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Ultra-densification of radio access network (RAN) is a key to efficiently support the exponentially growing mobile data traffic in 5G era.Furthermore,extremely high frequency band like mmWave band was utilized to solve the bandwidth shortage problem.However,untra-dense reusing the same radio resource produced severe interference.And the mmWave link was very harsh due to frequent blockage by obstacles.Therefore a new RAN architecture needed to be introduced to realize ultra-reliable communications in such a severe radio propagation environment.An architecture of distributed MIMO based RAN was presented.Then,enhanced interference coordination (eIC) was described.Finally,the effectiveness of distributed MIMO based RAN with eIC by computer simulation was showed.

In recent years, user cooperative traffic forwarding is a popular study topic and broadly seen as one of the important promising technologies to improve energy efficiency (EE) of the battery-driven mobile terminal (MT). However, the battery-driven devices always suffer from a problem of limited working time due to battery life. In this paper, we propose a simply machine learnable bandwidth allocation strategy for user cooperation-aided wireless communication systems and evaluate the power consumption of the systems via both theoretical and experimental approaches. By using the proposed bandwidth allocation strategy, we first derive the mathematical expressions to evaluate the transmission power of the MTs for non-cooperative and cooperative scenarios by a generalized channel model. In this generalized model, the spatially correlated shadowing and frequency selective fading are considered as channel effects, and this generalized model is mathematically analyzed for the consumed power via the proposed scenarios with the long-term evolution (LTE) power model for smartphones. In the final stage, we evaluate the results by our smartphone test-bed. The results obtained in this paper show that the benefits of the user cooperation-aided traffic forwarding are significant. Unfortunately, according to the numerical analysis, because there are some physical constraints for MTs, such as maximal transmit power, we cannot drastically obtain the benefits in real application cases. Some interesting points, such as how to use a machine learning approach to reduce the system complexity and thus improve transmission performances, are also discussed in this paper.

The dense deployment of heterogeneous networks (HetNets) has shown to be a promising direction to cope with the capacity demands in the future 5G wireless networks. The large number of small cell base stations (SBSs) in HetNets intended to help in achieving the capacity requirement of 5G networks can also result in a significant increase in energy consumption. This is due to the fact that there might be few associated users in certain SBSs, intelligently switching them to low energy consumption modes, or turning them off without seriously degrading system capacity is desirable in order to improve the energy savings in the HetNets. Also, the unnecessary handovers caused due to this dynamic power level switching in the SBS should not be neglected. In this paper, fuzzy logic-based game-theoretic framework is utilized to address these issues and examine the energy efficiency improvements in HetNets. We design fuzzy inference rules for handover decisions, and target base station selection is performed through a fuzzy ranking technique, while simultaneously considering both energy/spectral efficiency and signaling overhead. The results show that energy consumption can be improved considerably especially for high user velocities, while also managing ping-pong handovers.

The deployment of heterogeneous networks (HetNets) can significantly boost the network capacity. However, the large number of small cell base stations (SBSs) deployed in HetNets can result in an increased total energy consumption. One of the promising techniques to reduce the energy consumption of networks is base station (BS) ON/OFF switching (sleeping) approaches. Due to device lifetime and energy waste by unnecessary switchings, the number of switchings is considered as an important problem. In this paper, we formulate the ON/OFF switching problem as a satisfaction game, where BSs seek to meet certain performance constraints in order to avoid the frequent BS switchings. Furthermore, BSs can choose their transmission power levels according to the network conditions in a distributed manner. The proposed satisfaction game involves a multi-step process. In the first step, we aim at satisfying the players with the high satisfaction threshold in a predefined time interval. To measure a BS's satisfaction, a utility function is used that includes BS's load and power consumption, in which the load of each BS is coupled with the load of other BSs. Since all players cannot be simultaneously satisfied, unsatisfied players decide to reduce their thresholds, and form a game with the redefined thresholds. To solve the game, a regret-based satisfaction algorithm and a satisfaction equilibrium search algorithm are applied. Simulation results show that the proposed schemes can achieve significant reductions in the number of switchings compared with the benchmark methods.

Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 ), which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in L 1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p ∈ { 1 / 2 , 2 / 3 } based on an iterative L p thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA). Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L 1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.

In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods.

A heterogeneous wireless network is characterized by the presence of different wireless access technologies that coexist in an overlay fashion. These wireless access technologies usually differ in terms of their operating parameters. On the other hand, Mobile Stations (MSs) in a heterogeneous wireless network are equipped with multiple interfaces to access different types of services from these wireless access technologies. The ultimate goal of these heterogeneous wireless networks is to provide global connectivity with efficient ubiquitous computing to these MSs based on the Always Best Connected (ABC) principle. This is where the need for intelligent and efficient Vertical Handoffs (VHOs) between wireless technologies in a heterogeneous environment becomes apparent. This paper presents the design and implementation of a fuzzy multicriteria based Vertical Handoff Necessity Estimation (VHONE) scheme that determines the proper time for VHO, while considering the continuity and quality of the currently utilized service, and the end-users' satisfaction.

Frequency domain adaptive antenna array (FDAAA) is an effective method to suppress interference caused by frequency selective fading and multiple-access interference (MAI) in single-carrier (SC) transmission. However, the performance of FDAAA receiver will be affected by the antenna placement parameters such as antenna separation and spread of angle of arrival (AOA). On the other hand, hybrid frequency reuse can be adopted in cellular system to improve the cellular capacity. However, optimal frequency reuse factor (FRF) depends on the channel propagation and transceiver scheme as well. In this paper, we analyze the impact of antenna separation and AOA spread on FDAAA receiver and optimize the cellular capacity by using hybrid FRF.

MMSE-FDE can improve the transmission performance of OFDM combined with time division multiplexing (OFDM/TDM), but knowledge of the channel state information and the noise variance is required to compute the MMSE weight. In this paper, a performance evaluation of OFDM/TDM using MMSE-FDE with pilot-assisted channel estimation over a fast fading channel is presented. To improve the tracking ability against fast fading a robust pilot-assisted channel estimation is presented that uses time-domain filtering on a slot-by-slot basis and frequency-domain interpolation. We derive the mean square error (MSE) of the channel estimator and then discuss a tradeoff between improving the tracking ability against fading and the noise reduction. The achievable bit error rate (BER) performance is evaluated by computer simulation and compared with conventional OFDM. It is shown that the OFDM/TDM using MMSE-FDE achieves a lower BER and a better tracking ability against fast fading in comparison with conventional OFDM.