Inter-cell interference coordination (ICIC) is a promising technique to improve the performance of frequency-domain packet scheduling (FDPS) in downlink LTE/LTE-A networks. However, it is difficult to maximize the performance of FDPS using static ICIC schemes because of insufficient consideration of signal-to-interference-and-noise ratio distribution and user fairness. On the other hand, dynamic ICIC schemes based on channel state information (CSI) also have difficulty presented in the excessive signaling overhead and X2 interface latency. In order to overcome these drawbacks, we introduce a new concept of ICIC problem based on geometric network information (GNI) and propose an adaptive sector coloring game (ASCG) as a decentralized solution of the GNI-based ICIC problem. Furthermore, we develop an ASCG with a dominant strategy space noted as ASCG-D to secure a stable solution through proving the existence of Nash equilibrium. The proposed scheme provides better performance in terms of system throughput gain of up to about 44.1%, and especially of up to about 221% for the worst 10% users than static ICIC schemes. Moreover, the performance of the CSI-based ICIC, which require too much computational load and signaling overhead, is only 13.0% and 5.6% higher than that of ASCG-D regarding the total user throughput and the worst 10% user throughput, respectively. The most interesting outcome is that the signaling overhead of ASCG-D is 1/144 of dynamic ICIC schemes' one.

Phase noise is known to be a major bottleneck in full- duplex wireless systems. Current self-interference cancellation schemes with phase noise mitigation assume the perfect estimation of the self-interference channel. However, self-interference channel estimation schemes in the presence of phase noise have not been reported. In this paper, we propose an EM-based joint estimation scheme for self-interference channels with common phase errors. For simplicity, only the strongest echo path is considered. Simulation results show that the proposed scheme outperforms the conventional ML- based self-interference channel estimation scheme.

Small cells are considered an emerging technology for increasing the potential capacity of cellular networks. However, as the density of infrastructure increases, users have many choices for connection, and therefore, selecting an appropriate base station (BS) becomes an important issue. This study aims to provide an improved user association rule, where each user autonomously chooses the best among all the BSs in the vicinity, while considering their congestion levels. As the first step, an optimization problem is formulated, which emphasizes both the time-varying nature of the wireless channel and fairness among users. On the basis of this formulation, the influence of a specific handover event on system performance is investigated, and then, two versions of the handover frameworks are developed. Simulation results show that the proposed algorithms increase the throughput of every user in the network by 2.8–12% compared to the best conventional scheme. Furthermore, these schemes especially enhance the performance of users having low service quality by 5.0–85%, through efficient utilization of the pre-installed infrastructures.

In orthogonal frequency-division multiplexing (OFDM)-based wireless local area networks (WLANs), the phase noise (PHN) and a residual frequency offset (RFO) can cause the common phase error (CPE) and the inter-carrier interferences (ICI), which seriously degrade the performance of systems. In this paper, we propose a combined pilot symbol assisted and decision-directed scheme based on the least-squares (LS) and maximum-likelihood (ML) algorithms. In the proposed scheme, the CPE estimator is derived using the pilot symbols embedded in each OFDM symbol. Then, data symbols and channel response including the CPE are estimated using the CPE estimator. Simulation results present that the proposed scheme significantly improves the performance of OFDM-based WLANs.