Reconfigurable intelligent surfaces (RISs) are eminently suitable for improving the reliability of wireless communications by jointly designing the active beamforming at the base station (BS) and the passive beamforming at the RIS. Therefore, the accuracy of channel estimation is crucial for RIS-aided systems. The challenge is that only the cascaded two-hop channel spanning from the user equipments (UEs) to the RIS and spanning from the RIS to the BS can be estimated, due to the lack of active radio frequency (RF) chains at RIS elements, which leads to high pilot overhead. In this paper, we propose a low-overhead linear minimum mean square error (LMMSE) channel estimation method by exploiting the spatial correlation of channel links, which strikes a trade-off between the pilot overhead and the channel estimation accuracy. Moreover, we calculate the theoretical normalized mean square error (MSE) for our channel estimation method. Finally, we verify numerically that the proposed LMMSE estimator has lower MSE than the state-of-the-art (SoA) grouping based estimators.

Integrated Sensing and Communications (ISAC) is a new usage scenario for 6G. To enable studying ISAC solutions, standardization bodies such as European Telecommunications Standards Institute (ETSI) and Third Generation Partnership Project (3GPP) require specified ISAC channel models. 3GPP has been studying changes to the existing TR 38.901 channel models to model radio-frequency (RF) signal interactions between the transmitter, sensing target, receiver, and surrounding environment. In parallel, ETSI has been working on additional features to further support channel modelling for the rich variety of ISAC use cases envisioned in 6G. In this paper, we present an outline of the latest developments from industry and further provide an evaluation framework for assessing the models.

Reconfigurable holographic surfaces (RHSs) constitute a promising technique of supporting energy-efficient communications. In this paper, we formulate the energy efficiency maximization problem of the switch-controlled RHS-aided beamforming architecture by alternately optimizing the holographic beamformer at the RHS, the digital beamformer, the total transmit power and the power sharing ratio of each user. Specifically, to deal with this challenging non-convex optimization problem, we decouple it into three sub-problems. Firstly, the coefficients of RHS elements responsible for the holographic beamformer are optimized to maximize the sum of the eigen-channel gains of all users by our proposed low-complexity eigen-decomposition (ED) method. Then, the digital beamformer is designed by the singular value decomposition (SVD) method to support multi-user information transfer. Finally, the total transmit power and the power sharing ratio are alternately optimized, while considering the effect of transceiver hardware impairments (HWI). We theoretically derive the spectral efficiency and energy efficiency performance upper bound for the RHS-based beamforming architectures in the presence of HWIs. Our simulation results show that the switch-controlled RHS-aided beamforming architecture achieves higher energy efficiency than the conventional fully digital beamformer and the hybrid beamformer based on phase shift arrays (PSA). Moreover, considering the effect of HWI in the beamforming design can bring about further energy efficiency enhancements.

In this paper, we investigate channel estimation in a reconfigurable intelligent surface (RIS) assisted multi-user network while taking the mobility of users into consideration. Based on a time-varying channel model, we utilize Kalman filter (KF) that is able to exploit temporal correlation to track cascaded channel. In order to maintain a relatively low pilot overhead, we present a multiple sub-phases based transmission protocol where the number of pilot sequences in each sub-phase is less than the number of users, i.e., pilot contamination exists. For the sake of practicality, we directly utilize discrete Fourier transform (DFT) matrix as phase shift matrix. We analyze normalized mean square error and provide some asymptotic results. A more practical scenario with hardware impairments (HWI) at the transceiver and the RIS is also considered. Since HWI is also part of the measurement matrix and is unknown to the base station, we propose a joint estimation of the channel and HWI. Under this joint estimation framework, the underlying state space model becomes nonlinear. We develop an extended KF (EKF) algorithm to tackle the nonlinearity through which the model can be linearized. Numerical results show that the proposed KF and EKF algorithms outperform benchmark schemes under various scenarios.

Reconfigurable intelligent surfaces (RISs) are capable of enhancing the wireless propagation environment of the future Internet of Things (IoT). Recently, they have also been configured as a transmitter to realize information modulation at low hardware complexity. In this article, we conceive a transmitter relying on a single radio frequency (RF) chain for low-complexity RIS-aided multiuser downlink communication. More explicitly, in the proposed architecture, the multiuser information is transmit precoded and modulated at the RIS by appropriately configuring the phase shift and amplitude of each RIS element. We assume that the distribution of multiple users obeys on a Poisson point process (PPP), where we jointly optimize the total power reflected from the RIS and the power allocation fraction assigned to each user, under the practical constraint of a realistic amplitude limitation of each RIS element. Additionally, we theoretically analyze the ergodic rate, symbol error probability, outage probability, and coverage range of the proposed RIS-aided single-RF downlink and confirm the accuracy of our analysis by simulations. Finally, we compare its performance to that of the conventional multiple-input-multiple-output (MIMO) systems employing multiple RF-chains.

Reconfigurable intelligent surfaces (RIS) are capable of beneficially ameliorating the propagation environment by appropriately controlling the passive reflecting elements. To extend the coverage area, the concept of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has been proposed, yielding supporting 360° coverage user equipment (UE) located on both sides of the RIS. In this paper, we theoretically formulate the ergodic sum-rate of the STAR-RIS assisted non-orthogonal multiple access (NOMA) uplink in the face of channel estimation errors and hardware impairments (HWI). Specifically, the STAR-RIS phase shift is configured based on the statistical channel state information (CSI), followed by linear minimum mean square error (LMMSE) channel estimation of the equivalent channel spanning from the UEs to the access point (AP). Afterwards, successive interference cancellation (SIC) is employed at the AP using the estimated instantaneous CSI, and we derive the theoretical ergodic sum-rate upper bound for both perfect and imperfect SIC decoding algorithm. The theoretical analysis and the simulation results show that both the channel estimation and the ergodic sum-rate have performance floor at high transmit power region caused by transceiver hardware impairments.

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

Reconfigurable Intelligent Surface (RIS) consists of mostly-passive elements capable of electronically steering the impinging signal with configured phase shifts. However, achieving infinite phase resolution is infeasible and the phases needs to be quantized for practical implementation. In this paper, we propose an unsupervised learning-based method to estimate the optimal discrete RIS phase configuration by utilizing key eigen vectors of the available channel state information (CSI) as the input. The direct link between a base station (BS) and user equipment (UE) is not considered in the proposed method and only the CSI of the RIS-aided link is utilized. Further, the proposed network design is independent of the number of transmit and receive antennas at the BS and UE respectively, making the solution generalized and adaptable towards different BSs and UEs in the communication. Due to the unsupervised nature of the proposed method, the solution does not rely on any labeled data, obtaining which is otherwise an exhaustive process. The performance of the proposed solution is analyzed through rigorous simulations and shows the effectiveness of directly estimating discrete RIS configurations as compared to obtaining infinite resolution phase-shifts and then quantizing them. The performance analysis further demonstrates the efficacy of the proposed system with reduced computational complexity and generalized solution with respect to number of BS and UE antennas.

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

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

Reconfigurable intelligent surfaces (RIS) have the ability to steer the electromagnetic (EM) waves to a desired direction. This enables the improvement of the wireless link performance by allowing the illumination of receivers otherwise shadowed by buildings or hills. In this paper, a standards-compliant link-level simulator is developed to study the performance improvement offered by a RIS in 5G New Radio (NR) uplink operating at sub-6 GHz bands. At these frequencies the direct channel between the user and base station is rarely completely blocked, but given the stringent power restrictions of devices, the RIS is utilized for enhancing the coverage performance in the uplink direction. In the studied cases, the transmitter (TX) is close to the RIS and a line-of-sight (LoS) path between the TX and RIS is assumed. The channel between the TX and receiver (RX) is modeled as a non-line-of-sight (NLoS) channel with 5G NR clustered delay line A (CDL-A) profile. Both LoS and NLoS channels between the RIS and RX are considered. Under state-of-the-art system settings, the RIS is shown to increase the symbol error rate link performance by 6 dB. When the performance is measured with coded bit error rate, the performance gain in simulated cases is 1 dB. The coverage enhancement is measured with the throughput as a function of the distance between the TX and RX. The distance at which the maximum possible throughput can be achieved is increased about 5%. The coverage can be further extended if a lower than the maximum throughput is accepted.

Over the past decades, multiple antenna technologies have appeared in many different forms, most notably as multiple-input multiple-output (MIMO), that have transformed wireless communications for extraordinary diversity and multiplexing gains. The various MIMO technologies have been based on placing a number of antennas at some fixed locations which dictate the fundamental limit on the achievable performance. By contrast, this paper envisages the scenario in which the physical position of an antenna can be switched freely to one of the N positions over a fixed-length line space to pick up the strongest signal in the manner of traditional selection diversity. We refer to this system as a fluid antenna system (FAS) for tremendous flexibility in its possible shape and position. The aim of this paper is to study the achievable performance of a single-antenna FAS system with a fixed length and N in arbitrarily correlated Rayleigh fading channels. Our contributions include exact and approximate closed-form expressions for the outage probability of FAS. We also derive an upper bound for the outage probability, from which it is discovered that a single-antenna FAS given any arbitrarily small space can outperform an L-antenna maximum ratio combining (MRC) system if N is large enough. Our analysis also reveals the minimum required size of the FAS, and how large N is considered enough for the FAS to surpass MRC.

An integral component of radio access network (RAN) planning is in determining the coverage regions of base stations. Voronoi diagrams have long been used in this context but classical approaches are only applicable to unrealistic single-slope pathloss model. We provide a new algorithm which computes arbitrarily accurate boundaries of coverage regions under a very general pathloss model, which requires only that the radio signals decrease monotonically with distance and asymptotically as some power of the distance. The new algorithm is applicable to general cellular environments with multislope pathloss conditions, including those from 3GPP specifications.

In this work, we develop a novel channel estimation method using recurrent neural networks (RNNs) for massive multiple-input multiple-output (MIMO) systems. The proposed framework alleviates the need for channel-state-information (CSI) feedback and pilot assignment through exploiting the inherent time and frequency correlations in practical propagation environments. We carry out the analysis using empirical MIMO channel measurements between a 64T64R active antenna system and a state-of-the-art multi-antenna scanner for both mobile and stationary use-cases. We also capture and analyze similar MIMO channel data from a legacy 2T2R base station (BS) for comparison purposes. Our findings confirm the applicability of utilising the proposed RNN-based massive MIMO channel acquisition scheme particularly for channels with long time coherence and hardening effects. In our practical setup, the proposed method reduced the number of pilots used by 25%.

In this work, a deep learning-based symbol detection method is developed for multi-user multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold-based detection methods, which were designed for AWGN channels, are suboptimal in the context of MIMO fading channels. Furthermore, we propose a MIMO detection framework which replaces the linear thresholds with decision boundaries trained with neural network (NN) classifiers. The symbol error rate (SER) performance of the proposed detection model is compared against conventional methods under state-of-the-art system parameters. Here, we report to up to a 2 dB gain in SER performance using the proposed NN classifiers, allowing for exploiting higher-order modulation schemes, or transmitting with reduced power. The underlying gain in performance may be further enhanced from improvements to the NN architecture and hyper-parameter optimization.

This paper studies the physical (PHY)-layer security performance in full-duplex (FD) multiple- input multiple-output (MIMO) small-cell networks. Here, we take into account (i) residual self- interference (SI) over Rician fading channels, and (ii) mutual-interference (MI) under successive interference cancellation (SIC) mechanism. Considering linear zero-forcing (ZF) beamforming, the downlink (DL) and uplink (UL) average secrecy rates under both scenarios of passive and colluding eavesdropping are derived. Our findings indicate that the FD functionality can provide substantial improvements in the PHY-layer security performance, especially with the aid of MIMO communications and interference cancellation solutions.

Full-duplex (FD) has emerged as a disruptive solution for improving the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in the context of cellular networks, is however largely limited by the mutual interference (MI) between the downlink (DL) and uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic analysis of FD enabled cloud radio access network (CRAN) with finite user- centric cooperative clusters. Contrary to the most existing theoretical studies of C-RAN, we explicitly take into consideration non-isotropic fading channel conditions, and finite-capacity fronthaul links. Accordingly, we develop analytical expressions for the FD C-RAN DL and UL SEs. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient- capacity fronthaul links and advanced interference cancellation capabilities.

Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in 5G communication systems and the Internet of Things. In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely transmit covariance matrices and TS ratios, leads to an EE problem which is nonconvex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a suboptimal min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner-layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide two iterative resource allocation schemes under fixed auxiliary variables for solving the dual MAC problem. A subgradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.

We provide a theoretical study of physical (PHY)-layer security performance in full-duplex (FD) small-cell networks. Here, the multi-antenna base stations (BSs) and user equipments (UEs) follow from the homogeneous Poisson point process-based abstraction model. To facilitate FD communications, we take into account: 1) successive interference cancellation capability at the UE side via guard regions of arbitrary radii and 2) residual self-interference at the BS side using Rician fading distribution with arbitrary statistics. We investigate the small-cell network PHY-layer security performance in the presence of a Poisson field of eavesdroppers, under the different scenarios of passive and colluding eavesdropping. Considering linear zero-forcing beamforming, we characterize the downlink and the uplink ergodic secrecy rates and derive closed-form expressions for the different useful and interference signals statistics. In certain special cases of interest, we apply non-linear curve-fitting techniques to large sets of (exact) theoretical data in order to obtain closed-form approximations for the different ergodic rates and ergodic secrecy rates under consideration. Our findings indicate that the FD functionality, in addition to enhancing the spectral efficiency, can significantly improve the PHY-layer security performance, especially with the aid of multi-antenna communications and interference cancellation schemes.

In this paper, a fundamental study of energy efficiency (EE) optimization for coordinated multi-point (CoMP) simultaneous wireless information and power transfer (SWIPT) heterogeneous networks (HetNets) is provided. We aim to optimize the EE while satisfying certain quality-of-service requirements in regard to transmission rate and energy harvesting at both the macro cell and small cells. The corresponding joint beamforming and power allocation in the presence of intra- and inter-cell interference constitutes an EE maximization problem that is non-convex, and hence, very challenging to solve. In order to solve this problem, we propose to separate the beamforming design and power allocation processes. First, we adopt linear zero-forcing (ZF) beamforming to suppress the multi-user interference from both the energy harvesting users (EH-UEs) as well as the information decoding UEs (ID-UEs), thus transforming the HetNet under consideration to a virtual point-to-point system. An efficient power allocation algorithm is then developed to maximize the corresponding EE. On the other hand, the ZF strategy does not utilize the notion that interference benefits the EH-UEs. As a result, we propose a partial ZF approach by differentiating the EH-UEs and ID-UEs in order to further improve the EE. Our findings show that the EE can be significantly improved through the integration of CoMP-SWIPT in HetNets.

Full-duplex (FD) wireless has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference mitigation. The FD versus half-duplex (HD) SE gain in cellular networks is, however, largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process-based abstraction model of multi-antenna radio units and user equipments. We consider different network- and user-centric approaches toward the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo simulations. According to simulations using parameters of a state-of-the-art system, significant FD versus HD C-RAN SE gains can be achieved in the presence of advanced interference cancellation capabilities and sufficient-capacity fronthaul links.

In this work, we investigate the physical layer security (PHYLS) performance of full-duplex (FD) cellular networks, where the downlink (DL) and uplink (UL) occur over the same radio-frequency (RF) resources. Here, the locations of the base stations (BSs) and mobile terminals (MTs) are drawn from stationary Poisson point processes (PPPs). Moreover, the eavesdroppers (EDs) locations are unknown to the network, and are thus modeled from an independent PPP. We characterize the signal-to-interference-plus-noise ratio (SINR) distributions at the reference BS, MT, and most malicious EDs. Accordingly, we develop explicit expressions for the secrecy rates in both UL and DL of the FD cellular network under consideration. Our finding show that the choice of FD versus HD operation, in addition to improving the spectral efficiency, can enhance the secrecy rate, particularly for ultra-dense deployments.

In this paper, we provide joint subcarrier assignment and power allocation schemes for quality- of-service (QoS)-constrained energy-efficiency (EE) optimization in the downlink of an orthogonal frequency division multiple access (OFDMA)-based two-tier heterogeneous cellular network (HCN). Considering underlay transmission, where spectrum- efficiency (SE) is fully exploited, the EE solution involves tackling a complex mixed-combinatorial and non-convex optimization problem. With appropriate decomposition of the original problem and leveraging on the quasi-concavity of the EE function, the problem can be efficiently solved. On the other hand, the inherent inter-tier interference from spectrum underlay access may degrade EE particularly under dense small-cell deployment and large bandwidth utilization. We therefore develop a novel resource allocation approach based on the concepts of spectrum overlay access and resource efficiency (RE) (normalized EE-SE trade-off). Specifically, the optimization procedure is separated where the macro- cell optimal RE and the corresponding bandwidth is first determined, then the EE of small-cells utilizing the remaining spectrum is maximized. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation schemes can approach the optimal EE with each strategy being superior under certain system settings.

In this paper, we investigate joint antenna selection and spatial switching (SS) for energy efficiency (EE) optimization in a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigen-channel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigen-channel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming. We then provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.

In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (MIMO-BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely, transmit covariance matrices and TS ratios, leads to a EE problem which is non-convex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner- layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide an iterative resource allocation scheme under fixed auxiliary variables for solving the dual MAC problem. A sub-gradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.

We consider the case where a reference full-duplex (FD) node (e.g., base station), equipped with arbitrary number of transmit/receive antennas, utilizes generalized linear beamformers to simultaneously communicate with multiple FD radios (e.g., user equipments). The fading coefficients for the residual self-interference (SI) channels are drawn from the complex Gaussian distribution with arbitrary mean and variance. Here, it is not possible to directly derive the distribution of the bidirectional channel power gain. As a result, we adopt the method of moments in order to provide a new Gamma approximation for the residual SI distribution over FD multi-user MIMO Rician fading channels. The proposed theorem holds under arbitrary linear precoder/decoder design, number of antennas and streams, and SI cancellation capability. The validity of the theoretical findings is confirmed via extensive simulations of the entire transmit/receive processing chain.

We provide a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the residual self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed. In order to facilitate bi-directional wireless functionality, we adopt: 1) in the downlink (DL), a linear zero-forcing (ZF) with SI-nulling precoding scheme at the FD base stations and 2) in the uplink (UL), an SI-aware fractional power control mechanism at the FD mobile terminals. Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the FD baseline single-input single-output system can be overcome via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check, since we show that, under state-of-the-art system parameters, the spectral efficiency gain of FD massive MIMO over its half-duplex counterpart is largely limited by the cross-mode interference between the DL and the UL. In point of fact, the anticipated twofold increase in SE is shown to be only achievable when the number of antennas tends to be infinitely large.

In this paper, we investigate joint antenna selection and spatial switching for quality-of-service-constrained energy efficiency (EE) optimization in a multiple-input multiple-output simultaneous wireless information and power transfer system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigenchannel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigenchannel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming, iterative joint eigenchannel assignment and power allocation, and low-complexity multi-objective optimization-based approach. On the other hand, the number of active receive antennas induces a tradeoff in the achievable sum-rate and power transfer versus the transmit-independent power consumption. We provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.

We devise a framework using tools from stochastic geometry and queuing theory for the study of irregular cellular networks when user traffic varies randomly in time and space. We consider a typical wireless cell with a guard zone surrounded by an interference environment comprised of a dominant node at the guard-edge plus an outer-bound Poisson field of sources. A systematic approach is presented to characterize the flow rate in the presence of elastic data traffic with closed-form expressions of the intended signal power and bounded aggregate interference statistics over Nakagami-m fading channels accordingly derived. We then formulate and solve an optimization problem for the computation of the traffic capacity defined as the maximum elastic data flow intensity for which the system remains unsaturated.

We present an analytical model for the design and analysis of dense cellular networks (DenseNets) where load-awareness is explicitly incorporated in the system performance. New bounded expressions of aggregate interference and average rate are developed considering spatially-correlated heterogeneous sources. Subsequently, an optimization problem for pinpointing the optimal network density which minimizes the total energy expenditure is formulated and tackled. The validity of our framework and its advantages over the existing fully-loaded and interference- thinning methods are depicted via Monte-Carlo simulations. Based on state-of-the-art system parameters, a homogeneous pico deployment is revealed to be the most energy-efficient solution in future dense urban environments.

We propose an analytical stochastic geometry-based model for multiple-input multiple-output (MIMO) heterogeneous cellular networks (HetNets) with zero-forcing (ZF) precoding at transmitting base stations (BSs) and partial zero-forcing (PZF) beamforming at receiving user equipments (UEs). The user and area spectral efficiencies are characterized using a non-direct moment- generating-function (MGF) methodology with closed- form expressions of the intended signal power and aggregate network interference statistics accordingly developed. The impact of different cellular network deployments, antenna configurations, and transmission schemes on achievable performance are examined through theoretical and simulation studies. The results confirm the promising potential of multi-antenna communications and small-cell solution in emerging wireless environments.

The application of stochastic geometry theory for the study of cellular networks has gained huge popularity recently. Most existing works however rely on unrealistic assumptions concerning the underlying user traffic model. This paper aims to make a step in this direction by devising a new model for the performance analysis and optimization of heterogeneous cellular networks (HetNets) with irregular BS deployment and flow- level traffic dynamics. We provide a unified methodology for the evaluation of the flow rate with closed-form expressions of the useful signal power and aggregate network interference over Nakagami-m fading channels. The problem of computing the optimal loading factors which result in the greatest sustainable traffic whilst the system remains stable is formulated and tackled.

This paper presents a stochastic geometry-based framework for the design and analysis of downlink multi-user multiple-input multiple-output (MIMO) heterogeneous cellular networks with linear zero-forcing transmit precoding and receive combining, assuming Rayleigh fading channels and perfect channel state information. The generalized tiers of base stations may differ in terms of their Poisson point process spatial density, number of transmit antennas, transmit power, artificial-biasing weight, and number of user equipments served per resource block. The spectral efficiency of a typical user equipped with multiple receive antennas is characterized using a non-direct moment-generating-function-based methodology with closed-form expressions of the useful received signal and aggregate network interference statistics systematically derived. In addition, the area spectral efficiency is formulated under different space-division multiple-access and single-user beamforming transmission schemes. We examine the impact of different cellular network deployments, propagation conditions, antenna configurations, and MIMO setups on the achievable performance through theoretical and simulation studies. Based on the state-of-the-art system parameters, the results highlight the inherent limitations of baseline single-input single-output transmission and conventional sparse macro-cell deployment, as well as the promising potential of multi-antenna communications and small-cell solution in interference-limited cellular environments.

A stochastic radio resource allocation (RRA) algorithm is designed to maximize the total transmission rate of orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) with hybrid (i.e., joint underlay and overlay) spectrum access strategy. Our novel solution incorporates the probabilities of channel availability obtained through spectrum sensing for allocating power and subcarrier in a multi-user multi-band environment. In order to protect the licensed users from harmful intervention under imperfect sensing information, stochastic transmit and interference power constraints are imposed on the CRs. The performance of the proposed RRA algorithm and advantages over the conventional hard-decision-based approaches are demonstrated via simulation.

We provide new results on the signal-to-interference-plus-noise ratio (SINR) statistics considering a Poisson point process (PPP)-based heterogeneous interference field. In particular, closed-form expressions for the density functions of the reciprocal of the aggregate interference and signal-to-interference ratio (SIR) are developed. We prove that the effect of PPP-based interference on useful transmission is mathematically equivalent to the severe impact from a one-sided Gaussian fading channel. As an application example, the proposed approach is used to design and analyze the average SINR performance of a typical user in heterogeneous cellular networks (HetNets).

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for the mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

Spectral efficiency (SE) and energy efficiency (EE) are the main performance metrics for designing green radio (GR) networks; however they are conflicting criteria. Consequently, instead of separately focusing on either SE or EE, characterizing the fundamental trade-off between EE and SE of MIMO broadcast channels (BC) is significant for the development of green wireless communications. This paper investigates the fundamental EE-SE relationship in a multiple-input multiple-output (MIMO) broadcast channel, which is important for facilitating a desirable balance between energy savings and spectrum utilization. Through our investigation, we prove that EE-SE relationship for MIMO-BC is a quasiconcave function. Furthermore, EE is proved to be either strictly decreasing with SE or first strictly increasing and then strictly decreasing with SE. Based on these findings, we propose a two-layer resource allocation algorithm in order to tackle the comprehensive EE-SE trade-offs problem. The key of the proposed method lies in the inner-layer algorithm which is solved by applying the principle of multiple access channel - broadcast channel (MAC-BC) duality. The algorithm in its dual form is solved using sub-gradient method and bisection searching scheme. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE-SE trade-off for MIMO-BC.

We present a new unified model for the design and analysis of load-aware downlink heterogeneous networks (HetNets) where interferers are inherently spatially-correlated. A closed-form expression for the aggregate network interference statistics generated by correlated load-proportional tiers of base stations (BSs) over Nakagami-m fading interfering channels is developed. This approach allows for relaxation of several major limitations in the existing state-of-the-art models, in particular the always-on-BSs, uncorrelated interferers, and Rayleigh fading with no shadowing assumptions. The validity and advantages of the proposed load-aware framework over the heavily-adopted fully-loaded model and the more recent interference-thinning-based approximation are confirmed via extensive Monte-Carlo (MC) simulations. The results reveal several important trends and design guidelines for the practical deployment of HetNets.

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

Characterizing the fundamental energy efficiency (EE) performance of multiple-input–multiple-output interfering broadcast channels (MIMO-IFBC) is important for the design of green wireless system. In this paper, we propose a new network architecture proposition based on EE maximization for Multi-Cell MIMO-IFBC within the context of interference alignment (IA). Particularly, EE is maximized subject to maximum power and minimum throughput constraints. We propose two schemes to optimize EE for different signal-to-noise ratio (SNR) regions. For high-SNR operating regions, we employ a grouping-based IA scheme to jointly cancel intra- and inter-cell interferences and thus transform the MIMO-IFBC to a single-cell MIMO scenario. A gradient-based power adaptation scheme is proposed based on water-filling power adaptation and singular value decomposition to maximize EE for each cell. For moderate SNR cases, we propose an approach using dirty paper coding (DPC) with the principle of multiple access channel and broadcast channel duality to perform IA while maximizing EE in each cell. The algorithm in its dual form is solved using a subgradient method and a bisection searching scheme. Simulation results demonstrate the superior performance of the proposed schemes over several existing approaches. It also shows that interference-nulling-based IA approaches outperform hybrid DPC-IA approach in high-SNR region, and the opposite occurs in low-SNR region.

We derive new results for the higher order moments of signal-to-interference-plus-noise ratio (SINR) in the presence of an arbitrary Poisson point process (PPP)-based heterogeneous interference field. The analysis leverages on a moment-generating-function (MGF) methodology, which only requires the statistics of intended signal and aggregate interference, thus eliminating the need for the exact distribution of SINR. We extend the existing results on interference statistics by deriving a generalized closed-form expression of the interference MGF considering Nakagami-m fading channels with exclusion region. In certain special cases, explicit expressions for the averages of different functions of SINR are found, which also lead to closed-form solutions for the probability distributions of aggregate interference reciprocal and signal-to-interference ratio. We prove that in such cases the effect of total PPP-based interference power on useful transmission is mathematically equivalent to the severe fluctuations from a one-sided Gaussian fading channel. As an application example, the proposed methodology is used together with stochastic geometry theory to characterize the average SINR and rate in heterogeneous cellular networks. The validity of our analytical derivations is confirmed via Monte Carlo simulations for various system settings. We show that with cellular network densification there exists a tradeoff between the average SINR and rate performance.

Characterizing the fundamental metric of energy efficiency (EE) of multiple-input multiple-output interfering broadcast channels (MIMO-IFBC) is important for the development of green wireless communications. In this paper, we address the EE optimization problem for multi-cell MIMO-IFBC within the context of interference alignment (IA). We employ grouping-based IA scheme to cancel both inter-cell interference (ICI) and inter-user inference (IUI), and thus transform the MIMO-IFBC to a single cell single user MIMO scenario. A gradient-based optimal power adaptation scheme is proposed which utilizes water-filling approach and singular value decomposition (SVD) to maximize EE for each cell. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

Radio resource allocation (RRA) algorithms are developed to enhance the spectral efficiency of downlink adaptive modulation and coding (AMC)-based orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) under average transmit and peak interference power constraints. We consider the practical case of noisy channel-state-information (CSI) between cognitive transmitter (CTx) and primary receiver (PRx), and design novel interference management techniques to model and control the CR-inflicted interference on licensed spectrum. An expression for the cumulative density function (cdf) of the CRs' received signal-to-interference-plus-noise ratio (SINR) is developed to evaluate the resultant average spectral efficiency. Simulation results reveal that by adopting the joint resource allocation and interference management framework a considerable performance gain in the AMC-based OFDMA CR network is achieved over the conventional optimization methods.

We develop a unified framework for the performance analysis of arbitrary-loaded downlink heterogeneous networks (HetNets) in which interfering sources are inherently spatially-correlated. Considering a randomly-deployed multi-tier cellular network comprised of a diverse set of large-and small-cells, we incorporate the notion of load-awareness and spatial-correlations in characterizing the activities of base stations (BSs) using binary decision variables. A stochastic geometry-based approach is accordingly employed to systematically develop a bounded expression of ergodic rate with different cellular association and load-balancing strategies. Employing the proposed unified framework hence allows for relaxation of several major limitations in the existing state-of-the-art models, in particular the always-transmitting-BSs, uncorrelated interferers, and Rayleigh fading assumptions. We elaborate on the usefulness of adopting this methodology by providing detailed analysis of the aggregate network interference generated by interdependent load-proportional sources over Nakagami-m fading interfering channels. The analytical formulations are validated through Monte-Carlo (MC) simulations for various scenarios and system settings of interest. We observe that the heavily-adopted fully-loaded model as well as the more recent interference-thinning-based approximations are significantly limited in capturing the actual performance curve. The proposed bounded load-aware model and MC trials reveal several important trends and design guidelines for the practical deployment of HetNets.

In CDMA systems, outer loop power control (OLPC) determines the target value of SNR at the receiver, mostly by using look-up tables to map bit error rates (BERs) to SNRtargets. In this contribution, transmission delay and packet loss rate constraints in the data link layer (DLL) are invoked in order to determine the optimum outer loop SNR-target setpoint analytically, according to the number of active users in cell. Optimality is, in this sense, the maximization of system throughput. Using the optimum SNR-target, the optimal spreading factor is determined. Subsequently, the joint optimization of outer loop SNR-target and variable spreading factor (VSF), at the physical(PHY)-layer, with truncated automatic repeat request (ARQ) error control mechanism at the data link layer are proposed. Our scheme is compared with 'constant SNR-target' and 'PHY-layer based variable SNR-target' cases under continuous power and rate variation to show (analytically and by simulations) the achievable gain through the coupling of physical and data link layers parameters.

Joint optimization of outer loop power control (OLPC) signal to noise ratio-target (SNR-target) and variable spreading factor (VSF), at the physical(PHY)-layer, with truncated automatic repeat request (ARQ), at data link layer (DLL), in accordance to the number of active users in the cell is considered. This investigation is on a single cell conventional cellular DS-CDMA communication system with frequency-selective fading channels, where the number of active users is modelled through a one-dimensional discrete Markov chain. The optimally is in the sense of maximizing the sum-throughput, given a coherent RAKE receiver is employed with maximum ratio combining (MRC). We determine the optimal spreading factor using OLPC SNR-target at the PHY-layer, which satisfies the QoS imposed by packet error rate-target (PER-target) as a function of maximum number of allowed ARQ retransmissions at the DLL. The channel model considers independent paths with Nakagami fading characteristics. Total and truncated channel inversion techniques are used within the inner loop power control (ILPC) to adapt the transmission power to short time channel variations. Sum-throughput performance of the optimized system and non optimized system, where the SNR-target is assumed to be constant, under various multipath fading conditions are studied. A considerable gain in sum-throughput is achieved through coupling of PHY-layer and DLL parameters.

In CDMA systems, outer loop power control (OLPC) determines the target value of SNR at the receiver, mostly by using look-up tables to map bit error rates (BERs) to SNR-targets. In this contribution, transmission delay and packet loss rate constraints in the data link layer (DLL) are invoked in order to determine the optimum outer loop SNR-target setpoint analytically, according to the number of active users in cell. Optimality is, in this sense, the maximization of system throughput. Using the optimum SNR-target, the optimal spreading factor is determined. Subsequently, the joint optimization of outer loop SNR-target and variable spreading factor (VSF), at the physical(PHY)-layer, with truncated automatic repeat request (ARQ) error control mechanism at the data link layer are proposed. Hence, we show that quality of service (QoS) requirements at these layers can be simultaneously satisfied while maximizing throughput. Total and truncated channel inversion strategies are employed in the inner loop to adapt transmit power to short-time channel variations. We propose a system where the number of users in a cell is modeled by a one-dimensional discrete Markov chain, and design the adaptive continuous power and rate mechanism for the worst case packet error rate (PER) condition. The corresponding theoretical throughput, which can be regarded as upper-bound for discrete spreading factor case, is obtained numerically for various settings of system parameters. We have also provided simulation results for a practical channel condition. Our scheme is compared with "constant SNR-target" and "PHY-layer based variable SNR-target" cases under continuous power and rate variation to show the achievable gain through the coupling of physical and data link layers parameters.