Jinchun Gao

Optimisation of cooperative spectrum sensing in cognitive radio network

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.

A new UWB pulse design method for narrowband interference suppression

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.

Soft versus Hard Cooperative Energy Detection under Low SNR

Amidst conflicting views about whether soft cooperative energy detection scheme (SCEDS) outperforms hard cooperative energy detection scheme (HCEDS) greatly in cognitive radio, we establish the bridge that mathematically connects SCEDS and HCEDS by closed approximations. Through this bridge, it is demonstrate that, if the number of detectors of HCEDS is 1.6 times as that of SCEDS, they have nearly the same performance which is confirmed by numerical simulations, enabling a quantitative evaluation of the relation between them and an elucidation of the conflicting views.

Robust pilot-symbol-aided MIMO channel estimation and prediction

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.

Robust Energy Detection Based on Bayesian Estimation for Cognitive Radio

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.

Wideband Spectrum Sensing for Cognitive Radios: A Multistage Wiener Filter Perspective

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.

Robust multiuser MIMO scheduling algorithms with imperfect CSI

Robust multiuser multiple-input-multiple-output (MIMO) scheduling algorithms are proposed in this paper. With imperfect channel state information (CSI), traditional scheduling algorithms for the multiuser MIMO system based on the zero forcing precoding scheme will lose some performance due to the multi-user interference (MUI). In order to improve the system average throughput, we study the robust multiuser MIMO scheduling problem with imperfect CSI. From the average capacity formula, we derive a robust factor which can transform the robust multiuser MIMO scheduling problem into the traditional one, thus most existing nonrobust scheduling algorithms can be robust if this factor is adopted. Simulation results show that compared with the traditional algorithms, the proposed robust algorithms can improve the system average throughput significantly under the CSI error environment.

A low-complexity adaptive bit and power allocation algorithm for MIMO-OFDM systems

In this paper, we present a low-complexity adaptive bit and power allocation algorithm for MIMO-OFDM systems. The MIMO-OFDM channel can be decomposed into a group of parallel subchannels by singular value decomposition (SVD) [Branka and Vucetic, 2003]. To minimize the overall transmit power, we carry out a fixed bit and power allocation over these subchannels by exploring the characteristics of the ordered subchannel gains. The fixed bit and power assignment can be determined by greedy algorithm at the initial stage and utilized without change throughout the data transmission process. Simulation results show that the proposed algorithm has less than 1 dB performance loss relative to the optimal algorithm when the number of OFDM subcarriers is large, but drastically reduces the complexity.

Energy efficiency of massive MIMO wireless communication systems with antenna selection

Abstract Massive multiple-input multiple-output (MIMO) requires a large number (tens or hundreds) of base station antennas serving for much smaller number of terminals, with large gains in energy efficiency and spectral efficiency compared with traditional MIMO technology. Large scale antennas mean large scale radio frequency (RF) chains. Considering the plenty of power consumption and high cost of RF chains, antenna selection is necessary for Massive MIMO wireless communication systems in both transmitting end and receiving end. An energy efficient antenna selection algorithm based on convex optimization was proposed for Massive MIMO wireless communication systems. On the condition that the channel capacity of the cell is larger than a certain threshold, the number of transmit antenna, the subset of transmit antenna and servable mobile terminals (MTs) were jointly optimized to maximize energy efficiency. The joint optimization problem was proved in detail. The proposed algorithm is verified by analysis and numerical simulations. Good performance gain of energy efficiency is obtained comparing with no antenna selection.

Capacity limits of time-varying MIMO channels

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.