Mobility management is a major challenge for millimeter-wave (mmWave) cellular networks. In particular, directional beamforming in mmWave devices renders high-speed mobility support very complex. This complexity, however, is not limited to system design but also the performance estimation and evaluation. Hence, some have turned their attention to stochastic modeling of mmWave vehicular communication to derive closed-form expressions that can characterize the coverage and rate behavior of the network. In this article, we model and analyze the beam management for mmWave vehicular networks. To the best of our knowledge, this is the first work that goes beyond coverage and rate analysis. Specifically, we focus on a multi-lane divided highway scenario in which base stations and vehicles are present on both sides of the highway. In addition to providing analytical expressions for the average number of beam switching and handover events, we provide design insights for the operators to fine-tune their network through more informed choice of system parameters, including the number of resources dedicated to channel feedback and beam alignment operations.

We investigate the achievable rate of a single-cell distributed massive multi-input multi-output (MIMO) system underlaid a licensed primary multi-user co-located Massive MIMO network. We propose an access point (AP) selection procedure and power allocation (PA) method to improve the performance of the secondary network and derive the closed-form sum rate expression for Rayleigh fading channels by considering the effects of pilot contamination, inter-user interference and statistical downlink channel state information (CSI) at secondary users (SUs). Our results reveal that, the joint use of AP selection procedure and PA method significantly improve the achieved sum rate of the SUs while preserving the performance of the primary network.

Although the hybrid of cell-free (CF) massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) promises massive spectral efficiency gains, the type of precoders employed at the access points (APs) impacts the gains. In this paper, we thus comprehensively evaluate the system performance with maximum ratio transmission (MRT), full-pilot zero-forcing (fpZF) and modified regularized ZF (mRZF) precoders. We derive their closed-form sum rate expressions by considering Rayleigh fading channels, the effects of intra-cluster pilot contamination, inter-cluster interference, and imperfect successive interference cancellation (SIC). Our results reveal that this system supports significantly more users simultaneously at the same coherence interval compared to its OMA equivalent. However, intra-cluster pilot contamination and imperfect SIC degrade the system performance when the number of users is low. Moreover, with perfect SIC, mRZF and fpZF significantly outperform MRT. Also, we show that this system with either mRZF or fpZF precoding outperforms OMA systems with MRT. The analytical findings are verified by numerical results.

In this study, the authors propose four interference alignment (IA) schemes for multiple‐input multiple‐output (MIMO) interference broadcast channels (IBCs) under imperfect channel state information (CSI). They propose two new algorithms based on minimising the leakage of the interference signals under a generalised imperfect CSI model from two new points of view. The first algorithm is based on alternating minimisation algorithm. The second proposed algorithm is an improved version of minimum weighted leakage interference algorithm with a faster convergence rate. Inspired by the CSI uncertainty model, the authors present two robust IA algorithms based on exploiting stochastic properties of the CSI mismatch. The first one is based on joint signal and IA which reduces the leakage of the interference signals outside the interference subspace and forces the intended signal to fall into the orthogonal complement of the interference subspace. The second algorithm maximises the signal‐to‐interference‐plus‐noise ratio (SINR) in a per‐user‐based approach, while the existing maximum SINR (MSINR) algorithm in literature is a per‐stream‐based approach. The authors illustrate that the proposed MSINR algorithm requires less channel information and has less computational costs compared to the existing MSINR. The superiority of the proposed schemes over the existing algorithms are confirmed by simulation examples.

In this study, the authors present a method to derive sequential detector (SD) for a general class of composite hypothesis problems. The authors first explain how the SD is constructed by employing a ‘weight function’. Then, the authors employ this method in the cooperative spectrum sensing (SS) in cognitive radio networks where the primary user transmits a phase shift keying (PSK) signal with unknown complex amplitude in additive white Gaussian noise. The noise power is assumed known in the first scenario and unknown in the second one. To evaluate the performance of the resulting SDs, the authors obtain the required average sample number (ASN) function to meet the bounds of false alarm and missed detection probabilities through some numerical evaluations. The results illustrate that the average sensing delay of the proposed SDs are less than the required number of observations in the traditional fixed sample size detectors. In the proposed SDs, the authors also demonstrate that the increase of signal‐to‐noise ratio leads to decrease of ASN.

In this study, the authors concentrate on the designing of a deterministic measurement matrix. Unlike most of the studies, they employ the points on finite projective spaces rather than finite fields. These spaces provide more choices for the size of the proposed matrix. A new group of binary measurement matrices is presented through generalising DeVore's construction. For this purpose, homogenous polynomials over finite projective spaces are applied. To investigate the performance of the proposed matrix they provide an example on projective lines. It can be observed that the coherence of the result matrix is lower than DeVore's construction. The simulation results show that the proposed matrix outperforms the Gaussian matrix and DeVore's matrix in terms of noiseless and noisy signal recovery.

This letter provides a generalization of the well-known Wald test. The proposed generalized Wald test (GWT) is a Separating Function Estimation Test (SFET) which is a type of detector recently introduced for a wide class of composite problems. The test statistics of an SFET is an estimate of a real-valued Separating Function (SF). It is already proved that a Minimum Variance Unbiased Estimator of any SF leads to the optimal Uniformly Most Powerful unbiased detector. In many practical cases, such an optimal detector does not exist; hence, suboptimal ones are used, instead. Selecting an SF with a guaranteed performance is still an open problem which is investigated in this letter. First, we derive a lower bound for the detection probability of the SFET in terms of corresponding SF and the Fisher Information Matrix. Then we optimize the proposed bound with respect to the SF. The solution of the optimization problem leads to a generalization of the Wald test which is asymptotically optimal and reduces to the Wald detector in some special cases. Simulation results show the superiority of the proposed GWT over its counterparts, namely, the Wald test and GLR detector in some examples.

This paper addresses the joint detection and estimation approach for single-channel speech enhancement. In this approach, a detector decides on speech presence in each time-frequency unit and an estimator estimates the corresponding speech spectral amplitude. We utilize the concept of binary/continuous gain functions to study and extend the process of joint detection and estimation. The binary gains (BGs) have already shown an inferior performance compared to the continuous gains (CGs). In this paper, we propose a simultaneous detection and estimation (SDE) method where the detector structure is derived by the knowledge of the estimator. The proposed SDE method is a combination of Bayesian and Neyman-Pearson approaches and is expressed as a noncontinuous gain (NCG). It is expected that employing a superior detector, the proposed NCG improves the quality of the output speech. We concentrate on the derivation of the detector so that it minimizes the error caused by missed detection and/or wrong estimation of speech coefficients at a controlled level of falsely detecting high-energy noise as speech. Furthermore, an independent detection and estimation technique is proposed where the detector and the estimator are extracted in an independent manner. Simulation results demonstrate that the proposed SDE method minimizes the speech distortion at a controlled level of noise reduction. It is also shown that the performance of the proposed NCG is better than the CG and than the existing BGs in both noise reduction and speech distortion aspects.

In this paper we propose an algorithm with reduced complexity for the sphere detection (SD) which is used in multiple input multiple output (MIMO) detection algorithms without any performance degradation. The trade-off between the complexity and the bit error rate is a main challenge in wireless MIMO systems. The maximum likelihood (ML) detector considered as the optimum detector in the literatures. Since the complexity of the naive ML detectors is significantly high, the SD algorithms are proposed to lower the complexity. In this paper, we use the result of the geometrical decoder (GD) proposed in [8] which performs as the ML detector and has lower complexity than SD algorithm. We propose a method to further reduce the complexity of this SD algorithm. We show that the complexity is further reduced by almost 60%, i.e., the number of nodes visited by the proposed SD method is in average 60% less than that of the original one.

A fast algorithm to perform interference alignment (IA) in a cognitive radio network is proposed. Here, the authors consider a network consisting of multiple primary users (PUs) and secondary users (SUs). Although the PUs are cooperating through IA and their rates are maximised by water‐filling algorithm, in the secondary network, the authors try to choose the precoding matrices of the SUs such that the interferences at the primary receivers are aligned away from the desired subspaces of the primary receivers. Besides, the interference cancellation of the PUs on the secondary links is considered through designing the post‐processing matrices for the secondary links. In this way, their mutual harmful effects are suppressed and SUs could transmit their information simultaneously on the same frequency or time without causing/receiving any interference to/from the primary links. Meanwhile, the feasibility condition for the secondary network regarding the mentioned constraints is investigated. In addition, the rates of the secondary links are maximised by water‐filling algorithm. The proposed iterative algorithm is an improved version of minimum weighted leakage interference algorithm with a much faster convergence rate. The analytical results are confirmed through simulations.

In this letter, we construct a class of deterministic measurement matrices which are asymptotically optimal. For this purpose, we first apply the tensor product over the Reed Solomon (R-S) generator matrix to produce a new one; then employing this generator matrix, we construct a measurement matrix. If the R-S code is defined on \BBFq, then the resulting measurement matrices are of dimensions q2×q3, where q is an arbitrary prime power and its coherence would be1/q, which is desirable for compressive sampling. We also illustrate the effectiveness of our proposed matrices in compressed sensing with some simulation examples.

In this paper, we study the MIMO signal detection problem using widely separated antennas in Gaussian interference. The interference is assumed to be colored with unknown N × N covariance matrix. We derive the Uniformly Most Powerful Invariant (UMPI) test for this detection problem as the upper performance bound for invariant tests. Also the Separating Function Estimation Test (SFET) is derived for this problem using the Signal and Scatter to Interference Ratio (SIR). Then, based on the eigenvalues expansion of SIR, we propose a set of orthogonal signals in transmitters which maximizes the detection probability of UMPI and SFET. Simulation results show that 1) the performance of our proposed detector is close to the UMPI bound and 2) the performance of the optimal invariant bound improves when the transmitters use the proposed set of signals instead of the orthogonal complex exponential signals.

In this paper we introduce a new method for wideband spectrum sensing which uses the received signal compressed measurements to determine the occupancy status of each subchannel. In the cognitive radio systems, wideband spectrum sensing in high utilization regimes increases the speed of the access to the spectrum holes. In order to send data, the primary users employ different modulation types in each sub-channel; therefore, the transmitted signal is chosen among a signal space. Since just a few numbers of sub-channels are occupied by the primary user signals in each time instant, the received wideband signal has a sparse representation on the sub-channel signal space bases; then, the secondary user can compress the received signal samples. Knowing about the modulation types and therefore, the signal spaces, the information in each subchannel is separated using a projection matrix applied to the compressed measurements, directly. The separated compressed measurements are employed to derive a Generalized Likelihood Ratio detector and determine the presence or absence of the primary user signal in each sub-channel. We try to determine the sensing matrix so that the filtering and detection steps and, therefore, wideband spectrum sensing have low error.

The authors assume a linear equidistance antenna array as the receiver for a fixed frame-length multiple-access system, which employs variable-gain receiver power and repetition encoding. They propose a robust maximum a posteriori probability (MAP) blind bit-rate detector (BBRD). This detector considers the rate detection (RD) as a multi-hypothesis test and maximises the likelihood functions (LFs) to find the true bit-rate, whereas the complex amplitude of the received signal, the noise variance and the direction of arrival are unknown parameters. First, assuming that the location parameter is known and the information sequence are independent and uniformly distributed random variables, the authors propose a hybrid likelihood ratio test (HLRT). The proposed HLRT requires to solve a set of non-linear equations that have no closed-form solution. Thus, an iterative numerical algorithm is proposed. In addition, a quasi-HLR detector which has a significantly lower computational complexity is also proposed. In the case of unknown location parameter, the authors develop two quasi-HLR methods. They use fast Fourier transform and search to estimate the unknown location. In Q-HLRT-Method-I, the non-linear equation similar to the one in known location parameter is iteratively solved. In Q-HLRT-Method-II, a low complexity solution is proposed. Simulation examples evaluate and compare the performances of the proposed BBRDs.

The authors propose a computationally efficient spectrum sensing solution for an orthogonal frequency division multiplexing (OFDM) signal in a frequency-selective fading channel with additive white Gaussian noise (AWGN). The authors’ assumption is that the data symbols, channel coefficients and the noise variance are all unknown. The nature of the problem leads them to find an invariant detector. The optimum one is uniformly most powerful invariant (UMPI); their effort shows that this test does not exist, as the final decision statistic depends on some unknown parameters; although, they derive the most powerful invariant (MPI) detector, implanting these parameters, to provide an upper bound for invariant detectors. Instead, the authors develop the generalised likelihood ratio test (GLRT), substituting the unknown parameters by their maximum likelihood (ML) estimates in the Neyman–Pearson likelihood ratio. Furthermore, they propose a computationally efficient implementation of the resulting detector. Simulation results show a slight decrease in efficiency against gaining so much computational complexity (CC) improvement. For more comparisons they also derive the conventional energy detector, namely, generalised energy detector (GED) for a binary phase shift keying (BPSK) signal, to show the adequacy of the GLR detector performance which would be demonstrated by simulation results.

In this paper, we investigate a cooperative spectrum sensing scheme in which the local sensors at the secondary users perform an M-level quantization on the local decision statistic, and the quantized data are reported through erroneous channels, to be fused under Neyman-Pearson (N-P) criterion. The local quantization can be as fine as the bandwidth limitations permit; thus, the idea behind our effort is to smooth up the path towards the challenge of cooperative spectrum sensing under bandwidth constraints. We initially aim at formulating the N-P fusion rule with M-level quantization of the decision statistic. In this vein, we derive the required randomized test for the N-P fusion that represents the total performance of our spectrum sensing scheme. We further introduce a tight lower bound for the optimal performance of the primary user signal detection. An analytical procedure towards the bound and its relevant quantization setup at the local sensors are proposed and examined through case studies. The proposed near optimal bound gets closer to the optimal performance as the channel probability of error decreases, such that for ideal channels, it is seen to provide the exact optimal performance.

We consider a cooperative spectrum sensing scenario where the local sensors at the secondary users are viewed as one-level quantizers, and the quantized data are to be fused under Neyman-Pearson (N-P) criterion. We demonstrate how the N-P fusion results in a randomized test, which represents the total performance of our spectrum sensing scheme. We further introduce an upper performance bound for the overall primary user signal detection. An analytical procedure towards the upper bound and its relevant quantization setup at the local sensors are proposed and examined through simulations.

In this letter, we develop a computationally efficient algorithm for the Maximum Likelihood (ML) sequences estimation (MLSE) of an M-ary Phase Shift keying (M-PSK) signal transmitted over a frequency non-selective slow fading channel with an unknown complex amplitude and an unknown variance additive white Gaussian noise. The proposed algorithm also provides the ML estimates of the complex amplitude and the noise variance that are critical in signal activity detection and demodulation in the modern cognitive communication receivers. We prove the optimality of the proposed algorithm and compare its performance via simulation with a recent suboptimal algorithm.

This work presents a new perspective to the relationship between the composite binary hypothesis test and the estimation of its unknown parameters, i.e. the Uniformly Most Powerful (UMP) test and the Minimum Variance and Unbiased Estimator(MVUE). We show that for the one-sided binary composite hypothesis test, if the UMP test exists, it is nothing but comparing the MVUE for the unknown parameter with a threshold. The paper tries to make a link between the Cramer Rao Bound (CRB) in estimation theory and the UMP performance bound in detection theory. In addition to the intrinsic theoretical interest of such relationship discussed in the paper, it leads us to proposing a novel detection method. For such problems in which the UMP test does not exist, we suggest using a good estimator of the unknown parameter as the decision statistic. The simulation results confirm the idea that the closer we get to the CRB in estimating the unknown parameter, the more we get near to the UMP performance bound in detection.

The Uniformly Most Powerful (UMP) test, which is one with the highest probability of detection over all unknown parameters in a composite hypothesis test, does not exist in the most practical problems. It is common to derive the suboptimal Generalize Likelihood Ratio (GLR) detector, that is shown to perform close to optimal invariant tests in some problems. The Uniformly Most Powerful Invariant (UMPI) test is the one that is used for the performance evaluation of the GLR detector. However, the GLR or UMPI detectors are not simply derivable in many problems. In this paper, we suggest that the problem be transformed to a new space. We derive the conditions of the transformation, such that the detectors for the original problem and the transformed ones become equivalent. In addition, using this method, we discuss two practical problems (one in radar and other in communication) and derive the GLR detectors. We also find the UMPI detectors in these problems to verify the performance of the derived GLR detector.

The authors propose invariant tests for the detection of a complex signal with unknown constant amplitude and unknown phase variation in additive white Gaussian noise (AWGN). The authors show that in this problem, the uniformly most powerful invariant (UMPI) detector does exist only if the number of samples N is two. For more than two samples N≥3, the authors derive the most powerful invariant (MPI) detector in known signal-to-noise ratio (SNR) and use its performance as the upper bound benchmark for any invariant test. In addition, the authors derive the generalised likelihood ratio (GLR) detector and evaluate its performance against the MPI performance bound. This detector is very simple and represents the ratio of the L1-norm to the L2-norm of the data. Simulation results illustrate the close performances of the two detectors even at low SNRs, whereas in contrast to the MPI test the SNR is not required in the proposed GLR test. In order to understand why the knowledge of SNR is not so important in this detection problem, the authors also derive the GLR test for the case of known SNR. Interestingly, the resulting GLR detector (derived for the case of known SNR) turns out to be equivalent to the one derived for unknown SNR, i.e. a knowledge of the SNR is not used in any of the GLR tests. This reveals why the knowledge of the SNR is not so useful in this detection problem.

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal as well as the information sequence as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant test (UMPI) as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the above-mentioned GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors.

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal, as well as the information sequence, as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant (UMPI) test as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors