Gaussian Splatting (GS) offers a promising alternative to Neural Radiance Fields (NeRF) for real-time 3D scene rendering. Using a set of 3D Gaussians to represent complex geometry and appearance, GS achieves faster rendering times and reduced memory consumption compared to the neural network approach used in NeRF. However, quality assessment of GS-generated static content is not yet explored in-depth. This paper describes a subjective quality assessment study that aims to evaluate synthesized videos obtained with several static GS state-of-the-art methods. The methods were applied to diverse visual scenes, covering both 360° and forward-facing (FF) camera trajectories. Moreover, the performance of 18 objective quality metrics was analyzed using the scores resulting from the subjective study, providing insights into their strengths, limitations, and alignment with human perception. All videos and scores are made available providing a comprehensive database that can be used as benchmark on GS view synthesis and objective quality metrics.

This paper proposes a Question-Answering (QA) system for the telecom domain using 3rdGeneration Partnership Project (3GPP) technical documents. Alongside, a hybrid dataset, Telco-DPR11Telco-DPR dataset presented in this paper is available open-source [1]., which consists of a curated 3GPP corpus in a hybrid format, combining text and tables, is presented. Additionally, the dataset includes a set of synthetic question/answer pairs designed to evaluate the retrieval performance of QA systems on this type of data. The retrieval models, including the sparse model, Best Matching 25 (BM25), as well as dense models, such as Dense Passage Retriever (DPR) and Dense Hierarchical Retrieval (DHR), are evaluated and compared using top-K accuracy and Mean Reciprocal Rank (MRR). The results show that DHR, a retriever model utilising hierarchical passage selection through fine-tuning at both the document and passage levels, outperforms traditional methods in retrieving relevant technical information, achieving a Top-10 accuracy of 86.2 %. Additionally, the Retriever-Augmented Generation (RAG) technique, used in the proposed QA system, is evaluated to demonstrate the benefits of using the hybrid dataset and the DHR. The proposed QA system, using the developed RAG model and the Generative Pre-trained Transformer (GPT)-4, achieves a 14% improvement in answer accuracy, when compared to a previous benchmark on the same dataset.

Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. NeRF uses a volumetric functional representation that maps 3D points to their corresponding colors and opacities, enabling photorealistic view synthesis from arbitrary viewpoints. Despite its advancements, the efficient streaming of NeRF content remains a significant challenge due to the large amount of data involved. This paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming. While in the former, images are coded and then transmitted throughout the network, in the latter, the respective NeRF model parameters are coded and transmitted instead. This work also highlights the trade-offs in complexity and performance, demonstrating that the NN parameter-based strategy generally offers superior efficiency, making it suitable for one-to-many streaming scenarios.

This short paper proposes a new database - NeRF-QA - containing 48 videos synthesized with seven NeRF based methods, together with their perceived quality scores, resulting from subjective assessment tests; for the videos selection, both real and synthetic, 360 degrees scenes were considered. This database will allow to evaluate the suitability, to NeRF based synthesized views, of existing objective quality metrics, and also the development of new quality metrics.

Detecting and diagnosing the root cause of failures in mobile networks is an increasingly demanding and time-consuming task, given its technological growing complexity. This paper focuses on predicting and diagnosing low User Downlink (DL) Average Throughput situations, using supervised learning and the Tree Shapley Additive Explanations (SHAP) method. To fulfill this objective, Boosting classification models are used to predict a failure/non-failure binary label. The influence of each counter on the overall model’s predictive performance is performed based on the TreeSHAP method. From the implementation of this technique, it is possible to identify the main causes of low throughput, based on the analysis of the most critical counters in fault detection. Furthermore, from the identification of these counters, it is possible to define a system for diagnosing the most probable throughput degradation cause. The described methodology allowed not only to identify and quantify low throughput situations in a live network due to the occurrence of misadjusted configuration parameters, radio problems and network capacity problems, but also to outline a process for solving them.

With the increase in consumption of multimedia content through mobile devices (e.g., smartphones), it is crucial to find new ways of optimizing current and future wireless networks and to continuously give users a better Quality of Experience (QoE) when accessing that content. To achieve this goal, it is necessary to provide Mobile Network Operator (MNO) with real time QoE monitoring for multimedia services (e.g., video streaming, web browsing), enabling a fast network optimization and an effective resource management. This paper proposes a new QoE prediction model for video streaming services over 4G networks, using layer 1 (i.e., Physical Layer) key performance indicators (KPIs). The model estimates the service Mean Opinion Score (MOS) based on a Machine Learning (ML) algorithm, and using real MNO drive test (DT) data, where both application layer and layer 1 metrics are available. From the several considered ML algorithms, the Gradient Tree Boosting (GTB) showed the best performance, achieving a Pearson correlation of 78.9%, a Spearman correlation of 66.8% and a Mean Squared Error (MSE) of 0.114, on a test set with 901 examples. Finally, the proposed model was tested with new DT data together with the network's configuration. With the use case results, QoE predictions were analyzed according to the context in which the session was established, the radio transmission environment and radio channel quality indicators.

Performing an efficient management of mobile networks has become an increasingly difficult task, as these networks are becoming more complex, which results in more data to evaluate and optimize. This paper is focused on the performance evaluation of a Long Term Evolution (LTE) network using unsupervised learning techniques. The main objective is to detect groups of cells that show similar performances and, consequently, to identify the groups that perform below the desired threshold. Additionally, this work also aims to identify which cell configurations are associated with the best performance. In order to fulfill the first objective, a methodology based on the application of clustering algorithms to features extracted from Key Performance Indicators (KPIs) was developed. Regarding the second objective, statistical testing was applied to evaluate the dependence between the cells configuration and the respective cells performance. It was verified that there is not a significant difference in the obtained results using different clustering algorithms. In the majority of the cases, only two groups of cells were identified: one group consisting essentially on the cells with the best performance and the other group containing the worst performing cells. As far as the connection between configuration data and performance data is concerned, only one influencing case, referring to a parameter associated with the cell subscription capacity, was detected.

In this paper, a novel quality metric to evaluate depth-based synthesized views is proposed. This metric relies on a hybrid approach that uses features extracted in different phases of the image synthesis procedure, namely from the bitstream, from intermediate data produced during the synthesis process, and from the final synthesized view; these features are then combined using support vector regression. A new data set of synthesized images, with compression and rendering artifacts, was built and used to develop and assess the proposed metric. The metric performance is compared with conventional full-reference two-dimensional image quality assessment metrics and with quality assessment metrics developed specifically for synthesized images. The experimental results showed that the proposed solution outperforms the considered benchmark metrics, being able to predict the subjective quality scores of the synthesized images with a Pearson correlation coefficient close to 0.9.

This paper presents a novel Quality of Experience (QoE) prediction model, for voice and web browsing based on real Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) data. It estimates the user perceived quality, in a Mean Opinion Score (MOS) scale, by evaluating several Radio Frequency (RF) channel measurements and Quality of Service (QoS) metrics. Real Drive Test (DT) data and MOS measurements were used as reference data, in order to produce a new QoE prediction model, using machine learning techniques. The Support Vector Regression (SVR) algorithm was used to map the QoS metrics into MOS. The new developed model enables the application of QoE as a more realistic network optimization criteria. The QoE model for voice calls presented a Root Mean Square Error (RMSE) of 11% and a correlation of 62%, when comparing the predicted MOS to the one that was measured. The web browsing model showed an higher correlation (of 92%) and a lower RMSE (of 10%).

In the last years, the video content consumed by mobile users has increased exponentially. Since mobile network capacity cannot be increased as fast as required, it is crucial to develop intelligent schedulers that allocate radio resources very efficiently and are able to provide a high Quality of Experience (QoE) to most of the users. This paper proposes a new and effective scheduling solution—the Maximum Buffer Filling (MBF) algorithm—which aims to increase the number of satisfied users in video streaming services provided by wireless networks. The MBF algorithm uses the current buffer level at the client side and the radio channel conditions, which are reported to the network by the client, as well as the bitrate of the requested video segment. The proposed scheduling strategy can also fulfill different satisfaction criteria, since it can be tuned to maximize the numbers of users with high QoE levels or to minimize the number of users with low QoE levels. A simulation framework was developed, considering a Long Term Evolution (LTE) scenario, in order to assess the performance of the proposed scheduling scheme and to compare it with other well‐known scheduling solutions. The results show the superior performance achieved by the proposed technique, in terms of the number of satisfied and unsatisfied users.

In this letter, methods and corresponding complexities for fast matrix inversion updates in the context of massive multiple-input multiple-output (MIMO) are studied. In particular, we propose an on-the-fly method to recompute the zero forcing (ZF) filter when a user is added or removed from the system. Additionally, we evaluate the recalculation of the inverse matrix after a new channel estimation is obtained for a given user. Results are evaluated numerically in terms of bit error rate (BER) using the Neumann series approximation as the initial inverse matrix. It is concluded that, with fewer operations, the performance after an update remains close to the initial one.

This paper presents a novel solution for joint turbo equalization and carrier synchronization in SC-FDE schemes. The proposed solution resorts to the Bayesian principle for the synchronization operation. In fact, the so called sensor factor is conditioned with respect to the phase drift and the transmitted data symbol. The phase drift is the quantity of interest and the data symbol is, for synchronization purposes, considered a nuisance parameter. After marginalizing this nuisance parameter the sensor factor is expressed in terms of the observation and the phase drift only. This allows us to implement the recursive Bayesian filtering technique known as sequential importance sampling (SIS). Finally, we compare our solution with an alternative solution, namely, the a posteriori (MAP) estimator.

In this study the authors propose a novel joint detection and phase noise estimation scheme suited for severely time‐dispersive channels. The authors consider single‐carrier modulations combined with frequency domain equalisation schemes where the wireless transmission is impaired with phase noise. An iterative frequency‐domain equaliser is assumed on the receiver side and the phase noise is estimated and compensated for after the equalisation step and within each iteration of the equaliser. In fact, by exploiting the Gaussianity of the equaliser output the authors are able to track the phase noise using stochastic recursive filtering techniques. These techniques share the same dynamic state‐space (DSS) model. The observation equation corresponds to the measurement of the phase noise of a digitally‐modulated signal affected by additive white Gaussian noise, and the dynamics equation corresponds to the Wiener–Lévy model for the phase noise. Supported on this DSS model the authors aim at estimating the unknown phase noise value given all observations up to the current time instant. In a Bayesian context this represents estimating recursively in time the filtering and the predictive distributions. From these distributions a minimum mean‐squared error estimate of the phase noise is determined.

With this paper we aim to show that Gaussian sums particle filtering (GSPF) combined with iterative frequency-domain equalization (IFDE) for single-carrier (SC) modulations offer an appropriate solution for data transmission over highly frequency-selective channels with strong phase noise and linear drift. We present a promising IFDE scheme dubbed iterativeblock decision feedback equalization (IB-DFE) and propose a post-equalization phase noise estimation scheme within the iteration chain. In fact, by exploiting the Gaussianity of the equalizer output we are able to track the phase noise value by updating the state posterior distribution using the GSPF algorithm. Additionally, simulation results are presented considering QPSK, 16-QAM, and 64-QAM signalling in highly frequency-selective channels and different values for the phase noise variance and linear drift.

In this paper we show that Gaussian sum particle filters (GSPFs) present an effective solution for the problem of estimating phase noise in digital communications. We start by describing the problem of estimating the phase noise in terms of its general Bayesian formulation and then present the solution offered by the GSPF. Our filter is based in two central features. On the one hand, the system model comprising both the dynamics model and the observations model and on the other hand the sensor factor, a periodic function with respect to the current observation of the state. Although phase noise estimation algorithms are widely represented in the literature to our knowledge it has never been considered to use GSPFs for this purpose. Furthermore, other solutions using conventional particle filters assumed the modulation symbols to be PSK, i.e., phase-defined, while we assume phase- and amplitude-defined symbols, e.g., M-QAM.

In this paper the spatial distribution of the channel capacity of Multiple Input Multiple Output (MIMO) wireless systems within a microcell scenario is studied. Based on the capacity location dependency, made clear by realistic simulation results, a segmentation of the cell area into high and low capacity regions is proposed. The Base Station (BS) and Mobile Stations (MSs) inter-antenna spacing impact on these regions is also investigated. The results suggest the most efficient ways to enlarge the high capacity region, providing a framework to optimize the design and deployment of MIMO systems.

The International Telecommunication Union (ITU) established new requirements for the next generation of wireless communications. To meet those requirements, the concept of femtocells emerged as a solution, aiming at increasing capacity and offloading the macrocell network. In a co-channel deployment between macrocells and femtocells, the interference is a serious problem and that needs to be addressed. Since the femtocells are deployed by the end-user, the operator does not have a pre-planned solution to avoid interference, thus a self-organizing solution between femtocells located in the same area is required to manage the interference. In this paper we propose a flexible spectrum usage algorithm which runs at the femtocells, while the macrocells use a fixed reuse scheme in order to manage the interference in the network. In this study we verify that by assigning a fixed reuse scheme 1/3 for the macrocells, the performance achieved by the users is higher, when comparing the same scenario with reuse 1. We also propose a flexible spectrum usage algorithm for the femto cell, which is able to achieve higher user throughput in the femto layer and minimize the interference in the macro layer, when compared to a non-channel aware algorithm, which allocates the PRBs randomly.

Disaster relief networks have to be highly adaptable and resilient. Cognitive radio enhanced ad-hoc architecture have been put forward as a candidate to enable such networks. Spectrum sensing is the cornerstone of the cognitive radio paradigm, and it has been the target of intensive research. The main common conclusion was that the achievable spectrum sensing accuracy can be greatly enhanced through the use of cooperative sensing schemes. When considering applying Cognitive Radio to ad-hoc disaster relief networks, spectrum sensing cooperative schemes are paramount. A centralized cluster-based orchestration cooperative sensing scheme is proposed, where the cluster head controls and adapts the distribution of the cluster sensing's nodes according to the monitored spectrum state. The proposed scheme performance is evaluated through a framework, which allows gauging the accuracy of wideband multi-channel spectrum sensing cooperative schemes, showing that the proposed scheme performance reaches the theoretical maximum achievable accuracy while outperforming the round robin scheme.

A highly modular and configurable platform for designing parallel H.264 video encoders on multi-core processors is presented. Departing from the H.264/AVC reference software, preliminary optimizations were conducted and new data structures were developed, in order to support the encoder's parallelization and to confer the developed platform with a flexible, user configurable and highly scalable characteristics in what concerns the number of available cores to be used in the target concretization. After a careful assessment using different instantiations of the platform, the experimental results have shown that significant and close to linear speedups in what concerns the achieved frame-rate can be obtained, by simultaneously exploiting the several different parallelization models that are made available by this platform.

This paper presents an unified and empirical propagation model to obtain the received power in mixed scenarios, with outdoor and indoor environments, or in a scenario with only one kind of environment, either for an urban, sub-urban or rural scenario, with or without vegetation. This unified model is called the Lisbon University Institute (LUI) one and can be included into planning tools for wireless communication systems. The developed model is suitable for Path Loss prediction in mobile, as well as fixed wireless network systems, e.g., Wireless Fidelity, Universal Mobile Telecommunications System and Worldwide Interoperability for Microwave Access, considering Line-of-Sight or Non-Line-of-Sight propagation conditions.

This paper presents the validation of the unified propagation model, the Lisbon University Institute (LUI) model, which is valid for three wireless technologies: Wireless Fidelity, Universal Mobile Telecommunications System and Worldwide Interoperability for Microwave Access. With this purpose in mind, several experimental trials for those technologies were performed in different scenarios, where mixed environments (outdoor and indoor) were included. This generalized and unified propagation model was tuned by adjusting specific parameters for each different technology, based on the achieved results from the field experiments.

The analysis of the impact of voice-over-IP into HSPA and LTE multi-service traffic is a challenge. The validation of a Bernoulli-Poisson-Pascal multi-service traffic model in the presence of mobility is explored for different values for the number of channels. A mixture of voice (VOI) and video telephony (VTE) is considered. In a previous work, a critical situation was assumed where the total number of channels was exactly the same as needed by four VTE sessions. However, when few extra channels are added there is a significant reduction on VTE on/off blocking probability. With this few extra resources, VOI can now be easily supported. Since it is supported on top of a system saturated with VTE sessions, Pbonoffdecreases 4.6 times for VTE. Since the number of resources is not critical anymore, the range of values of the offered traffic for which the theoretical model agrees with simulation results becomes wider. There is no significative difference between the multi-service traffic results for the seven cell geometry and the roundabout (or ring) cellular topologies in the presence of mobility.

Disaster relief networks need to be highly adaptable and resilient in order to encompass the emergency service demands. Cognitive Radio enhanced ad-hoc architecture have been put forward as candidate to enable such networks. Spectrum sensing, the cornerstone of the Cognitive Radio paradigm, has been the target of intensive research, of which the main common conclusion was that the achievable spectrum sensing accuracy can be greatly enhanced through the use of cooperative sensing schemes. When considering applying Cognitive Radio to ad-hoc disaster relief networks, the use of spectrum sensing cooperative schemes becomes essential. A cluster based decentralized orchestration cooperative sensing scheme is proposed, where each node in the cluster decides which spectrum it should monitor, according to the past sensing decisions of all the cluster nodes. The proposed scheme performance is evaluated through a framework, which allows gauging the accuracy of multi narrow-band spectrum sensing cooperative schemes as well as to gauge the error in the estimation of each of the channels un-occupancy. Through that evaluation it is shown that the proposed decentralized scheme performance reaches the performance of the correspondent centralized scheme while outperforming the Round Robin scheme.

The trend on the proliferation of handheld devices triggered an increase on the development of pervasive and personalized mobile applications that exploit and support peer-to-peer/ad-hoc networks. This creates the conditions and motivation for a mobile electronic ID system, preferably one which provides a friendly privacy solution. The existing mobile identification solutions place the interviewer in a more authoritative position than the interviewee, while little or no privacy control is applied by the interviewee. With the mobile ID system (MobIS), this paper proposes a universal mobile ID system which is suitable for various scenarios (e.g. for professional, medical, or official uses) in a heterogeneous network environment. Such systems are highly prone to security and privacy threats, so a context aware privacy mechanism is developed in the proposed framework. The security and privacy frameworks are elaborated, and a demonstration of the prototype is presented in this paper.

In multiple input multiple output (MIMO) systems the antenna array configuration in the base station (BS) and mobile station (MS) has a large influence on the available channel capacity. In this paper, we first introduce a new frequency selective (FS) MIMO framework for macro-cells in a realistic urban environment. Next, MIMO configuration characteristics are investigated in order to maximize capacity, mainly the number of antennas and inter-antenna spacing. Channel and capacity simulation results are presented for the city of Lisbon, Portugal, using different antenna configurations. Two power allocations schemes are considered, uniform distribution and FS spatial water-filling. The results suggest optimized MIMO configurations, considering the antenna array size limitations, at the MS side.

The LMMSE (linear minimum mean square error) algorithm is one of the best linear receivers for DS-CDMA (direct sequence-code division multiple access). However, for the case of MIMO/BLAST (multiple input, multiple output/Bell Laboratories layered space time) with high loading, the perceived complexity of the LMMSE receiver is taken as too big, and thus other types of receivers are employed, yielding worse results. In this paper, we investigate the complexity of the solution to the MIMO LMMSE receiver's equations using block-Fourier algorithms, for both steady and unsteady channel situations.

In this paper a realistic model that is an extension of COST 273 channel model, introducing terrain and clutter information into cluster generation, is presented. Our model is based on a geometrical/stochastic approach, and allows a realistic treatment of both cluster positions and dynamics. This is done by means of visibility regions. Using terrain and clutter information, the cluster and visibility region generating PDFs are derived. Some simulation results over a GIS tool for the city of Lisbon, Portugal, are presented.

The paper introduces an algorithm that converts dynamically GIS terrain and clutter information into the flat terrain input parameterization needed for COST 231 Walflsch-Ikegami model. Under this algorithm, a new method for average building height calculation is presented, using Epstein-Peterson's multiple knife-edge diffraction method. Additionally, it is introduced a new realistic approach for line-of-sight stochastic modeling. The mobile station (MS) paths are generated using a new mobility model previously validated for the city of Lisbon, Portugal.

Abstract Low rate convolutional and turbo codes that output non‐linear cyclic (NLC) codewords of lengthn = 2m,mbeing a positive integer, are described. These codes have a very low coding rate, which makes them especially suitable for spread spectrum systems where they can be used for simultaneously achieving error correction and bandwidth expansion. Due to the cyclic properties and codeword length of the component codes, branch metrics can be efficiently computed using the fast Fourier transform (FFT), enabling simple implementation of the encoder and decoder. Among the possible NLC base codes, special attention is given to the Tomlinson, Cercas, Hughes (TCH) codes family due to their good autocorrelation properties. It is shown by simulation that the turbo codes schemes studied usually perform better than traditional turbo codes (in this paper the universal mobile telecommunications system (UMTS), rate 1/3 turbo code was used as a reference). This improvement is accomplished at the cost of bandwidth expansion. One of the advantages of the presented solutions over other low rate codes is their ability to improve the synchronization process at the receiver due to the good autocorrelation properties of the available NLC codes (especially TCH codes). A comparison of performance between the UMTS uplink connection and an equivalent system using the proposed codes for a multiuser scenario in a multipath fading channel is presented showing the possibility of capacity increase when using these codes. Copyright © 2006 John Wiley & Sons, Ltd.

The UMTS common transport channels forward access channel (FACH) and the random access channel (RACH) are two of the three fundamental channels for a functional implementation of an UMTS network. Most signaling procedures, such as the registration procedure, make use of these channels and as such it is necessary that both channels be available across the cell radius. This requirement makes the choice of the transmission parameters a fundamental one. This paper presents a sensitivity analysis regarding the transmission parameters of two UMTS common channels: RACH and FACH. Optimization of these channels is performed and values for the key transmission parameters in both common channels are obtained. On RACH these parameters are the message to preamble offset, the initial SIR target and the preamble power step while on FACH it is the transmission power offset.

Three different techniques for supporting packet data services in UMTS networks are compared in terms of packet call delays. The downlink shared channel (DSCH) is shown to have better performance when compared with the use of the dedicated channel (DCH) with a low rate reshuffling period.

This work considers asynchronous time division code division multiple access (TD-CDMA) systems with RAKE receivers and one stage of parallel interference cancellation (PIC) or serial interference cancellation (SIC). A general method based on the concept of spherically symmetric signals is presented for the evaluation of the average probability of error of uncoded TD-CDMA systems. Slow frequency-hopping (SFH) with frequency overlap (FO) between adjacent carriers and interference cancellation are also included in the analysis, which considers the multipath Rayleigh fading channel (which models indoor and outdoor vehicular radio propagation). We analyze quadrature phase shift keying (QPSK) modulation with coherent demodulation and multipath (frequency) diversity with maximal ratio combining (MRC). Power control, adaptive SFH, and interference cancellation are employed for improving the bit-error rate (BER) performance. It is found that the scheme with SFH, in spite of the FO, always improves the performance substantially, and interference cancellation, in general, provides the highest BER improvement.

This work deals with the asynchronous DS-CDMA reverse link with multiuser detection (MUD) receivers (two stages of parallel interference cancellation) the channel is multipath Rayleigh faded. The trade-off analysis between MUD and channel coding is the main topic. A comparison is made between the maximum throughputs (in bits/s/Hz) achievable using multiuser detection and single user detection, for both single cell and cellular systems. The effect of imperfect power control is also investigated. As in a DS-CDMA system a fixed bandwidth expansion can be allocated to either error correction coding or modulation signal spreading this trade-off is also analysed in conjunction with MUD vs. coding. We suppose that BPSK modulation with coherent demodulation is employed.

A multi-carrier code division multiple access (MC-CDMA) scheme is used in a multi-cellular environment in order to reduce the interference present in typical mobile systems. The conventional approach to limit the interference of the users in adjacent cells is frequency reuse (FR). In this paper, a new scheme-code reuse (CR)-is proposed in order to achieve a higher order of diversity. In this method, all the cells in the cluster use the total number of available carriers to transmit the users data signal. The distinction of the data signal of different users is made by using orthogonal Walsh-Hadamard codes for each user. A comparison between the frequency reuse and code reuse schemes is done as a function of the environment path loss, number of carriers, frequency gap between adjacent carriers, number of users and cluster size. The results obtained for a cluster size equal to four show that, increasing the number of carriers and the frequency gap between adjacent channels the performance of both schemes improve. When the number of carriers is increased, the best improvement is achieved for the CR scheme, while for the frequency gap increase it is the FR scheme that improves the most. When a cluster size equal to seven is considered, CR achieves a better performance than FR, since in this case, the co-channel interference is clearly reduced due to the larger distance between cells using the same codes.