Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we model the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Based on the proposed beam patterns and user association policies, we derive the conditional coverage probability and ergodic rate of communication and sensing dual functions for two association cases, as well as the marginal coverage rate using the distance-dependent thinning method. Taking into account the coupling effect of ISAC dual functions within the same network topology, we further calculate the joint coverage probability of ISAC performance. Simulation results verify the accuracy of derived theoretical formulations, and illustrate the impact of the RIS aperture, blockage, BS and RIS densities on ISAC coverage rates, which provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km${}^{-2}$BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from 62% to 97% with the deployment of RISs.

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green’s functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

The emerging concept of extremely-large holographic multiple-input multiple-output (HMIMO), beneficial from compactly and densely packed cost-efficient radiating metaatoms, has been demonstrated for enhanced degrees of freedom even in pure line-of-sight conditions, enabling tremendous multiplexing gain for the next-generation communication systems. Most of the reported works focus on energy and spectrum efficiency, path loss analyses, and channel modeling. The extension to secure communications remains unexplored. In this paper, we theoretically characterize the secrecy capacity of the HMIMO network with multiple legitimate users and one eavesdropper while taking into consideration artificial noise and max-min fairness. We formulate the power allocation (PA) problem and address it by following successive convex approximation and Taylor expansion. We further study the effect of fixed PA coefficients, imperfect channel state information, inter-element spacing, and the number of Eve's antennas on the sum secrecy rate. Simulation results show that significant performance gain with more than 100% increment in the high signal-to-noise ratio (SNR) regime for the two-user case is obtained by exploiting adaptive/flexible PA compared to the case with fixed PA coefficients.

In this paper, we study the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output systems to-wards the extension of the wireless cellular coverage in 3D, where multiple RISs, each equipped with an array of passive elements, are deployed to assist a base station (BS) to simultaneously serve multiple unmanned aerial vehicles (UAVs) in the same time-frequency resource of 5G wireless communications. With a focus on ensuring fairness among UAVs, our objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) at UAVs by jointly optimizing the transmit beamforming parameters at the BS and phase shift parameters at RISs. We propose two novel algorithms to address this problem. The first algorithm aims to mitigate interference by calculating the BS beamforming matrix through matrix inverse operations once the phase shift parameters are determined. The second one is based on the principle that one RIS element only serves one UAV and the phase shift parameter of this RIS element is optimally designed to compensate the phase offset caused by the propagation and fading. To obtain the optimal parameters, we utilize one state-of-the-art reinforcement learning algorithm, deep deterministic policy gradient, to solve these two optimization problems. Simulation results are provided to illustrate the effectiveness of our proposed solution and some insightful remarks are observed.

The recent progress of artificial intelligence (AI) opens up new frontiers in the possibility of automating many tasks involved in Telecom networks design, implementation, and deployment. This has been further pushed forward with the evolution of generative artificial intelligence (AI), including the emergence of large language models (LLMs), which is believed to be the cornerstone toward realizing self-governed, interactive AI agents. Motivated by this, in this paper, we aim to adapt the paradigm of LLMs to the Telecom domain. In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups. We consider training the selected models on 3GPP technical documents (Tdoc) pertinent to years 2009-2019 and predict the Tdoc categories in years 2020-2023. The results demonstrate that fine-tuning BERT and RoBERTa model achieves 84.6% accuracy, while GPT-2 model achieves 83% in identifying 3GPP working groups. The distilled BERT model with around 50% less parameters achieves similar performance as others. This corroborates that fine-tuning pretrained LLM can effectively identify the categories of Telecom language. The developed framework shows a stepping stone towards realizing intent-driven and self-evolving wireless networks from Telecom languages, and paves the way for the implementation of generative AI in the Telecom domain.

In this work, we study the problem of semantic communication and inference, in which a student agent (i.e. mobile device) queries a teacher agent (i.e. cloud sever) to generate higher-order data semantics living in a simplicial complex. Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations. For effective communication and inference, the teacher seeks minimally sufficient and invariant semantic structures prior to conveying information. These minimal simplicial structures are found via judiciously removing simplices selected by the Hodge Laplacians without compromising the inference query accuracy. Subsequently, the student locally runs its own set of queries based on a masked simplicial convolutional autoencoder (SCAE) leveraging both local and remote teacher's knowledge. Numerical results corroborate the effectiveness of the proposed approach in terms of improving inference query accuracy under different channel conditions and simplicial structures. Experiments on a coauthorship dataset show that removing simplices by ranking the Laplacian values yields a 85% reduction in payload size without sacrificing accuracy. Joint semantic communication and inference by masked SCAE improves query accuracy by 25% compared to local student based query and 15% compared to remote teacher based query. Finally, incorporating channel semantics is shown to effectively improve inference accuracy, notably at low signal-to-noise ratio (SNR) values.

Dual-polarized rate-splitting multiple access (RSMA) with polarization multiplexing has appeared recently as an attractive downlink transmission technique for dual-polarized massive multiple-input multiple-output (MIMO) systems. Dual-polarized RSMA does not require successive interference cancellation (SIC), which avoids practical issues of imperfect SIC decoding. Nevertheless, depolarization phenomena can still degrade the performance of this featured technique. In this work, we exploit the advanced capabilities of a dual-polarized intelligent reflecting surface (IRS) for unleashing an enhanced RSMA polarization multiplexing. To optimize the IRSs, we develop a multi-objective Frank-Wolfe-based algorithm for jointly mitigating cross-polar interference and improving the reception of common and private data streams at multiple users. Representative simulation examples demonstrate that dual-polarized IRS-MIMO-RSMA can efficiently mitigate detrimental depolarization phenomena and outperform conventional systems.

Response to industrial 4.0, massive devices are supposed to be equipped to collect and process a large amount of data. The high-dynamic environment is therefore created because of changes and moves of various equipments. In addition, indoor visible-light localization has attracted wide attention because of the popularity of the light-emitting diode (LED). However, most researches focus on the visible-light localization in the static environment. In this paper, we propose a high resolution visible-light localization approach in the dynamic industrial environment. This approach is based on the Particle Swarm Optimization (PSO) algorithm because it can reduce the influence of changes in a dynamic environment, but, it still be influenced. Thus, the PSO localization further supported by a neural network (NN) to achieve high and robust localization accuracy. Simulations are conducted to verify that the proposed approach has less sensitivity to environment's changes and three times coverage with localization less than 0.1 m compared to the conventional PSO algorithm.

In recent years, vehicular communication has attracted significant research attention for its potential as a fifth generation (5G) application. The IEEE 802.11p standard enables the wireless technology that defines vehicular communication and, due to the time-varying characteristic, one of its critical challenges is to ensure communication reliability. Moreover, this standard is based on orthogonal frequency division multiplexing (OFDM) transmission scheme, which may suffer from nonlinear distortions induced by high power amplifiers (HPA) at the transmitter, degrading the channel estimation and detection performance of the receivers. In this work, the application of deep learning (DL) based channel estimation schemes to the IEEE 802.11p standard in presence of HPA distortions is presented. Simulation results show that the deep neural network (DNN) based estimation schemes outperform conventional data-pilot aided (DPA) and spectral temporal averaging (STA) estimators in terms of bit error rate and normalized mean squared error, evidencing their superiority in providing reliable estimation in mobility scenarios in presence of HPA nonlinear distortions. Furthermore, the hybrid solution, employing a joint DNN-based post-processing and conventional estimation, achieves less computational complexity than the DL-based proposal on top of the initial DPA estimation.

Index Modulation (IM) techniques enable a significant Spectral Energy (SE) and/or Energy Efficiency (EE) enhancement. In this paper, a novel Filter IM domain is being explored, where a novel scheme Filter Shape Index Modulation (FSIM) is proposed. In contrast to most existing Single-Input Single-Output (SISO) IM systems, the proposed scheme achieves a higher SE/EE gain since it exploits all available time and frequency resources. The FSIM system transmits any conventional modulation (QAM, PSK,…) while conveying additional information bits by the index of pulse shaping filter shape. The optimal joint Maximum Likelihood detector is presented, and a low complexity optimal detector is proposed. In addition, an Inter-Symbol Interference (ISI) estimation and cancellation technique is developed to enable the usage of non-Nyquist filters. Finally, the results reveal that FSIM has a similar performance with the two proposed detectors, and it achieves an important performance gain of up to 6 dB compared to equivalent schemes of the same SE in AWGN and frequency selective channels.

In this paper, we address a multiple-input multiple-output (MIMO) communication system based on generalized spatial modulation (GSM) and dual-polarized (DP) antennas in sub-TeraHertz (sub-THz) bands to improve the Spectral Efficiency (SE) while reducing the spatial correlation effect and the space occupancy. Moreover, the joint Maximum Likelihood (ML) detector and a Modified Ordered Block Minimum Mean-Squared Error (MOB-MMSE) detector algorithm are proposed to detect jointly the complex symbols and the virtual bits conveyed by the activated polarization and the transmit antenna combination index. MOB-MMSE can achieve near-ML performance with low complexity. The performance of DP-GSM system over correlated Rayleigh/Rician fading and indoor sub-THz channels is studied. Provided results show that DP-GSM system is robust to the spatial correlation effects under Rician fading channel. In addition, an average bit-error probability (ABEP) upper bounding is derived and analyzed for the DP-GSM system over correlated Rayleigh/Rician fading channels. Since the sub-THz band suffers from many technological limitations and severe RF-impairments such as low transmit output power and important phase noise (PN), the DP-GSM system is studied by considering the sub-THz impairments. Moreover, the Dual Polarized Spatial Multiplexing (DP-SMX) and the Uni-Polarized GSM (UP-GSM) are compared with the DP-GSM system. The results reveal that DP-GSM with low order modulation schemes as QPSK outperforms the UP-GSM. Besides, DP-GSM and DP-SMX provide a good performance up to medium PN, which is interesting for sub-THz bands, but DP-GSM can reduce the transceiver cost by using a lower number of RF chains when the used transmitter architecture is based on RF-switching.

A novel domain for Index Modulation (IM) named “Filter Domain” is proposed. This new domain generalizes many existing modulations and IM domains. In addition, a novel scheme “Filter Shape Index Modulation” (FSIM) is proposed. This FSIM scheme allows a higher Spectral Efficiency (SE) gain than the time and frequency IM dimensions in Single-Input Single-Output (SISO) systems. In the FSIM system, the bit-stream is mapped using an Amplitude Phase Modulation (APM) as QAM or PSK, and an index of a filter-shape changing at the symbol rate. This filter shape, being changed at each symbol, enables a SE gain in SISO system without sacrificing any time or frequency resources. Compared to an equivalent 8QAM and 16QAM schemes and at the same SE, the FSIM with QPSK using 2 and 4 non-optimal filter shapes achieves a gain of 3.8 dB and 1.7 dB respectively at BER= 10-4, and this superiority is maintained in frequency selective fading channel compared to equivalent SISO-IM schemes. A low complexity detection scheme, approaching the maximum likelihood detector performance, is proposed along with a full performance characterization in terms of theoretical probability of filter index error and BER lower bound. Finally, FSIM can achieve better spectral and energy efficiencies when a filter bank and an ISI cancellation technique are optimally designed.

Multiple-Input Multiple-Output (MIMO) technique with Index Modulation (IM) over sub-TeraHertz (sub-THz) bands represent a promising solution to design new wireless ultrahigh data rate systems. However, the system design over sub-THz bands suffers from many technological limitations and severe RF-impairments such as low output power, limited resolution of high-speed low-power Analog-to-Digital Converters and important Phase Noise (PN) introduced by the Local Oscillator (LO). In this paper, different modulations schemes with Generalized Spatial Modulation (GSM) are compared from different perspectives while considering the sub-THz impairments. The effect of PN has been investigated for these modulation schemes in sub-THz channels using uniform linear and rectangular antenna arrays. The obtained results reveal that QPSK-GSM system is the best combination compared to GSM systems with any other Mary modulation scheme (e.g. PSK, DPSK, QAM, PAM). Compared to DQPSK-GSM and 4PAM-GSM at 12bpcu, same number of receive and activated transmit antennas, the QPSK-GSM system offers a gain ranging from 3.4 dB up to 5 dB. The results reveals that low to medium residual PN in distributed oscillator architecture can be tolerated when using GSM-QPSK without phase noise mitigation. Thus, enforcing the GSM to be a promising candidate for ultra-high wireless data rate communication in sub-THz bands.

In this paper, we address multiple-input multiple-output (MIMO) communication technique based on generalized spatial modulation (GSM) and dual-polarized (DP) antennas in sub-TeraHertz (sub-THz) bands to improve the Spectral Efficiency (SE) and to reduce the spatial correlation effect, and the space occupancy. The proposed system adds the polarization dimension to the conventional GSM scheme and enhance the SE. Moreover, the joint Maximum Likelihood (ML) detector and a Modified Ordered Block Minimum Mean-Squared Error (MOB-MMSE) detector algorithm is proposed to detect jointly the complex symbols and the virtual bits conveyed by the activated polarization and the transmit antenna combination index. MOB-MMSE can achieve near-ML performance with low complexity. The performance of DP-GSM system over (un)correlated Rayleigh/Rician fading and sub-THz channels has been studied and the results show that DP-GSM system is robust against channel deficiency such as Rician fading and spatial correlation. Moreover, the Dual polarized Spatial Multiplexing (DP-SMX) and the Uni-Polarized GSM (UP-GSM) are compared with the DP-GSM system. The results reveal that DP-GSM with low order modulation schemes as QPSK outperforms the DP-SMX at high spatial correlations, and thus it is a promising candidate for ultra-high wireless data rates communication in sub-THz band.

Non-Orthogonal Multiple Access (NOMA) is one of the promising techniques to ensure very high spectral efficiency in 5G mobile communications and beyond. In contrast to the orthogonal multiple access (OMA) technique, the NOMA shows outstanding performances in terms of throughput, user fairness, low latency and compatibility with the current and future communication systems. In this paper, we analyze the capacity region in NOMA system and the limited number of multiplexed users under a given power allocation vector (i.e. symmetric/asymmetric channel). In addition, we compare the downlink capacity of the OMA/NOMA systems. Moreover, we have investigated the effect of large constellation order on the power allocation and bit error rate (BER). Comparisons between the coded and uncoded schemes are also presented.

In this paper, a simple and accurate analytical framework for the duplex mode selection and the power allocation scheme for full duplex (FD) device-to-device (D2D) communications underlaying wireless cellular network is proposed. We first aim at maximizing the D2D links rate by properly selecting the duplex mode of the D2D pairs and allocating the power of the users while fulfilling the quality-of-service (QoS) of the cellular users (CUEs). Since the resulting optimization problem is non-convex, we derive a first-order optimal solution by leveraging an interesting optimization tool named as sequential convex optimization theory. We also discuss how to obtain an efficient sub-optimal solution in term of complexity and accuracy by deriving the optimal power ratio between the D2D devices assuming an interference limited system. The simulations have shown more than 95% accuracy of the proposed approaches and provided great insights on the solution design parameters taking into account the CU location and the self-interference threshold.

This paper studies the rate maximization problem of a Full duplex(FD) D2D underlaying cellular network. In the considered scenario, multiple FD-D2D pairs coexist with multiple cellular users which generate mutual interference between the two communication types. The complicated interference environment makes the optimization problem a non-concave problem. To solve this problem, we discuss how to leverage the monotonic optimization theory to obtain the global optimal solution at the cost of high complexity. We also derive a geometric based optimization framework, denoted as GALEN, that achieves the global optimality with much lower complexity, and also it provides a closed form expression for the solution. The simulation results show the importance of the proposed solution.

The low complexity, low cost of implementation, as well as the spectral and energy efficiency, are key features for the development of the internet of things (IoT) networks. In this context, the introduction of index modulation on single carrier-frequency division multiple access (SC-FDMA-IM) has reported significant gains in terms of energy efficiency. In this paper, we evaluate an SC-FDMA-IM scheme tailored for IoT devices taking into account its complexity and performance. For that end, computational simulations and a complexity analysis for different detectors are carried out. Our results show that a significant bit error rate gain is obtained in comparison to a conventional SC-FDMA scheme, while maintaining a reduced computational complexity and power consumption.

Wireless terabit-per-second (Tb/s) links will become an urgent requirement within the next 10 years. However, current methodology for high data rate wireless communication that keep increasing the M-ary modulation schemes and the order of MIMO spatial multiplexing cannot reach Tb/s with a low power consumption. Thus, a new methodology is required with a large bandwidth in the millimeter-wave (mm-Wave) and sub-Terahertz (sub-THz) bands above 90GHz. In addition, it must be able to provide an extremely high spectral efficiency with a low energy consumption. Note that this consumption can be reduced by using constant-envelope modulations such as continuous phase modulation (CPM). However, the CPM has a very low spectral efficiency that limits the desired data rate. This paper suggests a new methodology to reach 1 Tb/s with a low power consumption by using power efficient single carrier with Index Modulation (IM). Simulation results under various uncorrelated/correlated fading channels show that the systems with power efficient modulations as CPM or QPSK can achieve Tb/s with a good performance. Moreover, the link budget and power estimation prove that the constant and near-constant envelope modulations require less than 1-3 Watts for 1 Tb/s with 10-4un-coded BER. Finally, this paper shows that conveying most of the information bits using IM to reach an ultra-high data rate is more power efficient than high order MIMO spatial multiplexing with large M-ary QAM as used in LTE.

One of the most challenging issues of Internet of Things (IoT) devices is energy consumption. A massive deployment of connected devices is power hungry. Maximizing the energy efficiency of their batteries reduces the maintenance cost and extends their autonomy and then their utility. To address this issue, this paper proposes the introduction of index modulation on single carrier-frequency division multiple access (SC-FDMA) uplink transmissions for cellular IoT and machine-to-machine (M2M) networks. It is shown that the proposed SC-FDMA with index modulation (SC-FDMA-IM) scheme achieves 50% of energy efficiency increase, while improving the bit error rate performance by up to 3.5 dB of Eb/N0for the same spectral efficiency performance. However peak-to average power ratio (PAPR) performance are decreased, and PAPR reduction techniques have to be used for this scheme.

Narrow Band-Internet of Thing (NB-IoT) is a recently proposed technology by 3GPP in Release-13. It provides low energy consumption and wide coverage in order to meet the requirements of its diverse applications that span social, industrial and environmental aspects. Increasing the number of repetitions of the transmission has been selected as a promising approach to enhance the coverage in NB-IoT up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement compared with legacy LTE technologies, especially to serve users in deep coverage. However, a large number of repetitions reduces the system throughput and increases the energy consumption of the IoT devices, which reduces their battery lifetime and increases their maintenance cost. In this work, we propose a new method for enhancing the NB-IoT coverage based on machine learning algorithms. Instead of employing a random spectrum access procedure, dynamic spectrum access can reduce the number of required repetitions, increase the coverage, and reduce the energy consumption.

The strongest-cell criterion has been extensively used for handover algorithms during the last cellular-network generations. When network topologies become multi-layered, it results in abrupt behaviors such as the ping-pong effect as a consequence of the power gap between tiers and their irregular deployment. This effect not only affects users' quality of experience but also introduces a significant network overhead. Therefore, we propose an original handover algorithm based on predicted incomplete channel states from a Radio Environment Map to reduce this effect. The proposed algorithm is user triggered, network assisted, and fully backward compatible with LTE-A. Moreover, we evaluate the performance of our proposed algorithm against LTE-A in a two-tier cellular network for different user speeds following the guidelines outlined by the 3GPP on diverse matters (channel, mobility, wrapping, etc.). When applying realistic timing, our results reveal a highly substantial improvement in the number of ping-pong handovers regardless of the handover policy adopted in comparison to LTE-A without sacrificing users' experience; for instance, we obtain at least an order of magnitude decrease in the ping-pong rate at the expense of losing less than 9 percent in spectral efficiency.

In this paper, we explore the idea that 5G will permit the use of multiple waveforms, with each service employing a waveform that is best suited for it. We look at a 5G machine-type communication (MTC) scenario consisting of clustered user equipment employing device-to-device (D2D) communication, such as a smart factory with intercommunicating machinery. The overhead associated with synchronizing a large number of machine-type D2D user equipment (DUE) comes at a cost that may render synchronous communication infeasible or undesirable. Based on this motivation, we consider multiple possible combinations of prominent 5G waveform candidates for cellular users and DUEs, examining the asynchronous performance of all waveforms under consideration and using the performance of synchronous orthogonal frequency division multiplexing (OFDM) as a baseline for comparison. Specifically, we focus on the coexistence of waveforms in which the ordinary cellular users employ OFDM for synchronous communication, as in LTE, and the machine-type DUEs, operating asynchronously, employ a different waveform. When DUEs employ filter bank multicarrier with offset-QAM, the average achieved rate is marginally greater than the synchronous OFDM baseline case, and approximately 43% greater than the asynchronous OFDM case. This result is encouraging, as the benefits of asynchronous D2D communication could be enjoyed in MTC scenarios without suffering any performance reduction compared to the synchronous OFDM scenario. We then investigate how the relative performance of different waveform choices depends on the scenario by varying key parameters. Notably, for asynchronous communication, increasing the transmit power of DUEs results in diminishing benefits unless the DUEs employ a waveform that mitigates interdevice leakage interference.

In this paper, we propose a new adaptive modulation based on the wavelet packet transform, which targets a good resistance against frequency selective channels while avoiding a large PAPR. Classical multi-carrier modulation schemes divide the channel bandwidth into narrowband sub-channels to improve its robustness against frequency selective fading. However, they suffer from the peak-to-average power ratio (PAPR) problem, which occurs due to a random constructive addition of sub-carriers. By contrast, single carrier modulation schemes, where each transmitted symbol fully occupies the bandwidth, are more sensitive to frequency selective environments and less affected by the PAPR problem. In this paper, we show how the bandwidth division can be reconfigurable and adapted to the channel properties, and we provide several examples to prove that the proposed adaptive modulation represents an alternative modulation which is adjustable between two extreme cases: single carrier modulation and classical multi-carrier modulation.

The advent of 5G networks, where a plethora of spectrum-sharing schemes are expected to be adopted as an answer to the ever-growing users' need for data traffic, will require addressing mobility ubiquitously. The trend initiated with the deployment of heterogeneous networks and past standards will give way to a multitiered network where different services will coexist, such as device-to-device, vehicle-to-vehicle or massive-machine communications. Because of the high variability in the cell sizes given the different transmit powers, the classical handover process, which relies solely on measurements, will lead to an unbearable network overhead as a consequence of the high number of handovers. The use of spatial databases, also known as radio environment maps (REM), was first introduced as a tool to detect opportunistic spectrum access opportunities in cognitive radio applications. Since then, REM usage has been widely expanded to cover deployment optimization, interference management or resource allocation to name a few. In this paper, we introduce a handover algorithm that can predict the best network connection for the current user's trajectory from a radio environment map. We consider a geometric approach to derive the handover and handover-failure regions and compare the current handover algorithm used in Long-Term Evolution with our proposed one. Results show a drastic reduction in the number of handovers while maintaining a trade-off between the ping-pong shandover and the handover-failure probabilities.

This paper evaluates the capacity of weighted circularly convolved FBMC/OQAM systems. A rigorous mathematical model is derived to calculate the increase of capacity that can be obtained thanks to the lattice structure of the modulation and the exploitation of the intrinsic interference. The numerical results reveal that a signal to noise ratio gain of 2dB is obtained in one resource block of 12 subcarriers, translating into a capacity increase of 11% with respect to OFDM, which nuances some previous results on this topic in the literature.

This paper analyses a scenario where a Device-To-Device (D2D) pair coexists with an Orthogonal Frequency Division Multiplexing (OFDM) based incumbent network. D2D transmitter communicates in parts of spectrum left free by cellular users, while respecting a given spectral mask. The D2D pair is misaligned in time and frequency with the cellular users. Furthermore, the D2D pair utilizes alternative waveforms to OFDM proposed for 5G. In this study, we show that it is not worth synchronising the D2D pair in time with respect to the cellular users. Indeed, the interference injected into the incumbent network has small variations with respect to time misalignment. We provide interference tables that encompass both time and frequency misalignment. We use them to analyse the maximum rate achievable by the D2D pair when it uses different waveforms. Then, we present numerical results showing what waveform should be utilized by the D2D pair according to the time-frequency resources that are not used by the incumbent network. Our results show that the delay induced by linearly convolved waveforms make them hardly applicable to short time windows, but that they dominate OFDM for long transmissions, mainly in the case where cellular users are very sensitive to interference.

Multicarrier modulation systems suffer from large peak-to-average power ratio (PAPR). Tone reservation is a popular technique for PAPR reduction. It consists in reserving a number of carriers to produce a redundant additive signal which reduces the peak power. There are several schemes to select the reserved carriers. In this paper, we propose a new selection method for adaptive tone reservation. It is showed through simulations that this new proposed selection technique allows 5 dB gain in terms of signal-to- noise ratio for a bit error rate of 10-3, for different constellations, in frequency-selective Rayleigh fading channel.

This paper establishes a necessary condition that must be satisfied by the modulation waveforms of any generalized waveforms for multicarrier (GWMC) system with better peak-to-average power ratio (PAPR) than conventional orthogonal frequency division multiplexing (OFDM). GWMC systems include in particular all classical multicarrier modulation systems. As a consequence, we show that OFDM has the best PAPR performance over all GWMC systems that do not satisfy this necessary condition. We also identify an infinite family of GWMC systems with the same PAPR performance as OFDM. To illustrate our results, we present the simulations of the PAPR behavior for different GWMC systems, including some with better PAPR performance than OFDM.

This paper focuses on the use of a long term evolution (LTE)- like broadband system using filter bank multicarrier (FBMC) PHY layer in professional mobile radio (PMR) communication band. PMR communication systems have been designed for emergency, security, and disaster relief communications in the 400 MHz band. The main goal is to deploy a broadband LTE-like system to coexist without causing harmful interferences with actually deployed PMR systems in unused bands. Besides the use of FBMC scheme, some additional modifications on the resource block size and the pilot pattern structure have been introduced in LTE standard to be adapted and able to operate in PMR communication environment. Obtained results through simulations demonstrated the suitability of proposed changes in terms of achieved bit error rates compared with defined structure in LTE system.

In this study, the performance of different preamble‐based channel estimation techniques is analysed for orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) modulation systems. Three pilot allocation methods, the pair of pilot, the interference cancellation method, and the interference approximation method are considered. A theoretical expression of the estimation mean square error (MSE) is performed for each of them, which considers the selectivity of the channel and the interferences from all the neighbouring time–frequency positions. Moreover, an application of the linear minimum MSE estimator in OFDM/OQAM is investigated and an analysis of its MSE performance is provided as well. It is shown that, due to the intrinsic interference, the MSE of the channel estimation inevitably reaches an error floor whatever the applied method is. Numerical results show that the theoretical MSE assessments match those obtained by simulation. In addition to the MSE, the bit error rate performance for the different channel estimators is depicted.

This paper deals with the minimum mean square error (MMSE)-based multipath channel and noise variance estimation in the case of a pilot-aided orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) system. The theoretical expression of the LMMSE channel estimation is formulated and a simpler closed-form is derived. The MSE analysis shows that LMMSE inevitably reaches an error floor due to the interference inherent to OFDM/OQAM modulation. Furthermore, an algorithm for the joint MMSE estimation of both the channel and the noise variance (J-MCNE) is proposed and features a reduced complexity thanks to the low-rank approximation method. The noise level estimation represents a challenge as it is concealed in the intrinsic interferences generated by the OFDM/OQAM. Simulations reveal the capability of the proposed J-MCNE method to estimate the noise variance and show that the achieved bit error rate (BER) is close to that of the perfect estimation.

In this paper, we investigate the resource management problem in orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) cognitive radio (CR) systems. We propose performing resource allocation based on interference alignment (IA) in order to improve the spectral efficiency of CR systems without affecting the quality of service of the primary system. IA plays a role in the proposed algorithm to enable the secondary users (SUs) to cooperate and share the available spectrum, which leads to a considerable increase in the spectral efficiency of CR systems. However, IA based spectrum sharing is restricted to a certain number of SUs per subcarrier in order to satisfy the IA feasibility conditions. Accordingly, the resource allocation problem is formulated as a mixed-integer optimization problem, which is considered an NP-hard problem. To reduce the computational complexity of the problem, a two-phases efficient sub-optimal algorithm is proposed. In the first phase, frequency-clustering is performed in order to satisfy the IA feasibility conditions, where each subcarrier is assigned to a feasible number of SUs. Whenever possible, frequency-clustering stage considers the fairness among the SUs. In the second stage, the available power is allocated among the subcarriers and SUs without violating the constraints that limit the maximum interference induced to the primary system. Simulation results show that IA with frequency-clustering achieves a significant sum rate increase compared to CR systems with orthogonal multiple access transmission techniques.

In this paper, the problem of resource allocation in overloaded orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) cognitive radio (CR) system is considered. The objective is to allocate the different subcarrier and distribute the available user power in order to maximize the CR system throughput. The interference induced to the primary system should not be harmful and hence, should not exceed the prescribed limit. Interference alignment (IA) technique is employed in order to achieve an efficient use of the available radio resources. Without affecting the quality of service of the primary system, IA enables the secondary users to share the available spectrum which increases the CR system degrees-of-freedom. Due to IA feasibility condition, the spectrum sharing with perfect IA is restricted to a certain number of user per subcarrier. Accordingly, the resource management problem is formulated as a mixed-integer optimization problem which is considered as an NP-hard problem. To reduce the computational complexity of the problem, a two-phase efficient sub-optimal algorithm is proposed. Frequency-clustering is performed in the first phase to the overcome IA feasibility conditions while the power is distributed among subcarriers in the second phase. Simulations show that IA technique achieves a significant sum-rate increase of CR systems compared with the traditional CR systems that use orthogonal multiple access transmission techniques.

Our main emphasis is on the use of enhanced OFDM and filter bank based multicarrier (FB-MC) waveforms for utilizing effectively the available fragmented spectrum in heterogeneous radio environments. Special attention is on the broadband-narrowband coexistence scenario of the Professional Mobile Radio (PMR) evolution. The target here is to provide broadband data services in coexistence with narrowband legacy services of the TETRA family. The core idea is a multi-mode radio platform, based on variable filter bank processing, which is able to perform modulation/detection functions simultaneously for different signal formats with adjustable center frequencies, bandwidths and subchannel spacings.

Energy consumption in downlink mode is becoming an important topic as cellular communications grow into a large scale enterprise. The search for high rates keeping energy constraints low has put forward the idea that cells with smaller size may improve not only the capacity of the network, but also reduce the amount of energy that is needed to achieve such capacities. When using heterogeneous networks, users can be encouraged to handoff to a femtocell, that offers better capacity per unit energy spent, by means of different handoff strategies. These strategies may also improve the energy use of the network if the handoff priority is given to both, capacity, and energy use.

This paper considers the resources allocation problem in decode-and-forward (DF) relayed OFDM based cognitive radio (CR) system. The subcarrier matching and power allocation are optimized jointly under the individual power constraints in source and relay so that the sum rate is maximized while the interference induced to the primary system is kept below a pre-specified interference temperature limit. Therefore, the dual decomposition technique is adopted to obtain an asymptotically optimal solution. Moreover, a suboptimal scheme is presented to get rid of the high computational complexity of the optimal scheme. The suboptimal algorithm solves the problem in two phases. The subcarriers powers are adjusted in the first phase considering the interference that can be induced to the primary system in addition to the available power budgets. The subcarriers are matched in the second phase based on the fixed powers and the channel qualities. The performance of the different schemes is discussed throughout numerical simulation.

An OFDM based cognitive radio (CR) network is considered in this paper. The goal is to maximize the total sum rate of the CR system while ensuring that no excessive interference is induced to the primary system. The optimal scheme adopts the dual decomposition technique and in order to reduce the computational complexity of the optimal scheme, a greedy suboptimal algorithm is proposed. Selected numerical results is discussed to reveal the near-optimal performance of the suboptimal scheme and the superiority of the proposed algorithm over the random and the signal-to-noise ratio (SNR) based schemes.

Placing smaller cells in a heterogeneous network can be beneficial in terms of energy because better capacities can be obtained for a given energy constraint. These type of deployments not only highlight the need for appropriate metrics to evaluate how well energy is being spent, but also raise important issues that need to be taken into account when analysing the overall use of energy. In this study, handoff strategies, bandwidth allocation and path loss calculations in different scenarios, illustrate how energy can be consumed in a more efficient way when cell size is decreased. As a result, users can experience higher capacities while spending less energy, depending whether they handoff or not, increasing the overall performance of the network.

In this letter, the problem of resources allocation in decode-and-forward (DF) relayed OFDM based cognitive system is considered. The dual decomposition technique is adopted to obtain an asymptotically optimal subcarrier pairing, relay selection, and power allocation. The resources are optimized under the individual power constraints in source and relays so that the sum rate is maximized while the interference induced to the primary system is kept below a pre-specified interference temperature limit. Moreover, a sub-optimal scheme is presented to avoid the high computational complexity of the optimal scheme. The sub-optimal algorithm allocates jointly the different resources taking into account the channel qualities, the DF-relaying strategy, the interference induced to the primary system, and the individual power budgets. The performance of the different schemes and the impact of the constraints values are discussed through the numerical simulation results.

In this paper, the problem of resource allocation in OFDM based decode-and-forward (DF) cognitive radio (CR) system is considered. The objective is to maximize the CR system throughput by optimizing the subcarrier pairs and powers. The interference introduced to the primary system should not exceed the prescribed interference temperature limit. The resources are optimized jointly by applying the dual decomposition technique to get asymptotically optimal solution. In addition, a suboptimal algorithm that performs the resources allocation in two phases is proposed. By the suboptimal algorithm, the subcarrier pairing is first performed considering the channels quality as well as the interference introduced to the primary system. Afterwards, the powers are allocated in the second phase by applying a waterfilling-like algorithm. The suboptimal algorithm reduces significantly the computational complexity with small performance degradation. The simulation results confirm the efficiency of the proposed algorithm.

We consider a cognitive relay network with multi-relay nodes where the cognitive network transmits over the unused spectrum bands originally allocated to the primary system. This paper presents an optimal and suboptimal schemes that optimize the subcarrier pairs, relay selection and power allocation so that the total system capacity is maximized while guaranteeing that the interference introduced to the primary system is not harmful. The optimal scheme adopts the dual decomposition technique to allocate the different resources. In order to reduce the computational complexity of the optimal scheme, a greedy suboptimal algorithm is proposed. The suboptimal algorithm jointly allocates the different resources by considering channels quality, interference to the primary system and relaying strategy. The performance of the different scheme is discussed through the numerical simulations.

In this paper, an OFDM-based multi-relay cognitive radio network is considered where the relays use the decode-and-forward protocol to assist the source to relay transmission. The objective is to maximize the capacity of the CR network by optimizing the relay selection, subcarrier pairing and the power allocation. The optimization is performed under the interference to primary constraints as well as the individual power constraints. The problem is formulated as a mixed integer programming problem and the joint optimal solution is found using the dual decomposition technique. To reduce the computational complexity of the optimal scheme, a suboptimal algorithm is presented. Simulation results reveal that the suboptimal algorithm achieves a near optimal solution with less complexity.

Relayed transmission is a promising technique to increase coverage and achievable capacity of the communication systems. A cognitive OFDM-based decode-and-forward (DF) cooperative relay network is considered in this paper. The objective of the problem is to maximize the capacity of the cognitive radio (CR) system while guaranteeing that the interference introduced to the primary system is below the prescribed interference temperature limit. The optimal power allocation in the source and the relay is presented and a low computational complexity near-optimal algorithm is proposed. The simulation results confirm the efficiency of the proposed algorithm.

An efficient fairness-aware uplink resource allocation algorithm in orthogonal frequency-division multiplexing-based cognitive radio systems is presented. The proposed resource allocation algorithm is divided into two steps. The subcarriers to user assignment is first performed, and then the power is allocated to the different subcarriers. The objective is to maximise the total data rate while guaranteeing that the interference introduced to the primary system is under the prescribed interference temperature limit. The fairness among users is considered within the subcarrier allocation by reducing the probability of having users whose instantaneous rates are below a given minimum rate. Many factors will be considered during the resource allocation process like the channel quality, potential interference caused to the primary bands, instantaneous rate achieved by every user and the increment in the total data rate. Simulation results confirm the efficiency of the proposed algorithm.

This paper presents an efficient and fair uplink resource allocation algorithm in multicarrier based cognitive radio systems. The proposed resource allocation is divided into two steps. The subcarriers to user assignment is first performed and then the power is allocated to the different subcarriers. The subcarriers are allocated based on the channel quality, amount of interference imposed to the primary bands, instantaneous rate achieved by every user and the increment in the total data rate. The fairness among users is considered within the subcarrier allocation by reducing the probability of having users whose instantaneous rates are below a given minimum rate. The power is distributed among subcarriers so that the total data rate is maximized while the interference introduced to the primary system is guaranteed to be under the prescribed interference temperature limit. Simulation results confirm the efficiency of the proposed algorithm.

This paper presents a two-step downlink resource allocation algorithm for multicarrier based cognitive radio systems. The algorithm allocates the subcarriers to the users in the first step. In the second step, the power is allocated to these subcarriers in order to maximize the downlink capacity of the system without causing excessive interference to the primary user. The performance of the proposed algorithm is investigated using computer simulations to prove that it achieves near optimal performance and it is better than other existing algorithms. Moreover, the throughput of Orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier system (FBMC) based cognitive radio systems are compared to show the efficiency of using FBMC in future cognitive radio systems.

In this paper the combination of Transmit Antenna Selection with Linear Dispersion Code Selection is studied. Two optimization criteria are proposed (bit error rate minimization and throughput maximization). The performance of the proposed spatial link adaptation scheme is evaluated under low mobility environments concluding that maximum spatial diversity is achieved as well as a smooth transition between codes with low spatial multiplexing rate and high spatial diversity (suitable for low SNR), to codes with high multiplexing rate but low diversity order (suitable for high SNR) maximizing the overall system throughput.

The paper explores the technical limits and possibilities to transmit live audiovisual contents in the uplink mode by means of IP networks based on WiMAX broadband system using a professional camera. A previous evaluation of the WiMAX link performances in real urban environments is tackled, and then, obtained results used to implement an Automatic Video Coding Rate Adjustment (AVCRA) algorithm that adaptively adjusts the video codec parameters to the experienced wireless channel capacity. The experimental network also contains a designed antenna small enough to be incorporated in the camera. Special considerations regarding the radiation diagram have been taken into account for not affecting the head of the proper cameraman. Obtained experimental WiMAX throughput in function of the signal to noise ratio, and the Peak signal to noise ratio (PSNR) levels demonstrated the feasibility of live video links over WiMAX networks in urban environment.

Quick development of wireless communication makes the spectrum scarcity as a one of the serious problems. Cognitive radio (CR) is considered to be one of the possible solutions to solve the spectrum efficiency problem. Multicarrier communications are considered to be used in CR networks. Due to its high spectral efficiency, filter bank multicarrier (FBMC) can be considered as an alternative to conventional orthogonal frequency division multiplexing (OFDM) for transmission over the CR networks. This paper addresses the problem of resource allocation in multicarrier based CR networks. The objective is to maximize the downlink capacity of the network under both total power and interference introduced to the primary users (PU's) constraints. The optimal solution has high computational complexity which makes it unsuitable for practical applications and hence a low complexity suboptimal solution is proposed. The performance of using FBMC instead of OFDM in CR systems is investigated. Simulation results illustrate that the proposed resource allocation algorithm with low computational complexity achieves near optimal performance and prove the efficiency of using FBMC in CR context.

In this paper a new permutation zone which combines adjacent and distributed subcarrier permutation is proposed for Orthogonal Frequency Division Multiple Access (OFDMA) systems. For such purpose two types of burst are defined: localized and distributed. A new resource allocation and scheduling algorithm that exploits such structure is then proposed. Furthermore, the effects of limited feedback are also investigated where only the n-best subchannel quality metrics are feedback. As shown in the simulation results, a tradeoff between spectral efficiency, delay and load signaling requirements is obtained which can be adjusted by modifying the number of subchannels signaled in the uplink. Additionally, the proposed scheme turns out to be a very efficient way to increase the system fairness and therefore improving the quality of service.

Wireless communications are experiencing a great increasing demand, both user and bandwidth capacity. The efficiency in the resource management and usage will be a dramatically important issue since spectrum and power is a scarce resource, even more in the wireless environment. Under these circumstances, adaptive resource management will lead to high spectral efficiency rates and power saving algorithms. Previous works have focused on increasing the system capacity by means of complex power and rate adaptation techniques at the expense of unfair systems. The present proposal proposes a low complexity algorithm for adaptive resource allocation considering different quality of service classes in a multicarrier-code division multiple access (MC-CDMA) system, which is considered according to several researches, as a promising air interface scheme for the beyond 3G systems.

The increasing interest on multimedia applications and services in portable computing communication will create at same time a large demand for high speed wireless data on communication devices. The 4th generation (4G) mobile communication is expected to offer 100 Mbps or higher data rates in downlink. orthogonal frequency division multiplexing (OFDM) and multiple carrier code division multiple access (MC-CDMA) are potential candidates for next generation of mobile radio communications for achieving hight data rate transmissions. In cellular networks, the frequency reuse is one of the main parameters to consider since it is more suitable to optimize the spectrum reuse. Using a frequency reuse factor of one, it could be a possible system using the same frequency in all the network. In this proposal the sensitivity and the impact of an interfering base station based on a MC-CDMA system is investigated and analyzed using different channel coding and decoding processes.

Abstract ‘Always Best Connected’ (ABC) is considered one of the main requirements for next generation networks. The ABC concept allows a person to have access to applications using the devices and network technologies that best suits his or her needs or profile at any time. Clearly, this requires the combination of a set of existing and new technologies, at all levels of the protocol stack, into one integrated system. In this paper, a considerable set of the technologies, that are expected to play a key role towards the ABC vision, are presented. Starting from a reference architecture, the paper describes the required enhancements at certain levels of a traditional protocol stack, as well as technologies for mobility and end‐to‐end Quality of Service (QoS) support. The paper concludes with a case study that reveals the advantages of the ABC concept. This article replaces a previously published version (Wireless Communications and Mobile Computing; 5(2): 175‐191. [DOI: 10.1002/wcm.207]). Retraction notice DOI: 10.1002/wcm.426 . Copyright © 2006 John Wiley & Sons, Ltd.

The work presented in this proposal analyzes the effect of the pilot location process over both the time, and frequency directions within the OFDM frames. The optimum time-frequency pilot spacing that minimize the mean square error of the channel estimated has been analytically designed, and the system performance analyzed in terms of bit error rate using different bi-dimensional interpolation filters

This paper presents the emerging requirements users are imposing upon the evolving world of heterogeneous 4G mobile/wireless networks through their perception of final services. The mapping proposed in this paper groups together these user requirements in three main and distinguishable categories: service provision, connectivity, and adaptability and reconfigurability, by describing system concepts for each category from user terminal to network and services/applications.

We investigate the performance and the effects of the decorrelation process in the multi-user stage detection with several user's signals, using multiple carrier code division multiple access (MC-CDMA) modulation. We are dealing with the reverse link, assuming an asynchronous detection at the base station.

Requests for higher data transmissions rates and system performance in multiuser radio communications, needed for the future mobile generations or wireless local area networks, have centered them in the focus of interest of investigations in the signal processing area. MC-CDMA systems with orthogonal user codes based on Hadamard codes do not have any control of the transmitted waveform time spreading. In this case, a time guard is necessary to avoid the ISI effect. We have designed an alternative set of non-binary codes based on the time-concentration capability of prolate spheroidal functions. This property allows the use of several waveforms with almost ISI-free behavior. The advantage of using discrete prolate functions (DPF), instead of Hadamard codes, as users' codes is obvious; when the system is not fully loaded, users are able to transfer greater bit rates with better error performances. In a multiuser scenario, the receiver design is almost identical to the standard case. Exploiting also the DPF property of double orthogonality, implementation of a new set of matched filters in a shorter time interval is investigated.