We consider massive multiple-input multiple-output (MIMO) systems in the presence of Cauchy noise. First, we focus on the channel estimation problem. In the standard massive MIMO setup, the users transmit orthonormal pilots during the training phase and the received signal at the base station is projected onto each pilot. This processing is optimum when the noise is Gaussian. We show that this processing is not optimal when the noise is Cauchy and as a remedy propose a channel estimation technique that operates on the raw received signal. Second, we derive uplink-downlink achievable rates in the presence of Cauchy noise for perfect and imperfect channel state information. Finally, we derive log-likelihood ratio expressions for soft bit detection for both uplink and downlink, and simulate coded bit-error-rate curves. In addition to this, we derive and compare the symbol detectors in the presence of both Gaussian and Cauchy noises. An important observation is that the detector constructed for Cauchy noise performs well with both Gaussian and Cauchy noises; on the other hand, the detector for Gaussian noise works poorly in the presence of Cauchy noise. In other words, the Cauchy detector is robust against heavy-tailed noise, whereas the Gaussian detector is not.

Impulsive noise can appear in communication links. In the literature, it was demonstrated that when the noise is impulsive, standard Gaussian receivers perform poorly because of the outliers in the noise. Therefore, appropriate receivers must be used when the noise is impulsive. In this paper, we compare two types of massive multiple- input multiple-output (MIMO) receivers, namely those based on a Gaussian-mixture assumption and those based on a Cauchy assumption, in terms of channel estimation quality, when the noise is impulsive. Symmetric α-stable (SαS) noises are used to model impulsive noises in the paper. In the numerical results, the Gaussian-mixture receiver outperforms the Cauchy-based receiver.

In this paper, we work on channel estimation techniques for massive multiple-input multiple-output (MIMO) with Cauchy noise. In the standard massive MIMO setup, the users transmit orthonormal pilots during the training phase and the received signal in the base station is projected onto each orthonormal pilot signal. This process is optimum when the noise is Gaussian. In other words, the obtained signal after this process is the sufficient statistic and we do not lose any information. We show that this process is not optimum when the noise is Cauchy. Hence, we propose a channel estimation technique for the unprocessed received signal. The proposed channel estimation technique is compared with the channel estimates that are obtained from the projected signal.

In this paper, we evaluate the uplink spectral efficiency (SE) of a single-cell massive multiple-input-multiple-output (MIMO) system with distributed jammers. We define four different attack scenarios and compare their impact on the massive MIMO system as well as on a conventional single-input-multiple-output (SIMO) system. More specifically, the jammers attack the base station (BS) during both the uplink training phase and data phase. The BS uses either least squares (LS) or linear minimum mean square error (LMMSE) estimators for channel estimation and utilizes either maximum-ratio-combining (MRC) or zero-forcing (ZF) decoding vectors. We show that ZF gives higher SE than MRC but, interestingly, the performance is unaffected by the choice of the estimators. The simulation results show that the performance loss percentage of massive MIMO is less than that of the SIMO system. Moreover, we consider two types of power control algorithms: jamming-aware and jamming-ignorant. In both cases, we consider the max-min and proportional fairness criteria to increase the uplink SE of massive MIMO systems. We notice numerically that max-min fairness is not a good option because if one user is strongly affected by the jamming, it will degrade the other users' SE as well. On the other hand, proportional fairness improves the sum SE of the system compared with the full power transmission scenario.

In this paper, we analyze the uplink spectral efficiency (SE) of an arbitrary user in a single-cell massive multiple-input-multiple-output (MIMO) system in which there are jammers randomly distributed. We compare this SE with that of a user in a single-input-multiple-output (SIMO) system. We utilize two types of receivers that are widely used in massive MIMO literature: Maximum-ratio-combining (MRC) and zero-forcing (ZF). The jammers attack the base station (BS) during the training and data transmission phases. In order to estimate the channel vectors of the legitimate users, the BS uses either the linear minimum mean square error (LMMSE) estimator which requires information about the jamming power or the least squares (LS) estimator which does not require any knowledge about the jamming signals. We show that ZF gives higher SE than MRC, but interestingly the performance is unaffected by the choice of the estimators. Moreover, we derive the closed form signal-to-interference-noise ratio (SINR) for the MRC receiver when the jammers attack to the BS and based on this SINR expression we utilize power control algorithms that achieve max-min fairness and proportional fairness.

Nonlinearities in various stages of a transmitter may hinder and restrict the transmission rate. As observed in many studies, outermost constellation points are usually more adversely affected by these impairments. To observe these effects, we utilize two power amplifier models that have different effects on transmitted signals. The Rapp model considers only amplitude deformation and the resultant in-phase and quadrature errors can be assumed to be independent on the receiver side. Unlike the Rapp model, the Saleh model exerts both amplitude and phase deformations and the phase deformation introduces correlation between the in-phase and quadrature errors according to our observations. In addition to the correlation, the variances of in-phase and quadrature errors may not be equal to each other. In this paper, we propose receivers that consider error variances of each quadrature amplitude modulation (QAM) symbol. We compare the performances of the receivers with those of other receivers that take average error variances into account for decoding. Furthermore, we propose a practical receiver that directly works on digitized observations based on a look-up table that keeps log-likelihood ratios of the quantized regions in order to reduce computational complexity.

High data rate demands of new generations of communication systems necessitate the use of higher and higher order QAM schemes. However, the nonlinearities in various stages of the transmitter may hinder the performance of higher order QAM and limit the transmission rate enhancements. As observed in many studies in the literature, outermost constellation points are usually more adversely affected by nonlinearities and larger regions of uncertainty form around them. We focus on nonuniform error vector magnitude (EVM) values of QAM symbols in this paper and present achievable rates. Furthermore, we propose a simplified receiver that considers the unequal EVM distribution by splitting the constellation into multiple regions and compare the performance of the receiver to that of a conventional receiver taking into account average EVM.