In the framework of 5G-and-beyond Industry 4.0, jamming attacks for denial of service are a rising threat which can severely compromise the system performance. Therefore, in this paper we deal with the problem of jamming detection and mitigation in indoor factory deployments. We design two jamming detectors based on pseudo-random blanking of subcarriers with orthogonal frequency division multiplexing and consider jamming mitigation with frequency hopping and random scheduling of the user equipments. We then evaluate the performance of the system in terms of achievable block error rate (BLER) with ultra-reliable low-latency communications traffic and jamming missed detection probability. Simulations are performed considering a 3rd Generation Partnership Project spatial channel model for the factory floor with a jammer stationed outside the plant trying to disrupt the communication inside the factory. Numerical results show that jamming resiliency increases when using a distributed access point deployment and exploiting channel correlation among antennas for jamming detection, while frequency hopping is helpful in jamming mitigation only for strict BLER requirements.

Current fourth-generation (4G) long-term evolution (LTE) base-stations (BSs) that are not capable of performing elevation beamforming are down-tilted in order to optimize the throughput of ground user equipment (GUE). As a consequence of this down-tilt, unmanned aerial vehicles (UAVs), especially those at higher altitudes, are forced to connect with the BS side-lobes, resulting in increased transmit powers and interference to other GUEs and UAVs. In this paper, we propose using passive reflectors placed close to the BS as a low-cost alternative to elevation beamforming to compensate for the reduced antenna gain, and consequently, the increased pathloss between the BS and UAVs. The effectiveness of such reflectors in lowering the interference and increasing throughputs is tested through extensive simulations using the recently introduced third-generation partnership project aerial channel models.

Security attacks at the physical layer, in the form of radio jamming for denial of service, are an increasing threat in the Industry 4.0 scenarios. In this paper, we consider the problem of jamming detection in 5G-and-beyond communication systems and propose a defense mechanism based on pseudo-random blanking of subcarriers with orthogonal frequency division multiplexing (OFDM). We then design a detector by applying the generalized likelihood ratio test (GLRT) on those subcarriers. We finally evaluate the performance of the proposed technique against a smart jammer, which is pursuing one of the following objectives: maximize stealthiness, minimize spectral efficiency (SE) with mobile broadband (MBB) type of traffic, and maximize block error rate (BLER) with ultra-reliable low-latency communications (URLLC). Numerical results show that a smart jammer a) needs to compromise between missed detection (MD) probability and SE reduction with MBB and b) can achieve low detectability and high system performance degradation with URLLC only if it has sufficiently high power.

Implementing large antenna arrays for massive multiple-input multiple-output (MIMO) base stations requires transmit radio frequency (RF) chains with low complexity and high power amplifier (PA) efficiency. One approach to achieve this goal is to relax PA linearity and to digitally compensate for some of the resulting non-linear effects such as an increased error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR). The required compensation method can be implemented in a non-linear precoding module that exploits the large spatial degrees of freedom in a massive MIMO system. In this paper, we propose using outphasing RF chains with low-resolution phase shifters to generate constrained amplitude and phase-modulated signals with very low hardware complexity. To improve transmit power efficiency and to control the ACLR and EVM, we develop a novel non-linear precoding algorithm that incorporates the quantized phase-shift constraints in the outphasing RF chains. The achieved flexibility to trade-off EVM and ACLR in favor of a higher PA efficiency (and vice versa) is demonstrated by means of simulations.

This paper studies the impact of additional pilot overhead for covariance matrix estimation in a time-division duplexed (TDD) massive multiple-input multiple-output (MIMO) system. We choose average uplink (UL) and downlink (DL) spectral efficiencies (SEs) as performance metrics for the massive MIMO system, and derive closed form expressions for them in terms of the additional pilot overhead. The expressions are derived by considering linear minimum mean squared error (LMMSE)-type and element-wise LMMSE-type channel estimates that represent LMMSE and element-wise LMMSE with estimated covariance matrices, respectively. Using these expressions, a detailed theoretical analysis of SE behavior as a function of pilot overhead for both LMMSE-type and element-wise LMMSE-type channel estimation are presented, followed by simulations, which also demonstrate and validate theoretical results.

An analytical lower bound on uplink channel capacity of a user in a massive multiple-input multiple-output system where the channel vector and the covariance matrices of the users in that cell are unknown is derived in this paper. This analytical bound enables us to choose appropriate sample size for covariance matrix estimation to meet the spectral efficiency requirements. The accurate agreement between the derived bound and the simulated bound based on random samples of channel vectors and covariance matrices is shown.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) transceivers employ narrow beams to obtain a large array-gain, rendering them sensitive to changes in the angles of arrival and departure of the paths. Since the singular vectors that span the channel subspace are used to design the precoder and combiner, we propose a method to track the receiver-side channel subspace during data transmission using a separate radio frequency (RF) chain dedicated for channel tracking. Under certain conditions on the transmit precoder, we show that the receiver-side channel subspace can be estimated during data transmission without knowing the structure of the precoder or the transmitted data. The performance of the proposed method is evaluated through simulations.

We propose a novel pilot structure for covariance matrix estimation in massive multiple-input multiple-output systems in which each user transmits two pilot sequences, with the second pilot sequence multiplied by a random phase shift. The covariance matrix of a particular user is obtained by computing the sample cross-correlation of the channel estimates obtained from the two pilot sequences. This approach relaxes the requirement that all the users transmit their uplink pilots over the same set of symbols. We derive expressions for the achievable rate and the mean-squared error of the covariance matrix estimate when the proposed method is used with staggered pilots. The performance of the proposed method is compared with existing methods through simulations.

In this paper, we investigate the downlink throughput performance of a massive multiple-input multiple-output system that employs superimposed pilots for channel estimation. The component of downlink (DL) interference that results from transmitting data alongside pilots in the uplink (UL) is shown to decrease at a rate proportional to the square root of the number of antennas at the BS when the least-squares channel estimate is employed in a matched-filter precoder. The normalized mean-squared error (NMSE) of the channel estimate is compared with the Bayesian Cramér-Rao lower bound that is derived for the system, and the former is also shown to diminish with increasing number of antennas at the base station. Furthermore, we show that staggered pilots are a particular case of superimposed pilots and offer the downlink throughput of superimposed pilots while retaining the UL spectral and energy efficiency of regular pilots. We also extend the framework for designing a hybrid system, consisting of users that transmit either regular or superimposed pilots, to minimize both the UL and DL interference. The improved NMSE and DL rates of the channel estimator based on superimposed pilots are demonstrated by means of simulations.

In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed (TM) pilots exhibit a complementary behavior, with the former and latter schemes offering a higher throughput in high and low inter-cell interference scenarios, respectively. Based on this observation, in this paper, we propose an algorithm for partitioning users into two disjoint sets comprising users that transmit TM and SP pilots. This selection of user sets is accomplished by minimizing the total inter-cell and intra-cell interference, and since this problem is found to be non-convex, a greedy approach is proposed to perform the partitioning. Based on simulations, it is shown that the proposed method is versatile and offers an improved performance in both high and low-interference scenarios.

Superimposed pilots are proposed as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output systems. Provided that the uplink duration is larger than the total number of users in the system, superimposed pilots enable each user to be assigned a unique pilot sequence, thereby allowing for a significant reduction in pilot contamination. Channel estimation performance in the uplink is further improved using an iterative data-aided algorithm. Based on approximate expressions for the uplink signal-to-interference-plus-noise ratio, it is shown that superimposed pilots provide a better performance when compared with methods that use time-multiplexed data and pilots. Numerical simulations are used to validate the approximations and the improved performance of the proposed method.