Search

In this paper, we investigate a three-phase two-way (TW) amplify-and-forward (AF) relaying for cognitive radio networks. By utilizing the direct communications, the end user can employ maximal ratio combining to achieve the full diversity. We derive the closed-form and asymptotic expressions for user and system outage probabilities which allow us to highlight the advantage of cooperative cognitive communications. The numerical results, obtained through compact forms of these outage probabilities, yield that the cognitive TW AF relaying scheme can significantly enhance the reliability of unlicensed networks in which the transmit power at each secondary user is strictly governed.

This paper introduces for the first time a relay-assisted heterogeneous vehicular model including numerous stationary small cells, a mobile relay and a mobile receiver with unreliable backhaul. In this proposed system model, a macro-base station connected to the cloud communicates to numerous small cells through wireless backhaul links. A Bernoulli process is utilized to model the backhaul reliability. A relay using decode-and-forward protocol is considered to help the transmission from the stationary small cells to the mobile receiver. Moreover, at the mobile relay side, a selection combining protocol is applied to maximize the received signal-to-noise ratio. The links between stationary small cells and mobile relay are Rayleigh fading channels, and the link between mobile relay and mobile receiver is double-Rayleigh fading channel. A closed-form expression for outage probability is provided to evaluate the influence of the number of small cells and the backhaul reliability on the proposed system performance.

A cognitive heterogeneous network with unreliable backhaul connections is studied in this paper. In this system, a macro-base station connected to cloud transmits information to multiple small cells via backhaul links. In addition, multiple small cells acting as secondary transmitters send information to a receiver by sharing the same spectrum with multiple primary users. Bernoulli process is adopted to model the backhaul reliability. Selection combining protocol is used at the receiver side to maximize the received signal-to-noise ratio. We investigate the impacts of the number of small-cells, the number of primary users as well as the backhaul reliability on the system performance, i.e., outage probability in Rayleigh fading channels. Closed-form expressions are derived and asymptotic analysis is also provided.

This paper proposes the adaptive indirect learning architecture (ILA) based digital predistortion (DPD) technique using a recursive prediction error minimization (RPEM) algorithm for linearizing radio frequency (RF) power amplifiers (PAs). The RPEM algorithm allows the forgetting factor to vary with time, which makes the predistorter (PD) parameter estimates more consistent and accurate in steady state, and hence reduces mean square errors. The proposed DPD technique is evaluated with respect to the error vector magnitude (EVM) and the adjacent channel power ratio (ACPR). The simulated PA Wiener model is used to validate the efficiency of the proposed algorithms. The simulation results have confirmed the improvement of the proposed adaptive RPEM ILA based DPD in terms of EVM and ACPR.