We consider fronthaul-limited generalized zero-forcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multiple-antenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral efficiency (SE) performance of the system wherein the users use the minimum mean-squared error-based successive interference cancellation (MMSE-SIC) scheme to detect the desired signals. Specifically, we formulate an optimization problem for the user association and power control for maximizing the sum SE. The formulated problem is under per-AP transmit power and fronthaul constraints, and is based on only long-term channel state information (CSI). The challenging formulated problem is transformed into tractable form and a novel algorithm is proposed to solve it using minorization maximization (MM) technique. We analyze the trade-offs provided by the CF-mMIMO system with different number of CB clusters, hence highlighting the importance of the appropriate choice of CB design for different system setups. Numerical results show that for the centralized CB, the proposed power optimization provides nearly 59% improvement in the average sum SE over the heuristic approach, and 312% improvement, when the distributed beamforming is employed.

This paper analyzes the performance of linearly precoded time division duplex based multi-user massive MIMO downlink system under joint impacts of channel non-reciprocity (NRC) and imperfect channel state information. We consider a generic and realistic NRC model that accounts for transceiver frequency-response as well as mutual coupling mismatches at both user equipment (UE) and base station (BS) sides. The analysis covers two most prominent forms of linear precoding schemes, namely, zero-forcing (ZF) and maximum-ratio transmission (MRT), and assumes that only the statistical properties of the beamformed channel are used at the UE side to decode the received signal. Under the approximation of i.i.d. Gaussian channels, closed-form analytical expressions are derived for the effective signal to interference and noise ratios (SINRs) and the corresponding capacity lower bounds. The expressions show that, in moderate to high SNR, the additional interference caused by imperfect NRC calibration can degrade the performance of both precoders significantly. Moreover, ZF is shown to be more sensitive to NRC than MRT. Numerical evaluations with practical NRC levels indicate that this performance loss in the spectral efficiency can be as high as 42% for ZF, whereas it is typically less than 13% for MRT. It is also shown that due to the NRC, the asymptotic large-antenna performance of both precoders saturate to an identical finite level. The derived analytical expressions provide useful tools and valuable technical insight, e.g., into calculating the NRC calibration requirements in BSs and UEs for any given specific performance targets in terms of effective SINR or the system capacity bound.

In high-density low-bitrate Internet-of-Things (IoT) use case of 5G networks, the terminals and sensors are to be of extremely low-cost and low energy-consuming. Typically, the analog-to-digital converters (ADCs) dominate the power-budget of receiver chains, in particular if the quantization resolution is high. Hence, receiver architectures deploying 1-bit ADCs are of high interest towards realizing low-cost, high energy-efficiency device solutions. In this paper, we study the waveform design and optimization for a narrowband low-bitrate massive MISO downlink targeting to achieve rates higher than 1 bits/sec (per real-dimension) where the terminal receivers adopt only simple 1-bit quantization (per real-dimension) with oversampling. In this respect, first we show that for a particular precoder structure, the overall link is equivalent to that of an AWGN SISO with controlled intersymbol interference (ISI). The filter design problem for generating the desired ISI in such SISO links has been studied in previous works, however, the only known method in literature is a computationally demanding brute force search method. As a novel contribution, we develop models and tools that elaborate on the conditions to be satisfied for unique detection and existence of solution for the filter coefficients. Then, as a concrete example, the developed models and tools are utilized to show that in the absence of noise, five-times oversampling is required for unique detection of 16-QAM input alphabet. Building on these findings, we then develop novel algorithms that can efficiently design the filter coefficients. Examples and simulations are provided to elaborate on filter coefficient design and optimization, and to illustrate good SER performance of the MISO link with 1-bit receiver even at SNRs down to 5 dB.

Direct-conversion radio receivers can offer highly integrated low-cost hardware solutions for cognitive radio (CR) devices. Such receivers are, however, also very sensitive to various radio frequency (RF) impairments such as IQ imbalance, which can considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and hence neglect the impacts of such practical RF hardware limitations. In this article, we study energy detection (ED) based spectrum sensing in both single-channel and multi-channel direct-conversion receiver scenarios impaired by IQ imbalance. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form for both receiver scenarios. The analytical results, confirmed through extensive simulations, show that while the single-channel receiver scenario is fairly robust to IQ imbalance, the wideband multi-channel sensing receiver is very sensitive to the image channel crosstalk induced by IQ imbalance. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to ideal RF receiver case, and the exact performance depends on the image channel power level and IQ imbalance values. In order to prevent such degradation in the ability to identify free spectrum, a waveform level interference cancellation method is then proposed to mitigate the image channel crosstalk. The optimum cancellation coefficient yielding interference-free signal is first derived, being also complemented with a practical coefficient sample estimator. An explicit condition is also derived for which the proposed cancellation scheme deploying the practical coefficient sample estimator provides performance gain in the sensing decisions compared to uncompensated energy detection. Extensive computer simulations with various signal and imbalance conditions are provided which demonstrate that the proposed enhanced energy detection can suppress the image channel crosstalk efficiently, yielding detection and false alarm probabilities essentially identical to those of an ideal RF receiver.

Direct-conversion radio receivers can provide highly-integrated and low-cost hardware solutions for cognitive radio (CR) devices. However, such receiver structures are also known to suffer from various RF circuit imperfections, especially IQ imbalance. When studying the performance of various spectrum sensing methods, most existing work in literature neglect the effects of such RF imperfections. In this paper, we analyze the performance of a well-known eigenvalue based sensing method, namely maximum-to-minimum eigenvalue test, in the challenging multichannel spectrum sensing context under sensing receiver RF IQ imbalance. It is analytically shown that under RF IQ imbalance, the false alarm probability of the spectrum sensor is always higher compared to ideal RF front-end case. Then, in order to compensate for such degradation in the ability to identify free spectrum, a waveform level interference cancellation solution is deployed to enhance the performance, and optimum cancellation coefficient together with a practical sample estimator are formulated. Extensive computer simulations are provided to illustrate the degradation of false alarm probability in the presence of practical IQ imbalance values. Furthermore, the simulations also show that the proposed interference cancellation method can efficiently suppress the IQ imbalance effects, achieving identical false alarm probability with the ideal RF front-end case.

OFDM and other multicarrier waveforms are in general very sensitive to RF non-idealities, such as phase noise and IQ imbalance, of transmitting and receiving devices. Extensive work has been carried out in the open literature in analyzing the performance of OFDM radio link under such RF impairments in terms of detection error rate and mostly concentrating on one impairment at a time. However, there is only very limited work on analytical investigations of mutual information and rate loss expressions, the heart of communication theory, as functions of RF impairment levels. In this article, we derive two closed-form mutual information expressions, in the form of infinite series representation, for an arbitrary subcarrier of a general OFDM radio link impaired with transceiver phase noise and IQ imbalance in frequency-selective Rayleigh distributed block-fading radio channel, covering both uncorrelated as well as fully correlated mirror subcarrier scenarios. We also show that the mutual information saturates to a finite value due to the inherent RF impairments even in the case that the symbol-to-noise ratio approaches infinity. Extensive comparisons with results obtained from full OFDM radio link simulations are also provided to illustrate and verify the accurate match between analytical and simulated mutual information behavior.