The time varying nature of the wireless propagation channel causes a mismatch between the true channel at the time of data transmission and its available estimate based on previously received pilot symbols, and is known to impair the performance of the massive multiple input multiple output (MIMO) systems. In this paper, we develop and evaluate adaptive data aided channel tracking and data detection algorithms to counter the effects of channel aging for uplink and downlink massive MIMO systems. We first present a recursive least squares (RLS) algorithm for tracking the matrix uplink channel at the base station (BS), and derive bounds on its MSE performance. We also derive a linear complexity stochastic gradient descent (SGD) algorithm for tracking the uplink channel, along with its performance bounds. Following this, we develop RLS and SGD based algorithms for tracking the scalar effective downlink channel at each UE, and derive their performance guarantees. Finally, via Monte Carlo simulations, we validate the efficacy of the algorithms in terms of their mean squared error performance, and demonstrate the gains achievable by channel tracking in the form of the improvement in the symbol error rates.

The use of massive multiple-input multiple-output (MIMO) technology has enabled increased efficiency and capacity of wireless communication systems. When the downlink channel to user terminals (UTs) is known at the base station (BS), the BS can precode transmission to simplify detection at the UTs. In frequency division duplexed (FDD) systems, obtaining CSI at the transmitter to fully exploit the advantages of massive MIMO is complicated, since the number of channel coefficients to be trained and fed back from the UT is very large. This is exacerbated in the case where the channel is time varying, where frequent retraining and feedback is required. However, if the channel coefficients are spatially correlated, the amount of feedback required can be reduced significantly. In this paper, we consider the case where the channel coefficients from the BS to the UTs are spatially correlated, and show that efficient allocation of training power based on eigenvalues of the channel correlation matrix significantly boosts achievable rates. Further, we show that using Kalman filters to track the evolving channel coefficients reduces the need to retrain channels, and the reduced training requirement translates to higher data rates. Simulations confirm that optimal training and tracking channel modes enhances rates significantly.

We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs), with the goal of maximizing the sum UL-DL spectral efficiency (SE). We develop a new, low complexity, greedy algorithm for the combinatorial AP scheduling problem, with an optimality guarantee theoretically established via showing that a lower bound of the sum UL-DL SE is sub-modular. We also consider pilot sequence reuse among the UEs to limit the channel estimation overhead. In CF systems, all the APs estimate the channel from every UE, making pilot allocation problem different from the cellular case. We develop a novel algorithm that iteratively minimizes the maximum pilot contamination across the UEs. We compare the performance of our solutions, both theoretically and via simulations, against a full duplex (FD) multi-cell mMIMO system. Our results show that, due to the joint processing of the signals at the central processing unit, CF-mMIMO with dynamic HD AP-scheduling significantly outperforms cellular FD-mMIMO in terms of the sum SE and 90% likely SE. Thus, DTDD enabled HD CF-mMIMO is a promising alternative to cellular FD-mMIMO, without the cost of hardware for self-interference suppression.

We analyze the effect of channel aging on the achievable rate of time division duplexed massive multiple input multiple output systems serving a number of users under aging channels, using nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA). Using the recently proposed shared uplink pilot based channel estimation for NOMA, we derive bounds on the channel estimation error variance for the two schemes. We then derive the achievable spectral efficiencies of the two schemes. Using numerical results, we show that, in slowly varying channels, using NOMA with shared pilots is preferable over OMA, while the reverse is true under fast varying channels.

In this paper, we consider physical layer security in wireless sensor networks (WSNs) using distributed co-phasing (DCP)-based transmissions. For this protocol, we first analyze the achievable ergodic secrecy rate of a single stream DCP system in the presence of one or more eavesdroppers. We show that the coherent combining gain offered by DCP leads to the signal-to-interference-plus-noise-ratio (SINR) over the main channel increasing as the square of the number of SNs N and that over the eavesdropper channel increasing linearly with N. This results in a strictly positive ergodic secrecy rate that increases as log N. We then analyze the performance of multi-stream DCP and show that using K data streams in DCP leads to a K -fold increase in the achievable secrecy rate at high SNRs. We also discuss an alternative power allocation scheme for multi-stream DCP, such as distributed maximal ratio transmission with a per-user power constraint and show that this improves the achievable secrecy rates as compared to standard multi-stream DCP. Finally, we analyze the role of artificial noise in improving the achievable secrecy rates. We validate the accuracy of these derived results and illustrate the efficacy of DCP in ensuring secure data fusion in WSNs using Monte Carlo simulations.

In this paper, we study the effect of channel aging on the uplink and downlink performance of an FDD massive MIMO system, as the system dimension increases. Since the training duration scales linearly with the number of transmit dimensions, channel estimates become increasingly outdated in the communication phase, leading to performance degradation. To quantify this degradation, we first derive bounds on the mean squared channel estimation error. We use the bounds to derive deterministic equivalents of the receive SINRs, which yields a lower bound on the achievable uplink and downlink spectral efficiencies. For the uplink, we consider maximal ratio combining and MMSE detectors, while for the downlink, we consider matched filter and regularized zero forcing precoders. We show that the effect of channel aging can be mitigated by optimally choosing the frame duration. It is found that using all the base station antennas can lead to negligibly small achievable rates in high user mobility scenarios. Finally, numerical results are presented to validate the accuracy of our expressions and illustrate the dependence of the performance on the system dimension and channel aging parameters.

We study the problem of throughput optimization in large multiple-input multiple-output (MIMO) beamforming systems with channel aging, as the number of transmit and receive antennas is increased. We quantify the effective channel coherence time in terms of the intertraining interval, which is the time after which the channel state information needs to be reestimated to avoid loss in performance due to outdated channel estimates. Conventional wisdom suggests that the coherence time of large MIMO systems should decrease with increasing number of antennas, because the system has a large number of independently varying channel coefficients. On the contrary, we show that the effective channel coherence time increases with the number of antennas. We optimize the throughput with respect to the symbol and pilot energies, the number of antennas, frame duration, and target SNR. This analysis brings out the important fact that the effective channel coherence time critically depends on the underlying signaling scheme, and not only on the temporal correlation coefficient of the wireless channel.

In this paper, we consider the problem of spectrum sensing of cyclostationary signals for cognitive radios. It is shown that the detection performance in this case may be improved by enhancing the cyclostationary features of the signal of interest. The optimal filter for cyclostationary features is revisited, following which an adaptive space-time structure exploiting the spatial, temporal and spectral coherence of a cyclostationary signal incident upon an antenna array is proposed for enhancing the signal. A low complexity adaptation algorithm for this structure is also proposed. The performance of this detector is evaluated and compared with standard spectrum sensing methods. Simulation results show that a suitable space-time FRESH filtering configuration may be used to yield gains of up to 10 dB over both the standard energy detector and the traditional cyclostationary detector.

Fuzzy classifiers are suited for pattern classification when there exist a large amount of imprecision, uncertainty and ambiguity in the patterns. One such fuzzy classifier is based on the possibilistic fuzzy membership function used for measuring the degree of class belongingness. However, the performance of possibilistic classifier depends heavily on the cluster parameters such as the 3-dB point and the parameter that controls the degree of fuzziness in the cluster. In this paper, we develop an iterative method for tuning these parameters so that the performance of the classifier is improved. The classifier considered in our work is a multi-prototype classifier and includes options for rejecting patterns that are ambiguous and/or do not belong to any class. In our proposed scheme, the slopes of the membership function are suitably varied via parameter tuning so that the membership of a pattern to a cluster in which it actually belongs is maximized while that to other classes are forced to be as small as possible. We evaluate our method using the Wisconsin Breast Cancer Dataset (WBCD). The results show that the recognition rate is improved by as much as 8% when the cluster parameters are tuned.