This paper addresses the integration of unmanned aerial vehicles (UAVs) into terrestrial networks to improve the coverage and throughput at ground users through an optimized integrated access and backhaul (IAB) framework. We propose a joint optimization strategy for power allocation, user association, and beamforming design to maximize network throughput while meeting backhaul constraints at UAVs and ensuring minimum rate guarantees at ground users. The non-convexity and combinatorial complexity of the problem are tackled via a hierarchical approach utilizing successive convex approximation for joint user association and power allocation, and an augmented weighted minimum mean-squared error method for beamforming. Simulation results demonstrate the efficacy of our proposed framework in achieving higher sum rates and reliably meeting higher minimum rate requirements than conventional benchmarks, while also ensuring efficient use of network resources.

Traditional cellular base stations with downtilted antennas are designed primarily to serve ground users. However, the advancements of drone technology and the increasing number of aerial users have made it challenging to provide reliable cellular coverage in the sky using conventional infrastructure. To address this, we propose overlaying a portion of existing terrestrial base stations with uptilted antennas, while retaining their downtilted antennas for ground coverage. This approach aims to provide seamless cellular connectivity for aerial users without compromising ground user performance. Using stochastic geometry, we analyze the coverage probability for aerial users and examine interference dynamics from uptilted and downtilted antennas. Our results demonstrate significant coverage improvements for aerial users even with small overlay percentages, offering a cost-effective solution. Furthermore, we find that this approach does not adversely affect ground user performance. These insights provide valuable guidelines for future research and deployment of overlaid networks supporting both aerial and ground users.

Unmanned Aerial Vehicles (UAVs) are increasingly used in a plethora of applications such as shipping, surveillance, and search-and-rescue. For UAVs to operate safely, reliable cellular connectivity is essential. Utilizing the terrestrial networks for aerial connectivity has been proposed, but the 3D radiation pattern of base station antennas significantly affects the performance of aerial links. To address this, we evaluate the coverage probability of cellular-connected UAVs, considering vertical antenna gain, by leveraging tools from stochastic geometry. We also analyze how the UAV hovering height, tilt angle and 3D antenna beamwidth influence the reliability of the communication link. Our results show that a down-tiled antenna does not only improve the connectivity of terrestrial users but also its cellularconnected UAVs counterpart. Moreover, the coverage probability of the UAV-UE becomes saturated at large down-tilt angles at the TBSs due to the antenna sidelobe gain at the serving and interfering TBSs. We also found that the significant increase of the vertical antenna beamwidth improves the UAV user coverage probability especially at relatively low hovering altitudes thanks to the increase of the desired signal strength compared to the interference power.

The reflection characteristics of a reconfigurable intelligent surface (RIS) depend on the reflection response of the constituent unit cells, which are necessarily frequency dependent. This paper investigates the role of an RIS comprised of unit cells with different phase-frequency profiles in improving the achievable rate of a wide-band orthogonal frequency division multiplexing (OFDM) system. Specifically, we propose phase-frequency profiles with both variable phase and variable slope that enable improvements in the spectral efficiency of a channel. We first propose a mathematical model for the frequency response of the reflection coefficient of a realizable RIS unit cell and parameterize the phase-frequency profile by its slope and by its resonance center frequency. Then, modelling each RIS element with b control bits, we propose a method for selecting the parameter pairs to obtain a set of$2^{b}$reflection profiles. We then use a low-complexity optimization algorithm to maximize the data rate through the joint optimization of (a) the reflection profile for each RIS unit cell from the available sets and (b) the power allocations across the sub-carriers. We show that the resulting RIS outperforms existing designs over a wide range of user locations in single-input single-output and multi-user multiple-input single-output OFDM systems.

Holographic multiple-input multiple-output (HMIMO) communication systems utilize spatially-constrained arrays equipped with large number of antennas (NoA) to benefit from increased spatial multiplexing and spatial resolution gains. We consider a multi-user HMIMO system under an electromagnetic wave compliant channel model, and study its energy-efficiency (EE). We first derive closed-form expressions of the ergodic achievable rates under maximum ratio transmission (MRT) and zero-forcing (ZF) precoding in the downlink, and maximum ratio combining (MRC) and ZF combining in the uplink, implemented at the base station (BS) with reduced complexity dictated by the number of degrees-of-freedom (DoF) offered by the channel. Using these expressions, we formulate an EE maximization problem with respect to the power allocation (PA) and the NoA arranged within spatially-constrained HMIMO surfaces at the BS and users, and solve it using an alternating optimization algorithm. For fixed PA, the optimal NoA is derived as the solution of two analytical equations, while for fixed NoA we use sequential fractional programming to obtain the optimal PA. Numerical results yield useful insights into the EE performance in different operating regimes and under different side-lengths of HMIMO surfaces. The presented results show that under MRT and MRC in the downlink and uplink respectively, deploying more antennas in excess of the number of DoF increases the EE in low power budget (noise-limited) regime, whereas fixing the NoA to the number of DoF maximizes the EE in higher power budget (interference-limited) regime. On the other hand, under ZF precoders and combiners, the NoA that achieve the optimal EE is larger than the DoF under all power budgets, with the number of additional antennas decreasing with increasing power budget.

Amidst the ongoing debate about limited spectral availability, there remains a persistent demand for the development of spectrally efficient self-sustainable network (SSN) models. This paper addresses this challenge by optimizing spectral efficiency (SE) in uplink transmissions for an energy harvesting (EH)-enabled secondary user (SU) that operates opportunistically among multiple primary users (PUs) in an Internet-of-things (IoT) network. The PUs are assumed to employ a rotational time division multiple access (TDMA) scheme for transmissions, where the signals are divided into time slots for each PU to transmit data in a cyclic manner, while the SU uses an opportunistic non-orthogonal multiple access (NOMA) technique to transmit data without interfering with the PU transmissions, such that, at any given time slot, a PU and a SU share the same frequency band simultaneously. The SE of the system is maximized jointly by employing convex optimization and a deep reinforcement learning (DRL) model, specifically the deep deterministic policy gradient (DDPG) algorithm. Simulations demonstrate that the proposed approach significantly improves the SE of the considered IoT network, highlighting its potential for efficient spectrum management in IoT networks. We present a comprehensive SE analysis of the system, which further underscores the robustness and adaptability of our approach in optimizing SE under diverse operational conditions.

Stacked intelligent metasurface (SIM) is an emerging programmable metasurface architecture that can imple-ment signal processing directly in the electromagnetic wave domain, thereby enabling efficient implementation of ultra-massive multiple-input multiple-output (MIMO) transceivers with a limited number of radio frequency (RF) chains. Channel estimation (CE) is challenging for SIM-enabled communication systems due to the multilayer architecture of SIM, and because we need to estimate large dimensional channels between the SIM and users with a limited number of RF chains. To efficiently solve this problem, we develop a novel hybrid digital-wave domain channel estimator, in which the received training symbols are first processed in the wave domain within the SIM layers, and then processed in the digital domain. The wave domain channel estimator, parametrized by the phase shifts applied by the meta-atoms in all layers, is optimized to minimize the mean squared error (MSE) using a gradient descent algorithm, within which the digital part is optimally updated. For an SIM-enabled multi-user system equipped with 4 RF chains and a 6-layer SIM with 64 meta-atoms each, the proposed estimator yields an MSE that becomes close to that achieved by fully digital CE in a massive MIMO system employing 64 RF chains. This high CE accuracy is achieved at the cost of a training overhead that can be reduced by exploiting the potential low rank of channel correlation matrices.

This work studies a multi-cell one-bit massive multiple-input multiple-output (MIMO) system that employs one-bit analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at each base station (BS). We utilize Bussgang decomposition to derive downlink signal-to-quantization-plus-interference-plus-noise ratio (SQINR) and ergodic achievable rate expressions under one-bit quantized maximum ratio transmission (MRT) and zero-forcing (ZF) precoding schemes considering scenarios with and without pilot contamination (PC) in the derived channel estimates. The results are also simplified for the mixed architecture that employs full resolution (FR) ADCs and one-bit DACs, and the conventional architecture that employs FR ADCs and DACs. The SQINR is shown to decrease by a factor of 2/π and 4/π2in the one-bit setting compared to that achieved in the mixed setting and conventional setting respectively under MRT precoding without PC. Interestingly, the decrease in SQINR is less when we consider PC, which is shown to adversely impact the conventional system more than the one-bit system. Similar insights are obtained under ZF precoding with the decrease in the SQINR with the use of one-bit ADCs and DACs being more pronounced. We utilize the derived expressions to yield performance insights related to power efficiency, the numbers of antennas needed by the three architectures to achieve the same sum-rate, and energy efficiency.

This paper aims to derive expressions of the downlink ergodic user rates in a multi-cell large-scale multi-user multiple-input single-output (MISO) system under the assumption that each base station (BS) employs maximum ratio transmission (MRT) precoding and that single-antenna users in each cell are divided into groups, where channels of users in the same group share common covariance matrices and follow the double scattering channel model. Moreover, both channel estimation errors and pilot contamination effects caused by re-use of pilot sequences in neighboring cells are taken into consideration in this work. The analysis is carried out using statistical tools under the exact and asymptotic regimes in which the number of antennas at BS$N$, the number of users in each cell$K$, and the number of scatterers$S$grow large at the same pace. Furthermore, the obtained exact expressions and deterministic approximations of the ergodic rates are expressed in simplified closed-forms under the special case of multi-keyhole channels to yield useful insights. They reveal that signal-to-noise plus interference ratio (SINR) without user grouping in a multi-keyhole channel is similar to that under standard Rayleigh channel in the asymptotic regime. However, under user grouping, we show that the massive multiple-input multiple-output (MIMO) gains promised by deploying large-scale antenna arrays in multi-cell settings are limited by the number of scatterers even if the number of antennas grows large. Simulation results illustrate the close match provided by the asymptotic analysis for moderate system dimensions and confirm the insights drawn from the theoretical findings.

The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which the reflecting elements only employ random phase rotations without requiring any CSI. The only CSI then needed is at the base station (BS) of the overall channel to implement the beamforming transmission scheme. Under this framework, we derive the sum-rate scaling laws in the large number of users regime for the IRS-assisted multiple-input single-output (MISO) broadcast channel, with optimal dirty paper coding (DPC) scheme and the lower-complexity random beamforming (RBF) and deterministic beamforming (DBF) schemes at the BS. The random rotations scheme increases the sum-rate by exploiting multi-user diversity, but also compromises the gain to some extent due to correlation. Finally, energy efficiency maximization problems in terms of the number of BS antennas, IRS elements and transmit power are solved using the derived scaling laws. Simulation results show the proposed scheme to improve the sum-rate, with performance becoming close to that under coherent beamforming for a large number of users.

This work makes the preliminary contribution of studying the asymptotic performance of a multi-user intelligent reflecting surface (IRS) assisted-multiple-input single-output (MISO) downlink system under imperfect CSI. We first extend the existing least squares (LS) ON/OFF channel estimation protocol to a multi-user system, where we derive minimum mean squared error (MMSE) estimates of all IRS-assisted channels over multiple sub-phases. We also consider a low-complexity direct estimation (DE) scheme, where the BS obtains the MMSE estimate of the overall channel in a single sub-phase. Under both protocols, the BS implements maximum ratio transmission (MRT) precoding while the IRS design is studied in the large system limit, where we derive deterministic equivalents of the signal-to-interference-plus-noise ratio (SINR) and the sum-rate. The derived asymptotic expressions, which depend only on channel statistics, reveal that under Rayleigh fading IRS-to-users channels, the IRS phase-shift values do not play a significant role in improving the sum-rate but the IRS still provides an array gain. Simulation results confirm the accuracy of the derived deterministic equivalents and show that under Rayleigh fading, the IRS gains are more significant in noise-limited scenarios. We also conclude that the DE of the overall channel yields better performance when considering large systems.

Multi-user (MU) diversity yields sum-rate gains by scheduling a user for transmission at times when its channel is near its peak. These gains are limited in environments with line- of-sight (LoS) channel components and/or spatial correlation. To remedy this, previous works have proposed opportunistic beamforming (OBF) using multiple antennas at the BS to transmit the same signal, modulated by time-varying gains, to the best user at each time slot. In this paper, we propose reconfigurable surface (RS)-assisted OBF to increase the range of channel fluctuations in a single-antenna broadcast channel (BC), where opportunistic scheduling (OS) strategy achieves the sum-rate capacity. The RS is abstracted as an array of passive reflecting elements that only induce random phase shifts onto the impinging electromagnetic waves. We develop the sum-rate scaling laws under Rayleigh, Rician and correlated Rayleigh fading and show that RS-assisted OBF with a single-antenna BS can outperform multi-antenna BS- assisted OBF using a moderate number of elements.

This work focuses on the downlink of a single-cell multi-user system in which a base station (BS) equipped with M antennas communicates with K single-antenna users through a reconfigurable intelligent surface (RIS) installed in the line-of-sight (LoS) of the BS. RIS is envisioned to offer unprecedented spectral efficiency gains by utilizing N passive reflecting elements that induce phase shifts on the impinging electromagnetic waves to smartly reconfigure the signal propagation environment. We study the minimum signal-to-interference-plus-noise ratio (SINR) achieved by the optimal linear precoder (OLP), that maximizes the minimum SINR subject to a given power constraint for any given RIS phase matrix, for the cases where the LoS channel matrix between the BS and the RIS is of rank-one and of full-rank. In the former scenario, the minimum SINR achieved by the RIS-assisted link is bounded by a quantity that goes to zero with K. For the high-rank scenario, we develop accurate deterministic approximations for the parameters of the asymptotically OLP, which are then utilized to optimize the RIS phase matrix. Simulation results show that RISs can outperform half-duplex relays with a small number of passive reflecting elements while large RISs are needed to outperform full-duplex relays.

This paper studies the ergodic rate performance of maximum ratio transmission (MRT) precoding in the downlink of a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled using the double scattering model. We utilize the minimum-mean-square-error (MMSE) channel estimate for this model, which is used in the design of the MRT precoding. Within this setting, we are interested in deriving tight approximations of the ergodic rate under the assumption that the number of BS antennas (N), the number of users (K) and that of scatterers (S) grow large with the same pace. These approximations are expressed in simplified closed-form expressions for the special case of multi-keyhole channels. They reveal that unlike the standard Rayleigh channel in which the SINR grows as O(N), K the SINR associated with a multi-keyhole channel scales as O(S). This particularly shows that the reaped gains of the K large-scale MIMO over double scattering channels do not linearly increase with the number of antennas and are limited by the number of scatterers. We further provide simulation results that confirm the close match provided by the asymptotic analysis for moderate system dimensions and provide some useful insights into the interplay between N, K and S.

This paper studies the ergodic rate performance of regularized zero-forcing (RZF) precoding in the downlink of a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled by the double scattering model. This non-Gaussian channel model is a function of both the antenna correlation and the structure of scattering in the propagation environment. This paper makes the preliminary contribution of deriving the minimum-mean-square-error (MMSE) channel estimate for this model. Then under the assumption that the users are divided into groups of common correlation matrices, this paper derives deterministic approximations of the signal-to-interference-plus-noise ratio (SINR) and the ergodic rate, which are almost surely tight in the limit that the number of BS antennas, the number of users, and the number of scatterers in each group grow infinitely large. The derived results are expressed in a closed-form for the special case of multi-keyhole channels. The simulation results confirm the close match provided by the asymptotic analysis for moderate system dimensions. We show that the maximum number of users that can be supported simultaneously, while realizing large-scale MIMO gains, is equal to the number of scatterers.

This paper studies the sum-rate performance of regularized zero-forcing (RZF) precoding in a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled by the double scattering channel model. This non-Gaussian channel accounts for both the spatial correlation in the antenna arrays and the structure of scattering in the propagation environment. The user population is divided into G groups, where the users in the same group experience similar propagation conditions and are characterized by common correlation matrices. Under this setting, we derive deterministic approximations of the signal-to-interference-plus-noise ratio (SINR) and the sum-rate with RZF precoding, which are almost surely tight in the large system limit. Simulation results confirm the close match provided by the asymptotic analysis for moderate system dimensions.

The 3GPP Release-13 has recently introduced full-dimension multiple-input multiple-output (FD-MIMO) technology as a practical way to deploy massive MIMO arrays within feasible base station (BS) form factors through the use of active antenna systems with two-dimensional (2D) planar array structures. The 2D arrangement of antenna elements, where the elements in each antenna port are fed with downtilt weights, allows for adaptive electronic beamforming in the elevation as well as the conventional azimuth dimensions. This work focuses on the previously unaddressed problem of determining the optimal downtilt weight vectors for the antenna ports in each cell of a multi-cell multi-user system. The optimization criterion is to maximize the minimum signal to intra-cell interference ratio within a cell while constraining the inter-cell interference leakage. The quasi-optimal weight vectors are obtained through the application of semi-definite relaxation and Dinkelbach's method. The proposed algorithm performs better than the existing approximate schemes even under the effects of pilot contamination.

This paper discusses full-dimension multiple-input-multiple-output (FD-MIMO) technology, which is currently an active area of research and standardization in wireless communications for evolution toward Fifth Generation (5G) cellular systems. FD-MIMO utilizes an active antenna system (AAS) with a 2-D planar array structure that not only allows a large number of antenna elements to be packed within feasible base station form factors, but also provides the ability of adaptive electronic beamforming in the 3-D space. However, the compact structure of large-scale planar arrays drastically increases the spatial correlation in FD-MIMO systems. In order to account for its effects, the generalized spatial correlation functions for channels constituted by individual elements and overall antenna ports in the AAS are derived. Exploiting the quasi-static channel covariance matrices of users, the problem of determining the optimal downtilt weight vector for antenna ports, which maximizes the minimum signal-to-interference ratio of a multi-user multiple-input-single-output system, is formulated as a fractional optimization problem. A quasi-optimal solution is obtained through the application of semi-definite relaxation and Dinkelbach's method. Finally, the user-group specific elevation beamforming scenario is devised, which offers significant performance gains as confirmed through simulations. These results have direct application in the analysis of 5G FD-MIMO systems.

Elevation beamforming and Full Dimension MIMO (FD-MIMO) are currently active areas of research and standardization in 3GPP LTE-Advanced. FD-MIMO utilizes an active antenna array system (AAS), that provides the ability of adaptive electronic beam control over the elevation dimension, resulting in a better system performance as compared to the conventional 2D MIMO systems. FD-MIMO is more advantageous when amalgamated with massive MIMO systems, in that it exploits the additional degrees of freedom offered by a large number of antennas in the elevation. To facilitate the evaluation of these systems, a large effort in 3D channel modeling is needed. This paper aims at providing a summary of the recent 3GPP activity around 3D channel modeling. The 3GPP proposed approach to model antenna radiation pattern is compared with the ITU approach. A closed-form expression is then worked out for the spatial correlation function (SCF) for channels constituted by individual antenna elements in the array by exploiting results on spherical harmonics and Legendre polynomials. The proposed expression can be used to obtain correlation coefficients for any arbitrary 3D propagation environment. Simulation results corroborate and study the derived spatial correlation expression. The results are directly applicable to the analysis of future 5G 3D massive MIMO systems.

Motivated by the recent interest in 3D beamforming to enhance system performance, we present an information-theoretic channel model for multiple-input multiple-output (MIMO) systems, that can support the elevation dimension. The principle of maximum entropy is used to determine the distribution of the channel matrix consistent with the prior angular information. We provide an explicit expression for the cumulative density function (CDF) of the mutual information in the large number of transmit antennas and paths regime. The derived Gaussian approximation is quite accurate even for realistic system dimensions. The simulation results study the achievable performance through the meticulous selection of the transmit antenna downtilt angles. The results are directly applicable to the analysis of 5G 3D massive MIMO systems.

In this paper, an exact closed-form expression for the Spatial Correlation Function (SCF) is derived for the standardized three-dimensional (3D) multiple-input multiple-output (MIMO) channel. This novel SCF is developed for a uniform linear array of antennas with non-isotropic antenna patterns. The proposed method resorts to the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials to obtain a closed-form expression for the SCF for arbitrary angular distributions and antenna patterns. The resulting expression depends on the underlying angular distributions and antenna patterns through the Fourier Series (FS) coefficients of power azimuth and elevation spectrums. The novelty of the proposed method lies in the SCF being valid for any 3D propagation environment. Numerical results validate the proposed analytical expression and study the impact of angular spreads on the correlation. The derived SCF will help evaluate the performance of correlated 3D MIMO channels in the future.

Multiple-input-multiple-output (MIMO) systems of current LTE releases are capable of adaptation in the azimuth only. Recently, the trend is to enhance system performance by exploiting the channel's degrees of freedom in the elevation, which necessitates the characterization of 3D channels. We present an information-theoretic channel model for MIMO systems that supports the elevation dimension. The model is based on the principle of maximum entropy, which enables us to determine the distribution of the channel matrix consistent with the prior information on the angles. Based on this model, we provide analytical expression for the cumulative density function (CDF) of the mutual information (MI) for systems with a single receive and finite number of transmit antennas in the general signal-to-interference-plus-noise-ratio (SINR) regime. The result is extended to systems with finite receive antennas in the low SINR regime. A Gaussian approximation to the asymptotic behavior of MI distribution is derived for the large number of transmit antennas and paths regime. We corroborate our analysis with simulations that study the performance gains realizable through meticulous selection of the transmit antenna downtilt angles, confirming the potential of elevation beamforming to enhance system performance. The results are directly applicable to the analysis of 5G 3D-Massive MIMO-systems.

Electricity costs are a significant fraction of a cellular network's operations costs. We present Low-Carb, a practical scheme to decrease electrical energy consumption in operational cellular networks by coupling Base Transceiver Station (BTS) power savings with call hand-off—two features commonly used by cellular operators. Motivated by the practical observation that most callers are in the vicinity of multiple BTSs, Low-Carb presents and solves an optimization problem, allowing calls to hand-off from one BTS to another so that BTS power savings can be applied to a maximal number of BTSs throughout the cellular network. We use BTS locations and traffic volume data from a large live GSM network to evaluate the power savings possible using our proposed approach in Low-Carb. Our results indicate that for a GSM 1800 network operator with 7000 sites in an urban setting, a total of up to 35.36 MWh may be saved annually. This is at least 9.8% better than the energy savings obtained by just using BTS power savings alone. Other cellular operators can use the Low-Carb formulation with their own network data to estimate the electricity savings they may achieve on their networks.