Edward Chu Yeow Peh

Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks

When secondary users (SUs) in a cognitive radio network (CRN) are battery-powered wireless devices, energy resources become very precious. Therefore, it is important that their energies are used efficiently. In this paper, we define the energy efficiency as the ratio of the average throughput of the CRN over the average energy used by the CRN. In cooperative spectrum sensing, the fusion rule threshold, detectors thresholds at the SUs, length of the sensing time, and the number of cooperating SUs will affect both the average throughput and the average energy consumed by the CRN. Therefore, in this paper, we optimize and evaluate these parameters with the aim of maximizing the energy efficiency of the CRN.

Reliability of Spectrum Sensing Under Noise and Interference Uncertainty

Spectrum sensing is a fundamental problem in cognitive radio. As a result, it has been reborn as a very active research area in recent years despite its long history. Although various sensing methods have been proposed, their reliability at very low signal-to-noise ratio (SNR) and noise/interference varying environment remains to be investigated. In this paper, the noise power and interference uncertainty models are discussed first. Subsequently the reliability of single sensor is studied and its performance is quantized. Then cooperative sensing schemes are reviewed and their capability to combat noise power and interference uncertainty is analyzed. It is mathematically proved that cooperative sensing can alleviate the adversary impact of the uncertainty, but cannot eliminate it completely.

Power control for physical-layer network coding in fading environments

In a three-node wireless relay network, two nodes, BS1 and BS2, exchange information through a relay node, RL. Suppose time division duplex is used, physical network coding (PNC) uses two time slots for the information exchange instead of four time slots needed by the conventional method. In the first time slot, both BS1 and BS2 transmit simultaneously to RL. The relay node, RL does a PNC mapping based on the received signal and broadcast the mapped signal back to BS1 and BS2 simultaneously during the second time slot. The nodes, BS1 and BS2 are able to decode their desired information based on the received mapped signal and the signal which they had transmitted during the first time slot. In this paper, we analyze the average BER of the information exchanged between the two nodes in Rayleigh fading environments. We also derive the average BER of the mapped signal at the relay during the first time slot. With the derived BER of the mapped signal at RL, we propose to use power control at BS1 and BS2 to minimize the instantaneous BER of the mapped signal at RL. The proposed technique improves the BER of the desired information decoded at the two nodes. The solution turns out to be channel inversion based power control at both BS1 and BS2. The proposed power control technique improves both the average BER of the mapped signal at RL and the desired information at BS1 and BS2.

Power and modulo loss tradeoff with expanded soft demapper for LDPC coded GMD-THP MIMO systems

Tomlinson-Harashima precoding (THP) can be combined with geometric mean decomposition (GMD) to decouple a multiple-input multiple-output (MIMO) channel into multiple single-input single-output (SISO) subchannels with identical signal-to-noise ratios (SNRs). The combined system is called GMD-THP MIMO system. As all subchannels for this system have identical SNRs, it is more convenient to design modulation/demodulation and coding/decoding schemes than other MIMO systems that have different SNRs among the subchannels. In this paper, we consider low-density parity-check (LDPC) coded GMD-THP MIMO systems. Modulo operation at the receiver is needed for THP decoding but it may generate modulo errors. These modulo errors cause the log likelihood ratio (LLR) values provided by conventional soft demapper to be very inaccurate. We propose an expanded soft demapper scheme to reduce the inaccuracy of the LLR values, by which significant performance improvement can be achieved for LDPC decoding. Furthermore, THP introduces power loss and modulo loss into the system. These two losses are related to the size of the modulo boundaries and therefore we derive the expressions for these two losses as functions of the modulo size factor. With these expressions, we find the optimal modulo size factor which achieves the minimum combined losses through doing a tradeoff between power loss and modulo loss. Computer simulations are presented to show that the tradeoff indeed improves the performance of the LDPC coded GMD-THP systems.

Power Control in Cognitive Radios under Cooperative and Non-Cooperative Spectrum Sensing

In an opportunistic spectrum access (OSA) based cognitive radio system, a secondary user (SU) is allowed to access the licensed spectrum of the primary user (PU) when it is inactive. Conventional power allocation strategies, which do not consider spectrum sensing information (SSI), may not be optimal in OSA based cognitive radio system because when the SU mis-detects the PUs presence, the interference from the PU will cause a lower data rate or a higher outage probability to the SU. In this paper, power allocation strategies for each frame are designed based on the SSI gathered by the SU during the sensing period of the frame. We consider both cooperative and non-cooperative spectrum sensing scenarios. In non-cooperative spectrum sensing, the SU transmitter (SU-Tx) has its SSI while in cooperative spectrum sensing, the SU-Tx has both its SSI and the SU receivers (SU-Rxs) SSI. Using the SSIs, power allocation strategies are designed to either maximize the average data rate or minimize the outage probability of the SU. The proposed power allocation strategies have to ensure that the PU is sufficiently protected and the SU satisfies its long-term transmit power budget. Optimization of the sensing time is also considered to further enhance the performances of the system.

Cooperative Covariance and Eigenvalue Based Detections for Robust Sensing

Spectrum sensing is a fundamental problem in cognitive radio. As a result, it has been reborn as a very active research area in recent years despite its long history. Although various sensing methods have been proposed, their robustness at very low signal-to-noise ratio (SNR) and uncertain noise/interference environment is generally not satisfactory. In this paper, the concept of robust sensing is discussed first. Subsequently the cooperative covariance and eigenvalue based detections are proposed for robust spectrum sensing. It is proved mathematically that under some conditions the proposed methods are robust to uncertain and unpredictable noise and interference. The performances of the methods are also verified by simulations.

Cooperative Spectrum Sensing in Cognitive Radio Networks with Weighted Decision Fusion Scheme

In cognitive radio networks, both the sensing time and the fusion schemes used for cooperative spectrum sensing affect the detection probabilities of the primary users and the throughput of the secondary users. Therefore, joint optimization of the sensing time and the cooperative fusion scheme has been studied before in terms of sensing-throughput tradeoff design. In this paper, different from the previous studies, we consider the case that each secondary user may have different detection signal- to-noise ratio (SNR), and requires different threshold for energy detection. Weightings are used to weigh the decisions from the secondary users before combining. A new algorithm is proposed to compute the thresholds for the secondary users and the optimal weightings for the decisions are shown. Computer simulations are presented to show the performance of the proposed algorithm.

Optimal Cooperative Sensing and Its Robustness to Decoding Errors

We have derived the optimal cooperative sensing schemes for distributed sensors with time independent signals and shown that the optimal scheme is simply a linear combination of the normalized energies from all sensors. To avoid using the SNR information and simplify the decision process and threshold setting, some sub-optimal schemes have been analyzed and compared. In general, the proposed AOCS has relatively good performance at all cases. If the SNR gap is not large, EGC achieves good performance. On the other hand, if the SNRs vary greatly among the sensors, it is better to choose the MNE. Finally the impact of decoding error in the reported results has been investigated. It has been proved that the impact of decoding error is equivalent to the reduction of sensing time.

Sensing and power control in cognitive radio with location information

In a cognitive radio network, the secondary user (SU) will cause the primary user (PU) to be in outage when the SU miss-detects the PU and transmits with a power that prevents the PU receiver from decoding its data. Hence, the outage of the PU caused by the SU depends on the SUs transmission power and its probability of detection. For a given outage probability, it is possible for the SU to lower its transmission power in exchange for a lower probability of detection. The SU may not even require spectrum sensing to protect the PU if the SUs transmission power is low enough or when it is very far from the PU. In this paper, we first determine the distance of the SU from the PU such that the SU could use the channels without spectrum sensing. Then, at locations where the SU requires spectrum sensing, we determine the sensing parameters and the transmission power that will maximize the SUs throughput subject to an outage constraint for the PU.

Unified structure and parallel algorithms for FBMC transmitter and receiver

In recent years, filter bank multicarrier (FBMC) has recaptured widespread interests for its possible applications in cognitive radio and dynamic spectrum access. A distinctive feature for cognitive radio is its adaptivity to environment. When environment changes, a cognitive radio will change its parameters to optimize the transmission and receiving. Thus it is desirable to design a unified structure and algorithm for FBMC that needs little change for different parameters. In this paper, we propose a unified structure and parallel algorithms to implement the FBMC. The FBMC system and parallel algorithms are constructed based on the normalized prototype filter. The coefficients of the normalized prototype filter can be pre-computed and stored. The proposed parallel algorithms have the same structure for various choices of time duration, subcarrier spacing and bandwidth. Combined with known parallel algorithms for the fast Fourier transform (FFT), the proposed algorithms fully parallelize the computations for the transmitter and receiver, which can run much faster than conventional serial algorithms as modern processors usually have massive parallel capability.