Secure deletion plays a crucial role in safeguarding privacy and maintaining confidentiality. Historically, secure deletion was achieved on magnetic storage devices by data overwriting. However, with the rapid advancements in magnetic storage technology, existing secure deletion strategies are still oblivious to the unique characteristics of these storage devices. As a result, there is currently a lack of research effectively addressing the implementation of secure deletion for the emerging interlaced magnetic recording (IMR) technology. Regrettably, applying traditional read-modify-write (RMW) processes to enable secure deletion on IMR technology leads to significant challenges such as severe write amplification (WA) and performance degradation. In this paper, we propose move-on-cover (MOC), a novel secure deletion strategy for securely deleting on IMR, to optimize the performance of secure deletion. Specifically, when securely deleting a bottom track, the MOC first moves one of affected top track to a free top track, and then covers the to-be-deleted bottom track with another top track, minimizing redundant data backups and hiding secure deletion latency of bottom tracks. A series of evaluation experiments show that MOC is highly effective in reducing extra I/Os and improving the performance of secure deletion.

Due to high labeling cost, it is inevitable to introduce a certain proportion of noisy correspondence into visual-text datasets, resulting in poor model robustness for cross-modal matching. Although recent methods divide the datasets into clean and noisy pair subsets to yield promising achievements, they still suffer from deep neural networks over-fitting on noisy correspondence. In particular, the similar positive pairs with partially relevant semantic correspondence are easily partitioned into noisy pair subset by mistake without carefully selection, which brings harmful impact on robust learning. Meanwhile, the similar negative pairs with partially relevant semantic correspondence lead to ambiguous distance relations in common space learning, which also damages the stability of performance. To solve the coarse-grained dataset division problem, we propose Correspondence Tri-Partition Rectifier (CTPR) to partition the training set into clean, hard, and noisy pair subsets based on the memorization effect of neural networks and prediction inconsistency. Then, we refine the correspondence labels for each subset to indicate the real semantic correspondence between visual-text pairs. The differences between rectified labels of anchors and hard negatives are recast as the adaptive margin in the improved triplet loss for robust training in a co-teaching manner. To verify the effectiveness and robustness of our method, we conduct experiments by implementing image-text and video-text matching as two showcases. Extensive experiments on Flickr30 K, MS-COCO, MSR-VTT, and LSMDC datasets verify that our method successfully partitions the visual-text pairs according to their semantic correspondence and improves performance under noisy data training.

With the bit density improvement and the 3-D flash techniques, modern NAND flash-memory-based solid-state disks (SSDs) dramatically increase the flash storage capacity. However, in high-density SSDs, the long read latency overheads due to massive read-retry steps have become a serious performance concern to develop flash memory in storage devices. In this article, we proposed a parallel read-retry scheme (PaRR) to utilize the read-retry characteristics among flash memory cells. Specifically, when reading the multiple data pages simultaneously, PaRR reduces the read-response time by parallelizing read-retry operations. PaRR reduces the number of read-retry operations by parallelizing read-retry steps with the different aggressive read reference voltages if simultaneously reading the data pages that exhibit virtually equivalent reliability characteristic. Our evaluation shows that PaRR improves the I/O performance by 33.77% on average compared with the state-of-the-art scheme.

Video-text retrieval is a crucial task that has been a powerful application for multi-media data analysis and attracted tremendous interest in the research area. The core steps are feature representations and alignment to overcome the heterogeneous gap between videos and texts. Existing methods not only take advantage of multi-modal information in videos but also explore local alignment to enhance retrieval accuracy. Although performing well, these methods seem deficient at three perspectives: a) The semantic correlations between different modal features are not considered, which introduces irrelevant noise in feature representations. b) The cross-modal relations and temporal associations are ambiguously learned by a single self-attention manipulation. c) The training signal to optimize the semantic topic assignment for local alignment is missing. In this paper, we proposed a novel Temporal Multi-modal Graph Transformer with Global-Local Alignment (TMMGT-GLA) for video-text retrieval. We model the input video as a sequence of semantic correlation graphs to exploit the structural information between multi-modal features. Graph and temporal self-attention layers are leveraged on the semantic correlation graphs to effectively learn cross-modal relations and temporal associations respectively. For local alignment, the encoded video and text features are assigned to a set of shared semantic topics, and the distances between residuals from the same ones are minimized. To optimize the assignments, a minimum entropy-based regularization term is proposed for training the overall framework. Experimental results are carried out on the MSR-VTT, LSMDC, and ActivityNet Captions datasets. Our method outperforms previous approaches by a large margin and achieves state-of-the-art performance.

Video-text retrieval is one of the most popular branches in the cross-modal research area facing the exponential growth of multimedia services. Present methods typically mea-sure cross-modal similarities only by the cosine metric in a common space. However, this function ignores the in-herent discrepancy between videos and texts for reflecting spatial-temporal contents and fails to align them under the weakly supervised setting. To address this issue, we propose a novel Spatial-Temporal Optimal Transport (STOT) frame-work building upon advances in optimal transport. The spatial and temporal characteristics of videos and texts are carefully considered in STOT and associated by calculating a spatial-temporal alignment distance, which can be implemented as a regularizer for existing models to improve retrieval accu-racy. Extensive experiments are conducted on two video-text datasets with two models as base architectures to demonstrate the outperformance of our framework due to the powerful spatial-temporal alignment capability.

A novel dimming control scheme, termed as generalized dimming control (GDC), is proposed for visible light communication (VLC) systems. The basic idea of GDC is to exploit adaptively time, spatial and signal domain resources for enhancing communication performance under practical illumination constraints. In particular, to satisfy the illumination constraints, an incremental algorithm for index mapping is first proposed to achieve target optical power and uniform illumination. Next, GDC having the optimal activation pattern is investigated to improve the bit-error rate (BER) performance of VLC. In particular, the BER performance of GDC is analyzed using the union bound technique. Based on the analytical BER bound, the optimal activation pattern of GDC scheme having the minimum BER criterion (GDC-MBER) is obtained by exhaustively searching all conditional pairwise error probabilities. However, since GDC-MBER requires high search complexity, two low-complexity GDC schemes having the maximum free distance criterion (GDC-MFD) are proposed. The first low-complexity GDC-MFD scheme, termed as GDC-MFD1, is derived by a lower bound of the free distance using the Rayleigh-Ritz theorem. The second low-complexity GDC-MFD scheme, termed as GDC-MFD2, is proposed to reduce further the computation by exploiting the time-invariance characteristics of the VLC channel. Simulation and numerical results show that GDC-MBER, GDC-MFD1 and GDC-MFD2 have similar BER performance, and they can achieve more than 3 dB SNR gains compared with conventional dimming control schemes for a BER of 10-4and a dimming level of 50%.

Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which existing methods pay less attention. To address this issue, we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit reasoning between frame regions. Specifically, we consider frame regions as vertices and construct a fully-connected semantic correlation graph. Then, we perform reasoning by novel random walk rule-based graph convolutional networks to generate region features involved with semantic relations. With the benefit of reasoning, semantic interactions between regions are considered, while the impact of redundancy is suppressed. Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity. Extensive experiments on two public benchmark datasets validate the effectiveness of our method by achieving state-of-the-art performance due to the powerful semantic reasoning.

This paper proposes a simple dimming control scheme for multi-LED visible light communications (VLC), termed as generalized dimming control (GDC) scheme. The GDC performs dimming control by simultaneously adjusting the intensity of transmitted symbols and the number of active elements in a space-time matrix. The indices of active elements in each space-time matrix, as well as the modulated constellation symbols, are used to transmit information base on the space-time index modulation. Furthermore, the tradeoff between the intensity of transmitted symbols and the number of active elements in the space-time matrix is analyzed for enhancing the reliability of communication. To improve the communication reliability, a GDC with the minimum bit error rate (BER) criterion is first proposed to select the optimal dimming control pattern based on exhaustive search. Furthermore, to reduce the computational complexity, a low complexity GDC with the maximum free distance criterion is proposed. Simulation and numerical results show that the proposed scheme can outperform conventional dimming control schemes at both low and high dimming levels in terms of the BER performance.

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device- to-device (D2D) enabled caching cellular networks with time-varying user terminal (UT) movement and content popularity, we model these dynamic networks as a stochastic game to design a cooperative caching placement strategy. We consider the long-term caching placement reward of all UTs. Each UT becomes a learning agent and the caching placement strategy corresponds to the actions taken by the UTs. In an effort to solve the stochastic game problem, we propose a multi- agent cooperative alternating Q-learning (CAQL) caching placement algorithm. We discuss the convergence and complexity of CAQL, which can converge to a stable caching policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic environment.

In device-to-device (D2D) enabled caching cellular networks, user terminals (UTs) collaboratively store and share a large volume of popular contents from the base stations (BSs) for traffic offloading and delay reduction. In this paper, the multi-winner auction based caching placement in D2D-enabled caching cellular networks is investigated for UT edge caching incentive and content caching redundancy reduction. Firstly, a multi-winner once auction caching placement (MOAC) algorithm is proposed. The maximum social welfare problem is solved by semidefinite programming (SDP) relaxation to obtain a near optimal caching placement. Moreover, the pricing strategy of the auction is developed as a Nash bargaining game. We further propose a multi-winner repeated auction based caching placement (MRAC) algorithm, which can greatly reduce the computation complexity with tiny performance loss. Simulation results show that the proposed algorithms can reduce the traffic load and the average content access delay effectively compared with the existing caching placement algorithms.

Caching at base stations (BSs) has been proposed as a promising technology to address the challenges of the increasing traffic. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul is analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. By utilizing the hybrid caching policy, analytical expression of successful content delivery probability (SCDP) is derived. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. SCDP increases with the increasing backhaul capacity until converging, whose convergence rate increases with the increment of cache capacity.

Uplink transmission is an issue for visible light communications due to the unpleasant irradiance from the source light when placed close to the users. To overcome this problem, this paper proposes the use of relays to lower the required source optical power. A popular multi-carrier modulation scheme, termed direct-current biased optical orthogonal frequency division multiplexing, is employed to achieve high spectral efficiency. In addition, both amplitude-and-forward (AF) and decode-and-forward (DF) protocols are used. The theoretical models of AF and DF protocols are also obtained and verified by simulations. To minimize the source optical power while satisfying reliable communications, this paper formulates the associated optimization problems for AF and DF protocols. Exhaustive search is first used to obtain the optimal configuration for the system. As exhaustive search requires high computation efforts and can be time-consuming, two low-complexity suboptimal designs for AF and DF protocols are then proposed, and the proposed suboptimal designs can approximate the performance of exhaustive search in high signal-to-noise ratio regions. Numerical and experimental results indicate that when compared with the counterpart without a relay, the proposed relay-assisted system requires much lower source optical power under the constraints of reliable transmissions.

Caching at the edge has emerged as a promising technology to enhance the quality of service (QoS) of users and mitigate the backhaul load. Since the cache capacity is not arbitrarily large to store all contents, the backhaul can assist to fetch the contents from the core network. The network performance analysis of base station (BS) caching should consider the limited backhaul capacity. In this paper, we analyze the energy efficiency of the cache-enabled cellular networks considering the backhaul with stochastic geometry. First, the content coverage probability (CCP) based on the limited backhaul is analyzed. With the obtained CCP results, the expressions of throughput, power consumption and energy efficiency for a general case and a specific case with the mean load approximation are derived respectively. Simulation results confirm the accuracy of theoretical analysis and verify that BSs caching can dramatically improve energy efficiency on the condition that the content popularity is skewed and the backhaul capacity is relatively small.

Caching in the cellular networks has been proposed as a promising technology for reducing the content delivery latency and backhaul cost. Since the backhaul capacity is limited in the practical scenario, the network performance analysis of base station (BS) caching should address the effects of the limited backhaul. This paper investigates the energy efficiency of the cache‐enabled cellular networks with the limited backhaul based on the stochastic geometry method. First, the successful content delivery probability (SCDP), which depends on the successful access delivery probability, successful backhaul delivery probability, and cache hit ratio, is analyzed under the limited backhaul. Based on the obtained SCDP results, we derive the analytical expressions of throughput, power consumption, and energy efficiency for various scenes including the general case, the interference‐limited case, and the mean load approximation case. The accuracy of theoretical analysis is verified by the Monte Carlo simulation. The simulation results show that BS caching can dramatically improve energy efficiency when the content popularity is skewed, the content library size is small, and the backhaul capacity is relatively small. Furthermore, it is confirmed that there exists an optimal BS density which maximizes the energy efficiency of the cache‐enabled cellular networks.

In cognitive vehicular networks (CVNs), many envisioned applications related to safety require highly reliable connectivity. This paper investigates the issue of robust and efficient cooperative spectrum sensing in CVNs. We propose robust cooperative spectrum sensing via low‐rank matrix recovery (LRMR‐RCSS) in cognitive vehicular networks to address the uncertainty of the quality of potentially corrupted sensing data by utilizing the real spectrum occupancy matrix and corrupted data matrix, which have a simultaneously low‐rank and joint‐sparse structure. Considering that the sensing data from crowd cognitive vehicles would be vast, we extend our robust cooperative spectrum sensing algorithm to dense cognitive vehicular networks via weighted low‐rank matrix recovery (WLRMR‐RCSS) to reduce the complexity of cooperative spectrum sensing. In the WLRMR‐RCSS algorithm, we propose a correlation‐aware selection and weight assignment scheme to take advantage of secondary user (SU) diversity and reduce the cooperation overhead. Extensive simulation results demonstrate that the proposed LRMR‐RCSS and WLRMR‐RCSS algorithms have good performance in resisting malicious SU behavior. Moreover, the simulations demonstrate that the proposed WLRMR‐RCSS algorithm could be successfully applied to a dense traffic environment.

Uplink transmission design for visible light communication (VLC) is challenging due to the high optical power requirement for reliable links, which may result in unpleasant irradiance to humans. Current studies have proposed to use radio frequency (RF) communication for uplink while treating VLC as a downlink transmission technique. However, in typical scenarios such as hospitals or aircraft cabins, non-RF communications are preferred. Thus, it is beneficial to design a system which can utilize VLC for uplink transmission while minimizing its optical power. In this paper, we propose a amplify and forward based VLC uplink system which can significantly mitigate the optical power of the source light. The multi- carrier modulation scheme termed DC biased optical orthogonal frequency division multiplexing is employed, and the theoretical model of the proposed uplink system is studied. Based on this analytical model, the key system parameters are optimized. Numerical and simulation results verify that, when compared to an equal power allocation based relay system and a reference system without a relay, the proposed system can simultaneously achieve higher spectral efficiency and lower the source optical power.

Heterogeneous network (HetNet) has been widely accepted as a viable way to deal with the increasing traffic demand. However, the deployment of small cell base stations (SBSs) inevitably triggers a tremendous escalation of energy consumption. In this paper, we study the energy efficient HetNet deployment problem taking the disparities of the traffic load distribution into account. The optimization task is to select a set of SBSs from the candidate sites to maximize the energy efficiency while satisfying the traffic load requirement of the traffic demand points (TDPs). A two- stage approximation algorithm is introduced to solve the optimization problem by decomposing the original problem into two sub problems and settling with the greedy algorithm and the modified matching algorithm respectively. Simulation results verify that the proposed deployment strategy can greatly improve the energy efficiency compared with the random deployment strategy and is suitable for the scene with a large number of TDPs in the serving area whose minimum traffic load is large and the traffic load disparities are great.

Content centric networking (CCN) is a promising architecture for the future networks. In-networking caching of CCN can significantly improve the content delivery efficiency. Content popularity is one of the key factors considered in the design of the caching strategy. However, the existing research ignores the timeliness of content popularity statistics, which makes the changing of popular contents cached in the network lag behind the changing of the user preference. In this paper, a content clustering and popularity prediction based caching strategy (CPC) is proposed to solve this problem. Firstly, the massive contents are divided into different content types using cluster analysis. Then, the popularity of different content types is predicted by the autoregressive integrate moving average (ARIMA) model. Finally, based on the predicted content popularity, the process of the caching placement decision is given. The proposed caching strategy is a distributed caching management method, which can be implemented without centralized controller. Simulation results show that the proposed caching strategy can achieve better performance in terms of cache replacement rate, cache hit ratio and average hop count.

Cognitive heterogeneous cellular network (CHCN), comprised of small cells overlaid with macrocells, is a promising topology to fulfill the explosive demand for high-data-rate transmission by employing cognitive radio technologies. In this paper, a joint polarization and power allocation (JPPA) scheme is proposed for spectrum sharing in CHCN. By exploiting spectrum opportunities in polarization and power domains simultaneously, the proposed algorithm jointly optimizes the polarization states and transmitting power of small cell to maximize its downlink capacity subject to its own transmit-power constraint and interference-power constraint of macrocell. Numerical results reveal that the proposed scheme provides capacity and spectrum efficiency improvements compared with polarization-based spectrum sharing and power allocation schemes. In addition, it approaches the performance of the optimal exhaustive search with a much lower computational complexity.

With the diversification of mobile services, mobile traffic exhibits new characteristics which is different from traditional voice traffic. In this paper, we investigate the optimal base station (BS) density in cellular networks by taking into account the self- similar characteristics of mobile traffic using stochastic geometry approach. Based on the traffic load distribution satisfying self-similar characteristics, traffic coverage probability is defined and its relation with BS density is derived. An optimization problem with the aim of minimizing the BS density is formulated under the constraint of traffic coverage probability. Using binary search algorithm, the optimal BS density and the bounds (both upper and lower bound) for simplified analysis is obtained. The simulation results confirm the accuracy of theoretical analysis and verify the impact of the self-similar characteristics on the optimal BS density.

In this study, the authors focus on energy efficiency (EE) optimisation via the cooperation of small cell base stations (SBSs) in dense small cell networks (DSCNs) where the control plane (C‐plane) and data plane (D‐plane) are decoupled. They propose a coalition‐based sleep mode and power allocation (CSMPA) scheme to improve the DSCN EE while guaranteeing the target rates of users and maintaining the system capacity. In the CSMPA scheme, the cooperation of SBSs is formulated as a coalitional game in partition form and a centralised heuristic coalition formation algorithm without power cost is developed to achieve the final stable coalition structure. Each SBS can serve users in the active subframes and sleep in those subframes without data transmission. Afterwards, since the interference between coalitions affects EE, a distributed price‐based power allocation algorithm is presented to optimise the transmit power of active SBSs per subframe. System‐level simulation results show that the proposed CSMPA scheme can yield less number of outage users and significant EE performance gain without jeopardising system capacity.

This paper proposes a novel polarization-based underlay spectrum sharing scheme in cognitive heterogeneous cellular network (CHCN). Distinguished from traditional spectrum sharing exploiting spectrum opportunities in time, frequency, space and power domains, we exploit spectrum opportunities in polarization domain to enable small cell to coexist with macrocell. The proposed scheme optimizes the transmitting and receiving polarization states (PSs) of small cell to maximize the downlink capacity of small cell under the interference constraint of macrocell. Virtual polarization adaptation is adopted in small cell to generate the desired PSs by digital signal processing. Simulation results reveal the superiority of the proposed scheme in interference avoidance and spectrum efficiency improvement by exploiting polarization in CHCN.

According to echo model and properties of azimuth multichannel ScanSAR with up and down chirp modulation, an effective imaging approach to handle the raw data of this mode is proposed. First, echoes corresponding up and down chirp modulation pulses are individually range compressed. Afterwards, azimuth multichannel ScanSAR raw data with azimuth nonuniform sampling are reconstructed, and the resulting raw data are focused via the following modified Stolt interpolation and spectral analysis (SPECAN) steps. The effect on range ambiguity suppression of alternately sending up and down chirp signals is analyzed via simulation experiments on point and distributed targets. Simulation results validate the proposed imaging approach.

To improve the energy efficiency of power amplifier in OFDM systems, an adaptive polarization-QAM modulation scheme is proposed considering the data rate requirement variation at the transmitter. Since QAM and polarization modulation have different energy efficiency performance in the linear and nonlinear region of power amplifier respectively, thus the optimal energy efficiency can be obtained by combining the two modulation schemes together and adjusting their modulation order adaptively to fulfill the varying data rate requirement. The power series model of power amplifier is used to analyze the effects of both the in-band distortions and the out-of-band emissions on OFDM signals. Based on above, the proposed scheme can be formulated as an optimization problem regarding the output back off value as well as the modulation order of polarization modulation and QAM. Simulations under the same symbol error rate threshold and data rate requirement show that, the energy efficiency of the proposed scheme is nearly twice as that of the traditional QAM scheme in OFDM systems.

In visible light communication (VLC) systems, optical orthogonal frequency division multiplexing (O-OFDM) is an appealing modulation scheme. Recently, a number of O-OFDM schemes have been proposed. Direct current optical orthogonal frequency-division multiplexing (DCO-OFDM) is one of the known O-OFDM schemes for its high spectral efficiency and low-complexity. Since VLC involves a combination of illumination and communication, different optical power is often required to achieve a certain illumination level. However, the performance of DCO-OFDM will severely degrade when a relatively high or low optical power constraint is imposed. To solve this problem, an enhanced DCO-OFDM (eDCO-OFDM) scheme is proposed in this paper. By introducing a piecewise function with adaptive slopes according to the required optical power, eDCO-OFDM is able to demonstrate significant performance advantages over conventional DCO-OFDM in terms of spectral efficiency and biterror rate. Comprehensive simulations were conducted to verify the efficiency of the proposed scheme.

This paper is focused on the issue of complexity and low accuracy of downlink (DL) power control in dense heterogeneous network. A novel power control algorithm based on the interference statistic properties that is accurate and results in easy-to-evaluate interference is proposed. Compared to the recent work on DL power control using complex iterations to manege interference, the proposed algorithm differs in two key features. First, the interference statistic model based on the stochastic geometry is exploited to estimate cross-tier interference that follows alpha stable distribution. Second, a centralized power control algorithm with the complementary cumulative distribution function (CCDF) of alpha stable distribution is utilized to constrain interference effectively. Simulation results show that the proposed algorithm can maintain the outage probability of macrocell user (MUE) within a certain range and improve the throughput with less exchange of information.

In multiuser MIMO systems, the required feedback rate per user increases linearly with the number of transmit antennas in order to achieve full multiplexing gain. When it comes to massive MIMO systems, the feedback overhead grows unacceptable. This motivates us to explore a novel feedback reduction scheme based on principal component analysis (PCA). The proposed PCA based feedback scheme exploits the spatial correlation characteristics of massive MIMO channel model, since transmit antennas are deployed compactly at base station (BS). In the proposed scheme, mobile station (MS) utilizes compression matrix to compress spatially correlated high-dimensional channel state information (CSI) into low-dimensional one. Then the compressed low-dimensional CSI is fed back to BS instantaneously with reduced feedback overhead and codebook search complexity. The compression matrix is attained by operating PCA on CSI which is estimated over a long-term period by MS. In order to recover high-dimensional CSI at BS, compression matrix is refreshed and fed back from MS to BS every long-term period. Numerical results and feedback overhead analysis show that the proposed PCA based feedback scheme can offer a tradeoff between system performance and feedback overhead.

This paper formulates a joint base station operation and user association optimization problem in the cloud based heterogeneous cellular networks with hybrid energy sources, where all the base stations are connected to the cloud computing center and powered by both on-grid power and renewable energy. The combinatorial optimization problem is decomposed into two subproblems based on the time scale separation. For the base station operation problem, a simple algorithm is designed to determine the active base stations using a Lagrange multiplier as the threshold, which is inspired by the submodularity maximization problem. For the user association problem, an iterative algorithm implemented by the user and base station side is proposed to achieve load balancing. Simulation results demonstrate that the proposed algorithm can reduce energy consumption, and enable a flexible tradeoff between energy consumption and load balancing.

Dense small cell networks (SCNs) has emerged as a cost-effective way of supporting a rapid growth of traffic demand. However, a large number of small cell base stations (SBSs) will consume a lot of energy, which is not conductive to improve the SCNs energy efficiency (EE). In this study, a novel small cell grouping based power control scheme is proposed to save energy as well as to maintain the network throughput for dense SCNs. Specifically, small cells are divided into different groups according to both the load and the inter-cell interference levels between the dense small cells, and accordingly the transmission power of the SBSs is reduced based on the proposed utility function. The existence of the optimal solutions to the problem is discussed, then the differential evolution (DE) is applied to obtain the optimal transmission power. System level simulation results demonstrate that the proposed scheme can significantly improve the SCNs EE without sacrifice of the network throughput.

Per-cell codebook based limited feedback technique in the coordinated multi-point transmission (CoMP) system has received more and more attention because of its flexibility and scalability. In practical CoMP systems, quantized channel state information (CSI) is subject to a feedback delay due to signal processing at the receiver, finite bandwidth of feedback links, and finite capacity of backhaul links. In this paper, we study the effect of different codeword selection schemes based on per-cell codebook on data rate in CoMP systems. The impact of CSI feedback delay is considered by using Gauss-Markov temporal correlation channel model. We derive the theoretical upper bounds of data rate for two codeword selection schemes, joint codeword selection (JCS) and independent codeword selection (ICS) respectively. Simulation results show that the practical data rate is close to the theoretical data rate upper bound both for JCS and ICS, which has validated the availability of the theoretical data rate upper bounds.

In this paper, we consider the problem of spectrum sensing in cognitive radios by exploiting Stokes subvector, which can completely describe energy and polarization information of the received vector signal captured by dual-polarized antennas. We first find that both component correlation between Stokes variables (i.e., the elements of Stokes subvector) and vector correlation between Stokes subvectors containing signal and noise are different from that of noise only with high probability. Therefore, two new blind detectors, namely, component-correlation-based energy-polarization detection (CCB-EPD) and vector-correlation-based energy-polarization detection (VCB-EPD), are proposed, respectively. The analysis results reveal that CCB-EPD and VCB-EPD are all constant false alarm rate detectors, and the VCB-EPD method achieves better performance than CCB-EPD when channel is low depolarized and vice versa. Simulations show that the proposed two methods exhibit better performances than other multiantenna-based detectors whether priori polarization information of primary user is known or not. We also show that the two proposed methods have performance improvement with respect to existing polarization-based detectors due to the exploitation of both energy and polarization information and the unaffectedness by noise uncertainty. The experimental results verify that the proposed two methods can satisfy the performance requirement specified by the IEEE 802.22 standard.

Cell coverage and network capacity are two major challenges for the evolving 4G cellular wireless communication networks. Hierarchical layering of cells with macro base stations coexisting with low-power base stations in a service area has been investigated as a promising technique for enhancing the coverage extension and reducing the transmit power consumption, which has emerged as a candidate for green communications. However, the cell splitting gain can only be achieved when both inter-layer and co-tier interference are properly handled. In this paper, a basic co-channel heterogeneous network (HetNet) deployment scenario with two cell layers is considered. The scheduler groups all the user equipments (UEs) into three sets according to the signal to interference plus noise ratio (SINR) and the factor RI. Furthermore, the total spectrum band is divided into three parts based on the size of user sets and the two layers both reduce the transmission power in some specific bands, so that contributes to interference mitigation and throughput enhancement. Simulation results show that the proposed method can not only promote the energy efficiency (EE) but also enhance system performance in terms of both cell edge users' SINR and system capacity.

Coordinated multiple point transmission (CoMP) technology becomes popular due to its potential for improving the performance of cell-edge UEs who undergo strong inter-cell interference (ICI). To fully exploit the potential CoMP gain, the appropriate method to converge cells into cluster is a prerequisite. However, the majority of existing research are based on theoretical hexagonal cell grid model solely. In this paper, a CoMP multi-cell clustering scheme which considers randomly located cellular network is proposed. In randomly located cells model, the layout of cells and UEs are generated via a Poisson Point Process (PPP). The proposed clustering scheme takes full account of both ICI suffered by UEs and cells distribution, by mapping the CoMP network onto a bipartite network, in which two types of nodes represent cells and UEs respectively, while edges are quantified to denote the channel gain. Meanwhile a projection method is introduced to project the bipartite network onto a Euclidean space where distance between UEs and cells is easily calculated. Finally the efficient K-mean method is utilized to extract nearby UEs and cells out of the space and form the clusters. The proposed scheme hardly incurs extra overheads since only UE's large scale fading information is required. The simulation results demonstrate a considerable throughput gain attained by UEs over comparing algorithm.

In the open subscriber group (OSG) mode of heterogeneous network (HetNet), the inter macro-user interference is the main interference to macro user equipments (UEs), and the inter cell interference (ICI), containing the inter picocell interference and the cross-layer interference from macro base station (BS), is the main interference to pico UEs. In this paper, we propose an interference alignment (IA) scheme for such HetNet. The transceiver is designed as follows. Firstly, the precoding matrix for macro BS and the interference suppression matrices for macro UEs are used to eliminate the inter macrouser interference. Secondly, the precoding matrices of pico BSs are designed to align the inter picocell interference onto the space of the strongest cross-layer interference produced by macro BS. Finally, the aligned interference is removed by the interference suppression matrices of pico UEs. In addition, we introduce a power allocation scheme for the macro BS to further reduce the interference to pico UEs. The simulation results and the complexity analysis show that the proposed scheme can reduce the requirement of the receive antennas number and improve the system throughput of HetNet with lower complexity.

Envelope tracking (ET) is a significant technique to improve the efficiency of power amplifier (PA) in the power back-off region, especially in modern mobile communication system with a high peak-to-average power ratio (PAPR). Different ET implementation solutions, especially the way of choosing shaping table, have a great impact on the performance of ET system. In this paper, a transistor-level PA and a behavior-level supply modulator are introduced to analyze the performance of ET PA system. The power-added efficiency (PAE), AM-to-AM distortion, AM-to-PM distortion and Adjacent Channel Leakage Ratio (ACLR) are investigated to explain the impacts of different commonly-used shaping methods. Simulation results indicate that adopting ET technology increases the efficiency of RF PA largely, and the shaping methods applied in supply modulation will affect a lot in efficiency and linearity. Consequently, a new dualmode ET shaping method is proposed in this paper to guarantee high efficiency as far as possible without introducing too much distortions. Besides, it also improves the linearity where the PAE is at a lower level.

In this work, a joint polarization adaption and beamforming technique is proposed for horizontal spectrum sharing for TD-LTE and FD-LTE users. The polarization and spatial information of multiple dual-polarized antennas on current LTE nodes is exploited to mitigate the mutual interference among the primary, TD-LTE and FD-LTE users. By this means, the opportunities in polarization and spatial domains are exploited, which means the licensed spectrum can be shared by TD-LTE and FD-LTE users with primary users simultaneously, thus the spectrum efficiency is improved. Simulation results show that the proposed technique can improve the spectrum efficiency in comparison traditional spectrum sharing approaches that utilize the spectrum opportunities in temporal domain, while acceptable interference is introduced to the primary user.

Deployment of small cells within macrocell layout is a promising approach to meet the rapid growth of traffic demand. Cell range expansion (CRE) has been introduced into LTE-Advanced to balance cell load. Small cell CRE users may suffer severe downlink (DL) co-tier and cross-tier interference introduced by dense deployment of small cells. In this paper, we propose a joint coloring-based resource allocation and power reduction scheme to mitigate interference to CRE users while maintaining the network throughput. In order to mitigate the co-tier interference, we firstly group CRE users based on interference graph by coloring and allocate orthogonal resources to different groups. Then, small base stations (SBSs) reduce power on resources occupied by CRE users in other SBSs. In order to alleviate the cross-tier interference, macro base stations (MBSs) power reduction is provided based on a utility function. We apply particle swarm optimization (PSO) strategy to obtain the optimal power. System level simulation results show that our proposed algorithm can greatly improve the CRE user throughput with slightly sacrificing the macrocell throughput. Meanwhile, it can improve the network energy efficiency (EE).

To adapt to variation of polarization dependent loss (PDL) in depolarization channel, an adaptive polarization modulation scheme which can improve the spectral efficiency of polarization modulation system is proposed. In the proposed scheme, the transmitter adapts the optimal constellation size to the variation of the channel due to PDL while fulfilling a fixed target BER constraint, which is based on the received feedback information of channel state information estimated at the receiver. Our simulation results and analysis show that, adaptive polarization modulation (APM) can achieve higher spectral efficiency (SE) than nonadaptive polarization modulation, subject to the same fixed target BER constraint.

Joint relay selection and spectrum allocation scheme with multiple source-destination pairs and multiple potential relays in cooperative relay networks is proposed in this paper. In cooperative relay networks, the selected relay nodes and spectrum resource allocated to each source-destination pair have great impact on the system performance. It is an optimization problem for which relay nodes are selected and which spectrum resources are assigned. The exhaustive search can solve this problem but the complexity will increase factorially with the network size (i.e., the number of source-destination pairs and the number of relays in the network) and exponentially with the number of available spectrum resources. This paper proposes a novel quantum particle swarm optimization (QPSO) based relay selection and spectrum allocation scheme which can maximize three definitions of system throughput but has less computational complexity. Simulation results show that QPSO based scheme has the ability to find global optimal solution compared with other schemes in the literature.

The access point sleeping scheme is an efficiency method for power saving in the cellular networks. This paper proposes a QoS-aware adaptive access point sleeping scheme designed for energy efficiency in the relay cellular networks. To achieve the best tradeoff between QoS guarantee of users and the power saving of the network, the optimization problem of the access point selection for each user is given, and a dynamic energy control (DEC) algorithm is presented, in which, a best access point (base station or relay station) is allocated to each user and the access point without user association will be switched off for energy saving. We evaluated the network performance of power consumption, system throughput and energy efficiency by simulation, which show that, compared other schemes, the proposed access point sleeping scheme with DEC algorithm can improve the energy efficiency with the QoS guarantee for relay cellular networks, especially in low traffic condition.

The long term evolution (LTE) networks provide high data rate access to support multi-media services at the expense of more power consumption in user equipment (UE). Discontinuous reception (DRX) mechanism is one of the promising technologies for energy saving in LTE networks, which extends UE battery lifetime effectively. The DRX parameters configuration should make a compromise between power saving and wake-up delay, which can be achieved by the DRX parameters configuration according to different traffic types with specific quality of service (QoS) requirement. In this paper, we model the DRX mechanism with web traffic using a semi-Markov process. Based on the proposed model, the performance of power saving and wake-up delay with various DRX parameters is analysed, which provides insight on effects of the DRX parameters on the power saving and wake-up delay performance. The analytical results are verified by the simulation, which shows that LTE DRX achieves much power saving gains at the cost of tolerable delay.

Spectrum opportunities in time, space and power domain are exploited by secondary users in traditional cognitive radio studies. While spectrum opportunities in polarization domain, namely simultaneous utilization of spectrum between primary and secondary system with polarization signal characteristics, is exploited inadequately. With the merits of polarization mismatch, primary system lies in the orthogonal polarized domain of secondary system, and interference to primary system can be mitigated. This work studies the cognitive underlay TV spectrum sharing with the merits of polarization mismatch for TD-LTE system. In this direction, a novel Polarized Underlay Spectrum Sharing (PUSS) scheme is proposed for the coexistence of TD-LTE and DTV system. It aims to characterize the spectrum efficiency of the cognitive radio setup while transmission of Polarization States (PSs) is affected by channel depolarization effect and interference constraint of DTV receiver is taken into account. Transmitting and receiving PSs are optimized by Particle Swarm Optimization with the polarized channel state information and primary PSs. Theoretical analysis and numerical results indicate that the proposed PUSS scheme could enable TD-LTE system share TV spectrum without causing much interference to incumbent DTV communication, while improvement in spectrum efficiency is achieved.

In this paper, we propose a new blind spectrum sensing method based on the polarization characteristic of the received signal, which is completely represented by the orientation of a polarization vector. We first discuss a spectrum sensing model based on polarization vectors' orientation. Then we develop the directional statistics of polarization vectors that contain both the signal and noise or noise only. The distinctive difference between the two statistics can be used to decide whether the primary signal exists or not. Based on this, by using the well-known generalized likelihood ratio test (GLRT) paradigm, a new polarization sensing algorithm GLRT-polarization vector (GLRT-PV) is proposed. By applying directional statistics, we derive closed-form expressions for the probability of false alarm and the probability of detection under both dual-polarized additive white Gaussian noise (AWGN) and Rayleigh-fading channels. Our numerical simulation and experimental results show that the proposed method exhibits better performance than other existing methods in the case of unknown primary transmitter polarization and/or presence of noise power uncertainty.

This paper addresses how to improve the energy efficiency of OFDMA femtocell networks with sleep mode. Firstly, the energy consumption of the sleep mode is analyzed. Considering the requirements of improving the energy efficiency and ensuring the QoS of users, we extend the definition of effective capacity to formulate the tradeoff relationship between the energy efficiency and the waiting delay of users. Then two optimization schemes of sleep mode parameter are proposed: (1) Maximize the energy efficiency with effective capacity constraint; and (2) Maximize the effective capacity with energy efficiency constraint. And the performances are analyzed, including average energy consumption, energy efficiency, effective capacity and waiting time. Simulation results show that the effective capacity-based parameter optimization of sleep mode can achieve a good tradeoff between energy efficiency and QoS of users in OFDMA femtocell networks.

This paper addresses the cross-tier interference issue in two-tier OFDMA femtocell networks using hybrid spectrum allocation. Firstly, we propose a classification method for users. The macro users are classified into several categories based on their geographic location, and femto users are classified according to the categories of macro users. Then, based on such user classifications, a hybrid spectrum allocation (HSA) scheme is proposed. HSA employs a dynamic orthogonal spectrum allocation for macro user categories according to the number of MUEs in two-tier networks, and fractional spectrum reuse for femto user categories based on their different communication environments. Simulation results show that the proposed scheme can improve outage and throughput performance of macro users with less impact on femto users.

The optimum power allocation of the one-side interference channel with the non-cognitive relay node was studied. Assuming the orthogonal resources were used on the channels between the sources and relay node, we first derived a transmission scheme based on the dirty paper coding and the interference cancellation. Then with this transmission scheme the rates that was achievable in both the weak and strong interference regimes were given. A joint power allocation scheme among the sources and relay node was proposed, which maximized the sum-rate. The performance of the proposed power allocation scheme was proved. More explicit analysis investigated the effects of the noncognitive feature of the relay node on the power allocation and sum-rate. The relationship between the channel gains and the optimum joint power allocation had also been analyzed.

This paper addresses the cross-tier interference mitigation problem in two-tier OFDMA femtocell networks using power control. We model the objective of power control with utility functions for macrocell and femtocell, respectively. And we discuss the existence of optimal solutions to power control problems. Then a power control algorithm with Differential Evolution (DE) is developed to find the optimal power settings which maximize the utility function. Through numerical calculation, we study how to properly set the parameters of DE for our proposed utility functions. Furthermore, the performance of power control algorithm with different tunable coefficients of utility function is also analyzed by system level simulation. Simulation results show that our proposed algorithm can improve the outage and throughput performance of macro users with minor impact on femtocell users. Meanwhile, our proposed algorithm is easy to implement in distributed manner.

This paper addresses the power control problem in two-tier OFDMA femtocell networks to alleviate the cross-tier interference. We model the objective of power control with utility function, and proof the proposed utility function is strictly concave with respect to transmission power. Then a power control algorithm with Particle Swarm Optimization is developed to find the optimal power settings to maximize the utility function. Simulation results show that our proposed algorithm can improve the outage and throughput performance of macro users with less impact on femtocell users. Meanwhile, our proposed algorithm is easy to implement in distributed manner and efficient in computation.

This paper investigates the performance of clustered base station (BS) coordination with limited feedback and channel state information (CSI) delay. Given imperfect CSI caused by the limited feedback and channel delay, the expression of data rate per cell is derived. Moreover, compared to the rate with perfect CSI, a rate loss upper bound is obtained. A optimal feedback bits expression are derived to minimize the rate loss caused by limited feedback and channel delay. The numerical results show that the rate loss reduces when the number of feedback bits increases and the channel delay decreases. Meanwhile, the rate loss value reaches to a fixed value when feedback bits increase gradually. On the other hand, when the channel delay increases gradually, the rate loss can be improved little by the increasing of feedback bits. Lastly, as SNR increases, the system performance with the imperfect CSI improves more slowly than that with the perfect CSI.

In coordinated multi-point transmission (CoMP) systems, the optimal remote radio unit (RRU) location is analyzed theoretically and a RRU location design scheme for energy efficiency in practical scenarios is given. An average minimum access distance criterion is given for RRU location optimization. By minimizing the average distance between users and RRU, the optimal RRU distribution can be obtained when users are located uniformly in the cell. Taking into account the fact that user distribution will not be completely uniform in a practical environment, the k-means clustering algorithm is used to get the optimized RRU deployment in a practical user distribution. Simulation results show that the uplink transmission power can be greatly reduced with the RRU optimized location design in both the uniform and non-uniform user distribution.

The fundamental tradeoff between the transmission rate and the reliability of an one-side interference channel with relay is studied using the diversity-multiplexing tradeoff (DMT) as a measure. By analyzing the capacity and the outage probability, the DMT under strong interference condition is given. This paper concludes that the strength of the signal on interference channel will limit the maximum diversity and the strength of the signal forwarded by the relay will determine the diversity gain actually achieved. This paper also concludes that the power allocation ratio at the relay for the interference and the intended signal for one receiver does not affect the DMT.

In this paper, a concept of utility proportional fairness is defined, which achieves fairness of utility for different applications. The optimization problem of utility proportional fairness has been formulated meanwhile the optimal solution is presented. The utility proportional fairness is applied in the orthogonal frequency division multiplexing (OFDM) wireless networks, and the optimal subcarrier allocation for utility proportional fairness is deduced. An effective and practical simplified subcarrier allocation algorithm is proposed, in which the instantaneous data rate is substituted by average rate via exponentially weighted low pass time window. Simulation results illustrate that both the optimal and simplified subcarrier allocation algorithms guarantee the utility fairness between different applications well, and the simplified algorithm achieves a perfect tradeoff between utility fairness and system throughput and provides the quality of server (QoS) for multimedia applications.

Radio resource allocation is one of the key technologies in orthogonal frequency division multiplexing (OFDM) cellular systems, where subcarriers and power are schedulable resources. In this paper, we solve the fair resource allocation problem based on the idea of the Nash bargaining solution (NBS) from cooperative game theory, which not only provides the resource allocation of users that are Pareto optimal from the view of the whole system, but also are consistent with the fairness axioms of game theory. We develop a suboptimal solution of NBS via low-pass time window filter and first-order Taylor expansion, and then propose an efficient and practical dynamic subcarriers allocation algorithm. Simulation results show that the proposed dynamic subcarriers allocation algorithm providing utility fairness and improving system capacity.