Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), have significantly reduced the number of trainable parameters needed in fine-tuning large language models (LLMs).The developments of LoRA-style adapters have considered two main directions: (1) enhancing model expressivity with high-rank adapters, and (2) aiming for further parameter reduction, as exemplified by vector-based methods.However, these approaches come with a trade-off, as achieving the expressivity of high-rank weight updates typically comes at the cost of sacrificing the extreme parameter efficiency offered by vector-based techniques.To address this issue, we propose a vector-based random Tensor network for high-Rank Adaptation (TeRA), a novel PEFT method that achieves high-rank weight updates while retaining the parameter efficiency of vector-based PEFT adapters.This is achieved by parametrizing the tensorized weight update matrix as a Tucker-like tensor network (TN), whereby large randomly initialized factors are frozen and shared across layers, while only small layer-specific scaling vectors, corresponding to diagonal entries of factor matrices, are trained.Comprehensive experiments demonstrate that TeRA matches or even outperforms existing high-rank adapters, while requiring as few trainable parameters as vectorbased methods.Theoretical analysis and ablation studies validate the effectiveness of the proposed TeRA method.The code is available at https://github.com/guyuxuan9/TeRA.

The reasoning abilities of Large Language Models (LLMs) can be improved by structurally denoising their weights, yet existing techniques primarily focus on denoising the feed-forward network (FFN) of the transformer block, and can not efficiently utilise the Multi-head Attention (MHA) block, which is the core of transformer architectures. To address this issue, we propose a novel intuitive framework that, at its very core, performs MHA compression through a multi-head tensorisation process and the Tucker decomposition. This enables both higher-dimensional structured denoising and compression of the MHA weights, by enforcing a shared higher-dimensional subspace across the weights of the multiple attention heads. We demonstrate that this approach consistently enhances the reasoning capabilities of LLMs across multiple benchmark datasets, and for both encoder-only and decoder-only architectures, while achieving compression rates of up to ~ 250 times in the MHA weights, all without requiring any additional data, training, or fine-tuning. Furthermore, we show that the proposed method can be seamlessly combined with existing FFN-only-based denoising techniques to achieve further improvements in LLM reasoning performance.1

3D single object tracking (SOT) based on point cloud has attracted much attention due to its important role in machine vision and autonomous driving. Recently, M2-Track proposes a two-stage tracking structure centered on motion, but they ignore the effect of segmentation errors in sparse point cloud scenarios, which hinder the ability of networks to accurately represent tracking targets. To solve the problems, we propose an efficient 3D single object tracker (Abbr. EST) that can effectively segment point cloud features. Firstly, the proposed fusion segmentation module makes up for the feature loss caused by the downsampling strategy and enhances the ability of the network to recognize foreground points. In addition, the global embedded module is used to further focus on the crucial features of the target. This module provides global information by using residual networks and adding background information. Numerous experiments conducted on KITTI and NuScenes benchmarks show that EST achieves superior point cloud tracking in both performance and efficiency.

In this paper, we describe a frame-interleaving hardware architecture for a generalized node-based successive cancellation list (SCL) decoder. By efficiently reusing otherwise idle computational units, two independent frames can be decoded simultaneously, resulting in a significant throughput gain. Based on this new architecture, we also exploit graph ensembles to diversify the decoding, enhancing the error-correcting performance by 0.28 dB and reducing the worst-case latency for serial graph processing by over 32%. Implementation results show that the proposed SCL decoder with frame-interleaving architecture achieves a throughput of 7.15 Gbps and an area efficiency of 37.63 Gbps/mm2, which is 1.56× and 1.11× better than the state-of-the-art node-based SCL decoders.

This paper investigates the problem of joint active user detection (AUD) and channel estimation (CE) for grant-free random access in a cell-free massive multi-input multi-output (MIMO) system. Due to the sporadic activation of users and the channel block sparsity caused by large-scale fading in the cell-free system, the effective channel matrix exhibits a dual sparsity property. Considering this dual sparsity, joint AUD and CE are formulated as a sparse signal reconstruction problem based on compressed sensing. A ‘three-choice-one’ prior assumption is employed to extract the block sparsity in the antenna dimension. Then a variational Bayesian inference-based algorithm is proposed, and the simulation results validate the reliability of the proposed algorithm.

Due to the continuous increase in data traffic, communication networks require increased densification and multi-layering. Different hotspot areas may access different networks. We aim to avoid interference from cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) sys-tems in hotspot areas. To this end, we consider a specific scenario involving the cell-free mmWave massive MIMO system and the mmWave Vehicle-to-Everything (V2X) network. To minimize interference on the mmWave V2X network while ensuring the sum spectral efficiency (SE) performance of the cell-free mmWave massive MIMO system, a novel joint problem of beam selection and user equipment (UE) association is formulated. We propose a deep reinforcement learning (DRL)-based distributed double deep Q-network (D-DDQN) algorithm to address this problem. Simulation results indicate that the algorithm we propose out-performs traditional algorithms.

Compared to block precoding (BP), interference exploitation symbol-level precoding (IESLP) can effectively reduce transmit power in multiantenna wireless communication systems. However, because most existing IESLP methods are symbol-level optimizations of zero-forcing (ZF) BP which is usually not the best BP, the performance and application range of IESLP are restricted. In this article, we investigate the symbol-level optimization of any given BP method to reduce transmit power. We first define generalized constructive interference (GCI) as a new constructive interference metric for any received BP signals, where the interference is constructive as long as it will not impair the symbol error rate (SER). Then, convex GCI regions (GCIRs) of received BP signals for both phase shift keying (PSK) and quadrature amplitude modulation (QAM) constellations are designed. With the aid of GCIR, we propose BP-based symbol-level precoding (BPSLP) methods to reduce transmit power. Moreover, due to the difficulty of obtaining perfect channel state information (CSI), we also propose robust BPSLP (RBPSLP) methods to reduce transmit power for PSK and QAM constellations, where outage probability (OP) is constrained for robustness. Numerical results show that compared to existing precoding methods, our proposed precoding methods effectively reduce transmit power. It is worth noting that due to the bounded or even one-point constructive interference region (CIR), there are usually no feasible solutions for existing robust IESLP methods under QAM constellations, but our proposed RBPSLP method for QAM constellations is feasible as long as a feasible robust BP matrix exists.

We consider the channel estimation performance for the uplink of one-bit massive multiple-input multiple-output (MIMO) system. Each antenna at the base station (BS) is equipped with two one-bit analog-to-digital converters (ADCs), which quantify the received signals into two levels. Inspired by the use of denoising neural network to enhance channel estimation results, we extend the thought to the one-bit case and figure out whether the improvement can obtain in the extremely deteriorated scenario. After achieving positive outcome, we analyze why it works according to the contrast of simulation results and summarize the condition when the method works. Then we generalize the method and find denoising neural network can also enhance the results of any channel estimation methods. Based on all of the numerical results, we finally proposed a two-step channel estimation strategy, in which denoising neural network is treated as a standardized process after any traditional channel estimation methods under any scenario, and then we discuss the necessity and feasibility of the proposed strategy.

In massive multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, traditional pilot-aided channel estimation methods usually need interpolation to obtain the channel response other than pilot subcarriers. Therefore, the accuracy of interpolation is crucial for channel estimation. In this paper, we propose a deep learning-based channel interpolation method that utilizes the sparsity and correlation of the massive MIMO-OFDM channel in the angular-frequency domain. Specifically, we first consider the angular domain least squares (LS) channel estimation matrix at pilot subcarriers as a low-resolution 2D image. Then, a residual channel attention network (RCAN) for channel interpolation is proposed to reconstruct the whole channel state information (CSI). Moreover, to make the approach more practical in real-world scenarios, we further introduce the Noise2Noise method, which can be used to restore channel response without clean labels. Finally, simulation results demonstrate the superiority of the proposed channel interpolation method in terms of performance and practicability.

This paper proposes a low-complexity groupwise detection scheme for massive multiple-input multiple-output (MIMO) systems based on two key features of massive MIMO channels. First, most inter-user correlations tend to be negligible as the number of antennas$M$at the base station (BS) increases, while some users might have a high correlation that is indistinguishable for linear projection-based detectors. We hence develop a variational Bayesian inference-based groupwise (VBI-G) detector based on a factorization approximation of the a posteriori probability of transmitted symbols, which sequentially performs nonlinear detection for small groups of highly correlated users without computations of large-scale matrices. Second, the variation of normalized inter-user correlation is insignificant over a wide range of frequency, thereby user grouping based on correlation coefficients can be performed on a wideband basis. Accordingly, we propose a low-complexity incremental greedy grouping algorithm for minimizing the Kullback-Leibler divergence of the unconditioned posterior probabilities with the constraint of a maximum group size. Theoretical analysis proves that for a BS equipped with a uniform linear array, the maximum group size grows logarithmically with the number of users$K$with high probability in the regime$M, K\rightarrow \infty $with$K/M < 1/2$under the worst line-of-sight propagation condition. Simulation results verify that the proposed VBI-G detector achieves superior performance with an extremely low computational overhead for massive MIMO systems.

In this paper we investigate the problem of joint active user detection (AUD) and channel estimation (CE) in massive machine-type communications (mMTC) scenario with a massive multiple-input multiple-output (MIMO) base station. Besides sporadic traffic of massive access, the block sparsity of angular domain channel in massive MIMO is additionally considered to reduce the pilot overhead. By formulating the joint AUD and CE problem by sparse Bayesian learning with the double-sparsity feature in the angular domain, we introduce the modified pattern-coupled prior model to capture the non-uniform block structure. Then an algorithm based on variational Bayesian inference is proposed to recover the angular-domain channel. Moreover, a novel AUD scheme using the recovered angular-domain channel is developed by exploiting the high array gain in massive MIMO systems. Numerical results demonstrate that our proposed method can significantly improve the channel estimation quality and detection accuracy especially in the low pilot-length region.

A massive multi-input-multi-output (MIMO) system can bring substantial improvement in spectral and energy efficiency for wireless communication systems. The high array gain and fine spatial resolution allow the utilization of relatively simple processing at the base station, such as the near-optimal regularized zero-forcing (RZF) precoding. Nevertheless, such precoding schemes require computing the inverse and multiplication of matrices, which leads to a large burden for baseband processing when the system dimension grows large. To take full advantage of the spatial sparsity of massive MIMO channels in the finite scattering environment, we propose a virtual channel precoding strategy that directly utilizes the sparse virtual channel representation (VCR) in precoding. Furthermore, we develop a virtual channel RZF precoding algorithm based on the pre-conditioned conjugate gradient method, namely PCG-VC-RZF, which can directly use the sparse virtual channel matrix while avoiding the complex computation of the Gram matrix. The computational complexity and transmission delay are shown to be reduced tremendously by the proposed algorithm when operating in a large-dimension system. We further provide a beam selection strategy for improving the performance of the proposed approach under a complexity constraint. The simulation results verify the efficiency of the proposed precoding scheme.

In millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and increase spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid architecture has attracted extensive attention. Channel estimation and beamformer design based on the hybrid architecture are two critical techniques. Motivated by the advantages of sparse recovery, we propose an algorithm based on compressive sensing (CS) for channel estimation to estimate the dominant paths in the sparse mmWave channel. Moreover, benefited by the limited components of multipath in the angle-domain, an overhead-efficient method for hybrid beamformer design without singular value decompose (SVD) for obtaining the optimal hybrid precoder is proposed followed by the mmWave channel estimation, and only the indices of orthogonal array response vectors of the estimated dominated paths need to be transmitted as the feedback information for hybrid precoder design. Numerical experiments demonstrate that the proposed algorithm for channel estimation can recovery the channel matrix by estimating the parameters of dominant paths. Through the theoretical analysis and numerical experiments, we prove that the proposed algorithm for hybrid beamformer design can perform near-optimal spectral efficiency. Finally, we analyze the feedback overhead cost by the proposed algorithm and prove that the proposed algorithm can significantly reduce the feedback overhead.

In recent years, Internet of Things (IoT) has become one of the most important technologies in academia and industry. However, in many application scenarios, smart devices are distributed over a remote area without terrestrial communication systems. Considering the power of smart devices is limited, it is infeasible for terrestrial access networks to effectively receive the data of smart devices. Therefore, as a supplement to the terrestrial networks, Space-Air-Ground networks are critical to the Internet of Remote Things (IoRT). In this paper, we use high-altitude platforms (HAPs) to assist data transmission from smart devices to low earth orbit (LEO) satellites. Aiming at minimizing system power consumption, we propose an algorithm that jointly optimizes the resource allocation scheme and the HAP relay deployment. Since the problem is a mix-integer non-convex programming which is prohibitive to solve, our proposed algorithm divides it into two sub-problems to find a near-optimal solution with low computational complexity. Simulation results show that compared with average resource allocation scheme, our proposed algorithm can significantly reduce the system power consumption while satisfying the rate requirements of smart devices.

In multi-user millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid precoding as a crucial technique has attracted extensive attention. Most previous works for hybrid precoding developed algorithms based on optimization or exhaustive search approaches that either lead to sub-optimal performance or have high computational complexity. Motivated by the thought of cross-fertilization between Data-driven and Model-driven approaches, we consider applying deep learning approach and introduce the Hybrid Precoding Network(HPNet), which is a compressed deep neural network exploiting the feature extracting (thanks to convolutional kernels) and generalization ability of neural networks and the natural sparsity of mmWave channels. The HPNet takes imperfect channel state information (CSI) as the input and predicts the analog precoder and baseband precoder for multi-user massive MIMO systems. Moreover, in order to make the approach more practical in real scenarios, we further introduce a model compression algorithm, using network pruning, to greatly reduce the computational complexity and memory usage of the neural network while almost retaining the model performance and then assess the influence of pruned parameters in the network. Numerical experiments demonstrate that HPNet outperforms state-of-the-art hybrid precoding schemes with higher performance and stronger robustness. Finally, we analyze and compare the computational complexity of different schemes.

In multi-user millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and spectral efficiency significantly. In this paper, we focus on the maximization of the weighted sum rate and the global optimization of the max-min fairness problem, which are not only important but also very hard to find the global optimal solutions due to their nonconvexity property. Some previous works on the power allocation for maximization of the weighted sum rate usually proposed methods that either have high complexity or achieve sub-optimal performance. And there are few previously developed algorithms for finding the global optimal solution of the max-min fairness optimization problem. Based on a quadratic transform (QT) technique, which is a novel fractional programming (FP) approach, we introduce efficient iterative algorithms with low complexity to find optimal solutions of the two typical and difficult power allocation problems in wireless communications. Simulation evaluations demonstrate that our proposed algorithms are able to provide the optimal solutions and outperform previous methods in both performance and computational complexity.

As the development of wireless communication system, high altitude platform(HAP) plays an important role in forwarding signal in the stratosphere and Ka-band attracts more and more attention because of larger transmission bandwidth. However, ka-band is more sensitive to the troposphere weathers. For Ka-band based HAP communication system, considering the scenario where the user moves over a large area and moves for a long time, the multi-states statistical channel model affected by the troposphere weathers and ground environment is proposed in this paper. Assuming that terrestrial terminal is in three scenarios, there are three state fading which are modeled by a Markov chain in each scenario. Firstly, the probability distribution function (PDF) of the received signal is analyzed; Secondly, a step-by-step methodology generating time-series of proposed channel model is presented; Finally, based on the proposed channel model, the time-series and the bit error rate (BER) performance are obtained.

Low Earth Orbit (LEO) satellite networks play a major role to provide communication support for the regions beyond the coverage of terrestrial network systems. The positions of gateway stations have influence on the time delay, power consumption and throughput of LEO satellite networks, making it important to deploy the gateway stations at the best position. Aiming at minimizing the inter-satellite hop count, we are the first to study the deployment problem of gateway stations. The gateway deployment problem is formulated as a non-convex optimization problem. According to the characteristics of the deployment problem, we propose an improved genetic algorithm (GA) and an improved simulated annealing algorithm (SA) not only to get the optimal deployment location, but also to greatly reduce the computational complexity. Simulation results and analyses show that compared with random deployment, the optimal locations of the gateway stations obtained by the proposed algorithms can decrease 42.6% average hop count of inter-satellite links and increase 4.4% average capacity of feeder links.

The spread spectrum signal detection problem is examined in colored noise with an unknown covariance matrix. When the receiver is equipped with a symmetrically spaced linear array, persymmetry exists in the received data. We exploit the persymmetric structures to design adaptive detectors according to the principles of generalized likelihood ratio test (GLRT), Wald test, and Rao test. It turns out that the proposed GLRT has the same form as the proposed Wald test, and the Rao test does not exist. We prove that the proposed detector exhibits a constant false alarm rate against the unknown noise covariance matrix. Numerical examples demonstrate that the proposed detector has better performance than its non-persymmetric counterpart.

Wireless Sensor Networks (WSNs) have gained global attention in recent times due to their vast applications in various fields. These networks can face the disruption of data transmission due to sensor node failures when placed in harsh, inaccessible, and adverse environments such as battlefields or monitoring in enemy territory. The specific tasks performed by the collaboration among the sensor nodes in WSNs by internode connectivity may be terminated. Besides this, due to the failure of sensor nodes, the area covered by the network may be limited, which can cause damage to the objectives for such a network, as there might be an unaware danger in the lost area. Connectivity is a big problem in mobile WSNs due to the mobility of nodes. Researchers have developed a lot of algorithms that are capable enough for connectivity problems, but they do not emphasize the loss of coverage. We try to fill these gaps by proposing the new hybrid algorithm PACR (Position-Aware protocol for Connectivity Restoration). The concept behind PACR is the same as a person who writes his will before death on a deathbed. In the same way, when the sensor energy is below the threshold, it is converted into a recovery coordinator and generates a recovery plan. This accelerates the recovery by decreasing the time needed for failure identification. For the recovery process, the neighbor’s nodes do not travel to the exact position of the failed node. Instead, they just move to the distance where they can build communication links with other nodes. This greatly prolongs the network lifetime. The simulation results show that PACR outperforms other techniques present in the literature.

Low Earth Orbit (LEO) satellites play a central role in Earth observation systems. Due to high-speed movement, LEO satellites have very limited contact time with earth stations (ESs). Satellites that store too much data need to wait until they reconnect to ESs. In this case, the system may not meet the delay requirement. Existing works have scheduled the data downloading from satellites to ESs and made use of inter-satellite links (ISLs) to offload data among other satellites. However, such algorithms only maximize the overall throughput but neglect the truth that energy is always a scarce resource for satellites. To solve this problem, based on the proposed enhanced superposed multi-power level multi-transmission (ESMLMT) graph, we propose an iterative algorithm to minimize the energy consumption of data transmission while maximizing the throughput and satisfying the delay requirement. It is shown by simulations that our proposed algorithm achieved significantly higher energy efficiency without data downloading throughput performance degradation in satellite systems.

Multiple access technology is facing a great challenge posed by the upcoming massive Machine-Type Communication (mMTC). Traditional grant-based random access (RA) schemes are not competent to tackle the massive connectivity and small infrequent data packets of mMTC, because of the intolerable signalling overhead and delay. Grant-free random access joint with non-orthogonal multiple access (NOMA) may be suitable for the mMTC services as it reduces the signaling overhead. However, user collisions are still possible and damage the system's performances. In this paper, we propose a grouping-based grant-free random access based on statistical user rate information in which the base station (BS) divides MTDs into several groups according to the users' rate distribution and allocates proper radio resources for each group. BS broadcasts this grouping determinations to all MTDs with a small amount of signaling and MTD then randomly selects a group to access the channel on their own instant data rates. We analyze the blocking rate and provide an optimization method for deciding the grouping policy. Simulations prove the proposed scheme outperforms the traditional non-grouping RA even if there are some estimation errors.

In 5G wireless communication network, massive machine type communication (mMTC) is an emerging research topic. For mMTC, non-orthogonal multiple access (NOMA) has been proposed to support its large-scale connectivity. Due to the sparsity of mMTC, compressed sensing based algorithms can be used to identify the active users and recover the sparse channel state information (CSI) vector. In this paper, we propose a Bayesian message passing algorithm based on expectation propagation (EP) for joint active user detection (AUD) and channel estimation (CE) in NOMA. The proposed method approximates the complicated target distribution with a Gaussian distribution to achieve linear complexity. Simulations demonstrate that the EP-based algorithm achieves better performance in joint AUD and CE than the exiting algorithms, especially in the low SNR regime.

Traditional low earth orbit mobile satellite systems (LEO-MSSs) cannot satisfy the demands of huge capacity in ultra-dense regions. In addition, due to the frequent passive group handover caused by high speed movement of LEO satellites, quality of service (QoS) can hardly be satisfied. In this paper, we propose an extensible multi-layer architecture model based on traditional LEO-MSSs to solve these problems. In the proposed architecture, we introduce high-altitude platforms (HAPs) and terrestrial relays (TRs) to cover ultra-dense regions and provide communication services. We also present the theoretical analysis of the system capacity and handover rate to evaluate the benefits brought by the proposed architecture. Moreover, we compare the system performance after adding HAPs and TRs in different scenarios. Simulation results validate that HAPs and TRs can increase capacity and reduce handover rate, and show how the capacity and handover rate are improved.

This correspondence presents a construction of quasicyclic (QC) low-density parity-check (LDPC) codes based on a special type of combinatorial designs known as block disjoint difference families (BDDFs). The proposed construction of QC-LDPC codes gives parity-check matrices with column weight three and Tanner graphs having a girth lower-bounded by 6. The proposed QC-LDPC codes provide an excellent performance with iterative decoding over an additive white Gaussian-noise (AWGN) channel. Performance analysis shows that the proposed short and moderate length QC-LDPC codes perform as well as their competitors in the lower signal-to-noise ratio (SNR) region but outperform in the higher SNR region. Also, the codes constructed are quasicyclic in nature, so the encoding can be done with simple shift-register circuits with linear complexity.

This correspondence presents a jointly designed quasicyclic (QC) low-density parity-check (LDPC) coded-relay cooperation with joint-iterative decoding in the destination node. Firstly, a design-theoretic construction of QC-LDPC codes based on a combinatoric design approach known as optical orthogonal codes (OOC) is presented. Proposed OOC-based construction gives three classes of binary QC-LDPC codes with no length-4 cycles by utilizing some known ingredients including binary matrix dispersion of elements of finite field, incidence matrices, and circulant decomposition. Secondly, the proposed OOC-based construction gives an effective method to jointly design length-4 cycles free QC-LDPC codes for coded-relay cooperation, where sum-product algorithm- (SPA-) based joint-iterative decoding is used to decode the corrupted sequences coming from the source or relay nodes in different time frames over constituent Rayleigh fading channels. Based on the theoretical analysis and simulation results, proposed QC-LDPC coded-relay cooperations outperform their competitors under same conditions over the Rayleigh fading channel with additive white Gaussian noise.

Routing remains a most challenging task in sensor networks because of constrained resources like battery power, processing, and memory. Many energy efficiency techniques for the sensor networks have been proposed, among which hierarchical routing is considered the most energy-efficient and extended network lifetime technique. This technique has a lesser number of transmissions in the network. On the contrary, zone-based routing claims lesser control and routing overhead on the overall network lifetime. In this research, a simulation-based comparison of zone-based routing with static clustering hierarchical routing is conducted. The simulation results show that the zone-based routing outperforms hierarchical routing with static clustering in terms of energy efficiency, network lifetime, and throughput.

This paper presents an energy-efficient technique to handle redundant traffic (EEHRT) in the zone-based routing for wireless sensor networks. In this technique, multihop routing is performed based on the remaining energy of the nodes. Afterwards, it performs position-based routing without the need for the nodes to know their respective position. The main objective of this paper is to handle the redundant packets generated in zone-based routing using short beacon messages. Nodes of lower zones route the data of the higher zone to base station (BS) with a minimum number of hops and utilize only those nodes on the path which are energy efficient and located closer to BS. Moreover, the source node is acknowledged by the relaying node using a wireless broadcast advantage (WBA) without sending any special ACK packet to the sender, which reduces the control overhead in the routing process. The EEHRT technique improves the routing against RARZ by ensuring only one copy of the packet is propagated at each hop along the routing path to BS. Simulation results show that EEHRT achieved 28% improvement in energy efficiency, 10% and 25% improvements in data throughput against total and distinct packet reception at BS respectively, 35% increase in overall network lifetime, and 100% reduction in redundant packets generation and propagation in the network against RARZ routing.

Human mobility is an interesting topic attracting researchers from various fields. In this paper, we investigate mobility of college students in campus during their full life cycle and find the demographic differences on the predictability of human mobility. We explore an extensive consuming record data set collected from a college campus. Specifically, our data set includes over 15 million consuming logs gathered from 4,741 students (with demographic information) from 2013 to 2016. We present and analyze the evolving patterns of mobility entropy by ages, academic majors, and genders. We find that entropy decreases with age increasing and entropy grouped by genders follows different distributions, which is opposite to previous studies. Our findings will supply novel insights in human mobility research and provide possible support for wireless networking design in campus.

Future fifth generation (5G) wireless networks will be challenged by the huge amount of mobile data traffic, especially from enormous Machine Type Communication(MTC) devices, so in this paper we proposed Aggregation Coding Multiple Access(ACMA) for MTC downlink transmissions, which exploits the inherent correlation among aggregated users in spatial domain to improve the spectrum efficiency. By assuming the data traffic together with the delay requirements of MTC devices can be perfectly predicted based on wireless big data analysis, the transmission order of multicast downlink data traffic from base stations to multiple terminals could be adjusted by proposed aggregation coding technique using much less timeslots. We then evaluated the performance of conditional random search(CRS), standard-row algorithm(SRA) and Genetic Algorithm(GA) to optimize the data traffic transmission order compared to Multimedia Broadcast Multicast Service (MBMS) and simulation results validated the performance of ACMA and the optimization of GA. Our work shed light on dealing with massive MTC data traffic for future wireless communications.

The explosive growth of cellular data traffic demands poses a heavy burden to the cellular service provider (CSP), making it urgent to offload part of the cellular traffic through already-deployed third-party owned small cells. Auction based offloading is a promising method of traffic offloading, but the existing related works haven't considered the utilization efficiency of the leased spectrum resource. In this paper, we present and analyze a reverse auction to deploy an innovative market for tradeoff between the lower leasing cost and larger amount of offloading traffic, aiming to maximize the benefit of CSPs. Furthermore, we propose a greedy offloading mechanism to reach the tradeoff and spectrum efficiency with low complexity. The simulation results demonstrate that the proposed greedy mechanism can make a good compromise between the offloading cost and the offloading traffic.

Massive machine-type communications (mMTC) are expected to play an essential role within future fifth generation (5G) wireless network. To deal with the challenge that a serving cell need to accommodate a massive number of MTC users, user clustering may become a promising scheme. In each cluster, there is a MTC header (MTH) directly connecting with the base station (BS) while MTC users in the cluster connect to the MTH with sparse code multiple access (SCMA). However, because all MTC users work in a distributed way, sometimes more conflicts than expected on each radio resource may occur, leading to high receiving complexity, severer interference and even outage. To tackle the problem, we propose a new method of uplink and downlink resource reuse (UDRR) at MTH under the constraints of BS-MTH downlink capacity. The simulation shows that UDRR can increase the uplink connectivity and improve the block probability effectively.

Cloud radio access network (Cloud-RAN) is a promising wireless network architecture that can satisfy the fast growing mobile data traffic and improve the performance of Internet of Things. In this paper, we propose an energy-efficient resource allocation scheme based on heterogeneous Cloud-RAN jointly considering the remote radio head (RRH) antenna resource with baseband unit (BBU) computation resource. We formulate our joint resource allocation problem and decompose it into two subproblems. The first subproblem is a network-wide beamforming vectors optimization problem, and it is solved by weighted minimum mean square error approach. Based on the optimized beamforming vector, we propose an algorithm to get the RRH-user equipment clusters. The second subproblem is a BBU scheduling problem, and we reformulate it as a bin packing problem which aims to minimize the number of BBUs in working model to save more energy. Compared to some existed works which form the BBU scheduling problem as a bin packing problem, we propose a bin packing algorithm based on the best-fit-decreasing method, which has better performance. With simulation results and detailed analysis, the system performance of our proposed joint resource allocation scheme is verified, which is more energy-efficient than other existing schemes.

In this paper, we focus on the energy efficient joint resource scheduling scheme in time varying Cloud-RAN with delay sensitive traffic. We jointly consider the computation resources provided by baseband units (BBUs), which are modeled as the data processing rate of each virtual machine (VM) provided by the BBUs, and the antenna resources provided by the remote radio heads (RRHs), which are modeled as the beamforming vectors for each user equipment (UE) considering the limited fronthaul capacity and per-UE QoS requirement. Based on the Lyapunov optimization method, we divide the original problem into two subproblems, i.e., the BBU processing rate scheduling problem and the network-wide beamforming strategy problem. The first subproblem can be formulated as a convex programming problem, and we can solve the second subproblem with a weighted minimum mean square error (WMMSE) approach. With the detailed theoretical analysis and simulation results, it is clear that we can achieve a trade- off between energy efficiency and traffic delay, which can be controlled by the control parameter V.

Dense femtocell deployment in macrocell system is an important tendency in future cellular networks due to the increasing demand in indoor communications, where the cross-tier interference mitigation between the macrocell and femtocells is one of the most critical issues. However, the current studies mostly focus on the cross-tier interference in two-dimensional scenarios with a few works considering the tridimensional cell. In this paper, based on the traditional fractional frequency reuse (FFR) method, we propose a novel tridimensional frequency reuse (TFR) strategy to mitigate the cross-tier interference in two-tier femtocell networks. We analyze the performance of the proposed strategy and compare it with the traditional FFR method and the original co-channel method. Simulation results demonstrate the effectiveness of our proposed strategy on interference mitigation in the tridimensional cell.

The explosive growth of cellular network users and traffic stimulates the current network architecture to be more spectral-, energy-, and operation efficient and flexible. In this paper, we propose a transformium network architecture comprising transformium core, body and cloud, where the core dynamically schedules the body and cloud orchestrated with wireless network environment. To enable the transforming ability, some new emerging technologies, including software defined networking, network virtualization are employed in the proposed architecture. As a consequence, the transforimium network is a promising network architecture to improve the network capacity, quality of service and solve the network ossification problem.

Considering a heterogeneous cellular network where macro base stations (MBSs) are powered by grid power and small base stations (SBSs) are powered by renewable energy. We investigate the grid energy minimization problem by optimizing both the MBS active probability (MAP) and the SBS transmit power (STP) under the constraints of user quality of service (QoS) and renewable energy. Using tools from stochastic geometry, we first derive the expression of the service outage probability to evaluate user QoS. And then, the impacts of the MAP and the STP on the service outage probability are characterized. At last, the optimization problem minimizing the network grid energy consumption is formulated, and the closed expressions of the optimal MAP and STP are derived by investigating the monotonicity of the service outage probability. Numerical results show that our strategy of jointly optimizing the MAP and the STP is superior to the strategy of merely considering MBS sleeping with fixed SBS transmit power, and the superiority is more attractive with lower circuit power of SBS.

Satellite communication plays an important role in the ubiquitous global communication. However, for a long time, the communication provided by satellite suffers from serious signal attenuation and transmission delay. Conventional techniques such as HARQ couldn't provide timely service unless powerful channel codes are used. Once channel code fails, retransmission will be inevitable, and unbearable delay may be incurred. Fortunately, things started to change when Luby proposed the landmark LT code. As a practical implementation of fountain code, LT code avoids frequent feedback during the transmission. However, due to the high packet error rate (PER), the original LT code couldn't work stably in satellite communication. In this paper, we propose a novel hybrid decoding scheme for LT code. It consists of two decoding procedures: normal decoding using BP algorithm and mining decoding. The idea behind mining decoding is to release as many corrupted original packets as possible and combine the "identical" ones as in HARQ. Actually we develop three types of packet mining techniques to make a tradeoff between performance and computation complexity. The final simulation shows that, the proposed hybrid decoding scheme makes LT code work more stably, and achieves significant advantages over HARQ in terms of delay.

The current cellular networks have evolved to be more randomly, irregularly, and heterogeneously deployed to meet the exponential growth of mobile data traffic and the demand for seamless coverage, making the signal-to-interference ratio (SIR) distribution more challenging to analyze. Therefore, in this paper, we propose two simple approximative approaches to the SIR distribution of general heterogeneous cellular networks (HCNs) based on the ASAPPP method, which stands for “approximate SIR analysis based on the Poisson point process” and the MISR (mean interference-to-signal ratio)-based gain for each individual tier of the HCNs. Specifically, we first establish a per-tier ASAPPP approximation to general HCNs and then present an effective gain ASAPPP method as a further simplification when the path loss exponents are the same for all the tiers, that is, we give an explicit expression for the effective gain Geffof general HCNs such that the SIR distribution is obtained by scaling the SIR threshold θ to θ/Geff. The asymptotic behavior for the tail of the SIR distribution is also given. Furthermore, to highlight the simplicity and effectiveness of the approximative approaches, we derive the exact distribution of the SIR in the two-tier HCNs modeled by β-Ginibre and Poisson point processes and compare it with the approximate results. The results demonstrate that the proposed approaches give a simple yet excellent approximation for the SIR distribution.

The crushing demands for mobile data traffic drive the current cellular networks to become more heterogeneous, making the signal-to-interference ratio (SIR) distribution more difficult to analyze. In this paper we propose a simple approximative approach to the SIR distribution of heterogeneous cellular networks (HCNs) based on the ASAPPP method which stands for ``approximate SIR analysis based on the Poisson point process'' and the MISR (mean interference-to-signal ratio)-based gain for each individual tier of the HCN. The results demonstrate that this approach gives a tight approximation and asymptotically a lower bound for the coverage probability.

In this paper, we consider the delay tolerable data traffic transmission in Heterogeneous Networks (HetNet) and propose a flow-level-delay constraint small cell sleeping scheme to achieve energy saving gain. We use the flow-level model to capture the process of data transmission in micro base station (BS) which is the small cell we consider. Our scheme takes advantage of the feature of HetNet and designs macro BS cooperation in the process of dynamic micro BS sleeping, which helps achieve more energy saving. Through the Markov state transfer diagram and solving the balance equation, we get the numeric performance of power consumption and delay in our scheme. Then we solve a cost function minimization problem to get the optimal energy-delay tradeoff curve which helps to get the sleep scheme parameter settings. Our work cast light on how the energy consumption and delay performance is affected by dynamic sleep control and HetNet environment. And finally simulation shows that our proposed scheme can achieve better energy saving.

In this paper, we conduct a detailed analysis on voice traffic intensity measurement of large-scale mobile communication base stations. Our data set tracks over 700 base stations inside one city in Southern China over a continuous period of 18 days. We cluster base stations with k-means method based on their average traffic and the result shows four distinctive clusters, among which one cluster consisting of base stations near university campuses reveals unique calling behavior of college students. The spatial characteristics of base station traffic from global and local perspectives demonstrates that the highest global spatial correlation is around 6 AM and local non-stationarity is identified. Our insights of spatial-temporal traffic dynamics in mobile network can be leveraged into network monitoring and resource management.

For heterogeneous cellular networks (HCNs) sharing the same spectra, the connection of high-mobility user equipment (UE) is a problem due to its short residence time in micro cell. In this paper, we try to reduce the handovers brought by high-mobility UEs through bandwidth reservation and connection admission control (CAC). We propose three modes of joint bandwidth reservation and CAC, from the points of increasing the residence time and providing large bandwidth for the high-mobility UE. Some high-mobility UEs in the micro cell can be connected to the macro BSs for larger residence time and the rest high-mobility UEs in the micro cell are connected to micro access points (APs) for much larger bandwidth. To deal with the tradeoff between network capacity and handover rate, a global network utility function is formulated based on stochastic geometry modeling for bandwidth reservation and CAC. A gradient descent method is given to obtain the optimal solutions of reserved bandwidths in macro BS and micro AP and the admission probability of high-mobility UE. It is observed from extensive simulation that these modes can significantly improve the global network performance and the third mode has the best performance.

Due to its tractability, a multitier model of mutually independent Poisson point processes (PPPs) for heterogeneous cellular networks (HCNs) has recently been attracting much attention. However, in reality, the locations of the BSs, within each tier and across tiers, are not fully independent. Accordingly, in this paper, we propose two HCN models with inter-tier dependence (Case 1) and intra-tier dependence (Case 2), respectively. In Case 1, the macro-base station (MBS) and the pico-base station (PBS) deployments follow a Poisson point process (PPP) and a Poisson hole process (PHP), respectively. Under this setup and conditioning on a fixed serving distance (distance between a user and its nearest serving BS), we derive bounds on the outage probabilities of both macro and pico users. We also use a fitted Poisson cluster process to approximate the PHP, which is shown to provide a good approximation of the interference and outage statistics. In Case 2, the MBSs and the PBSs follow a PPP and an independent Matern cluster process, respectively. Explicit expressions of the interference and the outage probability are derived first for fixed serving distance and second with random distance, and we derive the outage performance, the per-user capacity, and the area spectral efficiency (ASE) for both cases. It turns out that the proposed Case 2 model is a more appropriate and accurate model for a HCN with hotspot regions than the multitier independent PPP model since the latter underestimates some key performance metrics, such as the per-user capacity and the ASE, by a factor of 1.5 to 2. Overall, the two models proposed provide good tradeoffs between the accuracy, tractability, and practicability.

The spatial structure of transmitters in wireless networks plays a key role in evaluating mutual interference and, hence, performance. Although the Poisson point process (PPP) has been widely used to model the spatial configuration of wireless networks, it is not suitable for networks with repulsion. The Ginibre point process (GPP) is one of the main examples of determinantal point processes that can be used to model random phenomena where repulsion is observed. Considering the accuracy, tractability, and practicability tradeoffs, we introduce and promote the β-GPP, which is an intermediate class between the PPP and the GPP, as a model for wireless networks when the nodes exhibit repulsion. To show that the model leads to analytically tractable results in several cases of interest, we derive the mean and variance of the interference using two different approaches: the Palm measure approach and the reduced second-moment approach, and then provide approximations of the interference distribution by three known probability density functions. In addition, to show that the model is relevant for cellular systems, we derive the coverage probability of a typical user and find that the fitted β-GPP can closely model the deployment of actual base stations in terms of coverage probability and other statistics.

In heterogeneous cellular networks (HCNs), the macrocell network is usually assumed to be overlaid by multiple independent tiers of small cells. However, in reality, the locations of the base stations (BSs) belonging to different tiers are not fully independent. Accordingly, in this paper, we propose a two-tier HCN model with inter-tier dependence, where the macro-BS (MBS) and the pico-BS (PBS) deployments follow a Poisson point process (PPP) and a Poisson hole process (PHP), respectively. Under this setup, we derive bounds on the outage probabilities of both macro users and pico users, and then use a fitted Poisson cluster process to approximate the PHP, which is shown to provide a good approximation of the interference and outage. The results show that the model with inter-tier dependence appears closer to the real deployment than the two extremes with full regularity (the triangular lattice) and complete randomness (the PPP). An important conclusion is that significant gains can be obtained if PBSs are deployed smartly (away from MBSs).

In order to allocate frequency resource among users in heterogeneous Cloud-RAN (Het-Cloud-RAN) as well as avoid the co-channel interference, we propose a traffic-aware graph-based dynamic frequency reuse scheme. Cloud-RAN is a new network structure comprised of baseband units (BBU) and remote radio heads (RRH), which has the ability to reduce the energy costs of network. Two kinds of RRHs, including macro RRHs and pico RRHs, are considered to form the two tiers of the Het-Cloud-RAN. We construct the interference graph where the vertices represent different RRHs in the network, and then apply our proposed scheme in it. We use a graph coloring method to allocate different length of bandwidth to cells in different tiers based on their traffic demands. Our scheme also considers the time varying traffic and can adjust the frequency allocation based on the change of traffic demands in each cell without re-performing the whole scheme. With the application of our scheme in Het-Cloud-RAN, we can not only improve the spectrum utilization and efficiency, but also reduce the energy consumption through reducing the number of working BBUs. The system level simulation results show clearly that our new scheme in Het-Cloud-RAN outperforms the existing frequency allocation scheme based on traditional cellular system in many ways including throughput and energy efficiency.

One of the most critical issues in two-tier femtocell networks is the inter-cell interference management, in which Femtocell Base Station (FBS) locating in the edge area of the Macrocell Base Station (MBS) coverage suffers from interference produced by adjacent Macrocell User (MU), making this area a “Dead Zone” to the FBS. In this paper, we study the uplink cross-tier interference for “Dead Zone” problem in closed access femtocell networks from the aspect of resource allocation based on orthogonal frequency division multiple access (OFDMA). We propose a resource-reallocation based strategy to mitigate the cross-tier interference, which reassigns the subchannels and power among MUs and the impaired FUs after the latter ones handover from the serving FBS to the MBS. Theoretical analysis and simulation results confirm that higher throughput can be achieved by our proposed strategy.

The forwarding activities of Pocket Switched Network (PSN) under social scene has attracted increasing attention, which are deeply affected by the social features of PSN nodes, especially the social network structure and characteristics of selfishness among nodes that jointly construct their behavior model under an opportunistic forwarding environment. Through both analysis and simulation, we investigate how social network structure and node selfishness influence data forwarding activities of PSN nodes based on our proposed contact-and-cooperative model. Simulation results obtained by PROPHET algorithm show that PSN forwarding performance, including delay time and packet delivery ratio, are improved by the introduction of social network structure. Further, the differentiation of node selfishness according to nodes' relationships on social structure level also displays intrinsic benefit in network performance in the presence of the node selfish behavior that is inevitable in real world. The results also show that hub nodes in the network, which undertake heavy relay mission, may play a more important role in forwarding activities under our model with social selfishness compared with unconditionally cooperative cases.

This work aims to apply a new dynamic frequency reuse scheme based on fractional frequency reuse (FFR) in Cloud-RAN structure. FFR is one of the most efficient schemes to mitigate the inter-cell interference in OFDM system, while strict FFR in traditional cellular system is still facing some problems such as the inefficient use of spectrum. Cloud-RAN is a new network structure comprised of baseband units (BBU) and remote radio heads (RRH), which has the ability to reduce the energy costs of network. We use a graph coloring method to allocate spectrum resource among different cell zones depending on different traffic demands as well as avoiding the inter-cell interference. With the application of our proposed scheme based on Cloud-RAN structure, we can not only improve the spectrum utilization, but also reduce the energy consumption through reducing the working number of BBUs. The simulation results show clearly that our new scheme in Cloud-RAN structure can achieve higher throughput and energy efficiency compared with the strict FFR in traditional cellular system.

Time-varying property of practical wireless channels affects the performance of cooperative protocol in multi-relay networks. In this paper, we evaluate and compare four decode-and-forward (DF) retransmission strategies over time-correlated Rayleigh channels, which are the combinations of two relay selection (RS) schemes and two repeater switching patterns: RS can be based on either statistical or outdated channel state information (CSI); in the static switching pattern all forwardings are executed by the same relay that is selected at the beginning, while in the dynamic pattern relays that have not recovered the source message keep receiving from the forwarding relay to extend the decoding subset, from which the activated relay for next retransmission is reselected. Numerical simulations validate that statistical information is more beneficial than outdated CSI over weakly correlated channels. On the other hand, the dynamic switching pattern generally outperforms the static one especially with relatively large maximum transmission number, at the cost of higher complexity of decoding and signalling.

This paper evaluates the idle users' cell selection performance for 3GPP LTE system. The focus is on the impact that reference signal received power (RSRP) strength based cell selection algorithm have on the system performance measured in terms of number of cell reselections, time between two consecutive reselections and uplink SINR for an idle user to experience a reselection. Further, the call blocking probability when idle users initialize calls is studied as another important system performance indicator. Some cell selection algorithms based on RSRP measurements have been implemented in a dynamic system level simulator and have been studied for different parameter sets in a 3GPP LTE recommended simulation scenario. The results suggest some proper parameters set which can make good tradeoff between the average number of cell reselections and the average uplink SINR.

Micro base station (BS) is introduced to the cellular network to improve both system capacity and energy efficiency. This paper develops a traffic-aware micro BSs planning scheme that takes both physical and traffic layer characteristics into account. We formulate a general problem, which is a challenging combinatorial problem, pertaining to total BS number minimization while satisfying the requirement of inhomogeneous traffic in the network. To solve the problem efficiently, we propose a greedy algorithm which is shown to be a constant-factor approximation of optimal deployment under the proven of the submodularity of a function related with the system capacity. Extensive simulations under various traffic configurations demonstrate the performance of the proposed algorithm and potential performance gains that can be achieved.

In cellular/WLAN interworking, WLAN is usually deployed to offload the heavy traffic in hot spots. To improve the offloading efficiency, we proposed a new WLAN-first scheme with service rate differentiation and analyzed its performance. The analytical results are validated by computer simulation. It is observed that, under the premise of QoS guarantee, the proposed scheme can significantly improve the throughput compared with the existing WLAN-first scheme. And the proposed scheme is easy to implement as the AP could decide the admission of a TIE based on its RSS (received signal strength) and the admission region, which could be computed off-line according to the analysis in this paper. The proposed scheme could be used for the future cellular/WLAN integration and may provide some insights for future extension.

Vertical handover decision (VHD) and radio resource allocation for the handover TIE (RRA) are two important issues in the integration of heterogeneous wireless networks, which are mainly considered separately for network utility problems, although both of them interact from the perspective of temporal dimension. The ergodic utility based joint-optimization algorithms should outperform that independent ones in the sense of optimal resource configuration, although that are generally more complex due to dealing with a more huge state space. In this paper, we proposed a distributed algorithm to immensely decrease the complexity of this joint-optimization algorithm with slightly performance decay, which validated by simulation, and the simulation results demonstrate that separately considering VHD or RRA for ergodic utility maximization has feeble improvement, while jointly consideration shows more improvement.

In this paper, we study energy-efficient channel aggregation problem in cognitive radio networks, where a secondary user senses multiple channels simultaneously or sequentially in the sensing period and subsequently aggregate those channels sensed free. We aim at designing the optimal sensing time and power allocation to achieve the maximum energy efficiency, meanwhile considering the maximum transmit power and minimum rate constraints as well as the protection to primary users. Taking into account the dynamics of the primary users' activities and the impact of imperfect spectrum sensing, we formulate the above design problem as a sum-of-ratios problem and solve it by applying the theory of nonlinear fractional programs. Numerical results validate the optimality of our energy-efficient design. Moreover, the impacts of the spectrum sensing approaches, the maximum number of sensed channels and the maximum power and minimum rate constraints on energy efficiency are also investigated respectively.

Though cooperative relaying is believed to be a promising technology to improve the energy efficiency of cellular networks, the relays' static power consumption might worsen the energy efficiency therefore can not be neglected. In this paper, we focus on whether and how the energy efficiency of cellular networks can be improved via relays. Based on the spatial Poisson point process, an analytical model is proposed to evaluate the energy efficiency of relay-assisted cellular networks. With the aid of the technical tools of stochastic geometry, we derive the distributions of signal-to-interference-plus-noise ratios (SINRs) and mean achievable rates of both non-cooperative users and cooperative users. The energy efficiency measured by “bps/Hz/W” is expressed subsequently. These established expressions are amenable to numerical evaluation and corroborated by simulation results.

To achieve highly efficient and flexible spectrum sharing in cognitive radio networks, we proposed a queuing method in combined channel aggregation and fragmentation (CAF) strategy for dynamic spectrum access (DSA). We derived the balance equation and evaluated various system performance metrics including blocking probability, dropping probability, spectrum utilization, throughput and mean queuing time by a continuous time Markov chain (CTMC) model. Numerical results show that this CAF with queuing strategy greatly lowers the dropping probability without obviously impacting the blocking probability, and slightly increases the spectrum utilization and throughput of the secondary network compared with the non-queuing case. Moreover, by tuning the bandwidth requirement of each secondary user, different system performance can be achieved.

Cooperative communication with network coding can yield higher system diversity gain and improve the efficiency of cooperation, for which designing the proper and efficient coding coefficients is one key technique for implementation. The important contributions of our work reveal the existence of coding coefficient matrix of order M constructed in GF(M = 2n) and propose practical and effective method to search such matrix. An improved genetic algorithm solution is proposed by introducing the local gradient search mechanism for large searching space and the search results show that the proposed method can obtain satisfying solutions and better computing performance for M = 8 in GF(8). The discussion on improved local gradient search mechanism illustrates its underlying potentials in global search framework. This work gains an insight into the coefficient matrix construction for cooperative communication with network coding in GF(2n).

Energy efficiency in cellular networks has recently received a significant attention and incorporating cooperative relays in cellular networks is believed to be a promising technology to greatly lower the energy consumption of cellular networks while sustaining high data rates. In this paper, we focus on improving energy efficiency of a single cellular system assisted by relays. We propose a novel relay deployment strategy and a corresponding relay selection procedure. Using coded cooperation as the transmission scheme, we derive the area power consumption under a target area spectral efficiency. Besides, the impact of different parameters such as cell radius, target area spectral efficiency, number and position of relays on the area power consumption is also studied. Compared with direct transmission and decode-and- forward (DF) relaying, our work significantly improves the energy efficiency of cellular systems and sheds key insights into the tradeoff between the energy consumption and the spectral efficiency via dynamically adjusting the resource allocation between BS and relay in coded cooperation.

Mobile terminals in heterogeneous wireless networks continuously undergo network selection within the initial access process and handover process. In order for a mobile terminal to be connected to a network in the best possible way in terms of QoS performance and energy consumption, this paper presents a novel method that takes into account user preferences, network conditions, QoS and energy consumption requirements in order to select the optimal network which achieves the best balance between performance and energy consumption. The proposed network selection method incorporates the use of fuzzy logic because of the available sources of information from different radio access technology (RAT) are qualitatively interpreted and heterogeneous in nature, and adopts different energy consumption metrics for real-time and non-real-time applications. Finally, simulations confirm our scheme's suitability and effectiveness.

Adaptive Relay-Assisted/ Direct Transmission (ARDT) proposed in [13] is a simple and efficient protocol which adaptively utilizes the direct link between two sources with only channel state information at receiver (CSIR), and it validly enhances the spectrum efficiency of time division broadcast (TDBC) system. In this paper, we combine ARDT with joint power allocation and relay selection for multi-relay scenario. Each relay optimize its own forwarding power to maximize the minimum end to end signal to noise ratio (SNR) towards two sources, and the optimal relay is then selected to cooperate. System outage probability, average throughput and average bit error rate (BER) are analyzed with theoretical upper and lower bounds. Numerical simulations validate the rationality of bounds, and show that whether maximal ratio combining (MRC) is adopted for receiving makes little difference. The proposed relay selection scheme is verified to outperform that based on original TDBC in reference work.

In cellular networks, base station must reserve some resource for the possible handoff which affects the energy efficiency of the system. In this paper, we establish a theoretical model to maximizing the energy efficiency (EE) from the aspects of users' mobility and service duration. After analyzing the probability of handoff in service with user mobility and service characteristics, we introduce a dynamic energy efficiency (DEE) function. With the DEE function, an optimization problem under the constraint of maximal power is formulated to determine the optimal cell radius to maximize the DEE. We prove the existence of the unique optimal cell radius and propose an algorithm to solve the problem. Numerical results show the relationship between the optimal cell radius and different users' mobility and service characteristics, and indicate that the optimal radius is increasing both in the users' average velocity and in the average service duration. This work can provide a reliable theoretical basis for green design of cellular networks.

In this paper, performance of three-time slot (3TS) physical layer network coding (PNC) is investigated in interference-limited two-way relaying system. Effective lower and upper bounds are derived for important performance metrics like outage probability and average bit error rate (BER) with different modulation modes. Asymptotic behavior is also proposed to intuitively exhibit the trend of performance in high signal-to-interference and noise ratio (SINR) regime. We differentiate practical applications with different relay power for the bounds. A particular scenario with positional grid structure of sources, relay and co-channel interferers is established for numerical analysis, and the effects of some critical parameters, such as relay's location, forwarding capability and power allocation factor, are compared and discussed. The theoretic solutions are finally verified by simulation results.

In this paper, we study the communication between terrestrial source and destination terminals via a geosynchronous (GEO) multi-beam satellite in satellite mobile communication (SMC) system. The end-to-end transmission is modeled by a relaying process, in which inter-beam interference plays a key role due to frequency reuse among adjacent beam cells and side-lobe leaking of practical beam antenna. Through theoretic analysis we obtain the closed-form expressions of end-to-end outage probability and system achievable throughput as the major performance metrics. Both theoretic and simulation results, which coincide well with each other under diverse beam frequency planning, suggest that reuse factor of 1/3 is an appropriate compromise between system throughput and reliability in the GEO SMC system.

Though opportunistic relaying coded cooperation is known as an efficient cooperation scheme for cooperative multi-relay networks, the existing works have the disadvantage of low resource utilization or high system complexity. In this paper, we focus on how to achieve a balance between resource utilization and system complexity by proposing an novel opportunistic relaying coded cooperation with selective source-to-destination parity transmission over Nakagami-m fading channels without additional cost. We derive closed-form expressions of the outage probability and explicit upper bounds on bit error probability using the statistical characteristic of the signal-to-noise ratios (SNRs) and confirm that the proposed scheme outperforms both opportunistic amplify-and-forward (AF) and opportunistic decode-and-forward (DF). Simulation results on the unbalanced generalized Nakagami-m fading conditions show that both system complexity and resource utilization are ameliorated with a better performance compared to the previous works.

In this paper, we propose a new definition of energy efficiency with respect to a given target packet error rate (PER). It is shown that some previous reported definitions are actually special cases of our definition. Consequently we use the definition to analyze the energy efficiency of Multilevel quadrature amplitude modulation (MQAM) and find that for a specific transmission there exists an energy efficient optimal initial PER which is independent of the traffic QoS. Then we derive the optimal PER and corresponding transmission power, and analyze the properties under various channel gain conditions. We believe that the result may help improve the energy efficiency of adaptive modulation. Finally, we consider the delay constraints and propose an optimization method based on PER to achieve extra energy efficiency gain while satisfying the delay requirements.

SUMMARY Selfish behavior of nodes in opportunistic networks impact greatly on the network performance. Therefore, an effective mechanism should be proposed to resist such misbehavior and to stimulate the nodes willing to relay packets for other nodes toward their destinations. On the basis of the evolutionary reputation model proposed, this paper investigates the network performance, including message delivery ratio, average delivery delay, and average intermittent hops, under the influence of reputation degree and selfish degree. In addition, the reputation evolution process is described, which helps to understand the interaction among network nodes. Simulation results show that the opportunistic network adopting the model proposed can resist partially the node selfishness and maintain acceptable network performance. Copyright © 2011 John Wiley & Sons, Ltd.

Finding the minimum distance of low-density-parity-check (LDPC) codes is an NP-hard problem. Different from all existing works that focus on binary LDPC codes, we in this paper aim to compute the minimum distance of nonbinary LDPC codes, motivated by the fact that operating in a large Galois field provides one important degree of freedom to achieve both good waterfall and error-floor performance. Our method is based on the existing nearest nonzero codeword search (NNCS) method, but several modifications are incorporated for nonbinary LDPC codes, including the modified error impulse pattern, the dithering method, and the nonbinary decoder. Numerical results on the estimated minimum distances show that a code's minimum distance can be increased by careful selection of nonzero elements of the parity check matrix, or by increasing the mean column weight, or by increasing the size of the Galois field. These results support observations that have been made based on simulated performance in the literature. Finally, we provide an upper bound on the minimum distance for nonbinary quasi-cyclic LDPC codes.

In this paper, lifetime of user equipments in hotspots is prolonged by combining relay technology and power control policy. As to both cable relay and wireless relay scenarios, effective power control policies are adopted to maximize energy efficiency under the premise of guaranteeing QoS. In addition, the closed-form results for both power control and lifetime gain are obtained, which match well with numerical results. This work extends our understanding about the effect of relay technology on UE's lifetime prolongation.

In this paper, we investigate the utility-based resource allocation problem in OFDMA relay networks with service differentiation, where Rate Constraint (RC) and Best Effort (BE) services are supported. Our objective of resource allocation is to maximize the sum utility of BE service users, while guaranteeing that the utility of each RC service user is equal to one. A joint optimization problem for relay selection, subcarrier assignment and power allocation is formulated. Since the problem cannot be solved directly, we make continuous relaxation and solve it by Lagrangian dual method. The optimal allocation strategy is obtained using Karush-Kuhn-Tucker (KKT) conditions. As it is difficult to converge to the optimal solution, thus we further present a heuristic resource allocation algorithm with low complexity. Simulation results show that our proposed algorithm achieves higher utility of BE users and lower outage probability of RC users, and also yields a good tradeoff between system throughput and user fairness.

In this paper, we construct nonbinary quasi-cyclic low-density parity-check (QC-LDPC) codes whose parity check matrices consist of an array of square sub-matrices which are either zero matrices or circulant permutation matrices. We propose a novel method to design the shift offset values of the circulant permutation sub-matrices, so that the code length can vary while maintaining a large girth. Extensive Monte Carlo simulations demonstrate that the obtained codes of a wide range of rates (from 1/2 to 8/9) with length from 1000 to 10000 bits have very good performance over both AWGN and Rayleigh fading channels. Furthermore, the proposed method is extended to design multiple nonbinary QC-LDPC codes simultaneously where each individual code can achieve large girth with variable lengths. The proposed codes are appealing to practical adaptive systems where the block length and code rate need to be adaptively adjusted depending on traffic characteristics and channel conditions.

The mechanism for the formation of small-world networks is important but still unsolved. We proposed a network evolution model based on local interaction among rational individuals with fixed network dimensions. This model extends Barabasi's preferential attachment mechanism to consider two more realistic factors when choosing opponent to interact. Prisoner's dilemma game are utilized to model such local interaction between individuals. The edges of the network are regulated by one simple rule proposed which strengthen the edges with good interaction while weaken those with bad ones. Numerical results show that small-world network structure could be evolved.

In this paper, we propose a new double parameters controlled cross-layer adaptive resource allocation scheme for heterogeneous types of traffic in multiuser OFDM system. Unlike the existing resource allocation scheme, the proposed double parameters controlled scheme don't focus on the design of packet utility function (PUF), but by controlling values of two parameters: "delay compensatory factor" and "throughput compensatory factor" respectively, to achieve diverse levels of tradeoff between traffic QoS satisfaction and system power efficiency flexibly. The existing PUFs may be applied in the proposed scheme, where double parameters controlled scheduling is followed by subcarrier-power allocation, considering not only queue state, random packet arrival and delay in the MAC layer but also transmit power limit and time varying wireless channel in the PHY layer. In addition, PUFs tailored for a specific type of traffic may be suit to systems with other types of traffic as well as with heterogeneous types of traffic. Fairness among different types of traffic can be guaranteed as well. Simulations confirm the flexibility and efficiency by the proposed scheme.

In this paper, we propose a new fairness criterion based on QoS satisfaction of users. Unlike the existing throughput fairness criterion suitable for non-realtime traffic and the existing delay fairness criterion tailored for real-time traffic, the proposed QoS satisfaction criterion handles heterogeneous traffics in an unified fashion. Based on the new fairness criterion, we propose a cross-layer resource allocation scheme for downlink multiuser OFDM with heterogeneous traffics, where user scheduling is followed by subcarrier-bit allocation. The resource allocation depends on not only the channel state information (CSI) and QoS requirements of users, but also random packet arrivals and queue states. Simulations confirm significant performance improvement and user QoS satisfaction fairness by the proposed scheme.

Block-wise adaptive bit and power allocation in V-BLAST system with a target BER and a total transmit power constraint is discussed in this paper. While block-wise allocation scheme greatly reduces feedback overhead, its BER performance suffers from time-variance of channel and error propagation in V-BLAST detection. We analyze the BER performance of the block-wise adaptive V-BLAST system with different kinds of SIC ordering over time-varying channel and find the optimal SIC ordering scheme. In order to reduce the impact of error propagation, a new bit and power allocation algorithm based on error propagation compensation is proposed in this paper. Simulation results show, the new algorithm can effectively improve the BER performance of block-wise adaptive V-BLAST system, increase the block size and decrease feedback overhead

In this paper, a novel joint CDMA/RTDMA protocol for multimedia transmission in wireless networks is studied. In this scheme, the base station allocates terminals with code-slots instead of slots. Theoretical analysis of voice packet dropping rate and data delay are based on the M/M/n/m model and phase process respectively. Through simulation and numerical results, this new MAC protocol can provide multiple service QoS even under heavy load.

In this paper, we present a M-ary MC-CDMA system for 4G which can support data rates up to 20 Mbps. In the transmitter, data bits are serial to parallel converted, then applying M-ary orthogonal modulation, the modulated symbol is spread using each chip of complex orthogonal codes. In a frequency selective slowly fading channel, we analyze the system performance of the downlink.