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.

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.

In unmanned aerial vehicle (UAV)-assisted wake-up radio (WuR)-enabled internet of things (IoT) networks, UAVs can instantly activate the main radios (MRs) of the sensor nodes (SNs) with a wake-up call (WuC) for efficient data collection in mission-driven data collection scenarios. However, the spontaneous response of numerous SNs to the UAV’s WuC can lead to significant packet loss and collisions, as WuR does not exhibit its superiority for high-traffic loads. To address this challenge, we propose an innovative receiver-initiated WuR UAV-assisted clustering (RI-WuR-UAC) medium access control (MAC) protocol to achieve low latency and high reliability in ultra-low power consumption applications. We model the proposed protocol using the M / G / 1 / 2 queuing framework and derive expressions for key performance metrics, i.e., channel busyness probability, probability of successful clustering, average SN energy consumption, and average transmission delay. The RI-WuR-UAC protocol employs three distinct data flow models, tailored to different network traffic scenarios, which perform three different MAC mechanisms: channel assessment (CCA) clustering for light traffic loads, backoff plus CCA clustering for dense and heavy traffic, and adaptive clustering for variable traffic loads. Simulation results demonstrate that the RI-WuR-UAC protocol significantly outperforms the benchmark sub-carrier modulation clustering protocol. By varying the network load, we capture the trade-offs among the performance metrics, showcasing the superior efficiency and reliability of the RI-WuR-UAC protocol.

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.

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.

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.

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.

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 (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.

In designing ultra-reliable low-latency communication (URLLC) services in 5G-and-beyond systems, link adaptation (LA) plays a vital role in adjusting transmission parameters under channel and interference dynamics. Without capturing such dynamics (e.g., relying on average estimates), the LA algorithms fail to simultaneously meet the strict reliability and latency bounds of mission-critical applications. To this end, this paper focuses on interference prediction-based adaptive resource allocation of one-shot URLLC transmission, wherein our solution deviates from the conventional average-based interference estimation schemes. We predict the next interference value based on the interference distribution estimation using a discrete-time Markov chain (DTMC). Further, to exploit the time correlation of each interference source, we model the correlated interference variations as a second-order DTMC to achieve higher prediction accuracy. While accounting for the risk sensitivity of interference estimates, the prediction outcome is then used for appropriate resource allocation of a URLLC transmission under link outage constraints. We evaluate the complete solution, given in the form of an algorithm, using Monte-Carlo simulations, and compare it with the first-order baseline counterpart. The analysis shows that the second-order interference estimate can fulfill the target outage as low as 10–7and improve the outage probability more than ten times in some scenarios compared to the baseline scheme while keeping the same amount of resource usage.

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.

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.

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.

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.

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.

5G networks should support heterogeneous services with an efficient usage of the radio resources, while meeting the distinct requirements of each service class. We consider the problem of multiplexing enhanced mobile broadband (eMBB) traffic, and grant-free ultra-reliable low-latency communications (URLLC) in uplink. Two multiplexing options are considered; either eMBB and grant-free URLLC are transmitted in separate frequency bands to avoid their mutual interference, or both traffic share the available bandwidth leading to overlaying transmissions. This work presents an approach to evaluate the supported loads for URLLC and eMBB in different operation regimes. Minimum mean square error receivers with and without successive interference cancellation (SIC) are considered in Rayleigh fading channels. The outage probability is derived and the achievable transmission rates are obtained based on that. The analysis with 5G new radio assumptions shows that overlaying is mostly beneficial when SIC is employed in medium to high SNR scenarios or, in some cases, with low URLLC load. Otherwise, the use of separate bands supports higher loads for both services simultaneously. Practical insights based on the approach are discussed.

We address the problem of resource allocation and packet scheduling for a mixture of ultra- reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) traffic in a fifth generation New Radio (5G NR) networks. A novel resource allocation method is presented that is latency, control channel, hybrid automatic repeat request (HARQ), and radio channel aware in determining the transmission resources for different users. This is of high importance for the scheduling of URLLC users in order to minimize their latency, avoid unnecessary costly segmentation of URLLC payloads over multiple transmissions, and benefit from radio channel aware multi-user diversity mechanisms. The performance of the proposed algorithm is evaluated with an advanced 5G NR compliant system level simulator with a high degree of realism. Simulation results show promising gains of up to 98% latency improvement for URLLC traffic and 12% eMBB end-user throughput enhancement as compared to conventional proportional fair scheduling.

In this paper, we discuss the achievable transmission rates over collision-prone radio resources shared by a number of devices, representative of novel Internet-of-Things (IoT) scenarios. We consider Maximum Ratio Combining (MRC) and Minimum Mean Square Error (MMSE) receivers at the base station, and derive the relationship between target failure probability and saturation rate, which represents the maximum achievable rate over shared resources in the interference limited regime. MRC receiver is shown to be sensitive to the presence of statistically relevant interferers operating over the same resources, rapidly leading to rate saturation. The MMSE receiver adds a tier of protection to collisions thanks to its interference suppression capabilities, suffering for a rate penalty only in case of a high number of users. A realistic system analysis in an indoor hotspot scenario validates the analytical trends and suggests insights on practical link adaptation strategies.

A centralized joint cell selection and scheduling policy for ultra-reliable low latency communication (URLLC) is studied in this paper for the 5G new radio (NR). A low complexity cell association and scheduling algorithm is proposed, while maintaining attractive performance benefits. By being able to centrally control from which cells the different users are instantaneously scheduled, we show that the undesirable queuing delays for URLLC traffic can be significantly reduced. The proposed solution is evaluated in a realistic multi-cell, multi-user, dynamic network setting in line with the 5G NR system design specifications, and calibrated against 3GPP NR assumptions. The presented performance results show promising gains, where the proposed centralized solution can accommodate 38% higher traffic offered load than a traditional distributed network implementation, while still fulfilling the challenging reliability and latency targets for URLLC.

Full duplex (FD) communication promises significant performance gains under ideal network settings. Generally, it has been shown that the throughput and delay gains of FD communication are somewhat limited in realistic conditions, leading researchers to study other possible applications where significantly higher gains over half duplex communication can be availed. The potential of FD nodes in improving the physical layer security of a communication link is investigated in this contribution. Specifically, we present a thorough analysis of the achievable secrecy rate for a transceiver pair in FD mode in the presence of a passive eavesdropper assuming a generic system model. The ergodic secrecy rate with FD communication is found to grow linearly with the log of the direct channel signal to noise ratio as opposed to the flattened out secrecy rate with conventional half duplex communication and irrespective of the eavesdropper channel strengths.

Multiple transmit and receive antennas can be used to increase the number of independent streams between a transmitter-receiver pair, or to improve the interference resilience property with the help of linear minimum mean squared error (MMSE) receivers. An interference aware inter-cell rank coordination framework for the future fifth generation wireless system is proposed in this article. The proposal utilizes results from random matrix theory to estimate the mean signal-to-interference-plus-noise ratio at the MMSE receiver. In addition, a game-theoretic interference pricing measure is introduced as an inter-cell interference management mechanism to balance the spatial multiplexing vs. interference resilience trade-off. Exhaustive Monte Carlo simulations results demonstrating the performance of the proposed algorithm indicate a gain of around 40% over conventional non interference-aware schemes; and within around 6% of the optimum performance obtained using a brute-force exhaustive search algorithm.

Full duplex communication promises system performance improvement over conventional half duplex communication by allowing simultaneous transmission and reception. However, such concurrent communication results in strong self interference and an increase in the overall network interference, and can only be exploited when traffic is available in both directions. The potential throughput gains of full duplex communication over conventional half duplex transmission in a small cell network with asymmetric traffic conditions is investigated in this contribution. The throughput performance gains are analysed using tools from stochastic geometry, and further confirmed through extensive system level simulations. Our findings explicitly quantify how the gains from full duplex communication depend on the traffic profile and the inter-cell interference coupling. The demonstrated throughput gains and delay reduction make full duplex communication an attractive potential technology component for the fifth generation dense small cell cellular system.

In this paper, we present an inter-cell interference subspace coordination scheme for multiple-input multiple-output communications. The method relies on downlink precoding design for distributed multi-cell multi-user networks. In the proposed method, receivers benefit from minimum-mean square error structure. Each receiver separates the received signal space into desired/interference sub-spaces. The key idea in this contribution is to employ precoding algorithms at the transmission-end with the objective of jointly projecting transmitted signal over desired subspace and aligning most of the interference onto predefined interference subspaces at the interfered receiver end. This idea works with only local channel state information available at transmitter-side and benefits from low computational complexity. Simulation results show that the proposed method offers about 28\% throughput enhancements in networks with high dominant interference regimes.

Big expectations are put into vehicular communications (V2X) for a safer and more intelligent driving. With human lives at risk, the system cannot afford to fail, which translates into very stringent reliability and latency requirements to the radio network. One of the challenges is to find efficient radio resource management (RRM) strategies for direct vehicle-to-vehicle (V2V) communication that can fulfil the requirements even with high traffic density. In cellular networks, a device-to-device (D2D) communication is usually split into two phases: the discovery process, for node awareness of each other; and the communication phase itself, where data exchange takes place. In the case of V2V, the discovery phase can utilize the status information that cars broadcast periodically as the beacons to detect the presence of neighbouring cars. For the delivery of specific messages (e.g., a hazardous event), direct communications can then be set up in a very fast way, based on the previously collected information. Several aspects have been revealed as essential to ensure the reliability and latency requirements of the discovery, such as the autonomous selection of the resources, the duplexing mode and the interference cancellation receivers

Full-duplex communication enables simultaneous transmission from both ends of a communication link, thereby promising significant performance gains. Generally, it has been shown that the throughput and delay gains of full-duplex communication are somewhat limited in realistic network settings, leading researchers to study other possible applications that can accord higher gains. The potential of full-duplex communication in improving the physical-layer security of a communication link is investigated in this contribution. We specifically present a thorough analysis of the achievable ergodic secrecy rate and the secrecy degrees of freedom with full-duplex communication in the presence of a half-duplex eavesdropper node, with both single-user decoding and multi-user decoding capabilities. For the latter case, an eavesdropper with successive interference cancellation and joint decoding capabilities is assumed. Irrespective of the eavesdropper capabilities and channel strengths, the ergodic secrecy rate with full-duplex communication is found to grow linearly with the log of the direct channel signal-to-noise-ratio (SNR) as opposed to the flattened out secrecy rate with conventional half-duplex communication. Consequently, the secrecy degrees of freedom with full-duplex is shown to be two as opposed to that of zero in half-duplex mode.

Full duplex transmission is envisioned as one of the potential breakthrough for a 5th Generation (5G) radio access technology. In this paper, we evaluate the performance of full duplex transmission in a network of dense small cells by using real channel measurements. A large measurement campaign is carried out in an indoor office and an open hall scenario, and such measurements are then fed to a system level simulator for offline analysis. Cell throughput results show that the promised 100% performance gain of full duplex over traditional half duplex transmission is significantly jeopardized by the increase of the intercell interference due to the simultaneous transmission and reception, and is further reduced in case of low cell activity factor.

Research in 5G has so far been aimed towards laying out a conceptual vision and the engineering requirements. The focus is now shifting towards standardization through evaluation of potential solutions. 5G wireless communication system is expected to serve a diverse range of services with different design requirements. Dense small cells with multiple antenna nodes are believed to be key elements in meeting these challenging requirements. 5G will thus feature an adaptable air interface with carefully designed radio resource management techniques that can optimize each link according to its service requirements. This article provides an overview of key radio resource management techniques for 5G dense small cells and demonstrates how these techniques can contribute to fulfilling some of the important 5G requirements. Preliminary system level simulation results indicate that a mean throughput gain of around 63 %, and up to 84 % in latency reduction can be achieved utilizing the discussed resource management techniques.

Given the recent advances in system and antenna design, practical implementation of full duplex (FD) communication is becoming increasingly feasible. In this paper, the potential of FD in enhancing the performance of 5thgeneration (5G) ultra-dense small cell networks is investigated. The goal is to understand whether FD is able to boost the system performance from a throughput and delay perspective. The impact of having symmetric and asymmetric finite buffer traffic is studied for two types of FD: when only the base station is FD capable, and when both the user equipment and base station are FD nodes. System level results indicate that there is a trade- off between multiple-input multiple-output (MIMO) spatial multiplexing and FD in achieving the optimal system performance. Moreover, results show that FD may be useful for asymmetric traffic applications where the lightly loaded link requires high level performance. In such cases, FD can provide an average improvement of up to 116% in session throughput and 77% in packet delay compared to conventional half duplex transmissions.

Full duplex (FD) communication has attracted the attention of the industry and the academia as an important feature in the design of the future 5th generation (5G) wireless communication system. Such technology allows a device to simultaneously transmit and receive in the same frequency band, with the potential of providing higher throughput and lower latency compared to traditional half duplex (HD) systems. In this paper, the interaction between Transport Control Protocol (TCP) and FD in 5G ultra- dense small cell networks is studied. TCP is a well- known transport layer protocol for providing reliability, which comes at the price of increased delay and reduced system throughput. FD is expected to accelerate the TCP congestion control mechanism and hence mitigate such consequences. System level results show that FD can outperform HD and alleviate the TCP drawbacks when the inter-cell interference is not the main limiting factor. On the other hand, under strong inter-cell interference, results show that the capabilities of the system to cope with such interference dictates the gain that FD may provide over HD.

Full duplex communication promises a theoretical 100% throughput gain by doubling the number of simultaneous transmissions. Such compelling gains are conditioned on perfect cancellation of the self interference power resulting from simultaneous transmission and reception. Generally, self interference power is modelled as a noise-like constant level interference floor. However, experimental validations have shown that the self interference power is in practice a random variable depending on a number of factors such as the surrounding wireless environment and the degree of interference cancellation. In this study, we derive an analytical model for the residual self interference power, and demonstrate various applications of the derived model in analysing the performance of a Full Duplex radio. In general, full duplex communication is found to provide only modest throughput gains over half duplex communication in a dense network scenario with practical self interference cancellation models.

The further densification of small cells impose high and undesirable levels of inter-cell interference. Multiple Input Multiple Output (MIMO) systems along with advanced receiver techniques provide us with extra degrees of freedom to combat such a problem. With such tools, rank adaptation algorithms allow us to use our antenna resources to either exploit multiple spatial streams in low interference scenarios, or suppress interference in high interference scenarios. In this paper, we propose and evaluate an interference-aware distributed rank adaptation algorithm. The concept behind our approach is to discourage the choice of transmitting with multiple spatial streams in highly interfered scenarios, and to exploit and encourage the usage of multiple spatial transmission streams in low interference scenarios. We show that our proposed algorithm can be adjusted to preserve and guarantee a good outage performance, while providing the benefit of higher average throughputs, in both low and highly interfered scenarios, when compared to fixed rank configurations, and distributed selfish schemes.

Full duplex communication promises a 100% throughput gain by doubling the number of simultaneous transmissions. In a multi-cell scenario, increasing the number of simultaneous transmissions correspondingly increases the number of interference streams observed at a particular receiver. As such, the potential throughput gain may not be 100% as promised. In this study, we evaluate the performance of full duplex communication in a dense small cell scenario as targeted by future 5th Generation (5G) radio access technology under the ideal assumptions of a full buffer, always active traffic model and perfect self interference cancellation. Advanced interference suppression/cancellation receivers are featured as well. Full duplex communication is found to provide about 30-40% mean throughput gain over half duplex transmissions for indoor scenarios, which provides an indication of the maximum throughput gains that can be achieved with full duplex communication in indoor scenarios under such idealized assumptions.

Full duplex (FD) communication promise of doubling the throughput of half duplex (HD) communication makes such type of system an attractive solution to cope with the expected mobile data traffic increase. Nevertheless, simultaneous transmission and reception in dense deployment scenarios increases the inter- cell interference compared to a traditional HD communication, due to a larger number of nodes simultaneously transmitting. Moreover, FD communication can only be exploited when there is traffic in both uplink and downlink directions simultaneously. In this paper, FD communication is studied within the framework of 5th generation (5G) small cell systems in order to address its effective gain in such specific scenarios. The factors that affect FD performance are analysed, and its performance is evaluated against a traditional HD communication. System level simulations show that the gain of FD over HD in the considered scenarios is lower than the expected 100% gain, with a strong dependency on the traffic and the interference conditions.

Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank adaptation algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank adaptation algorithm based on an estimate of the mean signal-to-interference-plus- noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank adaptation method.

Ultra-dense small cells are foreseen to play an essential role in the 5th generation (5G) of mobile radio access technology, which will be operating over different bands with respect to established systems. The natural step for exploring new spectrum is to look into the centimeter-wave bands as well as exploring millimeter-wave bands. This paper presents our vision on the technology components for a 5G centimeter-wave concept for ultra-dense small cells. Fundamental features such as optimized short frame structure, multi-antenna technologies, interference rejection, rank adaptation and dynamic scheduling of uplink/downlink transmission are discussed, along with the design of a novel flexible waveform and energy-saving enablers.

The usage of Minimum Mean Square Error - Interference Rejection Combining (MMSE-IRC) receivers is expected to be a significant performance booster in the ultra-dense deployment of small cells envisioned by an upcoming 5th generation (5G) Radio Access Technology (RAT). However, hardware limitations of the radio- frequency front-end and poor covariance matrix estimation may severely compromise its ideal gains. In this paper, we evaluate the network performance of MMSE-IRC receivers by including the effects of the receiver imperfections as well as realistic covariance matrix estimates. System level simulation results confirm that a realistic MMSE-IRC receiver can achieve throughput gains close to ideal, provided a reasonably high resolution Analog-to-Digital Converter (ADC) as well as a supportive radio frame format design are used.

The ideal successive interference cancellation paradigm helps to achieve the capacity of some multiuser channels, such as the Gaussian multiple access and broadcast channels. However, its performance is much more modest under realistic constraint on the decodability of the interference signal. In this paper, we rely on basic stochastic geometry models to analytically evaluate the performance of interference cancellation receivers in a local area network scenario, under `realistic' rate-constraints on the decodability of the interference signal. Analytical findings are validated by extensive Monte Carlo experiments. Alongside, complementary system level simulations results are presented to demonstrate the performance in a `practical'-like system. Our findings explicitly quantify how the gains from interference cancellation techniques depend on the spatial density of the active interferers in the network, and their respective data rates. The findings further highlight the importance of properly dimensioning the system in order to fully benefit from such interference cancellation techniques.

Beyond 4th Generation (B4G) local area networks will be characterized by the dense uncoordinated deployment of small cells. This paper shows that inter-cell interference, which is a main limiting factor in such networks, can be effectively contained using Interference Rejection Combining (IRC). By simulation we investigate two significantly different interference scenarios with dense small cell deployment. The results show that IRC brings considerable improvement in outage as well as in peak and median throughputs in both scenarios, and thus has a big potential as a capacity and coverage enhancing technique for B4G. The IRC gain mechanism depends strongly on the interference scenario and to some extent on the use of frequency reuse. These results are achieved with no coordination between cells and suggests that MIMO rank adaptation and IRC can be done independently.

Cooperative communication is a promising strategy to enhance the performance of a communication network as it helps to improve the coverage area and the outage performance. However, such enhancement comes at the expense of increased resource utilization, which is undesirable; more so in the case of opportunistic wireless systems such as cognitive radio networks. In order to balance the performance gains from cooperative communication against the possible over-utilization of resources, we propose and analyze an adaptive-cooperation technique for underlay cognitive radio networks, termed as hybrid-cooperation. Under the proposed cooperation scheme, secondary users in a cognitive radio network cooperate adaptively to enhance the spectral efficiency and the error performance of the network. The bit error rate, the spectral efficiency and the outage performance of the network under the proposed hybrid cooperation scheme with amplify-and-forward relaying are analyzed in this paper, and compared against conventional cooperation technique. Findings of the analytical performance analyses are further validated numerically through selected computer-based Monte-Carlo simulations. The proposed scheme is found to achieve significantly better performance in terms of the spectral efficiency and the bit error rate, compared to the conventional amplify-and-forward cooperation scheme.

In an underlay Cognitive Radio Network, multiple secondary users coexist geographically and spectrally with multiple primary users under a constraint on the maximum received interference power at the primary receivers. Given such a setting, one may ask “how to achieve maximum utility benefit at the secondary users given the imposed interference temperature constraint”? In an attempt at answering this question, we introduce a measure of the marginal secondary utility per unit primary interference (termed as relative rate utility) and propose a distributed algorithm that tries to maximize this measure. We present selected computer based Monte-Carlo simulation results, demonstrating the effectiveness of the introduced measure, and the improved performance of the proposed algorithm.

The impact of high power amplifier (HPA) on the performance of orthogonal frequency division multiplexing (OFDM) based wireless systems using link adaptation (LA) is analyzed in this work. LA maximizes throughput while maintaining a target bit error rate (BER) at the receiver. The non linear behaviour of the HPA introduces distortion in the OFDM signal which has high peak to average power ratio (PAPR). It is found in this work that this causes LA schemes to fail to meet the target BER. It is shown in this work that to make the LA system satisfy the BER constraint, the signal to noise ratio thresholds for changing the adaptive modulation needs to be updated.