Of specific interest in this current work, high-speed signal waveforms were evaluated and analyzed through novel mathematical modeling in time domain. Using complete math equations with accurate amplitude and phase information, the intrinsic property and transmission characteristic of a specific pseudo random binary sequence (PRBS) were further studied and presented quantitatively, where unipolar and bipolar signals at different bit rates were simultaneously considered in-depth. Transmission investigations of high-speed interconnects under pristine and degraded conditions were both conducted, and predictions of the mathematical model were all verified by circuit simulations and measurements. In addition, the corresponding eye diagram parameters were obtained and compared based on the voltage waveforms, providing a further validation of the proposed mathematical interconnect modeling method.

This paper consider the joint data and pilot power control problem in uplink single-cell multi-user massive multiple-input-multiple-output (MU-Massive- MIMO) systems. Instead of using equal data and pilot power allocation among each user equipment (UE), in this paper, we formulate the power control as an optimization problem for weighted sum spectral efficiency (SE) maximization objective subject to per-user pilot and data power budget in the uplink. To this end, we derive a lower bound expression on the average uplink SINR with maximum ratio combination (MRC) receiver under imperfect channel knowledge. Since the optimization of weighted sum SE has been proved to be a NP-hard problem, we propose an efficient algorithm to obtain an approximated optimal solution with polynomial complexity, by joint iterative optimization over data power and pilot power for each UE. The convergence of the proposed algorithm is guaranteed and discussed. Numerical results demonstrate the optimality of the proposed algorithm and show notable SE performance gains over suboptimal algorithms in previous work.

Massive Multi-input Multi-output (MIMO) systems have been regarded as a promising technique for next generation wireless network. This work investigates the performance of spectral efficiency (SE) of the massive MIMO based on the finite-dimensional physical channel model instead of conventional i.i.d. Rayleigh model. More importantly, we consider an uplink pilot reuse scheme which can be used to eliminate pilot contamination effectively and improve the SE performance significantly. To this end, a lower bound on achievable uplink SE of MRC scheme is derived, and a closed-form expression on the SE is also obtained for infinite number of BS antennas. Simulations manifest that we can obtain a notable SE performance gains by the uplink pilot reuse scheme in the finite-dimensional physical channel model over existing results in previous work.

This paper focuses on detection for multi-cell Massive MIMO network. As the column vectors are independent and identically distributed with each other, channel fast fading coefficient matrix is the eigenvector matrix for the covariance of received signal when the number of antennas at base stations (BSs) tends to infinity. Thus, we can get a set of normalized base vectors by eigenvalue decomposition of the covariance matrix for received signal. Based on this close relationship, an EVD (Eigenvalue Decomposition) based detection algorithm, which could solve the pilot contamination problem completely when the number of antennas at BS tends to infinity, is proposed. Simulation results show that the bit error rate (BER) could be reduced by two orders of magnitude when signal-to-noise ratio (SNR) is 40dB, with EVD-based detection algorithm, comparing with the traditional maximum-ratio combining (MRC) detection.

A wideband spectrum sensing scheme for cognitive radios is put forward in this letter. The proposed method operates over the total frequency bands simultaneously rather than a single band each time. It first estimates the number of occupied subbands, and then determines the accurate locations of occupied subbands as well as the vacant ones. Applying the ideas of multi stage Wiener filter to Gerschgorin disk estimator, this approach jointly makes the decision. In this way, the detection performance is enhanced because of capturing the signal information and suppressing the additive noise. Meanwhile, this approach avoids estimating the covariance matrix as well as the eigenvalue decomposition, and thus achieves a low computational complexity. Distinct from the conventional algorithms, neither noise power estimation nor prior knowledge of primary user signal is required, making the proposed algorithm robust to noise uncertainty and suitable for blind detection. Simulations under various conditions are presented to demonstrate the benefits of this approach and the results indicate that it surpasses the existing sensing algorithms.

Conventional designs of OFDM-based cognitive radio (CR) systems mainly focus on the system throughput. Since nowdays the energy efficiency (EE) of wireless systems becomes more and more important, this paper intends to improve the system throughput for unit-energy consumption in OFDM-based CR systems. The energy-efficient power allocation problem of OFDM-based CR systems is studied under the total power constraint, the interference power constraint and the rate constraint. Since the energy-efficient power allocation problem is non-convex, in order to find the optimal power allocation at each subcarrier, we propose a novel method named water-filling factors aided search (WFAS) to solve the EE optimization problems with multiple constraints. The global ε-optimality and the convergence of the proposed method are presented. The numerical results confirm the theoretical conclusions. The comparisons of the energy-efficient power allocation and the spectrum-efficient power allocation in OFDM-based CR systems are given under different constraints via numerical simulations as well.

Spectrum sensing is a key problem in cognitive radio (CR). Because of a low SNR, fading and sensing time constraints, a single CR may not be able to reliably sense the presence of primary radios, which motivates the study of sensing by multiple cognitive users. Here, the authors consider cooperative spectrum sensing (CSS) using a counting rule where several cognitive users sense whether primary users exist or not and send their decisions to the centre where the final decision is made. Optimal strategies under both the Neyman–Pearson criterion and the Bayesian criterion for CSS are derived using a counting rule. In addition, the authors present simple methods to calculate the optimal settings. Another contribution here is the analysis of a randomised rule at the centre, which is a long-existing problem in the field of distributed detection systems.

The energy detection is recently studied extensively as a promising candidate for primary signals detection in cognitive radio. However, one fundamental disadvantage of energy detection is that its performance degrades in the presence of inaccurate knowledge of noise power (noise uncertainty) which inevitably occurs in practical implementations. In this paper, we introduce a novel Bayesian estimation based energy detection (BEED) which, confirmed by simulations, can almost completely eliminate detrimental effects of noise uncertainty. In addition, the consistency of BEED is proved which means that it will correctly detect primary users with probability one when the sampling number increases to infinity.

In hard cooperative spectrum sensing (HCSS) for cognitive radio (CR), where local secondary users (sensors) sense whether primary users exist or not and then send decisions to the center where the final decision is made, correlation between sensors has been conventionally believed and experimentally shown to have detrimental effects on performance of HCSS. In this paper, we restudy effects of correlation and demonstrate that, contrary to common belief, the correlation has beneficial effects if the fusion rule at the center is convex and has detrimental effects if the fusion rule is concave. Further, we specially analyze the effects of correlation on AND HCSS and OR HCSS, two existing HCSS rules in the literature.

In this paper, we propose double-orthogonal coded space-time-frequency spreading code-division multiple-access (DOC-STFS-CDMA) scheme for the downlink orthogonal frequency-division multiplexing (OFDM) systems employing multiple transmit and receive antennas. First, we introduce the so-called STFS-CDMA and the corresponding minimum mean-square error (MMSE) detection algorithms for three scenarios with different user information. Then, we propose a new class of spreading codes, called double-orthogonal code (DOC), to match the MMSE detection algorithms for STFS-CDMA system. With the special double orthogonality, DOC can effectively exploit space, time, and frequency diversity to enhance the performance of MMSE detectors, and provide the well-balanced signal-to-interference-and-noise ratio to different users. Finally, the performance gain of the proposed scheme over conventional schemes is shown by computer simulation.

In this paper, we present the ergodic capacity limits of time-varying multiple-input multiple-output (MIMO) channels in flat fading and frequency-selective fading environments. By using a nonstationary method, called generalized water-filling algorithm (GWFA), the transmitted power is optimally allocated over all the channel states in three dimensions including space, time and frequency. Therefore, the highest capacity (i.e., capacity limit) of time-varying MIMO channels is obtained. Compared with the conventional water-filling algorithm, GWFA can achieve higher capacity via more efficient computation.

A new carrier frequency offset estimation scheme in orthogonal frequency-division multiplexing (OFDM) systems is proposed in this paper. Both the carrier frequency offset acquisition and tracking are based on a fixed-length training-symbol-block, which consists of multiple small identical training symbols. When each training symbol is shortened, the number of training symbols in the training-symbol-block should be increased accordingly to keep the total training-symbol-block length fixed. The proposed scheme extends Moose's estimator, where the estimation error is only dependent on total training symbol energy and cannot be reduced any more, once the total training symbol energy is determined. The proposed scheme can shorten each training symbol in a training-symbol block and select an appropriate estimator simultaneously, which can lead to further reduction of estimation error and increase of acquisition range, even with the total training-symbol-block energy being fixed. Performance analyzes for the proposed scheme in both the additive white Gaussian noise channel (AWGN) and the multipath channel are also presented in this paper. All estimators in the proposed scheme are conditionally unbiased, and simulation results demonstrate that they can work well both in the multipath channel and in the AWGN channel.

In this paper, we present a new ultra-wideband (UWB) pulse design method for narrowband interference (NBI) suppression. With a short time duration, the obtained UWB pulses not only meet the Federal Communications Commission (FCC) spectral mask, but also dramatically suppress the mutual interference between UWB systems and multiple narrowband communication systems. This method successfully solves the coexistence problem between UWB systems and the existing narrowband systems without the need of reducing UWB pulse power spectral density (PSD) over the whole UWB frequency band. Therefore, the application range of UWB systems can be enlarged by increasing the transmitted UWB pulse power.

We investigate robust pilot-symbol-aided channel estimation and prediction for multiple-input multiple-output (MIMO) systems in fast fading environments. First, we derive the design criteria of the optimal pilot blocks for energy, power and bandwidth-limited systems, respectively. Then, in order to exploit channel time correlation sufficiently, we propose a robust minimum mean square error (MMSE) channel estimator. By simply adjusting some parameters, the MMSE estimator can work as an estimator and a predictor simultaneously. Finally, simulation results show the proposed MMSE estimator, with a little additional complexity, is considerably insensitive to channel statistics and significantly outperforms conventional estimators in fast fading environments.