Full-Duplex Systems Using Multi-Reconfigurable Antennas

Full-duplex systems are expected to achieve 100% rate improvement over half-duplex systems if the self-interference signal can be significantly mitigated. In this paper, we propose the first full-duplex system utilizing Multi-Reconfigurable Antenna (MRA) with ?90% rate improvement compared to half-duplex systems. MRA is a dynamically reconfigurable antenna structure, that is capable of changing its properties according to certain input configurations. A comprehensive experimental analysis is conducted to characterize the system performance in typical indoor environments. The experiments are performed using a fabricated MRA that has 4096 configurable radiation patterns. The achieved MRA-based passive self-interference suppression is investigated, with detailed analysis for the MRA training overhead. In addition, a heuristic-based approach is proposed to reduce the MRA training overhead. The results show that at 1% training overhead, a total of 95dB self-interference cancellation is achieved in typical indoor environments. The 95dB self-interference cancellation is experimentally shown to be sufficient for 90% full-duplex rate improvement compared to half-duplex systems.Comment: Submitted to IEEE Transactions on Wireless Communication

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

A Distributed Approach to Interference Alignment in OFDM-based Two-tiered Networks

In this contribution, we consider a two-tiered network and focus on the coexistence between the two tiers at physical layer. We target our efforts on a long term evolution advanced (LTE-A) orthogonal frequency division multiple access (OFDMA) macro-cell sharing the spectrum with a randomly deployed second tier of small-cells. In such networks, high levels of co-channel interference between the macro and small base stations (MBS/SBS) may largely limit the potential spectral efficiency gains provided by the frequency reuse 1. To address this issue, we propose a novel cognitive interference alignment based scheme to protect the macro-cell from the cross-tier interference, while mitigating the co-tier interference in the second tier. Remarkably, only local channel state information (CSI) and autonomous operations are required in the second tier, resulting in a completely self-organizing approach for the SBSs. The optimal precoder that maximizes the spectral efficiency of the link between each SBS and its served user equipment is found by means of a distributed one-shot strategy. Numerical findings reveal non-negligible spectral efficiency enhancements with respect to traditional time division multiple access approaches at any signal to noise (SNR) regime. Additionally, the proposed technique exhibits significant robustness to channel estimation errors, achieving remarkable results for the imperfect CSI case and yielding consistent performance enhancements to the network.Comment: 15 pages, 10 figures, accepted and to appear in IEEE Transactions on Vehicular Technology Special Section: Self-Organizing Radio Networks, 2013. Authors' final version. Copyright transferred to IEE

PAPR Reduction and Sidelobe Suppression in Cognitive OFDM - A Survey

Cognitive radio (CR) is one of the key technology providing a new way to enhance the utilization of available spectrum effectively. The multicarrier modulation (MCM) technique which is widely used is Orthogonal Frequency Division Multiplexing (OFDM) system, is an excellent choice for high data rate application. The main two limitations of this technology is the high peak-to-average power ratio (PAPR) of transmission signal and large spectrum sidelobe. This article describes some of the important PAPR reduction techniques and sidelobe suppression techniques

PAPR Reduction and Sidelobe Suppression in Cognitive OFDM - A Survey

Cognitive radio (CR) is one of the key technology providing a new way to enhance the utilization of available spectrum effectively. The multicarrier modulation (MCM) technique which is widely used is Orthogonal Frequency Division Multiplexing (OFDM) system, is an excellent choice for high data rate application. The main two limitations of this technology is the high peak-to-average power ratio (PAPR) of transmission signal and large spectrum sidelobe. This article describes some of the important PAPR reduction techniques and sidelobe suppression techniques

Power spectrum characterization of systematic coded UW-OFDM systems

Unique word (UW)-OFDM is a newly proposed multicarrier technique that has shown to outperform cyclic prefix (CP)-OFDM in fading channels. Until now, the spectrum of UW-OFDM is not thoroughly investigated. In this paper, we derive an analytical expression for the spectrum taking into account the DFT based implementation of the system. Simulations show that the proposed analytical results are very accurate. Compared to CP-OFDM, we show that UW-OFDM has much lower out-of-band (OOB) radiation, which makes it suitable for systems with strict spectral masks, as e. g. cognitive radios. Further, in this paper, we evaluate the effect of the redundant carrier placement on the spectrum

Narrowband Signal Detection in OFDM Systems Using Spectral Shaping Techniques

Abstract: Orthogonal Frequency Division Multiplexing (OFDM) allow data to be transmitted efficiently and reliably by using multiple orthogonal subcarriers. It provides robustness against noise and corruption in the channel. The channel can be either wired or wireless depending on the particular application. Due to the close spacing of subcarriers, OFDM is susceptible to corruption caused by various narrowband signals such as Narrowband Interference (NBI). Spectral shaping shapes the Power Spectral Density (PSD) in order to have certain properties. Spectral shaping might improve the effectiveness of OFDM and make it sustainable in the long run for applications beyond the 4th generation of mobile communications (4G) and Long Term Evolution (LTE). We make use of spectral null codes and load them onto OFDM subcarriers. Introducing narrowband signals in the channel degrades the system’s performance and also eliminates the designed spectral properties. From this observation we infer that some narrowband noise is present in the channel. Previously, carriers hit by NBI or other narrowband noise had to be switched off manually. We found that combining OFDM with spectral shaping allows the presence of Narrowband signals in the channel to be detected and conclusions can be drawn over the channel quality. This did not improve the system in terms of bit error rate performance

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig