We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to any nomographic function, b) scales to a massive number of user equipments (UEs) and edge devices (EDs), c) supports the computation of multiple independent functions (streams), and d) operates in a multi-access fashion whereby each transmitter can choose to transmit either data symbols, computing signals or both. For the sake of illustration, we design the proposed multi-stream and multi-access method under an uplink setting, where multiple single-antenna UEs/EDs simultaneously transmit data and computing signals to a single multiple-antenna base station (BS)/access point (AP). Under the communication functionality, the receiver aims to detect all independent communication symbols while treating the computing streams as aggregate interference which it seeks to mitigate; and conversely, under the computing functionality, to minimize the distortion over the computing streams while minimizing their mutual interference as well as the interference due to data symbols. To that end, the design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations coupled with closed-form combiners purposebuilt for the AirComp operation, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 300 UEs/EDs. The efficacy of the proposed method under different loading conditions is also evaluated, with the performance of the scheme shown to approach fundamental limiting bounds in the under/fully loaded cases.

In-band Full-Duplex (FD) Multiple-Input Multiple-Output (MIMO) systems present a major opportunity for Integrated Sensing and Communication (ISAC) by enabling simultaneous signal transmission and reception. In this paper, we study the performance and implementation of FD-ISAC for vehicle-to-infrastructure (V2I) communications using a more precise sensing model. A common assumption in ISAC performance analysis has been that all targets can be represented by asingle pointcoordinate - named single ray sensing model (SRSM). This assumption simplifies performance analysis and algorithm implementation for ISAC, but it overlooks the shape and size of the sensing targets. Thus, we model each sensing target as acluster of rayswith certain angular characteristics associated with its geometry - named cluster ray sensing model (CRSM). Then, we derive the Cramer-Rao Bound (CRB) for angular direction and the variance of cluster-modeled targets. We also provide an (easy to optimize) lower bound of CRB for angular direction in terms of eigenvalues of the covariance matrix and its derivative, which approaches the original value in the low signal-to-noise (SNR) and low angular spread regime. Our analysis shows that a more accurate model (with large angular variance) improves the estimation of the direction of arrival of targets. Based on the CRB expressions, we design a partially connected hybrid beamforming (HBF) algorithm for millimeter wave (mmWave) OFDM FD-ISAC systems. The simulation results show that assuming CRSM together with a proposed HBF increases the data rate of the system by 25% as compared to assuming SRSM and a max signal to interference plus noise ratio (SINR) scheme.

Massive multi-input multi-output (MIMO) combined with orthogonal time frequency space (OTFS) modulation has emerged as a promising technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) massive MIMO-OTFS systems. Specifically, first, an iterative basis expansion model (BEM) based UL channel estimation scheme is proposed to accurately estimate UL channels with the aid of carefully designed OTFS frame pattern. Then a set of Slepian sequences are used to model the estimated UL channels, and the dynamic Slepian coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels by iteratively extrapolating the Slepian coefficients. Simulation results verify that the proposed UL channel estimation and DL channel prediction schemes outperform the existing schemes in terms of normalized mean square error of channel estimation/prediction and DL spectral efficiency, with less pilot overhead.

In this paper, a new waveform called discrete Fourier transform spread orthogonal frequency division multiplexing with chirp modulation (DFT-s-OFDM-CM) is proposed for the next generation of wireless communications. The information bits are conveyed by not only Q-ary constellation symbols but also the starting frequency of chirp signal. It could maintain the benefits provided by the chirped discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM), e.g., low peak-to-average power ratio (PAPR), full frequency diversity exploitation, etc. Simulation results confirm that the proposed DFT-s-OFDM-CM could achieve higher spectral efficiency while keeping the similar bit error rate (BER) to that of chirped DFT-s-OFDM. In addition, when maintaining the same spectral efficiency, the proposed DFT-s-OFDM-CM with the splitting of information bits into two streams enables the use of lower-order constellation modulation and offers greater resilience to noise, resulting in a lower BER than the chirped DFT-s-OFDM.

We consider a novel anchorless rigid body localization (RBL) suitable for application in autonomous driving (AD), in so far as the algorithm enables a rigid body to egoistically detect the location (relative translation) and orientation (relative rotation) of another body, without knowledge of the shape of the latter, based only on a set of measurements of the distances between sensors of one vehicle to the other. A key point of the proposed method is that the translation vector between the two-bodies is modeled using the double-centering operator from multidimensional scaling (MDS) theory, enabling the method to be used between rigid bodies regardless of their shapes, in contrast to conventional approaches which require both bodies to have the same shape. Simulation results illustrate the good performance of the proposed technique in terms of root mean square error (RMSE) of the estimates in different setups.

We consider the peak-to-average power ratio (PAPR) reduction challenge of orthogonal frequency division multiplexing (OFDM) systems utilizing tone reservation (TR) under a sensing-enabling constraint, such that the signals placed in the reserved tones (RTs) can be exploited for Integrated Sensing and Communication (ISAC). To that end, the problem is first cast as an unconstrained manifold optimization problem, and then solved via an iterative projected gradient descent algorithm assisted by an approximation of the infinity norm. Simulation results show that the proposed method, while maintaining a level of PAPR reduction similar to state of the art (SotA), not only has lower computational complexity but also outperforms the alternatives in terms of sensing performance.

We consider the design of low complexity and highperforming mean square error (MSE) minimization combiners for over-the-air-computing (AirComp) applications operating over the uplink of a system with one multiple-antenna access point (AP) and multiple single-antenna edge devices (EDs). Within that paradigm, we offer two contributions, namely, a simple initial combiner based on a Rayleigh quotient (RQ) design, and a low-complexity refinement stage based on a convex concave procedure (CCP). The new refinement stage algorithm is further enriched with an efficient (offline) hyper-parameter tuning mechanism via Bayesian optimization (BO) and acceleration method based on a half-space constrained least square problem reformulation solved via the adaptive moment estimation (Adam) algorithm. The low complexity and good performance of the proposed method help address typical limitations of edge devices. Numerical results demonstrate that the proposed design can achieve MSE performances equivalent to those of the best stateof-the-art (SotA) alternatives currently known, at about 200-times less complexity than the highest-performing SotA, and about 4-times less complexity than its low-complexity counterpart.

The recently proposed multi-chirp waveform, affine frequency division multiplexing (AFDM), is considered as a potential candidate for integrated sensing and communication (ISAC). However, acquiring accurate target sensing parameter information becomes challenging due to fractional delay and Doppler shift occurrence, as well as effects introduced by the coexistence of near-field (NF) and far-field (FF) targets associated with large-scale antenna systems. In this paper, we propose a novel angle-delay-Doppler estimation scheme for AFDM-ISAC system in mixed NF and FF scenarios. Specifically, we model the received ISAC signals as a third-order tensor that admits a low-rank CANDECOMP/PARAFAC (CP) format. By employing the Vandermonde nature of the factor matrix and the spatial smoothing technique, we develop a structured CP decomposition method that guarantees the condition for uniqueness. We further propose a low-complexity estimation scheme to acquire target sensing parameters with fractional values, including angle of arrival/departure (AoA/AoD), delay and Doppler shift accurately. We also derive the Cramér-Rao Lower Bound (CRLB) as a benchmark and analyze the complexity of our proposed scheme. Finally, simulation results are provided to demonstrate the effectiveness and superiority of our proposed scheme.

We propose a new two-stage, low-complexity, and robust beamforming (BF) method for heterogeneous MIMO rate splitting multiple access (RSMA) systems. In the proposed method, the phases and powers of the BF weights are designed separately, the first based on a tensor factorization of the channels between the base station (BS) and each user, and the second based on a fractional programming (FP) formulation of the power allocation problem, which is offered in three distinct variations, aimed as sum rate maximization (SRM), minimum rate maximization (MaxMin) and the maximization of the geometric-mean (GMean) of achievable rates, respectively. Thanks to the twostage approach, the proposed method is capable of delivering robustness to both channel state information (CSI) and successive interference cancellation (SIC) errors (incorporated in the phase design), at a low complexity compared to state-of-the-art (SotA) alternatives. Also thanks to the approach, the scheme naturally handles heterogeneity in terms of the number of antennas at each user, which can be arbitrarily distinct. Direct comparisons between SotA and the proposed schemes demonstrate that the contributed method generally outperforms the best alternative at comparable complexity, while approaching the best-performing SotA method of significantly higher complexity. In fact, the computational cost advantage of the proposed technique over the latter is quantified analytically and shown to be proportional to the cube of the number of BS antennas.

Index modulation (IM) is one of the key enabling technologies for beyond fifth generation (B5G) and sixth generation (6G) wireless systems, attracting attention for its inherent energy and spectral efficiency resulting from conveying information through the indexation of the resources utilized in during signal transmission. However, a remaining critical bottleneck for large-scale IM is the consequently infeasible detection complexity of combinatoric order. Therefore in this article, in order to maximally reap the advantages of IM in large scenarios, we propose a novel message passing (MP) decoder designed under the Gaussian belief propagation (GaBP) framework exploiting a novel unit vector decomposition (UVD) of IM signals with purpose-derived novel probability distributions. The proposed method enjoys a low decoding complexity that is independent of previously prohibitive combinatorial factors, while still approaching the performance of unfeasible state-of-the-art (SotA) search-based methods. The effectiveness of the proposed approach is demonstrated via complexity analysis and numerical results for the exemplary piloted generalized quadrature spatial modulation (GQSM) systems of truly massive sizes (up to 96 antennas).

In this paper, a Turbo basis expansion modeling (BEM) orthogonal time frequency space (OTFS) scheme is proposed for high-mobility communications subject to either Doppler-shift or Doppler-spread channel. It consists of superimposed pilot power and BEM order optimization and Turbo BEM OTFS receiver. The signal-to-interference-and-noise ratio (SINR) is derived as functions of OTFS system parameters and several channel information (i.e., channel correlation matrix, noise variance, etc.). The optimal superimposed pilot power ratio is then derived with a closed-form solution by calculating the first derivative of derived SINR. BEM order is specially optimized for each channel, instead of simply increasing or reducing it. The Turbo BEM OTFS receiver is proposed with an exchange of soft information between BEM channel estimation, signal detection, and data decoding, leading to high reliability. By using superimposed pilots for initial BEM channel estimation, the proposed Turbo BEM OTFS scheme has zero dedicated pilot overhead, resulting in high spectral efficiency. Simulation results confirm that the proposed OTFS scheme outperforms the existing OTFS schemes in terms of bit error rate (BER) and spectral efficiency. Extrinsic information exchange transfer (EXIT) chart analysis and simulation results also exhibit the fast convergence speed of proposed OTFS scheme.

We consider an uplink integrated sensing and communications (ISAC) scenario where the detection of data symbols from multiple user equipment (UEs) occurs simultaneously with a three-dimensional (3D) estimation of the environment, extracted from the scattering features present in the channel state information (CSI) and utilizing the same physical layer communications air interface, as opposed to radar technologies. By exploiting a discrete (voxelated) representation of the environment, two novel ISAC schemes are derived with purpose-built message passing (MP) rules for the joint estimation of data symbols and status (filled/empty) of the discretized environment. The first relies on a modular feedback structure in which the data symbols and the environment are estimated alternately, whereas the second leverages a bilinear inference framework to estimate both variables concurrently. Both contributed methods are shown via simulations to outperform the state-of-the-art (SotA) in accurately recovering the transmitted data as well as the 3D image of the environment. An analysis of the computational complexities of the proposed methods reveals distinct advantages of each scheme, namely, that the bilinear solution exhibits a superior robustness to short pilots and channel blockages, while the alternating solution offers lower complexity with large number of UEs and superior performance in ideal conditions.

In this paper, a novel dual-rate orthogonal time frequency space (OTFS) system resilient to oscillator frequency offset (OFO) and Doppler spread is presented for high-mobility communications. To the best of the authors' knowledge, this is the first work in OTFS which introduces dual-rate frame and addresses both OFO and Doppler spread. By multiplexing low-rate data blocks at the front and back of OTFS frame, OFO is estimated without requiring extra pilots. Thanks to subspace-based algorithm, OFO is estimated with high accuracy, and the residual small OFO can be jointly estimated with Doppler-spread channel by utilizing a few pilots, without deteriorating performance. Simulation results verify the superior performance of the proposed dual-rate system over existing single-rate systems, in terms of bit error rate (BER), mean-square-error (MSE) of OFO estimation, and MSE of equivalent channel estimation, while featuring lower pilot overhead and power. The BER of the proposed system also approaches its lower bound, which assumes perfect estimation of OFO and Doppler-spread channel.

Orthogonal time frequency space (OTFS) modulation has become an effective technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) multi-input multi-output (MIMO) OTFS systems. Specifically, first, an iterative data-aided channel estimation scheme is proposed to accurately acquire UL channels with the aid of specifically designed frame pattern. Then the discrete prolate spheroidal basis expansion model (DPS-BEM) is used to model the time-varying UL channel estimates, and the dynamic DPS-BEM coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels for all antenna pairs and paths by iteratively extrapolating the fitting coefficients. Simulation results verify that the proposed scheme outperforms the existing schemes in terms of normalized mean square error of channel prediction and DL sum-rate.

In this paper, we propose a novel joint activity and channel estimation (JACE) algorithm for grant-free extra large MIMO (XL-MIMO) systems subject to spatial non-stationarity phenomena by means of a Bayesian bilinear inference framework. In XL-MIMO systems, the signal from each user is visible only by a small portion of its antenna arrays, which are typically distributed over the surface of a certain structure. The sporadic user activity due to grant-free access, as well as the spatial non-stationarity, jointly imposes a challenging JACE problem involving a nested Bernoulli-Gaussian random variable. In order to address this issue, we decompose the latter into a bilinear inference problem of two independent random quantities, deriving novel message passing rules based on Gaussian approximation and bilinear inference. Performance evaluation via software simulations is offered to demonstrate the effectiveness of the proposed algorithm, which achieves the Genie-aided ideal estimation performance.

In this paper, a near-optimal Karhunen-Loeve basis expansion modeling (KL-BEM) orthogonal time frequency space (OTFS) receiver with superimposed pilots has been proposed for high-mobility communications over time-varying channels with Doppler and delay spread. First, an initial KL-BEM channel estimation is conducted using superimposed pilots, followed by the removal of superimposed pilots from the received OTFS signal and equalization based on message passing (MP) algorithm. After that, the detected data symbols are utilized as pseudo pilots together with the superimposed pilots to refine both KL-BEM channel estimation and equalization in an iterative manner. Simulation results confirm the superior performance of the proposed KL-BEM OTFS receiver over prior art in terms of bit error rate (BER). The resulting BER performance is close to the lower bound obtained by assuming perfect channel estimation. Besides, the proposed scheme provides high spectral efficiency, while featuring fast convergence and affordable complexity.

Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.

In this paper, a new receiver design based on basis expansion model (BEM) orthogonal time frequency space (OTFS) is presented for high-mobility communications with Doppler-spread channel. By deriving an analytical BEM OTFS system model, a low-order generalized complex exponential BEM (GCE-BEM) aided rough channel estimation is proposed at the initial stage with low pilot overhead, followed by equalization. Then, the refinement of channel estimation and equalization is conducted iteratively, in which a high-resolution GCE-BEM model with a large BEM order is adopted and the detected data symbols are exploited as pseudo-pilots, leading to higher estimation accuracy. Simulation results show that the proposed BEM OTFS receiver significantly outperforms the existing OTFS receivers in terms of the mean square error (MSE) of channel estimation and bit error rate (BER), while featuring low pilot overhead. Results also show the near-optimal performance of the novel solution,i.e., achieved BER is very close to the case of perfect channel estimation. The theoretical lower bound on MSE of channel estimation is derived to verify the effectiveness of the proposed BEM OTFS receiver, which is shown to be close to simulation results.

Extra large MIMO (XL-MIMO) systems are subject to spatial non-stationarity forming visibility regions (VRs), which leads to a sub-array-wise sparse structure of the channel matrix. When XL-MIMO systems operate in grant-free access mode, in which only a fraction of the potential users are active during a given time slot, it follows that the channel matrix possesses a doubly-sparse and user-specific structure such that the activity of each user and each sub-array can be jointly modeled by a nested Bernoulli-Gaussian distribution. This article considers the joint activity and channel estimation (JACE) problem in XL-MIMO systems subject to this so-defined spatial non-stationarity, tackling this challenging inference problem. Our main contributions are 1) to introduce the novel Bernoulli-Gaussian model to simultaneously capture the aforementioned two distinct structured sparsities, and 2) a new bilinear Bayesian inference algorithm capable of jointly estimating the associated channel coefficients, user activity patterns, sub-array activity patterns ($a.k.a$. spatial non-stationarity), boosted by expectation maximization (EM)-based auto-parameterization. In addition, to shed light on a realistic modeling of VRs, we also introduce a Matérn-cluster point process (MCPP)-based approach to imitate the clustered activity pattern due to spatial non-stationarity. The efficacy of the proposed bilinear JACE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also can reach the performance of a genie-aided scheme over wide signal-to-noise-ratio (SNR) ranges, in both uniformly-random and MCPP-based sub-array activity scenarios.

We consider quadrature spatial modulation (QSM) schemes, which achieve high spectral efficiency (SE) via the dispersion of a relatively small number$P$of$M$-ary modulated symbols over a large number of combinations of$n_{T}$transmit antennas and$T$transmit instances. In particular, we design a new space-time block code (STBC)-based scalable QSM scheme combining high SE with maximum diversity and optimum coding gains. Deriving a closed-form expression for the optimum SE, we show that scaling the size$T$with$n_{T}$not only is required to achieve SE optimality, but also results in further gains in bit error rate (BER) performance. Building on the latter optimal parameterization, a fully optimized scalable QSM (OS-QSM) transmitter design is then obtained by introducing a new dispersion matrix index selection algorithm that ensures even utilization of spatial-temporal resources. Finally, a new greedy boxed iterative shrinkage thresholding algorithm (GB-ISTA) QSM receiver is proposed, which exploits the inherent sparsity of QSM signals and while detecting spatially and digitally modulated bits in a greedy fashion. The resulting low complexity of the new receiver, which is linear on$n_{T}$, enables the utilization of OS-QSM in systems of previously prohibitive dimensions.

Cellular vehicle-to-network (V2N) communication will be the backbone of the connected vehicles of future. One of the key requirements of the connected vehicles is a near universal coverage on the streets. Traditional radio network planning for cellular coverage is done with raster format whereby all pixels in a network area have equal weighting. Whereas, for V2N communication the target is to primarily ensure continuous network coverage on the streets. In this work, a route-based methodology which is pertinent for V2N coverage analysis is presented. This method adds another key parameter into consideration, namely, the base stations (BS) deployment schema. Existing cellular networks, whereby, small base stations (BSs) are deployed at the macro BS cell-edge, at traffic hotspots or to compensate for coverage holes, may not be sufficient for V2N coverage especially at millimeter wave (mmWave) carrier frequencies. Herein, we perform the coverage analysis for an existing small BS deployment as well as for an ultra-dense deployment at 2 GHz, 5 GHz and 28 GHz carriers. The statistics for signal-to-noise-plus-interference ratio (SINR) and achieved rate are aggregated by a large number of realistic vehicular routes from Google Directions application programming interface (API).

Recently, an active discussion on the feasibility of Millimeter Wave (mmWave) frequencies for the Vehicle-to-Vehicle (V2V) communication have been carried out in research community. We contribute to this discussion by providing a comparison between explicit three-dimensional ray-tracing simulations and field trial measurements on 39 GHz frequency. Three basic practical and relevant cases for V2V communications are considered covering several important scenarios of daily life traffic. A close match between the measured and simulated results is found through explicit ray tracing simulations; thus validating the feasibility of the simulation model and underlying assumptions. Moreover, these outcomes also shed light on the potential and challenges of using mmWave frequencies for V2V communication. The acquired results indicate that the Reference Signal Received Power (RSRP) levels are sufficiently above the noise level even up to 100 m distance between TX and RX in case of a single obstructing car. Results also reveal the impact of moving vehicle intersecting the LOS between the TX and RX vehicle at road intersection, and they indicate a notable blockage loss in case of short TX-RX separation.

In cellular networks, users are grouped into different cells and served by different access points (base stations) that provide wireless access to services and applications. In general, the service demand is very heterogeneous, non-uniformly distributed, and dynamic. Consequently, radio access networks create veryirregulartopologies with more access points, where service demand is concentrated. While this dynamism requires networks with the ability to adapt to time-varying conditions, the non-uniformity of the service demand makes the planning, analysis, and optimization difficult. In order to help with these tasks, a framework based on canonical domains and spatial mappings (e.g., conformal mapping) have recently been proposed. The idea is to carry out part of the planning in a canonical (perfectly symmetric) domain that is connected to the physical one (real-scenario) by means of a spatial transformation designed to map the access pointsconsistentlywith the service demand. This paper continues the research in that direction by introducing additional tools and possibilities to that framework, namely the use of centroidal Voronoi algorithms and non-conformal composite mappings. Moreover, power optimization is also introduced to the framework. The results show the usability and effectiveness of the proposed method and its promising research perspectives.

Cell switch-off (CSO) is an important approach to reducing energy consumption in cellular networks during off-peak periods. CSO addresses the research question of which cells to switch off when. Whereas online CSO, based on immediate user demands and channel states, is problematic to implement and difficult to model, off-line CSO is more practical and tractable. Furthermore, it is known that regular cell layouts generally provide the best coverage and spectral efficiency, which leads us to prefer regular static (off-line) CSO. We introduce sector-based regular CSO patterns for the first time. We organize the existing and newly introduced patterns using a systematic nomenclature; studying 26 patterns in total. We compare these patterns in terms of energy efficiency and the average number of users supported, via a combination of analysis and simulation. We also compare the performance of CSO with two benchmark algorithms. We show that the average number of users can be captured by one parameter. Moreover, we find that the distribution of the number of users is close to Gaussian, with a tractable variance. Our results demonstrate that several patterns that activate only one out of three sectors are particularly beneficial; such CSO patterns have not been studied before.

In vehicle-to-vehicle (V2V) channels, roadside scatterers (RSSs), such as houses, buildings, trees, and many more, play a crucial role in the determination of the Doppler power spectral density (DPSD) characteristics. However, the relevant research results are scarce due to the lack of computationally tractable analytic DPSD solutions. To fill this gap, we investigate an indirect method for the DPSD analysis of a generic 2-D RSS model for V2V channels. The indirect method, based on Hoeher's theorem, employs successive transformations of random variables to obtain the DPSD. Compared with the conventional methods, leading to impractical multiple integral solutions, our method yields a single integral form, more useful for analytic studies, model validation/parameter estimation, and fading simulator design. Using the new DPSD solution, the impact of different RSS layouts on the DPSD characteristics is further investigated, and several new insights are provided. The joint probability density function (PDF) of the angle-of-departure and the angle-of-arrival (AoA) and the joint Doppler-AoA PDF are newly presented in closed forms and analyzed with respect to the DPSD shape. Comparisons with the DPSDs measured in highway and urban canyon environments demonstrate not only the validity of the generic 2-D RSS model but also the significant contribution of RSSs to V2V channels.

From its origins, cellular networks have been in constant evolution. Nowadays, real-world deployments admit very irregular network topologies in order to satisfy nonuniformly distributed service demands, i.e., there are small cells in areas where users are concentrated. This makes the planning, analysis, and optimization of such irregular topologies difficult. Methods based on stochastic geometry have been proposed and successfully used to capture some characters of this heterogeneity; and recently, the analysis based on canonical domains and spatial transformations has also been proposed as a complementary tool to aid in network planning and optimization tasks. The key idea is to simplify the analysis by using a dual/canonical domain that is perfectly symmetric and homogeneous. Unfortunately, yet expected, these synthetic models have fallen into disuse as they cannot capture the heterogeneity of the increasingly complex real-world deployments. However, the introduction of the aforementioned framework creates an opportunity to revisit these reference models and to study them from several new points of view. In addition, the one-dimensional network model fits with several relevant study items, such as Intelligent Transportation Systems and road-side deployments. The contribution of this paper is to present the analysis of the one-dimensional canonical domain (and several applications of it) as an initial effort to pave the way towards further generalizations, such as the challenging two-dimensional case.

The pursuit of more bandwidth and more efficient spectrum usage has led to consider the use of Long Term Evolution (LTE) technology in unlicensed frequency bands, a concept known as LTE-Unlicensed (LTE-U). This feature would be especially useful in hot-spots and indoors, where short-range pico-base stations could be used. However, indoor WiFi on unlicensed bands calls for coexistence mechanisms between LTE and WiFi. Accordingly, methods including listen-before-talk, advanced channel selection, duty-cycle, and variations of them, have been proposed. While these protocols are of great importance, we are approaching the coexistence issue from the radio access planning/optimization point of view by presenting a statistical system model for LTE-U indoor planning. The proposed optimization framework allows to obtain network topologies that maximize the benefits from the LTE-U deployment and fulfill coverage criteria. The performance of the statistically-optimized network topologies has also been validated by means of system level simulations.

In order to cope with the rapidly increasing service demand in cellular networks, more cells are needed with better resource usage efficiency. This poses challenges for the network planning since service demand in practical networks is not geographically uniform, and to cope with the nonuniform service demand, network deployments are becoming increasingly irregular. This paper introduces a new idea to deal with the nonuniform network topology. Rather than capturing the network character (e.g., load distribution) by means of stochastic methods, the proposed novel approach aims at transferring the analysis from the physical (irregular) domain to a canonical/dual domain that simplifies the work due to its symmetry. To carry out this task, physical and canonical domains are connected using conformal (Schwarz-Christoffel) mapping, a rich and mature theory from complex analysis. Thus, the main contribution of this paper is to introduce the analysis through canonical domains and validate the usability of conformal mapping as a feasible solution approach for this problem.

The fifth generation (5G) of wireless networks will connect not only persons but also things, in a massive and previously unheard-of scale. Therefore, engineers and researchers need to develop methods and solutions to 1) satisfy the increasing demand, and 2) plan, maintain, and optimize networks that are envisioned to provide services to a highly dynamic, diverse, and heterogeneous traffic, both in time and space. This paper makes a contribution to the second goal by introducing an idea for planning and/or optimization of cellular networks based on the use of Centroidal Voronoi Tessellations. The framework allows obtaining (deterministically) network topologies that are compatible with the service demand, described statistically in terms of its spatial distribution and volume. While the method provides by itself a first network performance assessment, site locations and cell areas, are key references for planning and optimization.

The analysis of cellular systems has been carried out modeling the access network by means of deterministic and stochastic methods. Deterministic models include hexagonal geometries or real-world data. Stochastic methods, given their nature, capture (in a statistical manner) the inhomogeneities found in realistic deployments. Beyond pros and cons of each approach, the notion of ‘topology’ associated to these models is either geometric (regular or non-regular) or architectonic (heterogeneous or not), but it is not enough to describe many aspects of real-world deployments. Despite the term is widely used in the context of cellular communications, there is no precise (mathematical) definition for it. In the light of this situation, this paper presents a set of proper definitions to describe network topology and its compatibility with service demand distribution in a way that is useful from the planning and optimization perspective. In addition, a novel view of ‘irregularity’ is also introduced. Due to its generality, this statistical framework is timely for 5G where 1) hyper-dense and hierarchical deployments are foreseen, and 2) more complex performance indicators such as energy, spectral, and deployment efficiency per area unit need to be accurately estimated. In addition, operator-specific criteria such as QoS can be taken into account in the model presented herein.

The green communications paradigm has been receiving much attention in wireless networks in recent years. More specifically, in the context of cellular communications, the concept of Cell Switch Off (CSO) has been recognized as a promising approach to reduce the energy consumption. The need is expected to be pressing especially in the next decade with the increasing small cell deployment. However, the cell switch on/off decisions compounded by the resource allocation task in CSO constitute a highly challenging optimization problem due to the fact that this problem can be viewed as a generalized version of the resource allocation (scheduling) problem in the conventional cellular networks without CSO, which itself is already difficult. This paper introduces a novel framework to CSO based on multiobjective evolutionary optimization. The main contribution of this paper is that the proposed multiobjective framework takes the traffic behaviour in both space and time (known by operators) into account in the optimal cell switch on/off decision making which is entangled with the corresponding resource allocation task. The exploitation of this statistical information is done in a number of ways, including through the introduction of a weighted network capacity metric. This indicator prioritizes cells which are expected to have traffic concentration resulting in on/off decisions that achieve substantial energy savings in scenarios where traffic is highly unbalanced, without compromising the QoS. The proposed framework distinguishes itself from the CSO papers in the literature in two ways: 1) The number of cell switch on/off transitions as well as handoffs are minimized. 2) The computationally-heavy part of the algorithm is executed offline, which makes the real-time implementation feasible.

Intercell interference is the main issue limiting the capacity of modern orthogonal frequency-division multiple access based cellular networks. Recently, extensive research work has been carried out in this field, and intercell interference coordination techniques have been recognized as key enablers of current (and future) cellular technologies. In this article, (i) a comprehensive survey of the most representative contributions is provided together with (ii) a generic methodology to measure their actual merit. The performance of several interference avoidance strategies has been evaluated both from system and user point of view in the context of a Long Term Evolution (LTE)-based network considering not only synthetic cellular scenarios but also realistic deployments. Our literature review indicates that there is a need for adaptive/operatorcustomizable low-complex intercell interference coordination (ICIC) schemes suitable for realistic LTE deployments. Results obtained by means of a comprehensive set of simulations corroborate and support this premise. In this article, it is shown that simultaneous gains in terms of spectral/energy efficiency and fairness can be achieved through dynamic mechanisms with respect to both classic hard reuse schemes and static ICIC techniques. Besides numerical results, a novel merit assessment methodology based on several weighted performance metrics is proposed. Our findings show that dynamic schemes outperform static techniques by around 20–35% in realistic deployments.

Carrier Aggregation (CA) is one of the key features introduced in LTE Release 10 to achieve higher levels of throughput. Two or more component carriers (CCs) are aggregated and user equipments (UEs) simultaneously transmit in more than one. The use of non-contiguous resource allocation in the uplink (UL) implies higher peak to average power ratio, so power de-rating is essential to avoid inter-modulation distortion and adjacent channel leakage-power ratio. CC selection is a key radio resource management procedure by which the eNB assigns UEs to CCs. While most CC selection algorithms deal with downlink, existing literature lacks efforts on UL CA. Moreover the mechanism is determinant on the UL performance. Given the UE power limitation, different criteria must be used in each link. This work proposes a novel CC selection algorithm that distinguishes between power limited and non-power limited UEs. In particular it is proposed to introduce information on maximum power reduction in selection decisions. Besides, UEs are not directly rejected if they are power limited, an acceptance margin is defined. This contains information on throughput variations between the allocation in several or just one CCs. This novel approach is contrasted with other CC selection techniques. Results show cell edge throughput improvements and the benefit of allowing bandwidth aggregation in a subset of power limited users.

Interference management has been recognized by the industry as a key enabler for 4G systems. Emerging technologies include multicarrier systems such as LTE and WiMAX for which effective management of intercell interference is of utmost importance in order to improve the Quality of Service (QoS) at cell edges. Static Intercell Interference Coordination (ICIC) techniques such as Soft Frequency Reuse (SFR) are aimed at alleviating this problem; however the usage of baseline SFR designs (schemes without optimization) only offers tradeoffs between cell edge performance and spectral efficiency and performance is indeed far from optimal as results herein confirm. Thus, this paper presents a novel multiobjective algorithm in order to address this problem and achieve effective optimization of SFR implementations. Results show that the proposed algorithm succeeds in finding good-quality SFR configurations enhancing simultaneously network capacity and cell edge performance while reducing energy consumption with respect to baseline designs and previous proposals.

Effective interference management is a technical challenge of utmost importance for emerging OFDMA-based technologies such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). Static Intercell Interference Coordination techniques including Soft Frequency Reuse (SFR) have enjoyed acceptance among mobile operators as a solution to deal with this problem mainly due to their low complexity and easy implementability. However, recent results indicate that the performance of default SFR settings directly applied to realistic cellular deployments is quite suboptimal and hence optimization is mandatory. This paper addresses this issue by presenting a novel multiobjective framework able to achieve accurate fine tuning of SFR and hence, enhance system capacity and cell edge performance while reducing energy consumption.

Intercell Interference is the main issue limiting the capacity of modern OFDMA-based cellular networks. Recently, extensive research work has been focused on intercell interference coordination since it has been recognized as a key concept to achieve seamless levels of quality of service. Nevertheless, very often this problematic is addressed as a standalone issue. In this work, a novel channel state information feedback scheme is proposed aiming at improving the overall system performance when both channel state information and static interference coordination are jointly considered. By means of the proposed scheme, gains in terms of users throughput, cell edge performance and energy efficiency are achieved with respect to existing aperiodic LTE-based mechanisms without additional complexity.

Nowadays mobile operators are planning and designing their mid/long term strategies based on new OFDMA-based cellular technologies such as LTE and WiMAX. Co-siting and re-utilization of existing facilities are within their roadmap in order to keep CAPEX as low as possible. Therefore, the optimization of OFDMA networks subject to fixed/previous (and usually suboptimal) coverage is mandatory. In addition, recently there is a great interest in reducing OPEX mainly by means of energy savings. This paper presents a novel multiobjective formulation aiming at improving the performance of OFDMA networks where full frequency reuse is employed. The proposed framework is flexible and results indicate that it is also effective as it improves both spectral efficiency and cell edge performance while it simultaneously reduces energy consumption.

The provision of Quality of Service (QoS) to users of LTE networks depends to a large degree on the choice of an appropriate scheduling algorithm able to meet the requirements of mobile operators. Dynamic packet scheduling has been recognized as a key approach to maximize the utility of OFDMA-based systems due to its inherent ability to exploit the frequency selectiveness of wideband channels both in time and frequency domain. However, one of the main issues associated to dynamic scheduling is the high amount of signaling overhead required to provide users with resource allocation information. While the impact of control channel limitations on LTE VoIP capacity has been widely studied yet, tradeoffs associated to control channel usage and the provision of QoS for Non-Real Time (NRT) services has been basically omitted in current literature. In this paper, such tradeoffs have been addressed from several perspectives. Results show that the relationship between scheduling policies, offered levels of QoS (expressed in terms of guaranteed bit rates) and control channels capacity is not trivial and requires careful planning.

This paper presents a joint study of several intercell interference coordination strategies considering both static and dynamic approaches, and with different adjustments in their basic parameters. A wide evaluation is presented with special emphasis on the efficiency vs. fairness tradeoff. Besides, additional performance metrics have been considered as enablers of a full understanding of the strengths and weaknesses of each method. Results show that, although spectral efficiency can achieve similar values with proper tuning, certain schemas outperform others in important parameters such as the effectiveness in the utilization of resources. Dynamic semi-centralized approaches appear as an attractive option with an acceptable level of adaptability, moderate complexity and good performance.

3GPP LTE is the evolution of UMTS which will make possible to deliver high quality multimedia services with an improved user experience. Since Radio Resource Management (RRM) has been recognized as a key point to successfully accomplish this target, the performance evaluation of a multi-cell resource allocation scheme applied to LTE downlink (DL) is presented in this paper. A semi-distributed RRM framework is discussed and evaluated from a system level viewpoint. Detailed link level simulations have also been carried out to properly back up the results.

3GPP LTE is the evolution of UMTS which will make possible to deliver next generation high quality multimedia services according to the users' expectations. The flexibility of the downlink OFDM radio interface with Adaptive Modulation and Coding (AMC), MIMO and H-ARQ plays a crucial role in achieving the low latency and high spectral efficiency promised by the new radio access standard. This paper presents a LTE DL link level simulator whose main purpose is to generate suitable look-up tables to interface with a system level simulator. In this context, the Exponential Effective SIR (EESIR) metric is a link abstraction model that is used to properly characterize multistate channels. The reference BLER curves in AWGN channel and the parameters of the EESIR model are given for the complete list of CQI's specified for LTE DL. The obtained results also include curves of mean link level throughput for different AMC formats and MIMO configurations.