Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation and transformation, we obtain a submatrix with all zero elements to save computation resources. The proportion of zero elements in the matrix can reach up to 58.5% from the OEA for SPC when the code length and code rate are 2048 and 0.5, respectively. Furthermore, the proposed OEA is beneficial to hardware implementation compared with the existing recursive algorithms in which signals are transmitted bidirectionally.

As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively.

Although the traditional successive cancellation list (SCL) decoding algorithm can greatly improve the performance of successive cancellation (SC) decoding algorithm, multiple path splitting and sorting operations increase the complexity. In order to reduce the complexity of the SCL algorithm, a new path splitting decision strategy is proposed to reduce the number of path splitting (PSN). This strategy is based on a new auxiliary path metric (APM) proposed in this letter, which is used to identify whether the decoding path is wrong. Wrong paths can be avoided by comparing the predicted APM with the threshold. The simulation results show that the proposed path splitting decision-aided SCL (PSD-SCL) algorithm reduces the complexity compared with other list decoding algorithms with small performance loss at high signal-noise ratio (SNR).

To solve the problem of load imbalance and system interference in heterogeneous networks (HetNets), an energy efficiency (EE) optimized strategy based on enhanced Inter-Cell Interference Coordination (eICIC) technology is proposed. The optimization of the EE of the system is established by taking into account four components which are the connection between the users and base stations (BSs), almost blank sub-frame (ABS) ratio, resource allocation in the frequency domain, and the power transmission of the macro base stations (MBSs). In our optimized strategy, the user’s optimal BS connection status are determined according to the overall EE change of the system, and the allocation of sub-frames and resource blocks of the MBSs both in the time domain and frequency domain are optimized respectively. We use the Gauss-Seidel method to iteratively solve the optimization problem. Simulation results show that the optimized strategy outperforms other solutions in terms of EE and user fairness in average. The proposed optimization strategy increases the system EE by 24.2% and 19.1% respectively when compared with the maximal reference signal received power (MAX-RSRP) and the low power-ABS (LP-ABS) strategies. The Jain fairness index of our proposed optimization strategy is averagely increased by 33.8% and 23.4% respectively. In addition, the proposed strategy can further improve the user satisfaction and the performance in the system.

Although the performance of belief propagation (BP) decoder is slightly better than that of successive cancellation (SC) decoder, it has remarkable performance gap compared with improved SC decoders. In this paper, we propose BP bit-strengthening (BPBS) decoder and concept of relative code rate for polar codes. When conventional BP decoding fails, the stable information bits are found by submatrix check and relative code rate, then a priori message of stable information bits is strengthened by hard decision. The simulation results show that BPBS decoder can achieve significant performance gains compared with conventional BP decoder. Furthermore, as signal-to-noise ratio (SNR) increases, the performance of BPBS is close to that of successive cancellation list (SCL) decoder in the medium and high SNR regions.

In this letter, a new soft cancellation bit-flip (SCAN-BF) decoding algorithm is proposed, reducing errors caused by error propagation. In order to find the bit index with high error probability, a flipping set for soft cancellation (SCAN) decoding algorithm is constructed based on special error distribution regularity. Segmented submatrix check is adopted in the proposed SCAN-BF algorithm to find the subblock where decoding error occurred. Since the search range of finding candidate flipping bits is reduced, the decoding complexity of SCAN-BF algorithm is also reduced. Experimental simulation shows that SCAN-BF decoding obtains a large performance gain in short and medium code lengths.