the DTX is applied base stations delay transmissions until sufficient user data is available to transmit using 40 percent or more of the system bandwidth. This allows the base stations to remain inactive for larger periods of time, and therefore reducing energy consumption. The target is to reduce the overall network energy consumption per area with minimal impact to the throughput. The numerical results show that when the majority of macrocell users can be offloaded to be served by femtocells the mean power per area consumption will decrease by up to 34.7 percent and at the same time a larger portion of users are able to meet the intended throughput target. When DTX is applied energy consumption is reduced further still to 45.3 percent.

We design an efficient sensing order selection strategy for a distributed cognitive radio (CR) network, where two or more autonomous CRs sense the channels sequentially (in some sensing order) for spectrum opportunities. We are particularly interested in the case where CRs with false alarms autonomously select the sensing orders in which they visit channels, without coordination from a centralized entity. We propose an adaptive persistent sensing order selection strategy and show that this strategy converges and reduces the likelihood of collisions among the autonomous CRs as compared to a random selection of sensing orders. We also show that, when the number of CRs is less than or equal to the number of channels, the proposed strategy enables the CRs to converge to collision-free channel sensing orders. The proposed adaptive persistent strategy also reduces the expected time of arrival at collision-free sensing orders as compared to the randomize after every collision strategy, in which a CR, upon colliding, randomly selects a new sensing order.

The use of small cells serviced by low-power base stations such as femtocells is envisioned to improve the spectrum efficiency and the coverage of next-generation mobile wireless networks. However, one of the major challenges in femtocell deployments is managing interference. In this paper, we propose a novel cooperative solution that enables femtocells to improve their achievable data rates, by suppressing intratier interference using the concept of interference alignment (IA). We model this cooperative behavior among the femtocells as a coalitional game in partition form and we propose a distributed algorithm for the coalition formation. The proposed algorithm allows the femtocell base stations to independently decide on whether to cooperate or not, while maximizing a utility function capturing both the gains and costs from cooperation. Using the proposed algorithm, the femtocells can self-organize into a stable network partition composed of disjoint femtocell coalitions and which constitutes the recursive core of the game. Inside every coalition, cooperative femtocells use advanced IA techniques to improve their downlink transmission rate. Simulation results show that the proposed coalition formation algorithm yields significant gains, in terms of average payoff per femtocell, reaching up to 30 percent relative to the noncooperative case for a network of N=300 femtocells.

The design of distributed mechanisms for interference management is one of the key challenges in emerging wireless small cell networks whose backhaul is capacity limited and heterogeneous (wired, wireless and a mix thereof). In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed. The proposed approach enables macrocell user equipments (MUEs) to optimize their uplink performance, by exploiting the presence of neighboring small cell base stations. The problem is formulated as a noncooperative game among the MUEs that seek to optimize their delay-rate tradeoff, given the conditions of both the radio access network and the - possibly heterogeneous - backhaul. To solve this game, a novel, distributed learning algorithm is proposed using which the MUEs autonomously choose their optimal uplink transmission strategies, given a limited amount of available information. The convergence of the proposed algorithm is shown and its properties are studied. Simulation results show that, under various types of backhauls, the proposed approach yields significant performance gains, in terms of both average throughput and delay for the MUEs, when compared to existing benchmark algorithms.

Energy efficient beamforming and power control problem is considered for a MISO (multiple input single output) network. We consider an active femtocell within the coverage area of a macrocell. The femto base station is equipped with multi antennas and the users are considered to be single antenna users. A beamforming and power control problem is formulated in order to minimize the energy consumption per bit in the downlink transmission in the femtocell. The objective function is introduced as “sum power/sum rate” which has the unit J/bit to measure the energy efficiency of the network. The problem is non-convex. We introduce a novel method to solve this problem with an approximation. We show that the problem can be solved with convex optimization techniques which has more practical interest even though the solution is suboptimal. In order to measure the energy efficiency we apply an existing power model which considers the total energy consumption of a base station. Thus we expect that our solution indicates realistic energy consumption measurements. Then we introduce a beamforming and power control algorithm which minimizes the energy consumption per bit transmission. Finally, the behavior of the objective function is observed in different antenna configurations by varying the user density in different channel environments.

This paper considers a downlink linear transmit and receive beamformer design in a coordinated multi-cell network where each multiantenna base station (BS) serves its own set of multiple antenna users. Optimization objective is to minimize sum transmission power among coordinated BSs while satisfying user specific minimum SINR targets. The problem is jointly non-convex in transmit and receive beamformers. Hence, even in a centralized case only a local optimal solution can be found by alternating optimization in which the transmit and receive beamformers are updated consecutively. The problem becomes even more complicated for a decentralized case since even if the channels from the BS to the neighboring cells' users are known, the receivers they are employing may not be. In order to obtain a decentralized implementation, each BS assumes worst case receivers for other cells' users when designing its own users' beamformers. Using this design approach, user specific SINR targets can be guaranteed using only local channel state information at each BS and some limited backhaul signaling among coordinated BSs. The proposed decentralized transceiver design algorithm is solved by repeating the following two optimization steps separately at each BS: transmit and receive beamformer optimization via alternating optimization and inter-cell interference power optimization via primal decomposition. Decentralized implementation comes at a cost of somewhat increased sum power compared to the centralized case. Numerical examples demonstrate fast convergence and significant gain over MISO system when SINR targets are low.

We investigate a joint precoder decoder design scheme for multiple-input multiple-output (MIMO) channel physical layer network coding (PNC) based two-way relay system. Precoders at source nodes and decoder at relay node are designed to facilitate PNC operations at the relay node. We consider minimizing the weighted mean square error (MSE) at relay node to enhance the accuracy of the PNC. Formulated optimization problem is non-convex and we propose an algorithm to solve it optimally. Numerical results confirm that the joint precoder decoder algorithm provides the optimal solution to minimizing weighted MSE with the total available power. Effect of weighting parameters, relay location and number of antennas at nodes are considered in the system analysis.

We introduce a full duplex multiuser multiple-input multiple-output (FD MU-MIMO) system, and consider the total throughput maximization problem under a sum power constraint in the downlink (DL) channel and per-user power constraints in the uplink (UL) channel. Due to the nature of asymmetric DL/UL capacity, a trivial method to this problem is to optimize the DL and UL channels sequentially. However, when the self-interference (SI) is large, the sum rate of the UL channel in this sequential design is dramatically degraded. Herein, a joint design is proposed, in which the DL and UL channels are optimized simultaneously. Since the objective function of the throughput maximization problem is non-convex, it is difficult to find the optimal solution. Thus, we propose a joint iterative algorithm to find a suboptimal design, using a local optimization strategy. Simulation results demonstrate that the iterative joint design outperforms the sequential design, and the FD MU-MIMO system is superior to the conventional half duplex (HD) system in terms of the total system throughput when the SI is sufficiently small. This makes the FD MU-MIMO techniques promising for small cell deployments where the transmit power is relatively small.

The choice of a suitable backhaul constitutes one of the main performance bottlenecks in the emerging femtocell networks. In this paper, we study the impact of adopting a heterogenous backhaul (i.e., wired or over-the-air) with realistic quality-of-service requirements on coherent coordinated multipoint (CoMP) transmission in the downlink of femtocell networks. We formulate a cooperative game with continuum among the femtocell access points (FAPs) for performing CoMP in order to maximize the downlink rate while accounting for the constraints on the heterogeneous backhaul. In this respect, we propose a distributed algorithm that enables the FAPs to jointly decide on their cooperative partners as well as the choice of a backhaul strategy. In this respect, the proposed algorithm jointly addresses the problem of coalition formation as well as the optimization of the tradeoff between OTA and wired backhaul transmission modes, each of which is limited by a different factor such as delay or spectrum resources availability. We show that the proposed algorithm converges to a stable partition which constitutes the continuum core of the studied cooperative game. Simulation results show that our proposed scheme yields interesting gains in terms of the average downlink rate per FAP, reaching up to 26% relative to the classical of non-cooperative transmissions.

This paper studies the effect of feedback delay on the performance of amplify-and-forward (AF) relay selection over Nakagami-m fading environment. Spatial diversity can be improved by employing multiple relays with selection, however, this diversity gain cannot be fully realized when the feedback delay is present in the decision metric. Hence, we try to quantify this detrimental effect by deriving the exact closed form solution to outage probability and ergodic capacity for channel state information (CSI)-assisted relay. Further, we derive simple asymptotic results for outage to provide insight of the system performance and diversity. Monte Carlo simulation is used to verify the analytical work.

In this paper, we propose a method to connect a group of wireless sensors to LTE/LTE-A system by using the cellular users as mobile relays, with the basic principle of maintaining the normal traffic between the cellular users and eNodeB during the sensor data collection. The cellular spectrum is re-used on sensor-to-mobile relay link without employing additional frequency resources. Multi- carrier CDMA methods are suspected to be utilized by sensor nodes, where data of each sensor is spread to the whole re-used cellular spectrum to create a low data rate transmission. In order to avoid additional complex receiver on cellular terminals, the simple amplify-and-forward relaying scheme is used at mobile relays. Since eNodeB can observe the cellular traffic components in the re-used spectrum exactly, it has the capability of canceling them in the received overlapped signals, and detecting the sensor data by using advanced multi-user detection methods. Finally, the end-to-end outage probability is analyzed and a closed-form approximation is derived. The numerical results show that the approximate closed-form equation is significant and that the proposed scheme can be used to collect sensor data in LTE/LTE-A networks.

In this work we compare full-duplex (FD) and half-duplex (HD) relaying in terms of outage probability and throughput. We consider a practical FD relay model where the loop interference between transmitted and received signals is taken into account. We analyze two modes for FD transmission, block Markov encoding and multi-hop transmission without interference cancellation, and two different modes for HD transmission, which are based on selective and incremental decode-and-forward protocols. Results show that there is a tradeoff between SNR and information rate in which each scheme becomes more suitable.

We investigate error performance and an optimum power allocation scheme for multiple-input multiple-output (MIMO) channel physical layer network coding (PNC) based two-way relay system. Zero forcing precoding technique is used at source nodes to facilitate PNC operations at the relay node. First, system error performance is investigated by introducing upper and lower bounds for BPSK modulation system. They are validated with numerical results and bounds provide accurate results at high SNR regime. Then, we consider sum rate maximization under a total power constraint to obtain the optimum power allocation scheme. Analytical solutions are derived and compared with other possible schemes, which are suboptimal. Numerical results confirm that the power allocation scheme provides the optimal solution for the achievable sum rate with the total available power and the relay position.

This paper introduces a novel approach to evaluate the performance of ad hoc networks based on their spatial capacity, defined as the maximum spatial spectral efficiency supported by the network while a zero outage probability is guaranteed for all communication links. Specifically, the spatial capacity together with upper and lower bounds is derived in closed-form for networks where transmitters follow a Poisson point process and their respective receivers are located at a fixed distance. In this scenario, the spatial capacity is achieved using a rate adaptation technique that adjusts the link spectral efficiency in accordance with the distance between the receivers and their closest interferers. Besides, the spatial capacity is analytically proved to be always greater than or equal to the maximum spatial spectral efficiency achieved when a fixed spectral efficiency and an unbounded outage probability are considered. Numerical results show that the spatial-capacity-achieving setting leads to a spatial spectral efficiency about 125% higher than the one reached with the fixed spectral efficiency strategy.

In this paper, a new measure of outage-constrained network area spectral efficiency, coined as stable transmission capacity, is introduced, which is achievable under finite delay and queue-length stability. Specifically, this framework extends the transmission capacity formulation to scenarios with packet retransmissions and transmitters' queues with independent packet arrivals. This approach is applied to single-hop wireless networks with nodes being spatially distributed as Poisson point process, slotted ALOHA medium access protocol, packet arrivals following a geometrical distribution, and bounded number of retransmissions. The stable transmission capacity is then obtained as the solution of a constrained optimization problem with respect to the probability that a transmitter access the network, the link spectral efficiency, and the maximum number of retransmissions. Using the unconstrained stable transmission capacity as an upper bound, it is shown under which operating points and network parameters such limit can be achieved. Our numerical results also evince how the spatial density and the arrival process affect the network performance.

We propose and investigate distributed coordination mechanisms for controlling the co-channel interference generated by standalone femtocells in two-tier coexistence scenarios consisting of macrocells underlaid with short-range small cells. The rationale behind employing such mechanism is to opportunistically reuse resources without compromising ongoing transmissions on overlaid macrocells, while still guaranteeing Quality of Service in both tiers. Stochastic geometry is used to model network deployments, while higher-order statistics through the cumulants concept is utilized to characterize the probability distribution of the aggregate interference at the tagged receiver. To conduct our studies, we consider a shadowed fading channel model incorporating log-normal shadowing and Nakagami fading. In addition, various network algorithms, such as power control and frequency (re)allocation, are included in the analytical framework. To evaluate the performance of the proposed solutions, we also derive closed-form expressions for the outage probability and average spectral efficiency with respect to the tagged receiver. Results show that the analytical framework matches well with numerical results obtained from Monte Carlo simulations, and that the coordination mechanisms substantially improve the performance of overlaid macrocell networks, while also benefiting femtocells.

In this work we investigate whether it is preferable to have a large number of short single-hop links or a small number of long single-hops in a multi-hop wireless network. We derive analytical expressions to compute the metric aggregate multi-hop information efficiency under different hopping strategies, and analyze the trade-off involving robustness of single-hop links, interference and hopping strategy.

In this paper we investigate the transmission capacity of wireless networks when packet retransmissions are allowed. We consider networks modeled as a homogeneous Poisson point process operating under different medium access control schemes, namely unslotted and slotted ALOHA, and CSMA with carrier sensing at the transmitter and with carrier sensing at the receiver. For these scenarios, we derive analytical expressions to compute the maximum number of retransmissions attempts that leads to the optimal transmission capacity. Numerical results based on our formulation show that CSMA with carrier sensing at the receiver (asynchronous transmissions) reaches the highest maximum transmission capacity when traffic intensity is low, while slotted ALOHA (synchronous transmissions) is the best choice when traffic intensity is high.

This paper investigates the performance of amplify-and-forward (AF) two-way relay system with antenna correlation. Overall outage probability (OOP), average symbol error rate (SER) and ergodic capacity are analyzed. By considering general correlation structure with arbitrary eigenvalue distribution, we derive a tight lower bound for OOP and use it to evaluate average SER and ergodic capacity. Moreover, the system is studied in high signal-to-noise ratio (SNR) to obtain the insightful behavior of the performance and diversity gain. Finally Monte Carlo simulations are conducted to verify the accuracy of the results.

We investigate coordination mechanisms for controlling the co-channel interference generated by stand-alone femtocells in two-tier coexistence scenarios. Stochastic geometry is used to model network deployment scenarios, while the cumulants concept is utilized to characterize the probability distribution of the aggregate interference at a tagged user. The rationale for using coordination mechanisms is to opportunistically reuse resources without compromising ongoing transmissions on overlay macrocells, while still guaranteeing Quality of Service in both tiers. Results have shown that the analytical framework matches fairly well with numerical results obtained with Monte Carlo simulations. Yet coordination mechanisms improve performance of overlay macrocell network by substantially diminishing co-channel interference.

In this paper, we consider an optimal power allocation scheme for a physical layer network coding relay based secondary user (SU) communication in cognitive radio networks. SUs are located on two different primary user (PU) coverage areas and we introduce an energy and spectrally efficient SU communication scheme. The sum rate of relay based two way communications for SUs is maximized under a total power constraint and interference power threshold (IPT) constraints to PUs. The optimal power allocation schemes are illustrated for different cases of SUs and relay powers. Numerical results confirm that the power allocation scheme provides the optimal solution for the achievable sum rate and the IPT constraints have an effect on the sum rate variation with the total available power.

In this contribution, we consider the performance of the CSMA MAC protocol in multi-hop ad hoc networks in terms of "aggregate multi-hop information efficiency". Our model consists of a wireless network where transmitter nodes are distributed according to a homogeneous 2-D Poisson point process, and packets are generated following a Poisson distribution. Each packet is forwarded to its destination a fixed distance away from the source through an arbitrary number of hops. Approximate analytical expressions are derived for the outage probability of CSMA in its various incarnations, considering different values for the sensing threshold (related to the backoff decision) and the required communication threshold (which determines a correct packet reception). The aggregate multi-hop information efficiency is evaluated as a function of the transmission density, the communication rate, the maximum number of permitted backoffs and retransmissions, and the number of hops. Our results indicate the existence of optimal operating points for achieving maximal efficiency, and a basis is thus established for the optimization of the system parameters in order to improve the performance of multi-hop ad hoc networks.

We consider the problem of multicasting from a single source to multiple destinations over the erasure channel. We are interested in energy efficient communication. Our performance metric is the number of bits sent per joule of energy spent until a finite amount of packets is delivered from the source to all the destinations. We compare the performance of Random Linear Network Coding (RLNC) and Automatic Repeat reQuest (ARQ) with respect to the above performance metric. Our numerical results illustrate that RLNC is more energy efficient than ARQ when the unreliability of links is high.

We consider the problem of MaxWeight scheduling in wireless multihop networks. This problem is known to be NP-hard. We propose a solution method, based on the branch and bound technique, which solves globally the MaxWeight scheduling problem with an optimality certificate. Efficient analytic bounding techniques are introduced as well.

We consider the problem of weighted sum-rate maximization (WSRMax) in wireless networks. This problem is known to be NP-hard and it plays a central role in resource allocation, link scheduling or in finding achievable rate regions for both singlecast and multicast networks. We propose a solution method, based on the branch and bound technique, which solves globally the WSRMax problem with an optimality certificate. Efficient bounding techniques are introduced as well.

In this paper, we consider a decentralized downlink beamformer design problem in a multiantenna cognitive radio network where multiple primary and secondary transmitters serve multiple single antenna primary and secondary users, respectively. The design goal is to minimize the total transmitted power across the secondary transmitters subject to the constraints of the received SINR at each secondary user and the received interference power at each primary user.We propose two alternative decentralized approaches where iterative beamformer designs are based on primal and dual decomposition methods. Beamformers are calculated locally relying on the limited backhaul message passing between secondary transmitters. Since the original problem is convex, both decentralized approaches converge to the globally optimal solution for static users' channels. Moreover, a feasible set of beamformers are guaranteed at each iteration. The proposed beamformer designs apply directly to the case of hierarchical femtocell networks where multiple macro- and femtocells co-exist. Convergence behavior of the algorithms is demonstrated through simulations. In addition, sum power performance of the optimal decentralized beamforming is compared with zero-forcing beamforming in a quasi-static flat fading scenario.

In our study we have analyzed the interference, modeled as a Gamma variable, at the primary receiver by a cognitive radio adhoc network distributed in a Poisson point process (PPP) with relay assisted primary communication link. We have shown through the density functions that the signal to interference ratio (SIR) concentrates at high values with an increased number of relays leading to better performance. Outage probability, symbol error rate (SER) and ergodic capacity are analyzed to take more insight of the system and it is further shown that the best performance increase can be obtained with a single relay.

Space-frequency resource allocation and multiuser beamforming are crucial techniques to obtain high spectral efficiency requirements of IMT-A technologies. In this paper, we consider system level evaluation results of downlink TDD based LTE-A MIMO-OFDMA networks in ITU-R specific IMT-A evaluation environments. Efficient multiuser MIMO beamforming with and without intra-site coordinated multipoint (CoMP) and proportional fair space-frequency scheduling algorithms are particularly investigated. The system level results indicate that MU-MIMO and CoMP are needed in urban micro (UMi) environment to achieve the ITU-R specific spectral efficiency requirements. In ITU specific indoor scenario, MU-MIMO system is able to provide very high spectral efficiency because intercell interference is not limiting performance so strongly. The considered MU-MIMO CoMP seems to work also in rural macro environment with high mobility users and outdated CSI.

This paper investigates orthogonal space-time block coded transmission for a multiple-input multiple-output (MIMO) channel with non-coherent amplify-and-forward relaying in a situation where the source-relay and relay-destination channels undergo Rayleigh and Rician fading respectively. Fading coefficients of the Rician channel are independent but not necessarily identically distributed. We derive exact expressions for the moment generating function, first and second moments of the instantaneous signal-to-noise ratio at the destination to statistically characterize the system behavior. We then analyze the system performance by deriving new analytical expressions for the bit error rate and amount of fading. These performance metrics reveal that strong line of sight components between relay-destination link always limit the performance promised by MIMO scattering environment when both nodes have multiple antennas.

A new system model is introduced for spectrum sharing in a multi-operator environment. The infrastructure based cognitive radio network operates in underlay/overlay (hybrid) transmission mode sharing the spectrum of licensed users. Primary user networks (PUNs) are benefitted by the relaying capability of the cognitive radio base station so that their service degraded users due to low coverage, can enhance the performance. The cognitive radio network (CRN) also helps itself by transmitting its own data along with the primary data on the same band, while agreeing to maintain the signal to interference plus noise ratios of primary users' at a predetermined level. CRN operates in an underlay mode with an imposed interference threshold. In addition, maximizing the secondary user capacity by strategically changing the available frequencies from time slot to time slot is discussed with the introduction of two primary data relaying schemes (asynchronous / synchronous). The objective of the secondary user capacity maximization is reformulated into a linear programming problem with interference cancellation techniques. Power allocation for all the active users and the capacity of the secondary user is analyzed in both of the schemes.

We consider the problem of multicasting from a single source to multiple destinations over an erasure channel model. We are interested in energy-efficient communication. Our performance metric is the number of bits that the source delivers successfully to all destinations per joule of the overall energy spent. We compare the performance of Random Linear Network Coding (RLNC) and Automatic Repeat reQuest (ARQ) with respect to the above performance metric in the presence and absence of Forward Error Correction (FEC). Our numerical results illustrate that RLNC is more energy-efficient than ARQ when the links are highly unreliable. However, as the reliability protection provided to the links through FEC increases the energy-efficiency performance of RLNC deteriorates and it becomes suboptimal compared to ARQ methods.

Femtocells are envisioned to be deployed in indoor environments in order to improve both radio coverage and spectrum efficiency. This paper focuses on the self-organization of indoor femtocells, which includes mechanisms of cooperation. In this context, we propose a solution to automatically form cooperating groups among severely interfered femtocells in order to avoid interference. A hybrid access policy is proposed and compared to the closed and open policies. Results show that, as stated in the Braess Paradox, pervasive cooperation may be detrimental, when available resources are highly contended. Conversely, in particular cases, a marginal selfish behavior of femtocells can be preferable.

Overlaying low-power, low-cost, femtocells, over existing wireless networks has recently emerged as a means to significantly improve the coverage and performance of next-generation wireless networks. While most existing literature focuses on spectrum sharing and interference management among non-cooperative femtocells, in this paper, we propose a novel cooperative model that enables the femtocells to improve their performance by sharing spectral resources, minimizing the number of collisions, and maximizing the spatial reuse. We model the femtocell spectrum sharing problem as a coalitional game in partition form and we propose a distributed algorithm for coalition formation. Using the proposed algorithm, the femtocells can take autonomous decisions to cooperate and self-organize into a network partition composed of disjoint femtocell coalitions and that constitutes a stable partition which lies in the recursive core of the considered game. Whenever a coalition forms, the femtocells inside this coalition can cooperatively pool the occupied spectral resources. Additionally, the members of any given coalition jointly schedule their transmissions in order to avoid collisions, in a distributed way. Simulation results show that the proposed coalition formation algorithm yields a performance advantage, in terms of the average payoff (rate) per femtocell reaching up to 380% relative to the non-cooperative case.

We consider the problem of multicasting from a single source to a set of destinations. We assume that the source can either reach the destinations directly or forward its traffic through a set of relays. Due to the non-linear attenuation of the signal with distance, employing the relays can help to improve the signal quality at the destinations. Meanwhile, relays also consume energy for retransmission of the received information and the rate of communication can be decreased due to the multi-hop transmission. Under the performance objective of maximizing the common amount of information (number of bits) that the source sends to all destinations per Joule of the total energy spent, we wish to identify whether direct transmission from the source to the destinations is preferable as opposed to multi-hop forwarding through the relays. In the latter case, we also identify a) which subset of the relays should be activated, b) for how long, and c) the respective destinations that each relay has to serve. Finally, we provide a set of numerical results to support our analysis.

This letter considers the design problem of coordinated downlink minimum power beamforming for a multiuser multi-cell network, where each multi-antenna base station (BS) serves multiple single antenna users. We propose a decentralized primal decomposition based algorithm where limited amount of information is exchanged between coupled BSs at each iteration. Algorithm converges to the globally optimal solution for a static scenario. Unlike most of the previous decentralized methods, a feasible set of beamformers is guaranteed at each iteration even when the exchanged backhaul information is outdated. Consequently, the proposed approach naturally lends itself to realistic time-correlated fading scenarios.

Generalized Zero Forcing (GZF) is an extension of the zero forcing beamforming method to deal with downlink multiuser MIMO communications when users have more than one receive antenna. In literature, Singular Value Decomposition (SVD) based channel Block Diagonalization (BD) is proposed to eliminate multiuser interference for GZF, but it is computationally inefficient. In this letter, we propose to generate a GZF precoder by using the product of a channel pseudo inverse matrix and a block diagonal matrix with an appropriate form, whose optimality for solving weighted sum rate maximization and weighted sum power minimization problems is proven. Later by using the proposed precoder a novel BD method with significantly lower complexity is presented. The complexity analysis shows that our proposal is computationally more efficient than SVD-BD.

This paper studies downlink resource reuse schemes for cellular networks with coexisting femtocells and macrocells. We examine two reuse schemes, termed split reuse and shared reuse. In this paper, we develop an analytical model of resource allocation based on random graphs. In our model, arbitrarily chosen communication links interfere with each other with a certain probability, which depends upon whether the links belong to femtocell or macrocell users. Using this model, we establish asymptotic bounds on the minimum number of resource blocks required to make an interference-free resource assignment for all the users in the network for large numbers of users. We assess these bounds using a simple greedy resource allocation algorithm to demonstrate that the bounds are reasonable in finite networks of plausible size. By applying the bounds, we establish the expected impact of femtocell networks on macrocell resource allocation under a wide variety of interference scenarios. Further, we assess the efficiency loss associated with split reuse, as an aid to determining whether resource allocators should use the simpler split reuse scheme or attempt to tackle the complexity and overhead associated with shared reuse.

In a wireless network, the problem of self interference arises whenever a node transmits and receives simultaneously in the same frequency band. So far only two extreme approaches to circumvent this problem were thoroughly investigated in the literature. The first one prevents any node to transmit and receive simultaneously which may lead to a too conservative design. The second one assumes perfect self interference cancelation which can be too optimistic since it ignores all possible technological limitations. To fill this gap, we provide a method to evaluate the network layer benefits from simultaneous transmission and reception when the network nodes employ self interference cancelation techniques with different degrees of accuracy. From a network design perspective, the provided method can be used to find the required level of accuracy for the self interference cancelation such that certain gains are achieved at the network layer. Numerical results suggest that the accuracy of existing self interference cancelation techniques can provide significant gains for certain network setups.

Acquisition systems for sub-surface diagnostic (e.g., earthquake monitoring) require large number of sensors (geo-phones or accelerometers) to be deployed outdoor over large areas (tens of sqkm) to measure backscattered wave fields that are collected into a storage/processing unit (sink node). Aggregated data sets are analyzed to obtain an image of the sub-surface, monitor seismic activity, and declare possible alarm conditions. Cable based connectivity is the bottleneck of current systems, in terms of power consumption and degradation in accuracy. Replacing cables with wireless is now becoming attractive to improve the monitoring quality and reduce the probability of false negatives. Strict sampling synchronization constraint over large geographic areas, high precision sensor localization, high data-rate, and low delay are all topics that call for a scalable network system: Multi-Band Ultra Wide-Band radio transmissions (MB-UWB) play a key role as the only viable technology. This paper introduces the system and UWB network architecture based on ECMA-368 standard, moreover it provides a novel analytical tool to evaluate the energy consumption and delay during network set-up.

In this paper, the results of outdoor-to-indoor multiple-input multiple-output (MIMO) channel measurements at 780 MHz are presented. The motivation for these measurements was to provide actual measurement data needed to extend the International Mobile Telecommunications -Advanced (IMT-A) channel models to the frequencies around 400 - 900 MHz. There are publications reporting measurement results for these frequencies, but the results for the outdoor-to-indoor propagation scenario are basically non-existing. The measurement results presented in this paper include path loss, shadow fading, delay spread, Ricean K-factor and angular spread. The cross-correlations between large scale parameters are also given. The path loss results were compared to two modified versions of COST 231 path loss model for building penetration loss. The comparison showed that the COST 231 path loss model with modified indoor and outdoor loss sections can be applied also to frequencies around 800 MHz with suitable parameter values.

A new analytical model based on Parallel Space-Time Markov Chain concept is presented for performance evaluation of IEEE 802.11 DCF MAC in multi-hop ad hoc networks. The proposed framework is able to model hidden-terminal problem and the aforementioned unreachability phenomena in such networks precisely. To the best of our knowledge, the proposed framework is one of the leading approaches in finite load analysis of multi-hop ad hoc networks taking into account backoff and post-backoff processes, in addition to the MAC sub-layer transmission queue status in an integrated fashion. Based on the proposed model, we provide an extensive throughput performance evaluation of practical multi-hop networks based on IEEE 802.11 DCF MAC.

We consider the cross-layer utility maximization problem for wireless networks. It is well known that the optimal network control policy can be decomposed in three separate subproblems: 1) flow control at the network layer, 2) routing and scheduling at the network layer, and 3) resource allocation (RA) at the medium access control and physical layers. The main contribution of this paper is a power and rate control algorithm for the RA subproblem. In the case of single-hop networks, the proposed algorithm provides a locally optimal solution for the RA subproblem. Even though the global optimality of the solution cannot be guaranteed due to the nonconvexity of the problem, the numerical results show that the algorithm can provide significant gains at the network layer in terms of end-to-end rates and network congestion as compared to the optimal time division multiple access (TDMA) based RA. For solving the RA subproblem in the case of multi-hop networks, the proposed algorithm must be used in conjunction with an exhaustive search for the optimal set of transmitter nodes. However, the numerical results show that even with a random selection of the set of transmitter nodes, the proposed algorithm can provide significant improvement at the network layer in terms of end-to-end rates and network congestion as compared the optimal TDMA based RA.

A medium access control (MAC) protocol to address the so-called unreachability problem in mobile ad hoc networks is proposed. Stations maintain double hop neighborhood graphs and exchange designated eMAC tables to share their knowledge about their neighborhood topology. An adaptive table broadcasting technique to facilitate topology information dissemination in mobile ad hoc networks is also proposed. Performance of the proposed schemes is evaluated and compared with earlier schemes through simulations. Our results show performance enhancement due to better handling of unreachability, possible heterogeneous power distributions among contending stations, and mobility issues.

Generalized Zero Forcing (GZF) will be used in emerging commercial systems for Multi-User MIMO (MU-MIMO) broadcast communications. In this paper, the user scheduling problem related to it is studied. Instead of using Singular Value Decomposition based Block Diagonalization (SVD-BD) method, we construct the GZF precoder that optimally solves the Sum Rate Maximizing (SRMax) problem by a product of a channel pseudo inverse matrix and a block diagonal matrix. Later a Suboptimal User Scheduling (SUS) algorithm with Receive Antenna Selection (RAS) is designed by using a greedy user and antenna selection under SRMax and other simplified criteria. As the channel pseudo inverse can be sequentially calculated, the proposed algorithm is computationally efficient. It also achieves higher average sum rate which is verified by numerical simulations.

We consider the problem of cross-layer utility maximization subject to stability constraints for a multicommodity wireless network where all links are sharing a single channel. We assume a time slotted network and only one node is allowed to transmit at any given slot. The optimal cross-layer network control policy can be decomposed into three subproblems: (1) flow control at the transport and network layers, (2) routing and scheduling at the network layer, and (3) resource allocation (RA) at the medium access control and physical layers. Every time slot, a network controller decides the transmitter node, amount of each commodity data admitted to the network layer, schedules different commodities over networks' links and controls the power and rate allocated to every link. In this paper we provide solutions for the RA subproblem for two scenarios: (1) when the nodes are equipped with standard single user receivers, and (2) when the nodes are equipped with multiuser receivers performing successive interference cancelation. In addition we propose decentralized algorithms to obtain the solution of RA subproblem for each scenario. The numerical results show that as the signal-to-noise ratio increases, using multiuser receivers can provide significant gains at the network layer in terms of end-to-end rates and network congestion as compared to that of single user receivers.

In this paper, we investigate stochastic multichannel load balancing in a distributed cognitive network coexisting with primary users. In particular, we propose a probabilistic technique for traffic distribution among a set of data channels by incorporating statistical information of primary users' activities in different channels into the selection process without centralized control. Moreover, the proposed scheme is enabled by a multi-channel binary exponential backoff mechanism to further facilitate contention resolution in a multi-channel environment. It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end-to-end throughput and average frame end-to-end delay. Furthermore, its performance is also compared to two heuristic channel selection techniques in a multi-channel cognitive network, coexisting with incumbents.

Coordinated linear transmitter-receiver processing by block diagonalization (BD) with beam selection is a straightforward method to utilize all degrees of freedom available in multiuser multiple-input multiple-output (MIMO) networks in order to increase system capacity. By applying channel state information (CSI) in the transmitter, the BD criterion offers zero- forcing between the downlink data streams of different users. This paper proposes practical uplink MIMO schemes for time division duplex (TDD) systems to co-exist with downlink TX-RX zero-forcing so that the locally available CSI of the BD channel is used by the terminals in the uplink transmission. It is shown that the preceded pilot symbols are sufficient in both uplink and downlink to satisfy the needs of both transmission and reception. The achievable rate of the system is evaluated in conjunction with linear receivers in a time-varying fading channel and with channel estimation. According to the results the proposed uplink transmission strategy provides increased rates compared to user selection as well as non-precoded transmission, without being sensitive to CSI uncertainty.

Block diagonalization (BD) is a downlink multiuser multiple-input multiple-output (MIMO) strategy that utilizes transmitter channel state information (CSI). This paper proposes a practical uplink MIMO scheme for time division duplex (TDD) systems to co-exist with BD, so that the CSI of the BD channels is used also in the uplink transmission. It is shown that the precoded pilot symbols are sufficient in both uplink and downlink to satisfy the needs of both transmission and reception. The capacity of the system is analyzed in conjunction with linear receivers in a time-varying fading channel. According to the results the proposed uplink strategy provides capacity gain over non-precoded transmission, without being sensitive to Doppler.

Many performance evaluations for IEEE 802.11 distributed coordination function (DCF) have been formerly reported in the literature; most studies are based on saturation analysis, and a few models under a finite load condition adopt an M/G/l queuing system. However, using M/G/l queuing only considers the first moment of frame service time to derive the probability of transmission queue being vacant. In this paper, we model the DCF using Parallel Space-Time Markov Chain (PSTMC), in which frame arrivals are tracked by monitoring the transmission queue during transitions between successive states of the space-time Markov chain. The proposed framework provides the possibility of modeling the contention phase, backoff and post-backoff procedures, and the transmission queue status. The proposed framework is validated by the simulation results.

In this paper, using the so-called parallel space-time Markov chain (PSTMC) framework we analyze the IEEE 802.11 Distributed Coordination Function (DCF) frame service time, jitter, and queuing delay in a single-hop non-saturated wireless network. PSTMC framework provides the possibility of simultaneous modeling of backoff and post-backoff procedures, in addition to the transmission queue status of a non-saturated 802.11 station. To the best of our knowledge, the presented contribution is the first analysis of service time, i.e., access delay and retransmission delay, plus queuing delay at the same time, when the precise modeling of binary exponential backoff (BEB) scheme in medium access control (MAC) layer is the main issue of concern. The model is validated by extensive simulations, showing its remarkable level of accuracy.

Many performance evaluations for IEEE 802.11 distributed coordination function (DCF) have been formerly reported in the literature; most studies are based on saturation analysis, and a few models under a finite load condition adopt an M/G/l queuing system. However, using M/G/l queuing only considers the first moment of frame service time to derive the probability of transmission queue being vacant. In this paper, we model the DCF using parallel space-time Markov chain (PSTMC), in which frame arrivals are tracked by monitoring the transmission queue during transitions between successive states of the space-time Markov chain. The proposed framework provides the possibility of modeling the contention phase, backoff and post-backoff procedures, and the transmission queue status. The proposed framework is validated by the simulation results.

The uplink-downlink SINR duality theorem is a key tool which simplify substantially the problem of joint design of the linear transmit and receive beamformers in multiple-input multiple-output (MIMO) downlink channels. The theorem has been proved previously under the assumption that the cross- coupling matrix between the users is primitive or, alternatively, by postulating the nonnegativity of the resolvent. By using the Lagrange duality theory, we first give an alternative proof which holds for arbitrary cross-coupling matrices. The proof does not only extend the result to a larger set of practical applications, but it also reveal more insight on the sum power minimization problem under a set of minimum SINR requirements for data streams. As a practical application, we apply the uplink-downlink SINR duality to derive a general method for MIMO downlink linear transceiver optimization according to different system performance criteria, including weighted sum rate maximization, weighted sum mean square error minimization, and minimum SINR maximization. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The numerical simulations show that the sum rate achieved by the sum rate maximization algorithm is within 0.5-1.5 bits/second/Hz close to the sum capacity. When compared to the traditional zero forcing based solutions, the proposed method provides more than 4 dB SNR gain and up to 3.5 bits/sec/Hz better spectral efficiency.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. By exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. The simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient. Only one iteration of the proposed method is enough to substantially outperform the zero forcing based solution.

Opportunistic spectrum usage would enable enhancing the efficiency of existing and emerging wireless communication systems. One of the key issues related to those systems is spectrum opportunity estimation. In this paper, we are using a technique utilized earlier in narrowband signal detection, namely the localization algorithm based on double-thresholding (LAD), for sensing the existence of primary user signals in a cognitive radio systems. The LAD method requires no a priori information on the primary user statistics and it has a low computational complexity that will enable a low-cost real-time implementation. The LAD method is able to estimate the number of narrowband signals and their characteristics, including bandwidth and power. A simplified version of the LAD method which uses normalized thresholds (NT) as well as an enhancement of the scheme that uses adjacent cluster combining (ACC) are proposed. Simulation results show that the simplified version of the LAD method is useful in the considered situations, and the enhanced version of the LAD method improves the performance of the LAD and LAD NT methods significantly.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of linear transmit and receive beamformers, according to weighted sum rate maximization criteria. The proposed method can handle multiple antennas at the BS and at the mobile users with an arbitrary number of data streams per scheduled user. It can also handle a fairly general set of practical power constraints for the transmit beamformers, i.e., we can impose sum power constraints for different subsets of the transmit antennas.

Hidden terminal is one of the major problems in ad hoc networks. In addition, there are scenarios where the destination of interest is located in the radio range of other transmitters, so that the efforts on establishing data communication with this mobile station will fail due to collisions that may occur between transmitted control frames and undesired received control and data frames. This phenomenon becomes a bottleneck when most of data transmissions experience packet fragmentation. In such scenarios, the desired destination becomes unreachable during the data transfer of neighboring nodes. Using the same PHY of IEEE 802.11 and making slight modifications in its MAC, a new medium access control scheme is presented to address such problems. The performance of the proposed approach is compared with earlier schemes through simulation-based evaluations, showing performance enhancement due to considerable better handling of unreachability issues in the proposed MAC scheme

A novel low-complexity and provably convergent algorithm is proposed to find the sum capacity for vector Gaussian broadcast channels. Unlike the recently proposed sum-power constraint iterative waterfilling (SPC-IWF) algorithms, it has lower complexity and requires no additional precautions to ensure the convergence. We have proved analytically the convergence with probability one, and the computer simulations show that the proposed algorithm converges faster than the earlier variants of SPC-IWF algorithms. We formulate the problem in the context of a multiple-input multiple-output orthogonal frequency-division multiplexing system and discuss the simplifications provided by the block-diagonal channel structure

A MIMO broadcast channel with linear transceiver schemes is considered in this paper. The base station (BS) is equipped with a scheduler, using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS we propose an iterative algorithm for joint design of the transmit and the receive beamformers according to mean square error minimization criterion. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically non-tractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. There is no guarantee that the global optimum has been found due to the nonconvexity of the problem, but, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient

In this paper, a novel simple generalized Chase algorithm (GCA) based multiuser detector (MUD) is proposed for MC-CDMA systems. After getting the initial solution from the front-end frequency domain linear minimum mean-squared error (LMMSE) receiver, different Chase-type algorithms are adopted to extend the potential solution list. Among the list, the candidate which can achieve the minimum distance to the received signal, is selected as the final solution. Both bit- and symbol-level receivers for high level modulation schemes are considered. Compared to the conventional MMSE receiver, the proposed receiver can provide superior performance with little increase in complexity. Moreover, the robustness of the proposed receiver is studied by incorporating a semi-blind subspace based channel estimation.

Adaptive MIMO-OFDM systems employing eigenmode based signalling have a great potential to increase the spectral efficiency when the channel state information (CSI) is accurately known at transmitter (TX) side. However, the perfect CSI is a too strong assumption for a wireless system operating in frequency selective channels. In the presence of CSI errors, the eigenmodes orthogonality is lost and a spatial equalizer is used at each subcarrier to remove the inter-eigenmodes interference. In this paper we propose using a first order matrix inversion approximation (based on truncated Neumann expansion) to find an upper hound for the covariance matrix of the decision variable at equalizer's output. Based on this upper-bound we are able to find a new bit and power loading algorithm which maximizes the throughput subject to maximum transmit power and maximum frame error rate constraints. The effect of CSI errors on the achievable spectral efficiency is studied by computer simulations for different antenna correlation setups. The results clearly show that the proposed method is robust against CSI errors and channel spatial correlation. The achieved spectral efficiency at low and medium SNR is larger that the outage capacity with no CSI at TX side.

The performance of an interleaved multicarrier code-division multiple access system with a maximal-ratio combining receiver is studied with the help of the characteristic function of correlated Nakagami-m random variables. Computer simulations are used to demonstrate the accuracy of the analysis. Based on the analytical results, with an exhaustive search of different subcarrier interleaving schemes, the optimal subcarrier interleaver which provides the minimum bit-error rate (BER) can be achieved. Compared to a random interleaver, the optimal subcarrier interleaver offers significant performance improvement.

In this paper, a novel space–time-frequency minimum mean squared error (STF-MMSE)-based parallel interference cancellation receiver is proposed for space–time block-coded multicarrier code division multiple access systems in time-varying fading channels. The signal processing of this new detector is jointly implemented in space, time, and frequency domains, which leads to a powerful capability of combating interference coming from different sources. An adaptive implementation based on subspace estimation is proposed for slow-varying fading channels. Furthermore, based on the characteristic function of a complex Gaussian random vector, an analytical method to calculate the bit error probability of the proposed STF-MMSE receiver is presented. Representative examples of the detector are provided to demonstrate its superior performance.

In the bit-error rate (BER) analysis of code-division multiple-access (CDMA) systems, a Gaussian approximation is widely used to tackle the multiple access interference (MAI), although it does not always offer satisfactory accuracy. This paper investigates the BER performance of synchronous multicarrier (MC) CDMA systems over Nakagami-m-fading channels in a different way. We present an accurate and unified BER analysis for synchronous MC-CDMA systems. To facilitate our analysis, we assume a synchronous uplink, whose BER performance can be intuitively viewed as a lower BER bound of the more realistic asynchronous MC-CDMA. The basic idea is that, by using the Gauss-Chebyshev quadrature (GCQ) rule to perform inverse Laplace transform, an accurate BER can be numerically obtained from the moment generating function (MAG) of the output decision variable at a receiver, without any assumption about the MAI distribution. First, signals on all subcarriers of MC-CDMA systems are assumed to experience independent fading. Two standard diversity combining techniques, equal gain combining (EGC) and maximal ratio combining (MRC), are employed. The BER performance in both downlink and synchronous uplink is analyzed. We then consider a more general system model, in which signals on different subcarriers undergo correlated fading. The asymptotic (error floor) performance of downlink MC-CDMA with MRC is studied. In particular, we investigate the effects of spreading sequences and the delay spread of the channel on the system performance. Numerical examples are provided to show the main results of this paper. The accuracy of the GCQ and MGF based solution is verified by different approaches such as Monte Carlo integration and the exact residue method. In addition, the accuracy of the commonly used Gaussian approximation is also examined.

The knowledge of channel state information (CSI) at the transmitter (TX), which in case of time division duplex (TDD) can be easily obtained due to radio channel reciprocity, can dramatically increase the spectral efficiency of a multiple-input multiple-output (MIMO) system. This paper presents a robust link adaptation method for TDD systems employing MIMO-OFDM eigenmode based signalling. We propose a rather simple logarithm-free bit and power loading algorithm which requires low signalling overhead. For unbalanced MIMO systems with larger number of transmit than receive antennas, the achieved spectral efficiency at low and medium SNR is higher than the outage MlMO capacity with unknown CSI at the TX. The simulation results show that for a constant frame error rate, the throughput degradation comparing to the universally accepted Hughes-Hartogs algorithm is negligible.

We present a novel joint multiuser detection method based on sphere packing lattice decoding and semi-blind channel estimation for multicarrier code-division multiple-access (MC-CDMA) systems. After modelling MC-CDMA as a sphere packing lattice, a low-complexity maximum-likelihood (ML) detection, sphere decoding algorithm, is applied to jointly detect all users. The impacts of channel estimation errors are studied by incorporating a semi-blind subspace based channel estimation in the receiver. The selection of search radius and the complexity of the receiver are also investigated. Another promising detection technique, genetic algorithm (GA) based multiuser detector (MLJD) is also studied. Simulation results demonstrate the superior performance of the semi-blind receiver compared to the conventional receivers for MC-CDMA and its robustness to channel estimation errors.

Multicarrier code-division multiple-access (MC-CDMA) is a potentially attractive multiple access technique for future wireless communication systems. In this paper space-frequency minimum mean squared error based parallel interference cancellation receiver (SF-MMSE/PIC) is developed for MC-CDMA systems which produces the SF-MMSE combining as a byproduct. The signal processing of this new detector is jointly carried out in space and frequency domain which leads to the advantage of combating the interference from different sources simultaneously. Several representative simulation examples of the proposed SF-MMSE/PIC receiver are provided to demonstrate its powerful capability to suppress interference. In the fully loaded system, the proposed receiver can offer single user performance.

The bit error rate (BER) of an asynchronous multicarrier code-division multiple-access system in an additive white Gaussian noise channel is evaluated using Monte Carlo integration and moment-generating function methods. The BER performance for different families of spreading sequences is investigated. Numerical results show that the approach of a recently published paper (see Popovic, B.M., IEEE Trans. Common., vol.47, p.918-26, 1999) cannot give an accurate BER because the interference from other subcarriers has been omitted. Some new findings about the performance of different sequences are also presented.

In this paper, a new practical method for determining the bit-error rate (BER) of equal gain combining (EGC) receivers for multicarrier code-division multiple-access (MC-CDMA) systems is presented. This new method is based on an alternative Gaussian approximation (AGA) and it applies to the system with multiple active users in frequency selective Nakagami-m fading channels. This new procedure takes advantage of an alternate integral representation of the conditional BER along with characteristic functions. The AGA method has the advantage of simplicity in expression and computational efficiency. The accuracy of the proposed approach is demonstrated with computer simulations in a fading channel.

This paper studies in-band interference of different kind of ultra wideband (UWB) signals. UWB frequency spectra are produced by using several types of narrow pulse waveforms, all based on the Gaussian pulse. Due to the extremely wide bandwidth, these signals spread over the frequency bands allocated to other radio systems. In-band interference power is calculated over the IF bandwidths of two victim receivers as a function of pulse width. Also, the signal attenuation with distance is presented. The victim systems under the study are GPS (L1-band) and GSM900. Based on the results, the 3/sup rd/ derivative of a Gaussian pulse seems to be the best choice, from the interference point of view, among the waveforms included in this study. It causes less interference than the Gaussian pulse or Gaussian doublet if the pulse widths are shorter than 1 ns. The system utilizing the direct sequence concept caused less in-band interference power than the system utilizing time hopping amongst all waveforms studied. The studied UWB systems are based on bi-phase baseband data modulation.

Analytical results are presented for a direct sequence spread spectrum system code acquisition in a static multipath channel. The performance measure is the mean acquisition time. The constant false alarm rate criterion is used as threshold setting rule for comparator. The energies of the multipath components are combined already in the acquisition process by using a modified chip level post detection integration. Therefore, diversity combining in the acquisition is used. The method increases the probability of detection of the burst of multipath components, ie, it decreases the mean acquisition time for finding the existence of a multipath profile. The numerical results indicate that the method improves the acquisition performance significantly. Also total acquisition time, ie, time to acquire all the multipaths can be decreased by the proposed method.

A design method for recursive space-time trellis codes and parallel concatenated space-time turbo coded modulation was recently proposed as a new framework for building optimized, low-complexity, large equivalent constraint-length space-time trellis codes. In this paper the distance spectrum interpretation of the space-time turbo coded modulation is introduced to evaluate the union bound on the frame error rate over fast fading channels. The distance spectrum gives a good insight on the importance of multiplicities in the space-time coding design. Derived bounds are in some cases very tight and provide a designer with the practical tool for further optimization of the constituent space-time trellis codes within the space-time turbo coded modulation framework. Some interesting examples of the convergence of the sub-optimal iterative decoding were also presented.

This paper evaluates the level of interference caused by different ultra-wideband (UWB) signals to other various radio systems, as well as the performance degradation of UWB systems in the presence of narrowband interference and pulsed jamming. The in-band interference caused by a selection of UWB signals is calculated at GSM900, UMTS/wideband code-division multiple-access (WCDMA), and Global Position System (GPS) frequency bands as a function of the UWB pulsewidth. Several short-pulse waveforms, based on the Gaussian pulse, can be used to generate UWB transmission. The two UWB system concepts studied here are time hopping and direct sequence spread spectrum. Baseband binary pulse amplitude modulation is used as the data modulation scheme. Proper selection of pulse waveform and pulsewidth allows one to avoid some rejected frequency bands up to a certain limit. However, the pulse shape is also intertwined with the data rate demands. If short-pulses are used in UWB communication the high-pass filtered waveforms are preferred according to the results. The use of long pulses, however, favors the generic Gaussian waveform instead. An UWB system suffers most from narrowband systems if the narrowband interference and the nominal center frequency of the UWB signal overlap. This is proved by bit-error rate simulations in an additive white Gaussian noise (AWGN) channel with interference at global system for mobile communication (GSM) and UMTS/WCDMA frequencies.

A new bandwidth and power efficient signaling scheme is proposed that achieves high data rates over wideband radio channels exploiting the bandwidth efficient OFDM modulation, multiple transmit and receive antennas and large frequency selectivity offered in typical low mobility indoor environments. Owing to its maximum transmit diversity gain and large coding gain, space-frequency turbo coded modulation is demonstrated to perform within 2.5 dB of the 10% outage capacity for the variety of practical wideband MIMO radio channels, strongly out performing other space-frequency coding schemes proposed in literature. A simple way of combining space-frequency coding with OFDM delay diversity for cost effective further increase in bandwidth efficiency by exploiting more than two transmit antennas is also proposed.

The most important 3rd generation cellular communications standard is based on wideband CDMA (WCDMA). Receivers based on TDMA style channel equalization at the chip-level have been proposed for a WCDMA downlink employing long spreading sequences to ensure adequate performance even with a high number of active users. These receivers equalize the channel prior to the despreading, thus restoring the orthogonality of users and resulting in multiple access interference (MAI) suppression. In this paper four adaptive versions of chip-level channel equalizers are studied and their performance is evaluated in a Rayleigh fading multipath channel. The numerical results show a significant performance improvement over the conventional RAKE receiver with a large number of active users.

A new bandwidth and power efficient signaling scheme is proposed that achieves high data rates over wideband radio channels exploiting the bandwidth efficient OFDM modulation, multiple transmit and receive antennas and large frequency selectivity offered in typical low mobility indoor environments. Owing to its maximum transmit diversity gain and large coding gain, space-frequency turbo coded modulation is demonstrated to perform within 2.5 dB of the 10% outage capacity for the variety of practical wideband MIMO radio channels, strongly outperforming other space-frequency coding schemes recently proposed in the literature. A simple way of combining space-frequency coding with OFDM delay diversity for cost effective exploitation of more than two transmit antennas is also proposed.

A hard-decision parallel interference cancellation receiver suitable for closed-loop power controlled CDMA uplink is introduced. Receiver algorithms for cancellation and SIR estimation are presented. A simple model for inter-cell interference is proposed to gain insight into the detector performance in cellular CDMA networks. Multiantenna diversity reception with maximal-ratio combining is employed in reception. The receiver performance is evaluated in fading multipath environments with simulations. Performance gains with regard to the conventional receiver are found in both pico- and macro-cellular environments. Gain from cancellation is found to be larger in more rapidly fading vehicular environments, where power control has less effect on the fluctuation of the received power. Results suggest the utilisation of the near-far robustness of the HD-PIC in power control algorithm design.

With the finalization of new series of IEEE 802.11 and ETSI HIPERLAN standards, it becomes very important and interesting to study the methods to integrate geolocation functionalities into the next generation wireless LANs. We investigate geolocation methods and system architectures using OFDM signals in HIPERLAN/2 wireless LANs. We propose a novel method to measure geolocation metrics by exploiting the HIPERLAN/2 MAC frame structure. Computer simulation results are presented to show the performance of the geolocation systems using OFDM signals.

This paper examines the related issues of capacity, coverage and power control in a multi-cell WCDMA network. The network capacity in this case is based on required link quality thresholds. If all users achieve the required link SIR targets for a given percentage of all power control time slots, the network is deemed not to have exceeded its capacity. When this is 50%, the analysis reverts to the conventional mean SIR target performance. For network operators however a substantially higher link quality requirement is likely. In this case, 95% is considered. This means all users must achieve a SIR of greater than or equal to the target value for 95% of the time. The analysis is based on extended models used for IS-95 systems which relies on Gaussian assumptions of interference. This approach gives an insight into the effects of varying traffic types, different allowances for SIR targets and mixed traffic. This paper presents analysis which allows the minimum required power control to be determined based on network load and traffic mix. It also presents analysis which allows the impact of mobile by mobile power control to be examined.

The linear minimum mean-squared-error (LMMSE) criterion can be used to obtain near-far resistant receivers in direct-sequence code-division multiple-access systems. The standard version of the LMMSE receiver (postcombining LMMSE) minimizes the mean-squared error between the filter output and the true transmitted data sequence. Since the detector depends on the channel coefficients of all users, it cannot be implemented adaptively in fading channels due to severe tracking problems. A modified criterion for deriving LMMSE receivers (precombining LMMSE) in fading channels is presented. The precombining LMMSE receiver is independent of the users' complex channel coefficients, and it effectively converts the time-varying Rayleigh fading channel to an equivalent fixed additive white Gaussian noise channel from the point of view of updating the detector. The performance of the LMMSE receivers in fading channels is studied via computer simulations and numerical analysis. The results show that the postcombining LMMSE receiver has potentially larger capacity, but it cannot be used in fast fading channels. The precombining LMMSE receiver has slightly worse capacity than the postcombining LMMSE receiver, but remarkably larger capacity than the conventional RAKE receiver at the signal-to-noise ratios of practical interest.

Bit error probability of linear receivers for multiuser communications is analysed. The results can be applied both for centralised multiuser or joint receivers as well as for decentralised single-user receivers. The analysis is presented for AWGN and known or perfectly estimated fading multipath channels. The analysis is also extended to the estimated fading multipath channel case with data-aided (DA) channel estimation. Two ways to approximate the averaging over the interfering data symbol combinations are considered and compared. One is a semi-analytic method, where only part of the interfering data symbol combinations are taken into consideration in the averaging. The other is the Gaussian approximation. In addition to MAI, the analysis directly incorporates the impact of the interpath interference (IPI) or inter symbol interference (ISI) to the receiver performance. Numerical examples are presented to demonstrate the usefulness of the method.

Parallel interference cancellation (PIC) receivers are among the most promising receiver techniques for future code-division multiple-access (CDMA) systems. Interference cancellation efficiency relies on the knowledge of the number of users and propagation paths needed in multiple-access interference (MAI) estimation. In practice, the exact number of users and paths is not known exactly, e.g., due to inter-cell interference, unknown propagation paths or new users trying to connect to the base station. As a result, there will be some residual interference even with perfect cancellation of the known signal components. In many cases, the inter-cell interference can be large enough to significantly degrade the performance of the PIC receivers. For that reason, residual interference suppression is crucial to guarantee that the PIC receivers can operate reliably. Residual interference can be reduced by applying adaptive antennas to PIC receivers. In this paper, another possibility is considered. The approach taken is to combine PIC receivers with blind adaptive interference suppression techniques in single sensor receivers.

The bit-error probability of a linear receiver for code-division multiple-access communications is analyzed. The analysis is presented for the additive white Gaussian noise and fading multipath channels also with data-aided channel estimation. Two ways to approximate the averaging over the interfering data symbol combinations are considered and compared. Numerical examples are presented to demonstrate the usefulness of the analysis methods.

This paper introduces analytical results for a DS spread spectrum system with matched filter code acquisition in a fixed multipath channel. The performance measure is the mean acquisition time. Constant false alarm rate criteria are used as the threshold setting rule for the comparator. The energies of the multipath components are combined already in the acquisition process by using a chip level post detection integration. The method increases the probability of detection of the burst of multipath components, i.e., it decreases the mean acquisition time for finding the existence of the multipath profile. The numerical results indicate that the method improves the acquisition performance significantly, without requiring knowledge of the number of multipath components.

In this paper, the performance of some code acquisition schemes are studied in the downlink of DS-CDMA systems. The conventional matched filter, the minimum variance method, the MUSIC algorithms and the eigenvector method based delay acquisition schemes are studied in a frequency-selective fading channel. The three last algorithms are based on different estimation schemes for the inverse of the sample-covariance matrix. Based on the results, the minimum variance based method can be used to improve the acquisition performance in comparison to the conventional non-coherent matched filter method. The MUSIC algorithm becomes useless in highly loaded systems. The best performance, however, was obtained by using a longer despreading interval in the conventional delay estimator. Unfortunately, it would require either unmodulated pilot channel or a control channel with lower data rate, which may not be available in all applications.

Linear minimum mean squared error (LMMSE) criterion can be used to obtain near-far resistant receivers in direct-sequence code-division multiple-access (DS-CDMA) systems. The standard version of the LMMSE detector depends on the channel coefficients of all users and cannot be used in frequency-selective fading channels due to fast changes of channel phases which demands for continuous detector updating. In this paper, a modified criterion for deriving LMMSE receivers in fading channels is used, which effectively converts the pathological Rayleigh fading channel to an equivalent fixed AWGN channel from the detector point of view. Thus, the detector is independent from the user's complex channel coefficients. Adaptive versions of the modified LMMSE receiver require estimation of channel delays and complex coefficients. An adaptive least mean squares (LMS) algorithm for DS-CDMA downlink receivers utilizing a pilot channel for channel estimation is presented as an example of a practical implementation of the scheme. The numerical results show that the modified criterion can be applied in downlink receivers in fading channels and the adaptive version converges close to the optimal LMMSE solution.

Near-far resistant data detectors for frequency selective fading channels are proposed for the uplink of code-division multiple-access (CDMA) mobile communication systems. Parallel interference cancellation (PIC) is used to suppress the multiple-access interference (MAI) in the receiver. In order to improve the MAI estimates, the multistage principle is used. In this paper, three main issues have been studied: the effect of feeding back the channel estimates from the last stage with a different number of cancellation stages, the hang-up phenomenon as well as means to avoid that in PIC receivers, and the impact of channel estimators on the receiver performance. The PIC based multistage receiver developed in the course of these studies had good performance in fading channels according to the simulation results.

A near-far resistant iterative algorithm for multiuser signature sequence delay estimation is presented. It is shown that the maximum-likelihood delay estimator can be replaced by the so-called blind ML delay estimator which depends on delays only, not on transmitted symbols and complex channel coefficients. A variable projection type algorithm is presented to compute the blind ML estimator in an iterative way. According to the simulation results, the delays for all users can be estimated reliably both in AWGN and fading channels by using the scheme being proposed.

The performance analysis of a new concept of a code-division multiple-access (CDMA) based low Earth orbit (LEO) satellite network for mobile satellite communications is presented and discussed. The starting point was to analyze the feasibility of implementing multisatellite and multipath diversity reception in a CDMA network for LEO satellites. The results are used to specify the design parameters for a system experimental test bed. Due to the extremely high Doppler, which is characteristic of LEO satellites, code acquisition is significantly simplified by using a continuous wave (CW) pilot carrier for Doppler estimation and compensation. The basic elements for the analysis presented are: the channel model, the pilot carrier frequency estimation for Doppler compensation, and multipath and multisatellite diversity combining.