Intelligent Reflecting Surfaces (IRSs) are a promising technology for enhancing the spectral and energy efficiency of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In these systems, accurate channel estimation remains challenging due to the passive nature of IRS elements and the high pilot overhead in large-scale deployments. This paper presents a deep learning-based Multi-Block Attention (MBA) framework for efficient cascaded channel estimation in IRS-assisted mmWave MIMO systems that utilize orthogonal frequency division multiplexing (OFDM). First, we show the optimality of the discrete Fourier transform (DFT) and Hadamard matrices as phase configurations for least squares (LS) estimation. To reduce training overhead, we selectively deactivate IRS elements and compensate for induced feature loss using a two-stage architecture: (i) a Convolutional Attention Network (CAN) for spatial correlation recovery and (ii) a Complex Multi-Convolutional Network (CMN) for noise suppression. The MBA architecture mitigates error propagation through attention-guided feature refinement and denoising. Simulation results indicate that the MBA method reduces pilot overhead by up to 87% compared to the LS estimator. Additionally, at signal-to-noise ratios of 10 dB, our proposed method achieves approximately 51% lower normalized mean squared error (NMSE) than leading methods. It also maintains low computational complexity and adapts effectively to various propagation environments.

Selection of hyperparameters in deep neural networks is a challenging problem due to the wide search space and emergence of various layers with specific hyperparameters. There exists an absence of consideration for the neural architecture selection of convolutional neural networks (CNNs) for spectrum sensing. Here, we develop a method using reinforcement learning and Q-learning to systematically search and evaluate various architectures for generated datasets including different signals and channels in the spectrum sensing problem. We show by extensive simulations that CNN-based detectors proposed by our developed method outperform several detectors in the literature. For the most complex dataset, the proposed approach provides 9% enhancement in accuracy at the cost of higher computational complexity. Furthermore, a novel method using multi-armed bandit model for selection of the sensing time is proposed to achieve higher throughput and accuracy while minimizing the consumed energy. The method dynamically adjusts the sensing time under the time-varying condition of the channel without prior information. We demonstrate through a simulated scenario that the proposed method improves the achieved reward by about 20% compared to the conventional policies. Consequently, this study effectively manages the selection of important hyperparameters for CNN-based detectors offering superior performance of cognitive radio network.

In this paper, we consider spectrum sensing (SS) problems with various general noise models such as Middleton class A (MCA), isometric complex symmetric$\alpha $-stable ($\text{S}\alpha \text{S}$), and isometric complex generalized Gaussian distribution (CGGD). This approach enables us to examine the effect of practical phenomena such as impulsive noise on SS problems. In this general framework, we propose a detector based on convolutional neural networks (CNNs) with favorable performance under various noise models. The proposed model-free and data-driven CNN offers robustness in diverse noise scenarios. Thus, it can be utilized in environments with different physical behaviors. We demonstrate this method outperforms the highly regarded likelihood ratio test (LRT) in most cases. For all impulsive cases, the proposed CNN is the superior detector, providing a near-optimum performance for the conventional Gaussian noise. We indicate the proposed data-driven CNN offers an appropriate alternative solution to LRT. However, it requires more computational operations, a rich training dataset, and a training process, instead. Furthermore, the main rationale for proposing this CNN is that it enables the network to generalize its effective performance to various noise models and cases. To this end, quantitative simulations confirm superiority of the proposed CNN compared to other recent deep-learning methods.

To provide effective wireless connectivity, the use of aerial vehicles has been proposed as a promising solution in a wide variety of applications. In some of these applications, however, to achieve desirable performance, knowing the location of users may be a necessity. One promising approach for solving the problem of obtaining user locations is through the locations of some nodes (called anchors) and measuring the distance between connected nodes in the network. In this paper, we propose prominent techniques to improve the performance of the localization using multiple moving aerial anchors (MAA). In using MAAs as anchor nodes, the anchor nodes in our scenario have movement capability. This provides us with more flexibility to enhance the accuracy of the localization with taking into consideration the power consumption of MAAs. In fact, we propose an MAA placement method by which each user can be connected to at least one MAA while MAAs communicate with as small as possible power consumption. Then, we propose a path planning for MAAs by which distances between users and MAAs can be measured in order to estimate the locations of users. Our simulation results show that our proposed localization scheme can provide highly accurate location estimations.

The key role of spectrum sensing in cognitive radios attracted substantial research attention to improve the performance of detectors. We consider a general model for the receiver noise with the potential of generalization towards modeling noise in various environments. This general noise model describes accurately noise characteristics ranging from the Gaussian noise to the severe impulsive noise. However, many previous studies are based on the ideal Gaussian noise, and they cannot capture the non-Gaussian models. To provide a robust detector against different behaviors of the noise in various environments, we employ a convolutional neural network (CNN) compatible with different noise models. The proposed CNN detector is data-driven, and due to its single-dimensional input layer, it is consistent with the received signal and requires no pre-processing. The likelihood ratio test (LRT), the Wald, and the Rao tests for this problem are derived to enrich the paper with comparative evaluations of the proposed CNN and conventional model-based approaches and other neural networks. Although various simulated scenarios substantiate the general superiority and robustness of the CNN-based against impulsive noise and mismatch of parameters, it requires higher computational complexity than other discussed detectors.

In millimeter wave (mmW) dense femto-networks, major challenges are overcoming the performance loss imposed by the channel and managing the co-channel interference. The former is due to the mmW susceptibility to pathloss and shadowing and the latter is due to density of the network. We cope with both challenges by a clustering method designed for mmW environment. In our approach, the femto access points (FAP) and femto users (FU) are clustered based on having the most line of sight connectivity. We modify this binary optimization problem into a continuous problem using deductive penalty functions and solve it by difference of two convex functions (D.C.) programming. Our clustering algorithm achieves higher data rate compared to the foremost clustering method. We also propose a technique to assign FUs to FAPs in each cluster which has near-optimal performance and polynomial time complexity. We solve mixed integer nonlinear programming of power and sub-channel allocation by D.C. programming. Instead of using the deductive penalty terms in D.C. programming, we penalize the objective function in a multiplicative manner. Thus, the penalty term depends on both constraint violation and objective function. Our scheme achieves around 10% higher data rate compared to the method using deductive penalty terms.

Low complexity and spectral efficient carrier frequency offset (CFO) estimation in massive multiple input multiple output (MIMO) systems is a critical problem. Therefore, in this paper we propose a low complexity CFO estimation technique for single carrier frequency domain equalization (SC-FDE) and orthogonal frequency division multiplexing (OFDM) based Massive MIMO systems. In this technique, to increase the spectral efficiency, we use the pilots sent for channel estimation to estimate both the CFO and the channel, iteratively. Our analysis show that our proposed technique has better performance and lower complexity than previous techniques which utilize one pilot block for both CFO and channel estimation in massive MIMO systems.

This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality-of-service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For fair comparison between the conventional terrestrial network and the aerial-BS assisted one, we keep the total number of BSs identical in both networks. Obtaining the best performance in such networks highly depends on the optimal placement of the aerial-BS. To this end, an algorithm which can rely on general and realistic assumptions and can decide where to go based on the past experiences is required. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.

In this paper, we investigate the suitability of single carrier frequency domain processing (SC-FDP) as the downlink transmission scheme in a massive multiple-input multiple-output (MIMO) system. By deriving the sum-rate of the SC-FDP massive MIMO system theoretically, we show that this method obtains a sum-rate similar to that of orthogonal frequency division multiplexing (OFDM) massive MIMO. We also derive the theoretical sum-rate of both SC-FDP and OFDM in a non-synchronized massive MIMO scenario and show that the rate of the former is significantly larger than that of the latter. Moreover, we theoretically analyze the sum-rate of both systems in the presence of power amplifier non-linearity. All the sum-rates are derived for both zero forcing and matched filter precoding schemes. The results show that the effect of power amplifier non-linearity on the sum-rate of both systems is similar when the number of users is large. We also compare SC-FDP with OFDM from the peak to average power ratio (PAPR) and complexity viewpoints. Although the PAPR of SC-FDP signals is lower than that of OFDM signals, for MIMO systems, the difference between their PAPR decreases as we increase the number of users. Thus, both techniques can have similar PAPR in massive MIMO systems. The overall complexity of SC-FDP and OFDM is similar. Due to the mentioned facts, SC-FDP can be considered as a promising transmission scheme for the downlink of the massive MIMO systems in the presence of carrier frequency offset and power amplifier non-linearities.

In this paper, we investigate the effect of carrier frequency offset (CFO) on the sum-rate of block-wise single carrier(SC) massive MIMO systems. Based on the user knowledge regarding the channel conditions, we consider three different scenarios. In the first scenario, the users know the expectation of the effective channel including the CFO effect in the time domain. In the second scenario, the users know the expectation of the effective channel including the effect of the CFO in the frequency domain. In the third scenario, the users know the expectation of the effective channel without the effect of the CFO. We show that in the first scenario, the effect of the CFO can be completely compensated. We also show that for finite number of base station (BS) antennas, the sum-rate in the second scenario is higher than that of the third scenario. However, as the number of BS antennas tends to infinity, the sum-rate in the former case is bounded and in the later case it tends to infinity.

We investigate the optimal power allocation problem for a femtocell network where the utilized air interface is single carrier frequency division multiple access (SC-FDMA). To overcome the interference, the receivers are equipped by frequency domain equalizers designed based on the zero forcing criterion. We derive the optimal power allocation when we protect the data rate of macro users by imposing a temperature limit on the interference arising from femto users to the macro base station. This constraint confines the cross-tier interference. We show that the optimization problem is convex and solve it using dual decomposition technique to obtain the optimal power of each femto user.

This paper analyzes the bit error rate (BER) performance of grouped frequency division multiple access (GFDMA) using a tool known as finite length analytical tool (FLAT). We derive the equations of the FLAT method including the variance of the equalizer output signal for different iterations. We use this value to calculate the BER of the equalizer output when a specific set of sub-carriers are assigned to more than one user. Through comparative performance evaluation, we show that the proposed FLAT method can accurately evaluate the BER of the GFDMA system and the difference between the theoretical and simulated BER curves is at most 0.1 dB. Thus, we can efficiently utilize the FLAT method to design the GFDMA system and avoid extensive simulations required to determine the number of users transmitting over the same sub-carriers simultaneously.

In this paper, we consider a single carrier frequency division multiple access (SC-FDMA) system impaired by frequency instabilities and timing offset. In this system, both time and frequency offsets deteriorate the orthogonality of sub-carriers and generate multi-user interference which degrades the system performance. For fast and efficient design of such systems, it is crucial to have an analytical and accurate evaluation of the system performance. However, such closed-form expression for the error rate is available only for perfectly synchronized SC-FDMA systems in the literature. In fact, the performance of SC-FDMA systems in the presence of time or frequency offsets has only been evaluated based on simulations. In this paper, we analytically obtain the symbol error rate (SER) of an SC-FDMA system impaired by time or frequency offsets when data is transmitted over a frequency selective Rayleigh fading channel. Through comparative simulations, we show that our proposed analysis can accurately evaluate the SER performance of SC-FDMA systems in the presence of time and frequency uncertainties.

This paper presents the analysis of the impact of circuit nonlinearities on the transmission rate of a communication system. We consider a general transmission model that includes transmitter- and receiver-side nonlinearities, environmental thermal noise and receiver chain noise. Using this model we establish lower and upper bounds for the capacity of nonlinear additive white Gaussian noise channels in various thermal noise and receiver noise regimes.

We study the impact of nonlinearities on the transmission rate of a communication system by establishing lower and upper bounds on the channel capacity. Our analysis is based on a general transmission model that includes transmitter and receiver nonlinearities, environmental thermal noise, and receiver chain noise. Using this model, we derive lower and upper bounds for the channel capacity of additive white Gaussian noise channels, fading channels, and multiple input multiple output channels in various thermal noise and receiver noise regimes.

In this work, we propose a QPSK OFDM code that has both low peak to average power ratio and good bit error rate performance. We consider a Time-frequency Turbo Block code. This code is composed of the cosets of the first-order binary Reed-Muller (RM) code in the frequency domain, and Bose-Chaudhuri-Hocquenghem (BCH) code in the time domain. Our contributions are two-fold: 1) we introduce two encoding schemes that guarantee a PAPR of 3 dB for QPSK constellation, 2) we propose moderate complexity decoding procedures for these schemes. We evaluate the performance of the proposed schemes for the additive white Gaussian noise channel. Our code performs only slightly worse than the best known Turbo codes but has significantly lower PAPR. This gives a significant total power gain over the best known Turbo codes.

In this paper, we present an advanced solution for the long standing problem of large peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. Although the design of low PAPR codewords has been extensively studied and the existence of asymptotically good codes with low PAPR is also proven, still no code has been constructed to satisfy all requirements. The main goal of the paper is to develop a coding scheme that not only generates low PAPR codewords, but it also performs relatively close to the Shannon limit. We achieve this goal by implementing a time-frequency turbo block coded OFDM. In this scheme, we design the frequency domain component to have a tightly bounded PAPR. The time domain component code is designed to obtain good performance while the decoding algorithm has reasonable complexity. Through comparative performance evaluation we show that utilizing the proposed method, we achieve considerable improvement in terms of PAPR while we slightly loose the performance compared to capacity achieving codes with similar overall block length.

Dual Bose-Ray-Chaudhuri (BCH) codes, despite their favorable peak to average power ratio (PAPR) properties, have not been used in coded orthogonal frequency division multiplexing (OFDM) systems. This is due to unavailability of a practical decoder and large performance gap to the Shannon limit. In this paper, we propose an advanced approach to solve the aforementioned problems. We construct a new code with favorable PAPR properties based on dual BCH codes and develop the associated maximum a posteriori (MAP) decoding algorithm. By exploiting this code as the frequency domain constituent code in a time-frequency turbo structure, we reduce the gap between the performance and Shannon limit while the bounded PAPR of OFDM symbols is guaranteed. By comparative performance evaluation we illustrate that the performance of this system is comparable with that of capacity approaching codes while it has 7 dB lower PAPR.

In this paper we analytically evaluate the performance of turbo frequency domain equalizers when the frequency selective channel is estimated erroneously. We generalize the bit error rate transfer chart method proposed with the assumption of perfect channel information to the case with imperfect channel state information to analyze the performance of a turbo frequency domain equalizer. Through a comparative performance evaluation we show that the performance achieved by simulations is consistent with the one predicted by the bit error rate transfer chart. By this analysis tool we investigate the sensitivity of both adaptive and non- adaptive schemes to the processing gain of channel estimation. We show that the former case is less sensitive to channel estimation error.

In this paper we propose a synchronization method to compensate for the effect of frequency offset and phase noise generated by local oscillator instabilities for a Turbo Frequency Domain Equalizer (TFDE). TFDE is considered as an efficient equalization method which benefits from both good performance of iterative systems and reasonable complexity due to performing equalization in the frequency domain. We propose a joint turbo equalization and synchronization method in which the feedback information, generated iteratively by the decoder, is used for synchronization. As iterations continue, if the feedback information becomes more reliable, the performance of the synchronization method is continually improved. We propose two methods based on different criteria when the system suffers from only frequency offset. We also suggest a method based on Maximum A Posteriori (MAP) criterion in the presence of both frequency offset and phase noise which can be considered as a decision directed phase lock loop. These methods achieve considerable performance improvement with reasonable complexity.

This paper analyzes the performance of turbo frequency domain equalization (TFDE) with 16-QAM by bit error rate transfer (BERT) chart. We derive the BERT chart for the two cases in which the equalizer adapts or does not adapt from iteration to iteration. In the first case, the equalizer parameters change during iterations based on the feedback information and in the latter case they do not change. In the BERT chart analysis of TFDE, we theoretically find the mean and variance of the Gaussian-like distribution of the equalizer output. The mean and variance of the equalizer output depend on the channel parameters, channel signal to noise ratio and the decoder bit error rate in the previous iteration. Finally we compare the BERT chart results with simulation results and show that the predicted results are fairly accurate.

This paper1 presents a Turbo Equalization system using soft interference cancellation in the frequency domain, in which the filter is updated during the iterations with respect to the soft information it receives from the decoder. Simulation results confirm that this method outperforms the suggested one in [4,ll] while it does not add much complexity to the system. We also investigate the effect of two coding schemes, LDPC code and convolutional code, on the performance of Turbo Frequency Domain Equalization (TFDE). TFDE with these codes are compared in terms of performance and computational complexity. Finally we suggest using an LDPC based frequency domain turbo equalizer due to its good performance/complexity tradeoff.

This paper compares peak to average power ratio (PAPR) of multi carrier code division multiple access (MC-CDMA) and serial modulation-CDMA (SM-CDMA) with each other and with corresponding non-spread spectrum techniques which are orthogonal frequency division multiplexing (OFDM) and serial modulation (SM). In this comparison we consider the effect of two spreading types- binary and complex spreading, and equal-level and multi-level constellation on the amplitude distribution and out-of-band radiation. While MC-CDMA and OFDM have similar amplitude distribution, we show that the PAPR of SM-CDMA can be larger than that of SM. If the spreading is binary and the constellation is multi-level, this effect is so powerful that the PAPR of SM-CDMA becomes similar to that of MC-CDMA. Finally we use a modified selected mapping (SLM) algorithm to decrease the PAPR of SM-CDMA so that it would be equal to that of SM. Therefore SM-CDMA has lower PAPR if we use it together with modified SLM, and the system will need amplifiers with lower power back-off values

In this paper we analyze the performance of a turbo frequency domain equalizer using the BER transfer chart. This tool evaluates the signal to noise ratio improvement at the decoder input during iterations. We derive a formula for the variance of the equalizer output at each iteration as a function of the error probability of the previous iteration. By defining an equivalent SNR based on the equalizer output mean and variance, and knowing the decoder bit error rate curve, we are able to evaluate the bit error rate of the decoder output in each iteration. Compared to the initially proposed BER transfer charts, this method gives us a more accurate curve for the equalizer and follows the dynamic of the process. Simulation results show that this method can predict the performance of the system with reasonable accuracy.

We present a multiple access scheme for the uplink of broadband wireless systems. We consider SC (single carrier) based block transmission, with all users transmitting continuously, regardless of their data-rate. The transmitted signals can have very low envelope fluctuations. Moreover, the different users remain orthogonal, even for severe time-dispersive channels. By employing IB-DFE (iterative block decision feedback equalization) techniques we can have detection performances close to the matched filter bound, and even for fully loaded systems and severe time-dispersive channels.