Heterogeneous transfer learning is an effective approach for medical imaging problems with limited data and scarce public homogeneous resources, yet selecting the optimal domain for feature extraction remains an open, often intuition-driven challenge. This study proposes and validates a set of quantitative domain similarity measurements to a priori identify the most suitable intermediate domain for early laryngeal cancer detection within a two-step heterogeneous transfer learning (THTL) framework, thereby avoiding computationally expensive trial-and-error training. We introduce eight domain similarity measurements to access the similarity between intermediate domains and the target domain. Multiple common medical imaging modalities, including angiography, chest radiographs, lung computed tomography (CT), brain magnetic resonance imaging (MRI), pathological section images, diabetic retinopathy fundus images, skin lesion images, and gastroenteroscopy, are served as candidate intermediate domains. The resulting similarity scores are ranked and compared with actual THTL performance rankings. Finally, we employ normalized discounted cumulative gain (NDCG) to determine the most predictive measurement. Our findings reveal that Earth Mover’s Distance (EMD) is the most effective domain similarity measurement for grayscale images, while cosine similarity based on global features extracted from a convolutional neural network (CNN) is optimal for RGB images. Using these measurements, angiography and skin lesion images are identified as the most beneficial intermediate domains. This work establishes a validated, data-driven methodology that enables future researchers to replace subjective intuition in domain selection, thereby saving substantial computational resources while improving model performance.

In Intelligent Transportation Systems (ITS), reliable Vehicle-to-Infrastructure (V2I) communication is critical for real-time traffic management, collision avoidance, and environmental monitoring in high-mobility urban scenarios. To support real-time intersection collision-warning and traffic-signal-priority in ITS, we propose an RIS-assisted index modulation scheme that simultaneously increases road-safety message reliability and reduces roadside unit deployment density, termed Reflection Modulation-Assisted Adaptive Space Shift Keying (RM-ASSK). The RM-ASSK scheme leverages reconfigurable intelligent surface (RIS) technology to enhance the efficiency and reliability of wireless communication within transportation systems. By integrating RIS with adaptive space shift keying, the scheme aims to improve spectral efficiency and reduce bit-error rate performance. Meanwhile, we present theoretical analyses of the scheme’s spectral efficiency, average BER upper bounds, detection complexity, and ergodic channel capacity lower bounds. These analyses demonstrate the RM-ASSK scheme’s superior performance in terms of spectral efficiency, reduced complexity, and improved BER. Besides, to further enhance the scheme’s effectiveness and flexibility, we propose a novel antenna-combination selection algorithm, mixture optimized antenna combination selection, and a reflection-element subset design. These innovations optimize BER performance while balancing the trade-off between performance and complexity. Simulations show that RM-ASSK reduces data error rates by up to 25% in Nakagami-$m$and Rayleigh fading channels typical of urban V2I, enabling 15-20% faster dissemination of traffic alerts and improving vehicle throughput by supporting 30% more connected devices without spectral congestion.

Although many complex models were proposed to analyze time series data, some studies have demonstrated remarkable performance with simpler structures. A recent study proposed a non-parametric framework for 3D point cloud classification, which has the potential to be adapted for time series forecasting and enable interpretability. Inspired by the previous works, we present TSNN, a non-parametric and interpretable framework for traffic time series forecasting. TSNN consists of multiple layers that decouple the time series by matching the entries in a memory bank, where the memory bank is constructed using a similar matching process within the training set. It leverages the periodicity in traffic data to enhance forecasting accuracy while maintaining a simple model architecture. The proposed model operates without trainable parameters, preserving its inherent interpretability. In the experiments, TSNN achieves competitive performance compared to the typical deep learning models in four real-world traffic flow datasets. We also visualize the decoupling process to show the effectiveness of the components. Finally, we demonstrate the interpretability of the model and illustrate the contribution of each time step within the memory bank. Our code is available athttps://github.com/pzzzzzm/TSNN_release.

Accurate channel state information (CSI) feedback is essential in frequency division duplex massive multiple-input multiple-output systems, but increasing antennas cause the CSI matrix to grow exponentially, leading to significant feedback overhead. Inspired by the success of Transformers in natural language processing, recent Transformer-based CSI feedback methods have achieved excellent performance, though often with high computational costs that hinder real-time deployment on terminal devices. To address this challenge, in this paper, we present CsiMobileVit, a lightweight network that lowers computational complexity while maintaining reconstruction accuracy. The method achieves a good balance between simplicity and accuracy, making it practical for resource-limited devices. Extensive experiments confirm the effectiveness of this network.

A novel wireless transmission scheme, as named the reconfigurable intelligent surface (RIS)-assisted received adaptive spatial modulation (RASM) scheme, is proposed in this paper. In this scheme, the adaptive spatial modulation (ASM)-based antennas selection works at the receiver by employing the characteristics of the RIS in each time slot, where the signal-to-noise ratio at specific selected antennas can be further enhanced with near few powers. Besides for the bits from constellation symbols, the extra bits can be mapped into the indices of receive antenna combinations and conveyed to the receiver through the ASM-based antenna-combination selection, thus providing higher spectral efficiency. To explicitly present the RASM scheme, the analytical performance of bit error rate of it is discussed in this paper. As a trade-off selection, the proposed scheme shows higher spectral efficiency and remains the satisfactory error performance. Simulation and analytical results demonstrate the better performance and exhibit more potential to apply in practical wireless communication.

The pursuit of higher data rates and efficient spectrum utilization in modern communication technologies necessitates novel solutions. In order to provide insights into improving spectral efficiency and reducing latency, this study investigates the maximum channel coding rate (MCCR) of finite block length (FBL) multiple-input multiple-output faster-than-Nyquist (FTN) channels. By optimizing power allocation, we derive the system's MCCR expression. Simulation results are compared with the existing literature to reveal the benefits of FTN in FBL transmission.

Dual challenges of computational efficiency and representation effectiveness exist in processing point clouds. Inspired by the TDA (Topological Data Analysis), we propose to convert the point cloud to a transposed fully connected and weighted (t-FCW) graph in order to significantly decrease the computational complexity in the following processing steps. We design a TDA pipeline called Point-FCW with a series of vectorization techniques for the 3D object point cloud feature extraction, which is plugged into the non-parametric classification head. Our experimental results demonstrate that Point-FCW achieves 75.28% accuracy on the ModelNet40 dataset with 512 points, providing a tiny, consistent, and effective representation for TDA. Furthermore, when integrated with the state-of-the-art non-parametric network Point-NN, the mixture model performs better, with an improvement of 4.47% in the OBJ-BG split of the ScanObjectNN dataset. Similarly, when integrating Point-FCW into the parametric network, PointMLP yields a performance improvement of 3.54% in the PB-T50-RS split of the ScanObjectNN dataset. The proposed Point-FCW can serve as a complementary enhancement feature when integrated into the Point-NN and PointMLP models. Moreover, the t-FCW graph representation can be efficiently converted at a rate of 3739 samples/second. Our code is available inhttps://github.com/hawkinglai/Point-FCW.

Applying the deep learning in signal processing for communication systems, several models based on Real-Valued Deep Neural Network (RVDNN) and convolutional neural network (RVCNN) have been previously proposed to detect signals of generalized spatial modulation. This paper proposes a complex-valued deep neural network (CVDNN) and a complex-valued convolutional neural network (CVCNN) as detectors for reconfigurable intelligent surface (RIS)-assisted generalized spatial modulation. Contrary to previous models, the complex-valued signals are directly fed into the neural network, which requires few feature vector generators and therefore has a simpler structure. Simulation results show that the proposed CVNN detectors exhibit improved error performance and stability for various modulation schemes compared with other traditional detection schemes over Nakagami-m fading channels. The results are shown to be approaching that of maximum likelihood detection, while outperforming existing RVNN detectors.

Near-field reconfigurable intelligent surfaces (RISs) are unlocking promising potentials for the next generation of communications. Different from prior works that separately address phase shifts with errors and phase-dependent amplitudes (PDAs) in the RIS pixel hardware, this paper jointly studies power losses (PLs) caused by these two impairment factors. We propose three different pixel reflection models to accommodate different practical scenarios and derive their approximated upper bounds on the PL. It is important to note that neglecting uncertainties in the PDA may lead to an overestimation of the performance improvement offered by the RIS, thereby explaining the discrepancy between analytical and measurement results in several previous studies. Numerical simulations verify the correctness of the theoretical results.

In this paper, the channel coding performance with finite-alphabet inputs and finite blocklength in a two-user downlink non-orthogonal-multiple-access (NOMA) system is characterized from an information-theoretic perspective, and optimized with proper power allocation and constellation design. While previous works in NOMA were mainly focused on either infinite blocklength performance or finite blocklength performance with Gaussian inputs, we obtain the information-theoretic achievable rates accurate up to second-order as a function of the blocklength for both NOMA users employing finite-alphabet inputs subject to the average total power constraint. Taking advantage of the theoretical results, we formulate the rate and error-rate performance optimization problems in the finite blocklength regime for finite-alphabet inputs, from which the optimal power allocation and constellation-rotation can be derived. Furthermore, polar codes and LDPC are employed to demonstrate how close their performances are from the second-order achievable bound when the blocklength is short. Our results are important in machine-type or IoT communications where lightweight modulations and short blocklength are more relevant compared with traditional Gaussian-inputs assumption.

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique, which brings in intentional inter-symbol interference. This way it can significantly enhance spectral efficiency for practical pulse shapes such as the root raised cosine pulses. This paper proposes an achievable rate region for the multiple antenna (MIMO) asynchronous multiple access channel (aMAC) with FTN signaling. The scheme applies waterfilling in the spatial domain and precoding in time. Water-filling in space provides better power allocation and precoding helps mitigate inter-symbol interference due to asynchronous transmission and FTN. The results show that the gains due to asynchronous transmission and FTN are more emphasized in MIMO aMAC than in single antenna aMAC. Moreover, FTN improves single-user rates, and asynchronous transmission improves the sum-rate, due to better inter-user interference management.

In this paper, from an electronic reliability perspective, non-residual stochastic hardware aging (HA) effects are introduced to reconfigurable intelligent surfaces (RISs) aided communication systems, for characterizing the life cycle of the RIS. Different from traditional residual impairment factors such as RIS phase imperfections and transceiver noises, the impact of the stochastic HA effect on the RIS is related to RIS runtimes and lifetimes. Given this background, we first propose a Rician channel model for the RIS-aided system with the stochastic HA effect. Then, the definition for the lifetime of the RIS is mathematically given as the time at which 63.2% of the elements expire. Besides, closed-form achievable rate expression is also derived. Analytical and simulated results unveil an important insight that throughout the life cycle of the RIS, the residual impairment dominates when the runtime is shorter than the lifetime, otherwise the stochastic HA effect should be paid more attention to. This work can be regarded as the first guideline for evaluating and predicting the whole life cycle performance of the RIS-assisted system.

Internet of Things (IoT) networks require massive connections in dense areas. Therefore, a resource-efficient multiple access scheme seems inevitable to enable immense connectivity where multiple devices have to share the same resource block (RB). Nonorthogonal multiple access (NOMA) has been considered as the strongest candidate in recent years. However, in this article, by considering the practical implementation, we first provide a true power allocation (PA) constraint with finite alphabet inputs for conventional downlink NOMA and demonstrate that it cannot support massive connections in practical systems. To this end, we propose the bit-interleaved multiple access (BIMA) scheme in downlink IoT networks. The proposed BIMA scheme implements bitwise multiaccess interleaving and deinterleaving at the transceiver ends and there are no strict PA constraints, unlike conventional NOMA, thus allowing a high number of devices in the same RB. We provide a comprehensive analytical framework for BIMA by investigating all key performance indicators (KPIs) to present both information-theoretic [i.e., ergodic capacity (EC) and outage probability (OP)] and finite alphabet inputs [i.e., bit error rate (BER)] performance metrics with both instantaneous and statistical channel ordering. In addition, we define Jain’s fairness index and proportional fairness index (PFI) in terms of all KPIs. Based on the extensive computer simulations, we reveal that BIMA outperforms conventional NOMA significantly, with a performance gain of up to 20–30 dB in terms of KPIs in some scenarios. In other words, compared to conventional NOMA schemes, the same KPIs are met in BIMA with 20–30 dB less transmit power, which is quite promising for energy-limited use cases. Moreover, this performance gain becomes greater when more IoT devices are supported. BIMA provides a full diversity order for all IoT devices and enables the implementation of an arbitrary number of devices and modulation orders, which is crucial for IoT networks where a huge number of devices should be supported in a single RB in dense areas. In addition to the overall performance gain, BIMA guarantees a fairness system where none of the devices gets a severely degraded performance and the sum-rate is shared in a fair manner among devices. It guarantees QoS satisfaction for all devices. Finally, we provide an intense complexity and latency analysis for BIMA and demonstrate that it provides lower latency compared to conventional NOMA receivers, since it allows parallel computation at the receivers and no iterative operations are required. We show that compared to conventional NOMA receivers, BIMA reduces latency by up to 350% for specific IoT devices and 170% on average.

In this paper, we propose a novel anti-Doppler spread technique in IRS-aided predictable high-mobility vehicular communication. The predictable information of vehicle (e.g., speed, trajectory, and timetable) can be used to design a suboptimal phase shift set by solving a multi-objective problem, which can make IRS capable of maximizing instantaneous signal-to-noise ratio (SNR), minimizing Doppler spread, and keeping delay spread to a relatively low range simultaneously. The technique we proposed in this paper is more cost-effective, more practical, and of higher energy efficiency since the phase shift set by every element of the IRS for one vehicle pass can be designed and stored in advance, instead of processing in real-time. Simulation results show the effectiveness of the proposed phase shift set as compared to benchmark schemes.

Assuring an efficient Quality of Service (QoS) for heterogeneous Internet of Things services is a challenge, mainly due to spectrum scarcity and bandwidth limitations on the radio access network. To solve these problems, efficient management of airtime per station (STA) is necessary. In this work, we propose a scheduler to perform dynamic network slicing in IEEE 802.11ah Networks. The proposed scheduler is based on virtualization technologies to assure QoS restrictions per slice and can be deployed in the Access Point (AP) or in a virtual machine connected to the AP. Network metrics are used to verify QoS violations per slice over time. Once a QoS restriction is detected, the scheduler performs a reallocation of resources re-configuring the Restricted Access Windows parameters that compose a slice, adjusting airtime per STA. To evaluate the proposed scheduler, we consider a dynamic policy. Simulation results in a smart city scenario show that the dynamic policy is able to scale the network and satisfies QoS slice requirements.

Recently, Network Slicing in Radio Access Technologies (RATs) has been proposed as an approach to divide the wireless network infrastructure into isolated logical slices which are defined following their requirements and features in a service-driven way. To the best of our knowledge, the application of Network Slicing has not been explored in IEEE 802.11ah Networks. In this work, we propose a Virtual Network Slicing Broker (VNSB), a Virtual Network Function (VNF) instantiated inside a Software Defined Network (SDN) controller which communicates with an IEEE 802.11ah network Access Point (AP) via a southbound Application Program Interface (API). Based on information obtained by the northbound API, the slicing broker gets the information contained in the slicing templates, which describes the services features and respective Quality of Service (QoS) restrictions. With these data, the slicing broker makes logical slices in the context of the Restricted Access Windows (RAW) which are periodically sent by the AP to the stations (STA) via the Raw Parameter Set (RPS). Since there is no hardware with the IEEE 802.11ah standard available on the market, we validate the proposed solution through extensive simulations in a typical smart city scenario. Results showed the broker capacity to build and manage logical slices per service, respecting the QoS restrictions of each offered service. Moreover, the proposed slicing broker makes use of available resources of neighbor slices reducing delay, packet loss, and efficiently maximizing throughput per slice.

In this paper, we propose a novel spectrum sensing method to detect the presence of a primary user in a cognitive radio network when the secondary user is employing a large number of receive antennas. The new method is a suboptimal approach based on the generalized likelihood ratio test (GLRT), but unlike the previous GLRT-based methods which rely on large number of received samples obtained over time, we take advantage of the large number of samples available at the receive antennas. By exploiting the properties of the samples obtained at the receive antennas, effective spectrum sensing is achieved using mainly the spatial samples and therefore the sensing time is significantly reduced. With the suitable channel models and prior knowledge about the signal structure, the simulation results show that the proposed method offers performance comparable to well-known methods but with much smaller sensing time.

In this paper, we consider the optimization of Single-Carrier FDMA (SC-FDMA) performance using rotated constellation. Using maximum-likelihood (ML) detection, it was previously shown that the diversity order of SC-FDMA is upper-bounded by the number of multi-paths or the number of subcarriers employed. As in OFDM, channel coding is usually required in SC-FDMA system in order to maximize the frequency diversity performance, especially when number of subcarriers is small. By using rotated constellation and the proposed angles of rotation, we show that the SC-FDMA system may attain the maximum diversity and coding performance without a reduction in bandwidth-efficiency or a significant change in the peak-to-average power ratio. Furthermore, when the number of subcarriers allocated in a SC-FDMA system is small, it becomes feasible to employ high-complexity detection methods such as ML or QR-decomposition method. In particular, to handle resource blocks of various sizes, we propose that the block of subcarriers be divided into pairs so that rotation can be separately applied in each pair over the block. As such, ML detection and flexible allocation of subcarriers can be facilitated for short or long resource block in the proposed SC-FDMA system. Simulations have shown that the proposed SC-FDMA system may outperform the ordinary SC-FDMA system and OFDM system in situations where coding rate is high.

In this paper, a new class of equalization and channel estimation techniques, using the turbo frequency domain equalization (TFDE), is presented as a promising low-complexity detection method for single-carrier broadband wireless transmissions. Serial modulation (SM), being a direct counterpart of the well-known OFDM modulation, is receiving considerable attention recently, owing to the fact that it delivers comparable performance as OFDM while avoiding the problem of high peak-to-average power ratio. When frequency domain equalization (FDE) is applied, the complexity requirement is low and it becomes feasible to employ iterative processing which relies on decision feedback. This paper considers the turbo principle applied jointly to FDE, channel decoding and channel estimation. The result of this work is a set of effective iterative algorithms which may bring about 2-3 dB improvement over the linear FDE method. Furthermore, it is shown that they can provide performance comparable to the time-domain turbo equalization methods but with lower complexity. We then reach the conclusion that SM-based transmission with TFDE is a suitable technology for next generation wireless systems

In this paper, we consider the uplink of SC-based (single-carrier) block transmission systems where the transmission bandwidth associated to each channel, exceeds the symbol rate and we can have significant overlapping between adjacent channels. We present an iterative frequency-domain receiver for joint equalization and ACI (adjacent channel interference) suppression taking advantage of spectral correlations for the separation of cyclostationary signals. Our performance results show that the BER (bit error rate) performances with strong ACI levels can be similar to the ones without ACI. Moreover, after a few iterations these performances can be close to MFB (matched filter bound)

A novel iterative frequency domain equalization method is proposed for single-carrier (SC) based wireless systems operating over a time- and frequency-selective (or doubly-selective) channel. We adopt the basis expansion channel model (BEM) to approximate the doubly-selective channel with a set of Fourier exponentials. Using BEM, time-varying multipath interference is translated into inter-carrier-like interference in the frequency domain. We design a low-complexity iterative method which relies on a sliding-window-based MMSE widely-linear filter with inter-carrier interference cancellation, and iterated time-domain soft-decisions. Simulations are shown to justify the validity of our approach and the results indicate that this approach outperforms the high-complexity linear block MMSE equalizer and other iterative approaches of similar complexity.

In this paper, the coded performance of a previously proposed space-time multiuser multiplexing scheme called the spread space-spectrum multiple access (SSSMA) (Ng, BK et al.) is investigated for the forward link MIMO system. The key feature of SSSMA is that the number of user-channels is increased by exploiting additional degrees of freedom offered by multiple antennas. The large MIMO data pipe is divided and allocated to multiple coded user-channels over the entire transmission time interval and bandwidth. Thus, unlike the orthogonal multiple access scheme, the spatial multiple access interference (MAI) exists in SSSMA and is mitigated through the use of the space-time diagonal (STD) spreading sequences. When coding is introduced, each user may employ a user-specific optimal inter-leaver. It is theoretically shown that for two transmit antennas and optimal interleaving, the full diversity criterion is satisfied for all multiuser codeword pairs, thereby suggesting that spreading is an effective means to achieve spatial diversity at high bandwidth-efficiency. With suboptimal detector such as interference-cancelling receiver based on turbo processing, it is shown that the SSSMA offers near-capacity performance that is superior to many well-known MIMO transmission schemes.

A cellular system with overlapping sectors is proposed to enhance system performance through interference avoidance. Due to the overlapping effect, each user is allowed to select one sector out of many for operation. It is shown that the mutual interference among users in different regions within a cell can be minimized by choosing a good sector combination. The potential benefits of the proposed technique are investigated in two scenarios: orthogonal and nonorthogonal code division multiple access systems. In the former case, it is shown that when compared with the traditional nonoverlapping sectors system, one is able to reduce the number of spreading codes required to support all users by using appropriate code allocation in the proposed system. A theoretical framework based on combinatorial enumeration is developed to assess the performance of the proposed system. For the latter case, the enhancement takes the form of multiple access interference reduction. Simulation results show that its improvement is significant and can be further enhanced by increasing the number of overlapping sectors.

We study our previously proposed space-time multiple access framework (see Ng, B.K. and Sousa, E., WCNC'02, 2002) for MIMO forward link transmission in a multipath-rich environment. The primary goal is to achieve high bandwidth efficiency employing multicarrier modulation, while the number of user-channels, rather than the individual's data rate, is to be maximized. In the proposed framework, we focus on a single spreading period, in which the data from different users are multiplexed using space-time-frequency-diagonal (STFD) spreading sequences. It is shown that spectral efficiency of N users/Hz/sec can be achieved, where N is the number of transmit and receive antenna elements. Simulation results indicate that frequency diversity gain can be obtained with only linear-processing at the receiver.

We consider a new multiple access framework for the forward link transmission with multiple transmit and receive antennas. The primary goal is to achieve high bandwidth efficiency while the number of user-channels, rather than the individual's data rate, is to be maximized. In the proposed framework, we focus on a single spreading period, in which the data from different users are multiplexed using the space-time-diagonal (STD) spreading sequences. With a real constellation, N users/Hz/sec is achieved where N is the number of transmit and receive antenna elements. Any form of joint coding among these data over space-time cannot be considered within this period because they belong to different users. Hence, spreading over space-time becomes an effective approach for the purpose of multiplexing. Both analytical studies and simulations results show that our scheme has profound performance.

Signals designed for transmission over multiple transmit antennas are capable for achieving significant capacity gain. Traditional approaches aim at improving the single-user link with a centralized control over the set of transmit antennas. In this paper, by considering a set of independent and synchronized users communicating with the base station on the up-link, the joint signal can be viewed as space-time coded signal without a centralized control. Co-channel/inter-antenna interference presents a major impairment that limits the capacity. We propose a novel multiuser signal structure called interference-resistant modulation (IRM) to improve performance without coding nor bandwidth expansion. IRM can also be combined with fading-resistant modulation or space-time coding to yield additional gain when each user employs multiple transmit antennas. We prove that, both analytically and by simulations, the IRM with maximum-likelihood (ML) detection achieves the single-user performance asymptotically. Furthermore, to reduce the prohibitive complexity posed by ML detection, we propose a simple minimum-mean-square-error based precombining group detector and an interference cancellation scheme. It is shown that the proposed detector combined with IRM provides significant improvement over previous approaches.

The space-time code has shed new insights in designing signals over multiple transmit antennas. In this paper, by considering a set of independent and synchronized users communicating with the base station in the reverse link, the joint users' signals can be analyzed within the framework of the space-time code. As multiuser transmission requires that each user's signal is confined to its own transmitting antenna, co-channel/inter-antenna interference degrades the performance significantly. We propose a novel multiuser signal structure called interference-resistant modulation (IRM) to improve the performance without coding and bandwidth-expansion. We prove that, both analytically and by simulations, the asymptotic performance of IRM with maximum-likelihood detection achieves the single-user performance. Further, to reduce the prohibitive complexity posed by ML detection, we propose a simple MMSE-based pre-combining detector. It is shown that the proposed detector combined with IRM provides significant improvement over previous approaches with the same complexity.

As the information theory has suggested that deploying multiple transmit and receive antenna would bring about a huge capacity gain, future development is driven towards designing a system that would realize this gain in practice. Here, we consider the performance of a new space-time multilayered CDMA structure. The data stream is divided into layers, each of which is modulated with an unique space-time spreading sequence. At the receiver, the linear multiuser MMSE detector is used to recover all layers. Simulation results show that the capacity grows linearly with the number of transmit and receive antenna. We investigate the issue of selecting good space-time sequences to reduce co-interference from other layers. Results show that the joint space-time orthogonal codes exhibit good performance. The proposed structure also provides better immunity against signal loss, when any antenna element experiences failure.