Toward the Energy Efficiency of Resource Allocation Algorithms for OFDMA Downlink MIMO Systems

The problem of the simultaneous multi-user resource allocation algorithm in orthogonal frequency division multiple access(OFDMA)based systems has recently attracted significant interest.However,most studies focus on maximizing the system throughput and spectral efficiency.As the green radio is essential in 5G and future networks,the energy efficiency becomes the major concern.In this paper,we develop four resource allocation schemes in the downlink OFDMA network and the main focus is on analyzing the energy efficiency of these schemes.Specifically,we employ the advanced multi-antenna technology in a multiple input-multiple output(MIMO)system.The first scheme is based on transmit spatial diversity(TSD),in which the vector channel with the highest gain between the base station(BTS)and specific antenna at the remote terminal(RT)is chosen for transmission.The second scheme further employs spatial multiplexing on the MIMO system to enhance the throughput.The space-division multiple-access(SDMA)scheme assigns single subcarrier simultaneously to RTs with pairwise“nearly orthogonal”spatial signatures.In the fourth scheme,we propose to design the transmit beamformers based on the zero-forcing(ZF)criterion such that the multi-user interference(MUI)is completely removed.We analyze the tradeoff between the throughput and power consumption and compare the performance of these schemes in terms of the energy efficiency.

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station(BS)is equipped with a very large number of antennas(also referred to as"massive multiple-input multiple-output(MIMO)").We consider a time-division duplexing(TDD)scheme,in which reciprocity between the uplink and downlink channels can be assumed.Channel estimation is essential for downlink beamforming in massive MIMO,nevertheless,the pilot contamination effect hinders accurate channel estimation,which leads to overall performance degradation.Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals(AOAs)in the large-scale antenna system,we propose a multiple signals classification(MUSIC)based channel estimation algorithm during the uplink transmission.Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.

A Low Complexity Algorithm for Subcarrier-and-Bit Allocation in OFDMA-Based LTE Systems

In this paper, we propose optimum and sub-optimum resource allocation and opportunistic scheduling solutions for orthogonal frequency division multiple access （OFDMA）-based multicellular systems. The applicability, complexity, and performance of the proposed algorithms are analyzed and numerically evaluated. In the initial setup, the fractional frequency reuse （FFR） technique for inter-cell interference cancellation is applied to classify the users into two groups, namely interior and exterior users. Adaptive modulation is then employed according to the channel state information （CSI） of each user to meet the symbol error rate （SER） requirement. There then, we develop subcarrier-and-bit allocation method, which maximizes the total system throughput subject to the constraints that each user has a minimum data rate requirement. The algorithm to achieve the optimum solution requires high computational complexity which hinders it from practicability. Toward this suboptimum method with the reduced to the order of O（NIO, the total number of subcarriers end, we complexity propose a extensively where N and K denote and users, respectively. Numerical results show that the proposed algorithm approaches the optimum solution, yet it enjoys the features of simplicity, dynamic cell configuration, adaptive subearrier-and-bit allocation, and spectral efficiency.

Energy-Based Collaborative Spectrum Sensing for Cognitive UWB Impulse Radio

This paper focuses on the issue of collaborative spectrum sensing in cognitive ultra wideband（CUWB） impulse radio. We employ energy-based signal detection method and apply the Neyman-Pearson（NP） decision rule to determine the optimum threshold. Two cooperative spectrum sensing schemes are developed in this paper. The decision fusion scheme is based on hard decision, in which each cooperating cognitive user（CU） sends its own local decision to the fusion center（FC）. The FC then makes a final decision according to the majority voting rule. Alternatively, the data fusion scheme is based on soft decision, in which each local CU sends its observed value directly to the FC. The FC combines these values, compares to a threshold and then makes the final decision. The performances of both schemes are studied by using analytical tools and computer simulations. The receiver operating characteristics（ROC）, which reveal the probability of detection versus false-alarm curve, are employed to evaluate the system performance under different scenarios. Simulation results demonstrate that the data fusion scheme outperforms the decision fusion scheme and verify that the collaborative spectrum sensing has practical importance in CUWB networks.