In quantum state discrimination, typically the design of measurement operators or probe state is formulated assuming the set of possible states is perfectly known, but this may yield designs which are sensitive to deviations in the realized set of states. For example, the channel through which a transmitted state is sent may not be deterministic, but instead characterized by a classical distribution over quantum channels. In this paper, we consider the design of measurement schemes and probe states for quantum detection over an uncertain quantum channel. We present stochastic gradient-based algorithms to maximize the expected performance over the channel distribution for various design objectives, including the detection probability and mutual information. Furthermore, we introduce a scheme that leverages the isometric extension of a quantum channel to measure the channel output in an enlarged Hilbert space such that the channels are more distinguishable, while simultaneously reducing the effective dimension and thereby reducing the optimization complexity. Finally, we apply the proposed algorithms to multicopy channel discrimination.

We study covert classical communication over a quantum multiple-access channel (MAC) with a helper. Specifically, we consider three transmitters, where one transmitter helps the other two transmitters communicate covertly with a receiver. We demonstrate the feasibility of achieving a positive covert rate over this channel and establish an achievable rate region. Our result recovers as a special case known results for classical communication over classical MACs with a degraded message set, classical communication over quantum MACs, and classical communication over MACs with a helper. To the best of our knowledge, our result is the first to achieve covert communication with positive rates over both classical and quantum MACs.

We study covert communication and covert secret key generation with positive rates over channels with causal Channel State Information (CSI) at the transmitter. Specifically, we consider a state-dependent Discrete Memoryless Channel (DMC) where the transmitter has causal access to the CSI, and aims to communicate covertly with the receiver while simultaneously generating a covert secret key shared with the receiver. We derive an achievable rate region for this problem, which recovers as a special case the best-known results for covert communication over channels with CSI. To the best of our knowledge, our results are the first instance of achieving a positive rate for covert secret key generation.

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications, e.g., medical question-answering, mathematical sciences, and code generation. However, they also exhibit inherent limitations, such as outdated knowledge and susceptibility to hallucinations. Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to address these issues, but it also introduces new vulnerabilities. Recent efforts have focused on the security of RAG-based LLMs, yet existing attack methods face three critical challenges: (1) their effectiveness declines sharply when only a limited number of poisoned texts can be injected into the knowledge database (2) they lack sufficient stealth, as the attacks are often detectable by anomaly detection systems, which compromises their effectiveness, and (3) they rely on heuristic approaches to generate poisoned texts, lacking formal optimization frameworks and theoretic guarantees, which limits their effectiveness and applicability. To address these issues, we propose coordinated Prompt-RAG attack (PR-attack), a novel optimization-driven attack that introduces a small number of poisoned texts into the knowledge database while embedding a backdoor trigger within the prompt. When activated, the trigger causes the LLM to generate pre-designed responses to targeted queries, while maintaining normal behavior in other contexts. This ensures both high effectiveness and stealth. We formulate the attack generation process as a bilevel optimization problem leveraging a principled optimization framework to develop optimal poisoned texts and triggers. Extensive experiments across diverse LLMs and datasets demonstrate the effectiveness of PR-Attack, achieving a high attack success rate even with a limited number of poisoned texts and significantly improved stealth compared to existing methods. These results highlight the potential risks posed by PR-Attack and emphasize the importance of securing RAG-based LLMs against such threats.

Next-generation wireless technologies such as rate-splitting multiple access (RSMA) and massive MIMO are characterized by optimization problems too complex to solve in real-time, hence suboptimal heuristics are adopted in practice. As we explore in this paper, machine learning techniques have the potential to upend this paradigm, offering new algorithms customized for a particular distribution of problems. We consider MISO downlink beamforming optimization for NOMA, SDMA, and RSMA with sum rate and min rate criteria. We apply the framework of learning to optimize to learn an RNN optimizer that produces beamformers with much less computation than existing optimization algorithms such as weighted-MMSE. The RNN inference complexity scales linearly with the size of the antenna array and therefore is suitable for massive MIMO. We show that the learned optimizer is also compatible with a distributed beamforming scenario such as cell-free massive MIMO with information exchange facilitated by a central processor. Our simulation results show that the learned optimizer is competitive with state-of-the-art optimization methods, but requires a fraction of the computational cost.

We propose a new coded blockchain scheme suitable for the Internet-of-Things (IoT) network. In contrast to existing works for coded blockchains, especially blockchain-of-things, the proposed scheme is more realistic, practical, and secure while achieving high throughput. This is accomplished by: 1) modeling the variety of transactions using a reward model, based on which an optimization problem is solved to select transactions that are more accessible and cheaper computational-wise to be processed together; 2) a transaction-based and lightweight consensus algorithm that emphasizes on using the minimum possible number of miners for processing the transactions; and 3) employing the raptor codes with linear-time encoding and decoding which results in requiring lower storage to maintain the blockchain and having a higher throughput. We provide detailed analysis and simulation results on the proposed scheme and compare it with the state-of-the-art coded IoT blockchain schemes including Polyshard and LCB, to show the advantages of our proposed scheme in terms of security, storage, decentralization, and throughput.

Noiseless private side information does not reduce the download cost in Symmetric Private Information Retrieval (SPIR) unless the client knows all but one file. While this is a pessimistic result, we explore in this paper whether noisy client side information available at the client helps decrease the download cost in the context of SPIR with colluding and replicated servers. Specifically, we assume that the client possesses noisy side information about each stored file, which is obtained by passing each file through one of D possible discrete memoryless test channels. The statistics of the test channels are known by the client and by all the servers, but the mapping$\boldsymbol {\mathcal {M}}$between the files and the test channels is unknown to the servers. We study this problem under two privacy metrics. Under the first metric, the client wants to preserve the privacy of its file selection and the mapping$\boldsymbol {\mathcal {M}}$, and the servers want to preserve the privacy of all the non-selected files. Under the second metric, the client is willing to reveal the index of the test channel that is associated with its desired file. For both privacy metrics, we derive the optimal common randomness and download cost. Our setup generalizes SPIR with colluding servers and SPIR with private noiseless side information. Unlike noiseless side information, our results demonstrate that noisy side information can reduce the download cost, even when the client does not have noiseless knowledge of all but one file.

We study covert communication and covert secret key generation with positive rates over quantum state-dependent channels. Specifically, we consider fully quantum state-dependent channels when the transmitter shares an entangled state with the channel. We study this problem setting under two security metrics. For the first security metric, the transmitter aims to communicate covertly with the receiver while simultaneously generating a covert secret key, and for the second security metric, the transmitter aims to transmit a secure message covertly and generate a covert secret key with the receiver simultaneously. Our main results include one-shot and asymptotic achievable positive covert-secret key rate pairs for both security metrics. Our results recover as a special case the best-known results for covert communication over state-dependent classical channels. To the best of our knowledge, our results are the first instance of achieving a positive rate for covert secret key generation and the first instance of achieving a positive covert rate over a quantum channel. Additionally, we show that our results are optimal when the channel is classical and the state is available non-causally at both the transmitter and the receiver.

This paper studies covert communication over channels with Action-Dependent State Information (ADSI) when the state is available either non-causally or causally at the transmitter. Covert communication refers to reliable communication between a transmitter and a receiver while ensuring a low probability of detection by an adversary, which we refer to as “warden”. It is well known that in a point-to-point Discrete Memoryless Channel (DMC), it is possible to communicate on the order of$\sqrt {N}$bits reliably and covertly over N channel uses while the transmitter and the receiver are required to share a secret key on the order of$\sqrt {N}$bits. This paper studies achieving reliable and covert communication of positive rate, i.e., reliable and covert communication on the order of N bits in N channel uses, over a channel with ADSI while the transmitter has non-causal or causal access to the ADSI, and the transmitter and the receiver share a secret key of negligible rate. We derive achievable rates for both the non-causal and causal scenarios by using block-Markov encoding and secret key generation from the ADSI, which subsumes the best achievable rates for channels with random states. We also derive upper bounds, for both non-causal and causal scenarios, that meet our achievable rates for some special cases. As an application of our problem setup, we study covert communication over channels with rewrite options, which are closely related to recording covert information on memory, and show that a positive covert rate can be achieved in such channels. As a special case of our problem, we study the Additive White Gaussian Noise (AWGN) channels and provide lower and upper bounds on the covert capacity that meet when the transmitter and the receiver share a secret key of sufficient rate and when the warden’s channel is noisier than the legitimate receiver channel. As another application of our problem setup, we show that cooperation can lead to a positive covert rate in Gaussian channels. A few other examples are also worked out in detail.

In this article, we propose a novel spectral tensor layer for communication-free distributed deep learning. The overall framework is as follows: first, we represent the data in tensor form (instead of vector form) and replace the matrix product in conventional neural networks with the tensor product, which in effect imposes certain transformed-induced structure on the original weight matrices, e.g., a block-circulant structure; then, we apply a linear transform along a certain dimension to split the original dataset into multiple spectral subdatasets; as a result, the proposed spectral tensor network consists of parallel branches where each branch is a conventional neural network trained on a spectral subdataset with ZERO communication cost. The parallel branches are directly ensembled (i.e., the weighted sum of their outputs) to generate an overall network with substantially stronger generalization capability than that of each branch. Moreover, the proposed method enjoys a byproduct of decentralization gain in terms of memory and computation, compared with traditional networks. It is a natural yet elegant solution for heterogeneous data in federated learning (FL), where data at different nodes have different resolutions. Finally, we evaluate the proposed spectral tensor networks on the MNIST, CIFAR-10, ImageNet-1K, and ImageNet-21K datasets, respectively, to verify that they simultaneously achieve communication-free distributed learning, distributed storage reduction, parallel computation speedup, and learning with multiresolution data.

Noiseless private side information does not reduce the download cost in Symmetric Private Information Retrieval (SPIR) unless the client knows all but one file. While this is a pessimistic result, we explore in this paper whether noisy private side information available at the client helps decrease the download cost in the context of SPIR with colluding and replicated servers. Specifically, we assume that the client possesses noisy side information about each stored file, which is obtained by passing each file through one of$D$possible discrete memoryless test channels. The statistics of the test channels are known by the client and by all the servers, but the mapping$\mathcal{M}$between the files and the test channels is unknown to the servers. We study this problem under two privacy metrics. Under the first metric, the client wants to preserve the privacy of its file selection and the mapping$\mathcal{M}$, and the servers want to preserve the privacy of all the non-selected files. Under the second metric, the client is willing to reveal the index of the test channel that is associated with its desired file. For both privacy metrics, we derive the optimal common randomness and download cost. Our setup generalizes SPIR with colluding servers and SPIR with private noiseless side information. Unlike noiseless side information, our results demonstrate that noisy side information can reduce the download cost, even when the client does not have noiseless knowledge of all but one file.

We show that it is possible to achieve a positive covert communication rate over state-dependent quantum channels. Specifically, we consider fully quantum state-dependent channels when the transmitter shares an entangled state with the channel. To the best of our knowledge, this is the first instance of achieving a positive covert rate over a quantum channel. Our main results include a one-shot achievable covert rate and asymptotic achievable covert rates that recover, as a special case, known results for classical channels.

A key constraint that limits the implementation of blockchain in Internet of Things (IoT) is its large storage requirement resulting from the fact that each blockchain node has to store the entire blockchain. This increases the burden on blockchain nodes, and increases the communication overhead for new nodes joining the network since they have to copy the entire blockchain. In order to reduce storage requirements without compromising on system security and integrity, coded blockchains, based on error correcting codes with fixed rates and lengths, have been recently proposed. This approach, however, does not fit well with dynamic IoT networks in which nodes actively leave and join. In such dynamic blockchains, the existing coded blockchain approaches lead to high-communication overheads for new joining nodes and may have high-decoding failure probability. This article proposes a rateless coded blockchain with coding parameters adjusted to network conditions. Our goals are to minimize both the storage requirement at each blockchain node and the communication overhead for each new joining node, subject to a target decoding failure probability. We evaluate the proposed scheme in the context of real-world Bitcoin blockchain and show that both storage and communication overhead are reduced by 99.6% with a maximum 10−12 decoding failure probability.

Millimeter-Wave Massive MIMO is important for beyond 5G or 6G wireless communication networks. The goal of this paper is to establish successful communication between the cellular base stations and devices, focusing on the problem of joint user activity detection and channel estimation. Different from traditional compressed sensing (CS) methods that only use the sparsity of user activities, we develop several Approximate Message Passing (AMP) based CS algorithms by exploiting the sparsity of user activities and mmWave channels. First, a group soft-thresholding AMP is presented to utilize only the user activity sparsity. Second, a hard-thresholding AMP is proposed based on the on-grid CS approach. Third, a super-resolution AMP algorithm is proposed based on atomic norm, in which a greedy method is proposed as a super-resolution denoiser. And we smooth the denoiser based on Monte Carlo sampling to have Lipschitz continuity and present state evolution results. Extensive simulation results show that the proposed method outperforms the previous state-of-the-art methods.

Snapshot compressive imaging (SCI) cameras compress high-speed videos or hyperspectral images into measurement frames. However, decoding the data frames from measurement frames is compute-intensive. Existing state-of-the-art decoding algorithms suffer from low decoding quality or heavy running time or both, which are not practical for real-time applications. In this article, we exploit the powerful learning ability of deep neural networks (DNN) and propose a novel tensor fast iterative shrinkage-thresholding algorithm net (Tensor FISTA-Net) as a real-time decoder for SCI cameras. Since SCI cameras have an accurate physical model, we can trade training time for the decoding time by generating abundant synthetic data and training a decoder on the cloud. Tensor FISTA-Net not only learns a sparse representation of the frames through convolution layers but also reduces the decoding time and memory consumption significantly through tensor operations, which makes Tensor FISTA-Net an appropriate approach for a real-time decoder. Our proposed Tensor FISTA-Net obtains an average PSNR improvement of 0.79-2.84 dB (video images) and 2.61-4.43 dB (hyperspectral images) over the state-of-the-art algorithms, along with more clear and detailed visual results on real SCI datasets, Hammer and Wheel, respectively. Our Tensor FISTA-Net reaches 45 frames per second in video datasets and 70 frames per second in hyperspectral datasets, meeting the real-time requirement. Besides, the trained model occupies only a 12 -MB memory footprint, making it applicable to real-time Internet of Things (IoT) applications.

We propose a deep stochastic actor-critic algorithm with an integrated network architecture and fewer parameters. We address stabilization of the learning procedure via an adaptive objective to the critic's loss and a smaller learning rate for the shared parameters between the actor and the critic. Moreover, we propose a mixed on-off policy exploration strategy to speed up learning. Experiments illustrate that our algorithm reduces the sample complexity by 50%-93% compared with the state-of-the-art deep reinforcement learning (RL) algorithms twin delayed deep deterministic policy gradient (TD3), soft actor-critic (SAC), proximal policy optimization (PPO), advantage actor-critic (A2C), and interpolated policy gradient (IPG) over continuous control tasks LunarLander, BipedalWalker, BipedalWalkerHardCore, Ant, and Minitaur in the OpenAI Gym.

In order to fully unlock the transformative power of distributed ledgers and blockchains, it is crucial to develop innovative consensus algorithms that can overcome the obstacles of security, scalability, and interoperability, which currently hinder their widespread adoption. This paper introduces HybridChain that combines the advantages of sharded blockchain and DAG distributed ledger, and a consensus algorithm that leverages decentralized learning. Our approach involves validators exchanging perceptions as votes to assess potential conflicts between transactions and the witness set, representing input transactions in the UTXO model. These perceptions collectively contribute to an intermediate belief regarding the validity of transactions. By integrating their beliefs with those of other validators, localized decisions are made to determine validity. Ultimately, a final consensus is achieved through a majority vote, ensuring precise and efficient validation of transactions. Our proposed approach is compared to the existing DAG-based scheme IOTA and the sharded blockchain Omniledger through extensive simulations. The results show that IOTA has high throughput and low latency but sacrifices accuracy and is vulnerable to orphanage attacks especially with low transaction rates. Omniledger achieves stable accuracy by increasing shards but has increased latency. In contrast, the proposed HybridChain exhibits fast, accurate, and secure transaction processing, and excellent scalability.

In this work, we consider a backscatter communication system wherein multiple asynchronous sources (tags) exploit the reverberation generated by a nearby radar transmitter as an ambient carrier to deliver a message to a common destination (reader) through a number of available subchannels. We propose a new encoding strategy wherein each tag transmits both pilot and data symbols on each subchannel and repeats some of the data symbols on multiple subchannels. We then exploit this signal structure to derive two semi-blind iterative algorithms for joint estimation of the data symbols and the subchannel responses that are also able to handle some missing measurements. The proposed encoding/decoding strategies are scalable with the number of tags and their payload and can achieve different tradeoffs in terms of transmission and error rates. Some numerical examples are provided to illustrate the merits of the proposed solutions.

Considering the charging needs of the Internet of Things, we introduce the simultaneous wireless information and power transfer (SWIPT) technology into the ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul. However, SWIPT can bring potential eavesdropping issues. In this paper, we study the secure communication caused by SWIPT in the UD-CRAN network. Specifically, the transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense networks, such as base station diversity and high probability of line-of-sight transmission. Aiming at maximizing the security energy efficiency, we jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of remote radio heads (RRHs), and user-RRH association. We propose an iterative algorithm based on the Dinkelbach’s transform to deal with the fractional objective function. To solve the mix-integer non-convex inner problem, we design: (1) a successive convex approximation (SCA) based method in which the problem at each iteration is a mixed-integer second-order cone program; (2) and an alternating optimization algorithm based on semidefinite relaxation (SDR) to further balance the complexity and performance. Finally, numerical results are presented to demonstrate the efficiency of the proposed schemes. Moreover, the proposed SCA method can achieve excellent performance while preserving integer variables, which inevitably increases algorithm complexity. Furthermore, the proposed SDR method can avoid the iteration process of SCA and further reducing the algorithm complexity.

This paper studies covert communication over channels with action-dependent states when the state is available non-causally at the encoder. Covert communication refers to reliable communication between a transmitter and a receiver while ensuring low probability of detection at an adversary, which we refer to as "warden". It is well known that in a point to point Discrete Memoryless Channel (DMC), it is possible to communicate on the order of $\sqrt N $ bits reliably and covertly over N channel uses while the transmitter and the receiver are required to share a secret key on the order of $\sqrt N $ bits. This paper studies achieving positive covert and reliable communication rate, which is communication on the order of N bits over N channel uses, while the transmitter and the receiver share a secret key of negligible rate. We derive an achievable rate region by using block-Markov encoding and secret key generation from the Action-Dependent State Information (ADSI), which subsumes the best achievable rate region for channels with random states.

The design of transmission schemes and receiver techniques with high reliability and low latency for the massive machine-type communications (mMTCs) is an important challenge for the future Internet of Things (IoT) systems in the sixth generation (6G) wireless communication networks. In this article, we propose a new physical-layer transceiver scheme for mMTC, where users transmit their data based on a predesigned sparse Tanner graph, and the base station (BS) employs blind channel estimation and message-passing decoding to decode user data. In particular, low latency is achieved by the construction of the transmission Tanner graph, as well as a hybrid modulation scheme that consists of BPSK symbols to facilitate blind channel estimation, and general$M$-ary modulation to reduce the transmission delay. Moreover, high reliability is achieved by the proposed message-passing decoder that fully exploits the diversity of the transmitted signal and offers soft demodulation and decoding capabilities. We provide both performance analyzes based on density evolution, and simulation results, to demonstrate the superior performance of the proposed physical-layer transceiver solution for mMTC.

Blockchain is the foundation of emerging applications, such as smart contracts, nonfungible token (NFT), and metaverse. A key issue is that blockchain requires massive storage space, which limits its deployment in resource-limited end devices, e.g., Internet of Things. Recently, coded blockchain is proposed to reduce the storage requirement of blockchain while guaranteeing its security and data integrity. Coded blockchain encodes blocks into coded symbols, which are then distributively stored by clients. A key challenge when applying coded blockchain in resource-restricted networks is to ensure all clients store the same, and also the minimum, number of coded blocks. To this end, this article addresses a novel problem that minimizes the maximum (min–max) storage requirement of clients. It formulates the said problem as an integer linear program (ILP). It then proposes centralized algorithms to improve the computational efficiency of storage assignments. Moreover, it presents distributed algorithms that satisfy the distributive property of blockchain. Numerical results show that the proposed distributed algorithm with a short length code reduces the min–max storage of clients by 80% compared with traditional blockchain. In addition, the computational complexity of distributed algorithms is significantly lower than centralized algorithms.

Extracting information from large-scale high-dimensional data is a fundamentally important task in high performance computing, where the hierarchical Tucker (HT) tensor learning approach (learning a tensor-tree structure) has been widely used in many applications. However, HT tensor learning algorithms are compute-intensive due to the “curse of dimensionality,” i.e., the time complexity grows exponentially with the order of the data tensor. The computation of HT tensor learning algorithms boils down to tensor primitives, which are amenable to computing on GPU tensor cores. Existing work does not support HT tensor learning using GPU tensor cores. There are three main challenges to address: 1) to accelerate tensor learning primitives using GPU tensor cores; 2) to implement the tensor learning algorithms using GPU tensor cores and multiple GPUs; 3) to support large-scale data tensors exceeding the GPU memory capacity. In this paper, we present efficient HT tensor learning primitives using GPU tensor cores and demonstrate three applications. First, we utilize GPU tensor cores to optimize HT tensor learning primitives, including tensor contractions, tensor matricizations and tensor singular value decomposition (SVD). We employ the optimized primitives to optimize HT tensor decomposition algorithms for Big Data analysis. Second, we propose a novel HT tensor layer for deep neural networks, whose training process only involves a forward pass without back propagation. The forward pass consists of tensor operations, thus further exploiting the computing power of GPU tensor cores. Third, we apply the optimized primitives to develop a tensor-tree structured quantum machine learning algorithmtree-tensor network (TTN). Compared with TensorLy and TensorNetwork on NVIDIA A100 GPUs, our third-order HT tensor decomposition algorithm achieves up to$8.92 \times$and$6.42 \times$speedups, respectively, and our high-order case achieves up to$32.67 \times$and$23.97 \times$speedups, respectively. Our HT tensor layer for a fully connected neural network achieves$49.2 \times$compression at the cost of 0.5% drops in accuracy and$1.42 \times$speedup compared with the implementation on CUDA cores; for the AlexNet, our HT tensor layer achieves$9.45 \times$compression at the cost of 0.8% drops in accuracy and$1.87 \times$speedup compared with the implementation on CUDA cores. Our TTN algorithm achieves up to$11.17\times$speedup compared with TensorNetwork, indicating the potential of optimized tensor learning primitives for the classical simulation of quantum machine learning algorithms.

Tensor learning is a powerful tool for big data analytics and machine learning, e.g., gene analysis and deep learning. However, tensor learning algorithms are compute-intensive since their time and space complexities grow exponentially with the order of tensors, which hinders their application. In this paper, we exploit the parallelism of tensor learning primitives using GPU tensor cores and develop high-performance tensor learning algorithms. First, we propose novel hardware-oriented optimization strategies for tensor learning primitives on GPU tensor cores. Second, for big data analytics, we employ the optimized tensor learning primitives to accelerate the CP tensor decomposition and then apply it for gene analysis. Third, we optimize the Tucker tensor decomposition and propose a novel Tucker tensor layer to compress deep neural networks. We employ natural gradients to train the neural networks, which only involve a forward pass without backpropagation and thus are suitable for GPU computations. Compared with TensorLab and TensorLy libraries on an A100 GPU, our third-order CP tensor decomposition achieves up to$16.32\times$and$32.25\times$speedups; and$6.09\times$and$6.72\times$speedups for our third-order Tucker tensor decomposition. The proposed fourth-order CP and Tucker tensor decompositions achieve up to$30.65\times$and$5.41\times$speedups over the TensorLab. Our CP tensor decomposition for gene analysis achieves up to$5.88\times$speedup over TensorLy. Compared with a conventional fully connected neural network, our Tucker tensor layer neural network achieves an accuracy of$97.9\%$, a speedup of$4.47\times$, and a compression ratio of$2.92$at the cost of$0.4\%$drop in accuracy.

The Tucker Decomposition (TKD) is able to provide the low-dimensional and informative representations of real-world large-scale tensorial data, which are necessary to extract potential features and enhance the original data. However, computing such decomposition directly for a dense tensor is usually computationally elusive, due to the repetitive operations of computing large-scale tensor-matrix product. Instead of direct decomposition, this paper proposes an efficient algorithm for seeking the Faster TKD of the large-scale tensor, which is a lightweight decomposition approach based on the technique of randomized sampling. The proposed algorithm first converts the original large-scale tensor into a small-scale subtensor via full-mode sampling operation, and then the core tensor of TKD can be computed directly based on the subtensor with low complexity. Finally, an approximate TKD of the original large-scale tensor can be obtained after sequentially computing approximate full-mode factor matrices. A theoretical error analysis is provided to show that the approximation error approximates zero with high probability, and the proposed algorithm is verified based on real tensorial data of$\text{23821.24}~GB$.

Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. Since OAM requires precise alignment of the transmit and the receive antennas, the electronic beam steering approach has been proposed for the uniform circular array (UCA)-based OAM communication system to circumvent large performance degradation induced by small antenna misalignment in practical environment. However, in the case of large-angle misalignment, the OAM channel capacity cannot be effectively compensated only by the electronic beam steering. To solve this problem, we propose a hybrid mechanical and electronic beam steering scheme, in which mechanical rotating devices controlled by pulse width modulation (PWM) signals as the execution unit are utilized to eliminate the large misalignment angle, while electronic beam steering is in charge of the remaining small misalignment angle caused by perturbations. Furthermore, due to the interferometry, the receive signal-to-noise ratios (SNRs) are not uniform at the elements of the receive UCA. Therefore, a rotatable UCA structure is proposed for the OAM receiver to maximize the channel capacity, in which the simulated annealing algorithm is adopted to obtain the optimal rotation angle at first, then the servo system performs mechanical rotation, at last the electronic beam steering is adjusted accordingly. Both mathematical analysis and simulation results validate that the proposed hybrid mechanical and electronic beam steering scheme can effectively eliminate the effect of diverse misalignment errors of any practical OAM channel and maximize the OAM channel capacity.

In this paper we consider two Tanner-graph-based transmission schemes for massive multiple access, which combine non-orthogonal multiple access (NOMA) and grant-based random access. Each user transmits two data streams repeatedly using several channel time-frequency resource blocks (RBs) and the transmission schedule is represented by a Tanner graph, where variable nodes and check nodes represent the transmitted signals and the RBs, respectively. In Scheme 1 each variable node represents a data stream of a user, whereas Scheme 2 employs rate splitting and each variable node represents the superimposed data streams of a user. On the receiver side, we first consider peeling decoders that serve both as pilot-based channel estimators and baseline decoders. We then develop message-passing decoders for both transmission schemes that can fully exploit the diversity afforded by the repetitive transmission across different RBs, as opposed to the peeling decoders. We also propose a neural decoder by deep unfolding the message-passing decoder and further performing a small number of training epochs using the pilots. Simulation results show that for Transmission Scheme 1, the message-passing decoder offers decoding performance improvement over the peeling decoder by orders of magnitude; and as a result, Scheme 1 substantially outperforms Scheme 2. Moreover, the neural decoder further improves upon the performance of the message-passing decoder, as more information is learned about the transmitted data over the training epochs.

In this work, we exploit the radar clutter (i.e., the ensemble of echoes generated by the terrain and/or the surrounding objects in response to the signal emitted by a radar transmitter) as a carrier signal to enable an ambient backscatter communication from a source (tag) to a destination (reader). The proposed idea relies on the fact that, since the radar excitation is periodic, the radar clutter is itself periodic over time scales shorter than the coherence time of the environment. Upon deriving a convenient signal model, we propose two encoding/decoding schemes that do not require any coordination with the radar transmitter or knowledge of the radar waveform. Different tradeoffs in terms of transmission rate and error probability can be obtained upon changing the control signal driving the tag switch or the adopted encoding rule; also, multiple tags can be accommodated with either a sourced or an unsourced multiple access strategy. Some illustrative examples are provided.

In this paper, we propose new encoding and decoding schemes for the MIMO unsourced multiple access (UMA) systems. Each transmitter first encodes the information bits using an arbitrary channel code. The coded bits are divided into sub-blocks and each sub-block is mapped to a transmitted codeword using a common codebook. The codewords from all transmitters are transmitted through MIMO channels. We propose a sparsity-exploiting blind receiver algorithm that exploits the inherent codeword sparsity and a channel clustering technique to estimate the channel and decode the transmitted data. Either hard or soft estimates of the coded bits are given by the algorithm so we can apply single-user channel decoding to obtain the information bits of each transmitter. Simulation results are provided to illustrate the performance of the proposed algorithm for systems employing parity-check codes and Polar codes, respectively.

In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users. Upon resorting to an orthogonal frequency division multiplexing (OFDM) transmission format and a differential phase shift keying (DPSK) modulation, we study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.

We study online privacy-preserving anomaly detection in a setting in which the data are distributed over a network and locally sensitive to each node, and a probabilistic data model is unknown. We design and analyze a data-driven solution scheme where each node observes a high-dimensional data stream for which it computes a local outlierness score. This score is then perturbed, encrypted, and sent to a network operator. The network operator then decrypts an aggregate statistic over the network and performs online network anomaly detection via the proposed generalized cumulative sum (CUSUM) algorithm. We derive an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Additionally, we derive an asymptotic upper bound and asymptotic approximation for the average detection delay of the proposed algorithm under a certain anomaly. We show the analytical tradeoff between the anomaly detection performance and the differential privacy level, controlled via the local perturbation noise. Experiments illustrate that the proposed algorithm offers a good tradeoff between privacy and quick anomaly detection against the UDP flooding and spam attacks in a real Internet of Things (IoT) network.

We propose novel sparse-graph-based transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO channels. The channel coherence interval is divided into a number of sub-slots and each active transmitter selects certain sub-slots to repeatedly transmit its codeword according to a sparse Tanner graph. We propose iterative receiver algorithms that at each iteration decode either a single codeword, or two or three codewords jointly, and then subtract the decoded codewords from received signals during all sub-slots. The keys to these decoders are novel blind channel estimation algorithms when the received signal contains one, two, or three codewords. We perform density evolution analysis on the proposed UMA systems to obtain the asymptotic upper bounds on the maximum achievable rates for different decoders under both regular and irregular Tanner graphs. Extensive simulation results are provided to illustrate the performance of the proposed UMA systems, and its advantages over existing compressed-sensing (CS)-based UMA schemes.

5G millimeter-wave (mmWave) communication systems enable exciting new applications by significantly reducing the latency and increasing the data rate. However, this comes at a large computational cost, which results in long latency and large energy consumption. In this work, we aim to address this challenge in the problem of channel estimation of such systems through a set of algorithm-hardware co-optimizations. First of all, we employed a model-based neural network to improve the rate of convergence. We also optimized the neural network and achieved improved loss while using approximately the same number of operations. Furthermore, we were able to reduce the computational complexity through the use of sparsity inherent in mmWave channels. The proposed neural network for the channel estimation scales the computational complexity by more than two orders. Based on these innovations, we implemented a channel estimation subsystem on Zynq 7020 FPGA. The subsystem obtains an improvement in latency of up to ~10X and an improvement in energy consumption of up to ~300X over CPU and GPU based systems.

We present two model-based neural network architectures purposed for sporadic user detection and channel estimation in massive machine-type communications. In the scenario under consideration, a base station assigns the users a set of pilot sequences that is linearly dependent, but because user activity is sporadic the detection/estimation problem is amenable to sparse recovery algorithms. Further, we consider a millimeter-wave wireless channel, so that the channel vectors are sparse in a known dictionary. We apply the deep unfolding framework to design custom neural network layers by unrolling two iterative optimization algorithms: (1) linearized alternating direction method of multipliers, which we apply to a constrained convex problem, and (2) vector approximate message passing featuring a novel denoiser based on the iterative shrinkage thresholding algorithm. The networks thus inherit domain knowledge as encapsulated by the signal model, and suitable operations as informed by the algorithms—in the same spirit as convolutional networks that exploit structure inherent in images and audio, except grounded in optimization and statistics. The networks, trained on synthetic data generated from the block-fading millimeter-wave multiple access channel model, offer improved complexity and accuracy relative to their iterative counterparts, and are potentially a boon to cell-free MIMO systems.

Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in the resource-restricted environments is still a very challenging problem. In this paper, we propose to develop extremely compact RNN models with fully decomposed hierarchical Tucker (FDHT) structure. The HT decomposition does not only provide much higher storage cost reduction than the other tensor decomposition approaches, but also brings better accuracy performance improvement for the compact RNN models. Meanwhile, unlike the existing tensor decomposition-based methods that can only decompose the input-to-hidden layer of RNNs, our proposed fully decomposition approach enables the comprehensive compression for the entire RNN models with maintaining very high accuracy. Our experimental results on several popular video recognition datasets show that, our proposed fully decomposed hierarchical tucker-based LSTM (FDHT-LSTM) is extremely compact and highly efficient. To the best of our knowledge, FDHT-LSTM, for the first time, consistently achieves very high accuracy with only few thousand parameters (3,132 to 8,808) on different datasets. Compared with the state-of-the-art compressed RNN models, such as TT-LSTM, TR-LSTM and BT-LSTM, our FDHT-LSTM simultaneously enjoys both order-of-magnitude (3,985× to 10,711×) fewer parameters and significant accuracy improvement (0.6% to 12.7%).

We present two model-based neural network architectures purposed for sporadic user activity detection and channel estimation in the massive connectivity regime. In the considered scenario, the set of pilot sequences assigned to users is linearly dependent; but assuming user activity is sporadic, the detection/estimation problem is amenable to sparse recovery algorithms. We apply the deep unfolding framework to unroll two such algorithms, (1) linearized alternating direction method of multipliers and (2) vector approximate message passing, into a set of custom neural network layers. The networks thus inherit domain knowledge encapsulated in the signal model, plus suitable layer operations informed by the algorithms. The networks, trained on randomly generated data, offer improved complexity and accuracy relative to their iterative counterparts, and are a potential boon to cell-free massive MIMO systems.

This paper focuses on the channel estimation in cell-free hybrid massive multiple-input multiple-output (MIMO). Efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all access points (APs), which may enable the CPU to infer the private information of user locations. We therefore develop privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, a joint differentially private noisy matrix completion algorithm based on Frank-Wolfe iteration is presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we characterize the scaling laws of the estimation error in terms of data payload size. Simulation results demonstrate the tradeoff between privacy and channel estimation performance, and show that the estimation error can be mitigated by increasing the payload size while keeping the pilot size fixed.

High mobility channel estimation is crucial for beyond 5G(B5G) or 6G wireless communication networks. This paper is concerned with channel estimation of high mobility OFDM communication systems. First, a two-dimensional compressed sensing problem is formulated by approximately linearizing the channel as a product of an overcomplete dictionary with a sparse vector, in which the Doppler effect caused by the high mobility channel is considered. To solve the problem that the traditional compressed sensing algorithms have too many iterations and are time consuming, we propose an unfolded deep neural network (UDNN) as the fast solver, which is inspired by the structure of iterative shrinkage-thresholding algorithm (ISTA). All the parameters in UDNN (e.g. nonlinear transforms, shrinkage thresholds, measurement matrices, etc.) are learned end-to-end, rather than being hand-crafted. Experiments demonstrate that the proposed UDNN performs better than ISTA for OFDM high mobility channel estimation, while maintaining extremely fast computational speed.

We investigate the long-term scheduling and power control scheme for a wirelessly powered cell-free Internet-of-Things (IoT) network which consists of distributed access points (APs) and a large number of sensors. In each time slot, a subset of sensors is scheduled for uplink data transmission or downlink power transfer. Through asymptotic analysis, we obtain closed-form expressions for the harvested energy and the achievable rates that are independent of random pilots. Then, using these expressions, we formulate a long-term scheduling and power control problem to maximize the minimum time-average achievable rate among all sensors while maintaining the battery state of each sensor higher than a predefined minimum level. Using Lyapunov optimization, the transmission mode, the active sensor set, and the power control coefficients for each time slot are jointly determined. Finally, simulation results validate the accuracy of our derived closed-form expressions and reveal that the minimum time-average achievable rate is boosted significantly by the proposed scheme compared with the simple greedy transmission scheme.

We consider a system with one single-antenna reader and multiple hybrid backscatter-harvest-then-transmit (HTT) transmitters. The transmitters can operate in either the backscattering mode or the HTT mode; and the reader that supports both operating modes acts as a power transmitter and information receiver. The objective is to determine the transmission mode of all transmitters and minimize the total transmission time of the system. We provide problem formulations under both ideal and realistic power consumption models for HTT transmitters. We theoretically prove several key properties of the system under both models and develop bisection-based algorithms to solve the problems optimally with complexity$\mathcal {O}(\log K)$where$K$is the number of transmitters. The results are then further extended to the case of a massive MIMO reader.

Timely detection of abrupt anomalies is crucial for real-time monitoring and security of modern systems producing high-dimensional data. With this goal, we propose effective and scalable algorithms. Proposed algorithms are nonparametric as both the nominal and anomalous multivariate data distributions are assumed unknown. We extract useful univariate summary statistics and perform anomaly detection in a single-dimensional space. We model anomalies as persistent outliers and propose to detect them via a cumulative sum-like algorithm. In case the observed data have a low intrinsic dimensionality, we find a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold. Further, in the general case, we determine an acceptance region for nominal data via Geometric Entropy Minimization and evaluate whether the sequentially observed data persistently fall outside the acceptance region. We provide an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Moreover, we provide a sufficient condition to asymptotically guarantee that the decision statistic of the proposed algorithm does not diverge in the absence of anomalies. Experiments illustrate the effectiveness of the proposed schemes in quick and accurate anomaly detection in high-dimensional settings.

Bulk samples of the same patient are heterogeneous in nature, comprising of different subpopulations (subclones) of cancer cells. Cells in a tumor subclone are characterized by unique mutational genotype profile. Resolving tumor heterogeneity by estimating the genotypes, cellular proportions and the number of subclones present in the tumor can help in understanding cancer progression and treatment. We present a novel method, ChaClone2, to efficiently deconvolve the observed variant allele fractions (VAFs), with consideration for possible effects from copy number aberrations at the mutation loci. Our method describes a state-space formulation of the feature allocation model, deconvolving the observed VAFs from samples of the same patient into three matrices: subclonal total and variant copy numbers for mutated genes, and proportions of subclones in each sample. We describe an efficient sequential Monte Carlo (SMC) algorithm to estimate these matrices. Extensive simulation shows that the ChaClone2 yields better accuracy when compared with other state-of-the-art methods for addressing similar problem and it offers scalability to large datasets. Also, ChaClone2 features that the model parameter estimates can be refined whenever new mutation data of freshly sequenced genomic locations are available. MATLAB code and datasets are available to download at: https://github.com/moyanre/method2.

We propose new transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO and massive MIMO channels. Each active transmitter’s information bits are first channel encoded. The coded bits are divided into sub-blocks and each sub-block is modulated and transmitted. For both MIMO and massive MIMO channels, the conventional nonlinear modulation can be employed where each sub-block of coded bits is mapped to a transmitted signal vector. For the massive MIMO channel, we propose a new hybrid modulation scheme to reduce the receiver complexity, where the first sub-block is nonlinearly modulated, and the subsequent sub-blocks are linearly modulated and spread by the first sub-block signal. We also propose sparsity-exploiting blind receiver algorithms. Specifically, for the MIMO case, we exploit the codeword sparsity inherent in the UMA system, and a channel clustering technique, to estimate the channel and the transmitted signal of each transmitter. For the massive MIMO, in addition to the codeword sparsity, we further exploit the channel sparsity and user sparsity in estimating the channel and transmitted signal of each transmitter. The proposed receiver algorithms for both MIMO and massive MIMO channels output either hard or soft estimates of the coded bits, and therefore single-user channel decoding of the information bits can be performed for each transmitter. Extensive simulation results are provided to demonstrate the performances of the proposed algorithms.

Cloud removal is a ubiquitous and important task in remote sensing image processing, which aims at restoring the ground regions shadowed by clouds. It is challenging to remove the clouds for a single satellite image due to the difficulty of distinguishing clouds from white objects on the ground and filling the irregular missing regions with visual consistency. In this article, we propose a novel two-stage cloud removal method. The first stage is cloud segmentation, i.e., extracting the clouds and removing the thin clouds directly using U-Net. The second stage is image restoration, i.e., removing the thick cloud and recovering the corresponding irregular missing regions using generative adversarial network (GAN). We evaluate the proposed scheme on both synthetic images and real satellite images (over$20\,000\, \times \,20\,000$pixels). On synthetic images for cloud coverage less than 40%, the proposed scheme achieves improvements of 0.049–0.078 in Structural SIMilarity (SSIM) and 3.8–6.2 dB in peak signal-to-noise ratio (PSNR), while the$\ell _{1}$-norm error reduces by 49%–78%, compared with a state-of-the-art deep learning method Pix2Pix. On real satellite images, we demonstrate the consistent visual results of the proposed scheme.

Recently, the residual learning strategy has been integrated into the convolutional neural network (CNN) for single image super-resolution (SISR), where the CNN is trained to estimate the residual images. Recognizing that a residual image usually consists of high-frequency details and exhibits cartoon-like characteristics, in this paper, we propose a deep shearlet residual learning network (DSRLN) to estimate the residual images based on the shearlet transform. The proposed network is trained in the shearlet transform-domain which provides an optimal sparse approximation of the cartoon-like image. Specifically, to address the large statistical variation among the shearlet coefficients, a dual-path training strategy and a data weighting technique are proposed. Extensive evaluations on general natural image datasets as well as remote sensing image datasets show that the proposed DSRLN scheme achieves close results in PSNR to the state-of-the-art deep learning methods, using much less network parameters.

Phase retrieval refers to the problem of recovering a signal$ {x}_{\star }\in \mathbb {C}^{n}$from its phaseless measurements$\text {y}_{\text {i}}=| {a}_{i}^{ \mathsf {H}} {x}_{\star }|$, where$\{ {a}_{\text {i}}\}_{\text {i}=1}^{ {m}}$are the measurement vectors. Spectral method is widely used for initialization in many phase retrieval algorithms. The quality of spectral initialization can have a major impact on the overall algorithm. In this paper, we focus on the model where$ {A}=[ {a}_{1},\ldots, {a}_{ {m}}]^{ \mathsf {H}}$has orthonormal columns, and study the spectral initialization under the asymptotic setting$ {m}, {n}\to \infty $with$ {m}/ {n}\to \delta \in (1,\infty)$. We use the expectation propagation framework to characterize the performance of spectral initialization for Haar distributed matrices. Our numerical results confirm that the predictions of the EP method are accurate for not-only Haar distributed matrices, but also for realistic Fourier based models (e.g. the coded diffraction model). The main findings of this paper are the following: 1) There exists a threshold on$\delta $(denoted as$\delta _{ \mathrm {weak}}$) below which the spectral method cannot produce a meaningful estimate. We show that$\delta _{ \mathrm {weak}}=2$for the column-orthonormal model. In contrast, previous results by Mondelli and Montanari show that$\delta _{ \mathrm {weak}}=1$for the i.i.d. Gaussian model. 2) The optimal design for the spectral method coincides with that for the i.i.d. Gaussian model, where the latter was recently introduced by Luo, Alghamdi and Lu.

Motivated by the sequential detection of false data injection attacks (FDIAs) in a dynamic smart grid, we consider a more general problem of sequentially detecting a time-varying change in a dynamic linear regression model. To be specific, when the change occurs, a time-varying unknown vector is added in the linear regression model. The parameter vector of the linear regression model is also assumed to be unknown and time-varying. Thus, the pre- and post-change distributions are both unknown and time-varying. This imposes a significant challenge for designing a computationally efficient sequential detector. We first propose two Cumulative-Sum-type algorithms to address this challenge. One is called generalized Cumulative-Sum (GCUSUM) algorithm, and the other one is called relaxed generalized Cumulative-Sum (RGCUSUM) algorithm, which is a modified version of the GCUSUM. It can be shown that the computational complexity of the proposed RGCUSUM algorithm scales linearly with the number of observations. Next, considering Lordon's setup, for any given constraint on the expected false alarm period, a lower bound on the threshold employed in the proposed RGCUSUM algorithm is derived, which provides a useful guideline for the design of the proposed RGCUSUM algorithm to achieve any prescribed performance requirement in practice. In addition, for any given threshold employed in the proposed RGCUSUM algorithm, an upper bound on the expected detection delay is also provided. The performance of the proposed RGCUSUM algorithm is numerically studied in the context of an IEEE standard power system under FDIAs. Moreover, the numerical results demonstrate the superiority of the proposed RGCUSUM in computational efficiency.

Sequential attack detection in a distributed sensor network is considered, where each sensor successively produces one-bit quantized samples of a desired deterministic scalar parameter corrupted by additive noise. The unknown parameters in the pre-attack and post-attack models, namely the desired parameter to be estimated and the injected malicious data at the attacked sensors pose a significant challenge for designing a computationally efficient scheme for each sensor to detect the occurrence of attacks by only using local communication with neighboring sensors. The generalized Cumulative Sum (GCUSUM) algorithm is considered, which replaces the unknown parameters with their maximum likelihood estimates in the CUSUM test statistic. For the problem under consideration, a sufficient condition is provided under which the expected false alarm period of the GCUSUM can be guaranteed to be larger than any given value. Next, we consider the distributed implementation of the GCUSUM. We first propose an alternative test statistic which is asymptotically equivalent to that of GCUSUM. Then based on the proposed alternative test statistic and running consensus algorithms, we propose a distributed approximate GCUSUM algorithm which significantly reduce the prohibitively high computational complexity of the centralized GCUSUM. Numerical results show that the proposed distributed approximate GCUSUM algorithm can provide a performance that is comparable to the centralized GCUSUM.

High-accuracy location awareness in indoor environments is fundamentally important for mobile computing and mobile social networks. However, accurate radio frequency (RF) fingerprint-based localization is challenging due to real-time response requirements, limited RF fingerprint samples, and limited device storage. In this article, we propose a tensor generative adversarial net (Tensor-GAN) scheme for real-time indoor localization, which achieves improvements in terms of localization accuracy and storage consumption. First, with verification on real-world fingerprint data set, we model RF fingerprints as a 3-D low-tubal-rank tensor to effectively capture the multidimensional latent structures. Second, we propose a novel Tensor-GAN that is a three-player game among a regressor, a generator, and a discriminator. We design a tensor completion algorithm for the tubal-sampling pattern as the generator that produces new RF fingerprints as training samples, and the regressor estimates locations for RF fingerprints. Finally, on real-world fingerprint data set, we show that the proposed Tensor-GAN scheme improves localization accuracy from 0.42 m (state-of-the-art methods kNN, DeepFi, and AutoEncoder) to 0.19 m for 80% of 1639 random testing points. Moreover, we implement a prototype Tensor-GAN that is downloaded as an Android smartphone App, which has a relatively small memory footprint, i.e., 57 KB.

Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance.

Interferences due to non-orthogonality of signals usually exist in wireless networks when the number of users is larger than the sequence length, such as non-orthogonality of the pilots in multi-cell systems and non-orthogonality of the signature sequences in overloaded code-division-multiple-access (CDMA) systems. We address this effect from the perspective of non-orthogonal sequence design in a two-cell multiple-antenna network. Specifically, we aim at designing pilot sequences to minimize the sum mean-squared-error (MSE) of channel estimation with a given sequence length$\tau $where$\tau \in [K,2K]$and$K$is the number of users per cell. Considering the strength disparity between channels originating from the home cell and the neighbor cell, this problem boils down to minimizing the sum of squares of weighted correlations among sequences, whose lower bound is obtained inclosed formand can be regarded as a generalization of the well-known Welch bound (Welch, 1974). We prove this extended Welch bound is achievable, and design an algorithm based on the Davies-Higham method to generate the interference-minimizing sequences. Three fundamental properties of the proposed sequences are presented. Finally, we derive closed-form expressions of the average signal-to-interference-plus-noise-ratio (SINR) and rate for data transmission, based on which the optimal training duration can be found.

We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with K users and M access points (APs), each with Naantennas and Nraradio frequency (RF) chains. When Ka, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.

We consider a wirelessly powered Internet of Things (IoT) based on cell-free massive MIMO with energy harvesting. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. We assume that single antenna sensors send uplink pilots in order to allow APs to detect active users and estimate their channel coefficients. We assume that each AP is equipped with N ≥ 1 antennas and uses the linear minimum mean square error (LMMSE) channel estimation. Through an asymptotic analysis, we derive closedform approximations for the variance of the LMMSE channel coefficient estimates, the amount of harvested energy, and the achievable rates for the uplink data transmission. We next use these expressions to jointly optimize the charging duration and uplink and downlink power control coefficients to minimize the total transmit energy consumptions of APs and sensors, which is crucially important for IoT sensors. Simulation results verify the accuracy of the obtained expressions, and shows that significant gains in energy efficiency can be achieved by the proposed optimization algorithms.

This article investigates the coded cooperative data exchange (CCDE) problem, where a set of nodes initially hold a subset of packets and wish to retrieve all desired packets via direct wireless communication with neighbors. The CCDE problem in multihop wired networks has seen significant research recently and is proved to be NP-hard. The CCDE problem in multihop wireless networks (MWNs) must additionally consider half-duplex constraint, interference constraint, and channel constraint. Channel assignment brings new challenges to the CCDE problem in MWN, since it is also a well-known NP-hard problem even with one channel. In this article, we study the CCDE problem in MWN with multiple channels. We first construct a path network to evaluate the priority for each possible transmission and then construct a conflict graph (CG). This graph depicts the half-duplex, interference, and channel constraints. After that, a greedy channel assignment algorithm is proposed to obtain the nonconflict transmissions based on the constructed CG and assign a channel for each selected transmission. Finally, based on the selected transmissions, we construct a single-source multicast network exploiting the time expanded network, with which the network encoding and decoding schemes can be computed within polynomial time. Extensive simulations are conducted to demonstrate the efficiency of the proposed algorithm.

In this article, we propose a wirelessly powered Internet-of-Things (IoT) system based on the cell-free massive MIMO technology. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. Collocated massive MIMO and small-cell IoT can be treated as special cases of cell-free IoT. We derive the tight closed-form lower bound on the amount of harvested energy, and the closed-form expression of SINR as the metrics of power transfer and data transmission, respectively. To improve energy efficiency, we jointly optimize the uplink and downlink power control coefficients to minimize the total transmit energy consumption while meeting the target SINRs. Extended simulation results show that cell-free IoT outperforms collocated massive MIMO and small-cell IoT in terms of both downlink and uplink 95% likely performances. Moreover, significant gains can be achieved by the proposed joint power control in terms of both per user throughput and energy consumption.

This article presents a novel way to reduce blockchain nodes’ memory requirements using error correcting codes. In particular, LDPC codes are taken as examples to explicitly demonstrate the scheme. The proposed coding scheme encodes data across multiple blocks, respectively, block headers, in the blockchain. This leads to a significant reduction in required memory at each node. We then apply the proposed coding technique to blockchains organized in two different ways. Our first scheme has the same protocol for mining, broadcasting, and verification of blocks, as Bitcoin-type blockchains. Our scheme is different in thatfull nodesdo not have to store all blocks. Instead they will need to store only one block of a group of$t$blocks. In the second scheme, we consider a new block verification protocol and an account-based model under the assumption that transmission between any two nodes can be established, as well as the broadcast transmission. Our block verification protocol uses the Byzantine fault tolerance algorithm and requires sending a newly mined block to only a small number of verification nodes, instead of broadcasting it to the entire network, which leads to a reduction of the network load.

Backscatter communication can potentially find wide Internet of Things (IoT) applications because of its negligible energy consumption. Traditional backscatter communication relies on either dedicated radio-frequency (RF) sources, such as RF identification readers and power beacons, or ambient RF sources, e.g., TV and cellular signals. In this article, we study a backscatter-assisted wirelessly powered IoT system where devices can backscatter when nearby devices are actively transmitting via RF. Our objective is to determine the transmission schedule for all devices that maximizes the minimum system throughput. We formulate such a scheduling problem as a linear program for both linear and random networks. A key step in the formulation is to identify the groups of devices that can simultaneously backscatter without causing interference. Simulation results show that the max-min system throughput in linear and random networks can be increased by 46 and 180 times, respectively, by using the proposed backscatter-assisted schedule as compared with the traditional time-division multiple access (TDMA).

We consider the problem of clustering and completing a set of tensors with missing data that are drawn from a union of low-rank tensor spaces. In the clustering problem, given a partially sampled tensor data that is composed of a number of subtensors, each chosen from one of a certain number of unknown tensor spaces, we need to group the subtensors that belong to the same tensor space. We provide a geometrical analysis on the sampling pattern and subsequently derive the sampling rate that guarantees the correct clustering under some assumptions with high probability. Moreover, we investigate the fundamental conditions for finite/unique completability for the union of tensor spaces completion problem. Both deterministic and probabilistic conditions on the sampling pattern to ensure finite/unique completability are obtained. For both the clustering and completion problems, our tensor analysis provides significantly better bound than the bound given by the matrix analysis applied to any unfolding of the tensor data.

The problem of estimating high-mobility channels is a special case of the general problem of recovering multi-dimensional (MD) complex sinusoids. This paper is concerned with estimation of multiple frequencies with prior knowledge from incomplete and/or noisy samples. Suppose that it is known a priori that the frequencies lie in some given intervals, we develop efficient super-resolution estimators by exploiting such prior knowledge based on frequency-selective (FS) atomic norm minimization. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to lie in given intervals. We then propose to solve the FS atomic norm minimization problems for the low-rank spectral tensor recovery by converting them into semidefinite programs based on the MD Vandermonde decomposition. We also develop fast solvers for solving these semidefinite programs via the alternating direction method of multipliers (ADMM), where each iteration involves a number of refinement steps to utilize the prior knowledge. Extensive simulation results are presented to illustrate the high performance of the proposed methods.

We investigate the secure degrees of freedom (SDoF) for the two-way 2×2×2 MIMO interference channel (IC) under three wiretap models, i.e., the confidential messages (CM) model, the untrusted relays (UR) model, and the combined CM and UR (CM-UR) model. For the general case of arbitrary antenna configuration under each wiretap model, we derive the upper bound on SDoF with Markov chain and secrecy constraints, and obtain the achievability schemes with designed interference neutralization, cooperative jamming, and interference alignment schemes. To gain insight on these bounds, we further consider the special case where each user node has M antennas and each relay node has N antennas, and highlight the modification process when achieving the maximum SDoF. For such special case, we show that the optimum SDoF of the CM model is achieved in the regimes M ≥ N and M2M, and N = M.

In this paper, we consider a passive radar system that estimates the positions and velocities of multiple moving targets by using OFDM signals transmitted by a totally un-coordinated and un-synchronizated illuminator and multiple receivers. It is assumed that data demodulation is performed separately based on the direct-path signal, and the error-prone estimated data symbols are made available to the passive radar receivers, which estimate the positions and velocities of the targets in two stages. First, we formulate a problem of joint estimation of the delay-Doppler of reflectors and the demodulation errors, by exploiting two types of sparsities of the system, namely, the numbers of reflectors (i.e., targets and clutters) and demodulation errors are both small. This problem is non-convex and a conjugate gradient descent method is proposed to solve it. Then in the second stage we determine the positions and velocities of targets based on the estimated delay-Doppler in the first stage. For the second stage, two methods are proposed: the first is based on numerically solving a set of nonlinear equations, while the second is based on the neural network, which is more efficient. The performance of the proposed algorithms is evaluated through extensive simulations.

This paper studies the energy-efficient power allocation problem for physical-layer security in multi-user (MU) distributed massive multiple-input multiple-output (MIMO) systems. A new metric called global average secrecy energy efficiency (GASEE) is proposed to measure the MU secrecy energy efficiency (SEE) with a single eavesdropper (Eve). We first derive closed-form expressions for the signal to interference-plus-noise ratios (SINRs) of legitimate users and the Eve with pilot contamination. Under a power consumption model that incorporates transmit power, backhaul power, remote antenna unit (RAU) circuit and signal processing power, and with transmit power constraints as well as SINR constraints for both users and the Eve, the GASEE maximization problem is formulated as a joint optimization of power allocation, RAU clustering, RAU selection and artificial noise (AN) selection. The formulated problem is a mixed integer nonlinear program (MINLP), which is solved by a double-loop procedure. In the outer loop, the denominator of objective is approximated as a linear function. In the inner loop, an efficient algorithm is proposed to find a near-optimal solution to the approximated problem by solving a sequence of sub-problems. Simulation results demonstrate that the proposed algorithm converges fast and achieves a higher GASEE than some heuristics.

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We first propose a distributed state estimator assuming regular system operation that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to 1) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, 2) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and 3) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies.

This work considers the cooperative sequential hypothesis testing problem in a distributed network with quantized communication channels. The sensors observe independent sequences of samples and in the meantime, exchange their local information in the form of quantized statistics at every sampling interval. The communication links are represented as an undirected graph. In this distributed setup, every sensor performs its own sequential test based on the local samples and the messages from the neighbour sensors. Our goal is to devise the distributed sequential test that comprises the quantization scheme, the message-exchange protocol and the test procedure such that every sensor in the network fully exploits the network diversity and achieves the (asymptotically) optimal performance in terms of the stopping time. In particular, two distributed sequential tests are proposed based on different quantization schemes and a quantized message-exchange protocol that satisfies certain conditions. The first quantization scheme uniformly quantizes the local statistic at each sensor and at every sampling interval; the second one hinges on a modified level-triggered quantization technique, and resembles the Lebesgue sampling of the running local statistic. Our analyses show that the uniform quantization based distributed sequential test yields sub-optimal performance, while the one based on level-triggered quantization achieves the order-2 asymptotically optimal performance at every sensor for any fixed quantization step-size. Furthermore, we generalize the proposed sequential tests to the cluster-based network. Numerical results are provided to corroborate our analyses and demonstrate the effectiveness of the proposed sequential tests.

The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data. In this paper, we study the low-tubal-rank tensor completion problem, i.e., to recover a third-order tensor by observing a subset of its elements selected uniformly at random. We propose a fast iterative algorithm, called Tubal-AltMin, that is inspired by a similar approach for low-rank matrix completion. The unknown low-tubal-rank tensor is represented as the product of two much smaller tensors with the low-tubal-rank property being automatically incorporated, and Tubal-AltMin alternates between estimating those two tensors using tensor least squares minimization. First, we note that tensor least squares minimization is different from its matrix counterpart and nontrivial as the circular convolution operator of the low-tubal-rank tensor model is intertwined with the sub-sampling operator. Secondly, the theoretical performance guarantee is challenging since Tubal-AltMin is iterative and nonconvex. We prove that 1) Tubal-AltMin generates a best rank-r approximate up to any predefined accuracy ε at an exponential rate, and 2) for an n × n × k tensor M with tubal-rank r ≪ n, the required sampling complexity is O((nr2kIIMIIF2log3n)/σ2rk), where σ̅rk is the rk-th singular value of the block diagonal matrix representation of M in the frequency domain, and the computational complexity is O(n2r2k3logn log(n/ε)). Finally, on both synthetic data and real-world video data, evaluation results show that compared with tensor-nuclear norm minimization using alternating direction method of multipliers (TNN-ADMM), Tubal-AltMin-Simple (a simplified implementation of Tubal-AltMin) improves the recovery error by several orders of magnitude. In experiments, Tubal-AltMin-Simple is faster than TNN-ADMM by a factor of 5 for a 200 × 200 × 20 tensor.

We consider sequential detection based on quantized data in the presence of eavesdropper. Stochastic encryption is employed as a counter measure that flips the quantization bits at each sensor according to certain probabilities, and the flipping probabilities are only known to the legitimate fusion center (LFC) but not the eavesdropping fusion center (EFC). As a result, the LFC employs the optimal sequential probability ratio test (SPRT) for sequential detection whereas the EFC employs a mismatched SPRT (MSPRT). We characterize the asymptotic performance of the MSPRT in terms of the expected sample size as a function of the vanishing error probabilities. We show that when the detection error probabilities are set to be the same at the LFC and EFC, every symmetric stochastic encryption is ineffective in the sense that it leads to the same expected sample size at the LFC and EFC. Next, in the asymptotic regime of small detection error probabilities, we show that every stochastic encryption degrades the performance of the quantized sequential detection at the LFC by increasing the expected sample size, and the expected sample size required at the EFC is no fewer than that is required at the LFC. Then the optimal stochastic encryption is investigated in the sense of maximizing the difference between the expected sample sizes required at the EFC and LFC. Although this optimization problem is nonconvex, we show that if the acceptable tolerance of the increase in the expected sample size at the LFC induced by the stochastic encryption is small enough, then the globally optimal stochastic encryption can be analytically obtained; and moreover, the optimal scheme only flips one type of quantized bits (i.e., 1 or 0) and keeps the other type unchanged.

Data completion is a problem of filling missing or unobserved elements of partially observed datasets. Data completion algorithms have received wide attention and achievements in diverse domains including data mining, signal processing, and computer vision. We observe a ubiquitous tubal-sampling pattern in big data and Internet of Things (IoT) applications, which is introduced by many reasons such as high data acquisition cost, downsampling for data compression, sensor node failures, and packet losses in low-power wireless transmissions. To meet the time and accuracy requirements of applications, data completion methods are expected to be accurate as well as fast. However, the existing methods for data completion with the tubal-sampling pattern are either accurate or fast, but not both. In this article, we propose high-performance graphics processing unit (GPU) tensor completion for data completion with the tubal-sampling pattern. First, by exploiting the convolution theorem, we split a tensor least-squares minimization problem into multiple least-squares sub-problems in the frequency domain. In this way, massive parallelisms are exposed for many-core GPU architectures while still preserving high recovery accuracy. Second, we propose computing slice-level and tube-level tasks in batches to improve GPU utilization. Third, we reduce the data transfer cost by eliminating the accesses to the CPU memory inside algorithm loop structures. The experimental results show that the proposed tensor completion is both fast and accurate. Using synthetic data of varying sizes, the proposed GPU tensor completion achieves maximum 248.18×, 7, 403.27×, and 33.27× speedups over the CPU MATLAB implementation, GPU element-sampling tensor completion in the cuTensor-tubal library, and GPU high-performance matrix completion, respectively. With a 50 percent sampling rate, the proposed GPU tensor completion achieves a recovery error of 1.40e-5, which is comparable with that of the GPU element-sampling tensor completion and three orders of magnitude better than that of the GPU high-performance matrix completion. To utilize multiple GPUs in servers, we design a multi-GPU scheme for tubal-sampling tensor completion. The multi-GPU tensor completion achieves maximum 1.89× speedup on two GPUs versus on a single GPU for medium or big tensors. We further evaluate the performance of the proposed GPU tensor completion in three real applications, namely, video transmission in wireless camera networks, RF fingerprint-based indoor localization, and seismic data completion, and it achieves maximum speedups of 448.68×, 24.63×, and 311.54×, respectively. We integrate this high-performance GPU tensor completion implementation into the cuTensor-tubal library to support various applications.

Tensors are the cornerstone data structures in high-performance computing, big data analysis and machine learning. However, tensor computations are compute-intensive and the running time increases rapidly with the tensor size. Therefore, designing high-performance primitives on parallel architectures such as GPUs is critical for the efficiency of ever growing data processing demands. Existing GPU basic linear algebra subroutines (BLAS) libraries (e.g., NVIDIA cuBLAS) do not provide tensor primitives. Researchers have to implement and optimize their own tensor algorithms in a case-by-case manner, which is inefficient and error-prone. In this paper, we develop the cuTensor-tubal library of seven key primitives for the tubal-rank tensor model on GPUs: t-FFT, inverse t-FFT, t-product, t-SVD, t-QR, t-inverse, and t-normalization. cuTensor-tubal adopts a frequency domain computation scheme to expose the separability in the frequency domain, then maps the tube-wise and slice-wise parallelisms onto the single instruction multiple thread (SIMT) GPU architecture. To achieve good performance, we optimize the data transfer, memory accesses, and design the batched and streamed parallelization schemes for tensor operations with data-independent and data-dependent computation patterns, respectively. In the evaluations oft-product, t-SVD, t-QR, t-inverse and t-normalization, cuTensor-tubal achieves maximum 16.91x, 27.03x, 38.97x, 22.36x,15.43x speedups respectively over the CPU implementations running on dual 10-core Xeon CPUs. Two applications, namely, t-SVD-based video compression and low-tubal-rank tensor completion, are tested using our library and achieve maximum 9.80x and 269.26x speedups over multi-core CPU implementations.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. However, there are still big challenges in the multi-mode OAM generation, OAM antenna alignment and OAM signal reception. To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs). First, we verify that UCA can generate multi-mode OAM radio beam with both the RF analog synthesis method and the baseband digital synthesis method. Then, for the considered UCA-based LoS MCMM-OAM communication system, a distance and AoA estimation method is proposed based on the two-dimensional ESPRIT (2-D ESPRIT) algorithm. A salient feature of the proposed LoS MCMM-OAM and LoS MCMM-OAM-MIMO systems is that the channel matrices are completely characterized by three parameters, namely, the azimuth angle, the elevation angle and the distance, independent of the numbers of subcarriers and antennas, which significantly reduces the burden by avoiding estimating large channel matrices, as traditional MIMO-OFDM systems. After that, we propose an OAM reception scheme including the beam steering with the estimated AoA and the amplitude detection with the estimated distance. At last, the proposed methods are extended to the LoS MCMM-OAM-MIMO system equipped with uniform concentric circular arrays (UCCAs). Both mathematical analysis and simulation results validate that the proposed OAM reception scheme can eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.

We consider a continuous-time energy harvesting (EH) communication system consisting of an EH transmitter with random lifetime and a receiver. We assume that neither the fading channel state nor the EH state is available to the transmitter and the transmitter can only observe its battery level, based on which it adjusts its transmit power. Specifically, we quantize the battery capacity of the transmitter into a number of levels, and whenever the energy in the battery hits a certain level, the EH transmitter updates its transmit power. Our main objective is to find the optimal battery-level-triggered (BLT) control policy to maximize the expected total throughput of the transmitter in its lifetime. We model the system as an extended two-dimensional stochastic fluid model (2D-SFM), and derive the Laplace-Stieltjes Transform (LST) matrices of the imbedded process on the decision time sequence, based on which, we formulate the power control problem as a Markov decision process (MDP). We obtain the BLT control policy and the maximum expected throughput by solving the 2D-SFM induced MDP. We evaluate the performance of the BLT policy by comparing it with the conventional uniform-in-time control policies, and results show that the proposed BLT power control policy provides significantly higher expected throughput under the same total energy consumption and the same average control frequency.

This paper studies the multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio (SINR) requirement for each user, the sum power constraint at each gateway as well as the per feed power constraints at the satellite. We propose two algorithms to solve the formulated problem. In the first algorithm, by employing the successive convex approximation (SCA) approach, we iteratively approximate the original nonconvex problem to a second-order cone program (SOCP) which can be solved by modern solvers efficiently. For the second, we propose a modified joint power control and beamforming algorithm which computes QoS beamforming and geometric programming (GP) based power allocation iteratively. Compared to the traditional method [5] which uses a subgradient and projection based approach for the power control, the GP based solution is more implementation friendly and practical appealing which also achieves a slightly better sum rate performance. Finally, numerical results are presented to validate the efficiency of the proposed schemes.

Tensor provides a brief and natural representation for large-scale multidimensional data by way of appropriate low-rank approximations, thus we can discover significant latent structures of complex data and generalize data representation. To date, tensor has gained tremendous success in various science and technology fields, especially in machine learning and big data applications. However, tensor computation, especially tensor decomposition, is usually expensive due to the inherent large-size characteristic of tensors, and hence would potentially hinder their future wide deployment. In this paper, we develop a hardware architecture to accelerate tensor singular value decomposition (t-SVD), which is a new tensor decomposition technique that has been successfully applied to high-dimensional data classification and video recovery. Specifically, design consideration of each key computing unit is analyzed and discussed. Then, the proposed t-SVD hardware architecture is implemented and synthesized using CMOS 28nm technology. Comparison with real-world CPU-based implementations shows that the proposed hardware accelerator is expected to provide average 14× speedup on various t-SVD workloads.

BACKGROUND: Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile. This explains the source of genetic heterogeneity observed in tumor sequencing data. To make precise prognosis and design effective therapy for cancer, ascertaining the subclonal composition of a tumor is of great importance. RESULTS: In this paper, we propose a state-space formulation of the feature allocation model. This model is interpreted as the blind deconvolution of the expected variant allele fractions (VAFs). VAFs are deconvolved into a binary matrix of genotypes and a matrix of genotype proportions in the samples. Specifically, we consider a sequential construction of the genotype matrix which we model by Indian buffet process (IBP). We describe an efficient sequential Monte Carlo (SMC) algorithm, SeqClone, that jointly estimates the genotypes of subclones and their proportions in the samples. When compared to other methods for resolving tumor heterogeneity, SeqClone provides comparable and sometimes, better estimates of model parameters. By design, SeqClone conveniently handles any number of probed SNVs in the samples. In particular, we can analyze VAFs from newly probed SNVs to improve existing estimates, an attribute not present in existing solutions. CONCLUSIONS: We show that the SMC algorithm for deconvolving VAFs from tumor sequencing data is a robust and promising alternative for explaining the observed genetic heterogeneity in tumor samples.

Device-to-device (D2D) communications and distributed storage are enabling technologies for the future Internet of Things systems. In this article, we consider power-efficient content delivery in a D2D-assisted wireless distributed storage system, where the partial downloading scheme is employed such that the content requester can download a portion of the stored content from neighboring storage devices. The basic idea is that by downloading a small amount of data from many storage devices, the power consumption is much lower than downloading the entire content from a single device. In designing such a system, we aim to minimize the total power consumption by properly allocating the channel and the amount of transmitted data for each storage device. Moreover, to account for the case that there are more devices than communication channels, we allow multiple devices to share the same channel by employing successive interference cancelation (SIC) decoding. The optimization problem is an integer program and by taking the alternative minimization approach, we decouple it into two subproblems: 1) the packet allocation subproblem, for which we provide the optimal solution and 2) the channel allocation subproblem, for which we provide the efficient suboptimal solution.

We investigate the secure degrees of freedom (SDoF) for the two-user multiple-input multiple-output (MIMO) interference channel with a cooperative jammer, where each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the jammer is equipped with K antennas. For each one of the four regions of parameters (M, N), i.e., 1 ≤ N/M2M. Moreover, we quantify the SDoF gap of the network and reveal it as a function of the number of the antennas. For large K, the upper and lower bounds coincide in all regimes, and the exact SDoF can be achieved.

We consider a centralized caching network, where a server serves several groups of users, each having a common shared homogeneous fixed-size cache and requesting arbitrary multiple files. An existing coded prefetching scheme is employed where each file is broken into multiple fragments and each cache stores multiple coded packets, each formed by XORing fragments from different files. For such a system, we propose an efficient file delivery scheme by the server to meet the arbitrary multi-requests of all user-groups. Specifically, the stored coded packets of each cache are classified into four types based on the composition of the file fragments encoded. A delivery strategy is developed, which separately delivers a part of each packet type first, and then combinatorially delivers the remaining different packet types in the last stage. The rate, as well as the worst rate of the proposed delivery scheme, are analyzed. We show that our caching model and delivery scheme can incorporate some existing coded caching schemes as special cases. Moreover, for the special case of uniform requests and uncoded prefetching, we make a comparison with existing results, and show that our approach can achieve a lower delivery rate. We also provide numerical results on the delivery rate for the proposed scheme.

We consider a system where a massive multiple-input multiple-output (MIMO) base station (BS) transmits information and energy to multiple energy harvesting receivers. Each receiver has no power source and needs to harvest sufficient energy in order to decode its message from the received signal. Under either the power splitting mode or the time switching mode at the receivers, we consider two design problems. One is to maximize the minimum transmission rate among all receivers and the other is to optimize the system energy efficiency (EE) through jointly designing the power allocation proportions at the BS and the power splitting (or time switching) factors at the receivers. The optimal solutions to these problems are obtained either in terms of closed-form expressions or efficient algorithms by leveraging the asymptotic channel orthogonality and hardening effects of massive MIMO. The simulation results indicate that the power splitting mode outperforms the time switching mode in terms of both the minimum transmission rate and the system EE.

Penetrating radar systems are widely employed to scan the objects that are placed behind or buried inside mediums (such as walls, ground, and so on). As the clutter is much stronger than the target echo, clutter removal must be performed before imaging. The moving average subtraction, spatial notch filtering, and singular value decomposition methods are commonly used to remove clutter. However, the drawback is that these methods eliminate some of the target spectrum information, which causes target energy losses and generates side lobes. To solve this problem, two spectrum recovery methods are proposed in this paper. The first method recovers the spectrum magnitude and phase via sinc interpolation and linear fitting, respectively, which is fast and suitable for real-time processing. Although the second method recovers the spectrum based on matrix completion with prior information, which is more accurate and more computational expensive. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed methods can improve various traditional clutter removal methods, the side lobes are clearly suppressed, and the signal-to-clutter ratio is significantly improved.

Due to its flexibility and low cost, the bistatic forward-looking synthetic aperture radar (BFSAR) which employs side-looking transmitter and forward-looking receiver has been studied in recent years. Sparsity-based techniques have been applied in the field of BFSAR imaging and show great potential. In sparsity-based BFSAR imaging, compensation of the motion errors is crucial to get a well-focused image. For fields that admit a sparse representation, we propose a sparsity-based imaging approach integrated with motion error estimation and compensation in this paper. First, a novel joint phase-amplitude compensation-based motion error correction scheme is developed to cope with the spatial variance of motion error. Then, an inversion observation model of the range-Doppler algorithm combined with motion error correction is derived, based on which a joint problem of BFSAR imaging and motion error estimation is formulated as a sparse recovery problem and solved in an iterative way, where in each iteration, both image formation and motion error correction are carried out. Experiments on both the simulated and real BFSAR data show that the proposed method can obtain a more accurate estimation result, and generate better focused images compared with the existing methods.

For a safe and reliable operation of the smart grid, timely detection of cyber-attacks is of critical importance. Moreover, considering smarter and more capable attackers, robust detection mechanisms are needed against a diverse range of cyber-attacks. With these purposes, we propose a robust online detection algorithm for (possibly combined) false data injection and jamming attacks, that also provides online estimates of the unknown and time-varying attack parameters and recovered state estimates. Further, considering smarter attackers that are capable of designing stealthy attacks to prevent the detection or to increase the detection delay of the proposed algorithm, we propose additional countermeasures. Numerical studies illustrate the quick and reliable response of the proposed detection mechanisms against hybrid and stealthy cyber-attacks.

We investigate the fundamental conditions on the sampling pattern, i.e., locations of the sampled entries, for finite completability of a low-rank tensor given some components of its Tucker rank. In order to find the deterministic necessary and sufficient conditions, we propose an algebraic geometric analysis on the Tucker manifold, which allows us to incorporate multiple rank components in the proposed analysis in contrast with the conventional geometric approaches on the Grassmannian manifold. This analysis characterizes the algebraic independence of a set of polynomials defined based on the sampling pattern, which is closely related to finite completability of the sampled tensor, where finite completability simply means that the number of possible completions of the sampled tensor is finite. Probabilistic conditions are then studied and a lower bound on the sampling probability is given, which guarantees that the proposed deterministic conditions on the sampling patterns for finite completability hold with high probability. Furthermore, using the proposed geometric approach for finite completability, we propose a sufficient condition on the sampling pattern that ensures there exists exactly one completion of the sampled tensor.

We investigate the upper and lower bounds on the quantization distortions for independent and identically distributed continuous sources in the finite block-length regime. We derive a lower bound on the quantization distortion, as well as an upper bound on the quantization distortion based on random quantization codebooks. Moreover, we apply the obtained bounds to the continuous Gaussian source. For the Gaussian source, we propose a computationally tractable method to numerically compute the upper and lower bounds, for both bounded and unbounded quantization codebooks. Numerical results show that the gap between the upper and lower bounds is small for the block length of several hundreds, and the upper and lower bounds are tighter than those proposed in the existing works.

This paper investigates the sequential hypothesis testing problem with online sensor selection and sensor usage constraints. That is, in a sensor network, the fusion center sequentially acquires samples by selecting one “most informative” sensor at each time until a reliable decision can be made. In particular, the sensor selection is carried out in the online fashion since it depends on all the previous samples at each time. Our goal is to develop the sequential test (i.e., stopping rule and decision function) and sensor selection strategy that minimize the expected sample size subject to the constraints on the error probabilities and sensor usages. To this end, we first recast the usage-constrained formulation into a Bayesian optimal stopping problem with different sampling costs for the usage-contrained sensors. The Bayesian problem is then studied under both finite- and infinite-horizon setups, based on which, the optimal solution to the original usage-constrained problem can be readily established. Moreover, by capitalizing on the structures of the optimal solution, a lower bound is obtained for the optimal expected sample size. In addition, we also propose algorithms to approximately evaluate the parameters in the optimal sequential test so that the sensor usage and error probability constraints are satisfied. Finally, numerical experiments are provided to illustrate the theoretical findings, and compare with the existing methods.

As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestion and pollution for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving a sufficient number of customers with a minimum number of vehicles and the lowest possible cost. A key component of the PV system is the online ride-sharing scheduling strategy. In this paper, an efficient path-planning strategy based on a greedy algorithm is proposed, which focuses on a limited potential search area for each vehicle by filtering out the requests that violate the passenger service quality level, so that the global search is reduced to a local search. Moreover, the proposed heuristic can be easily used in the future globally optimal algorithm (if it will exist) to speed the computation time. The performance of the proposed solution, such as reduction ratio of computational complexity, is analyzed. Simulations based on the Manhattan taxi data set show that the computing time is reduced by 22% compared with the exhaustive search method under the same service quality performance.

In this paper, we consider an un-cooperative spectrum sharing scenario, where a radar system is to be overlaid to a pre-existing wireless communication system. Given the order of magnitude of the transmitted powers in play, we focus on the issue of interference mitigation at the communication receiver. We explicitly account for the reverberation produced by the (typically high-power) radar transmitter whose signal hits scattering centers (whether targets or clutter) producing interference onto the communication receiver, which is assumed to operate in an un-synchronized and un-coordinated scenario. We first show that the receiver design amounts to solve a joint (non-convex) interference removal and data demodulation problem. Next, we introduce two algorithms exploiting sparsity of a proper representation of the interference and the vector containing demodulation errors of the data block. The first algorithm is basically a relaxed constrained atomic norm minimization, while the latter relies on a two-stage processing structure and is based on alternating minimization. The merits of these algorithms are demonstrated through extensive simulations; interestingly, the two-stage alternating minimization algorithm turns out to achieve satisfactory performance with moderate computational complexity.

In massive multiple-input multiple-output (MIMO) systems with hybrid analog/digital architectures, large training overhead is required for conventional pilot-only methods to estimate channel accurately before detecting data. To reduce the training overhead, a semi-blind detection method is proposed for data detection without knowing channel in an uplink multi-user system. The main idea is to exploit the received signal corresponding to both the pilot and data payload for channel estimation or data detection via a low-rank matrix completion formulation. The leveraged low-rank property stems from the fact that the number of active users K is typically much smaller than the number of antennas Naat a base station and the number of time slots Tcin a coherence interval. Compared with the pilot-only method, the number of pilots required is reduced from an order of Nato K. Two iterative algorithms are introduced to solve the low-rank matrix completion problem: regularized alternating least squares and bilinear generalized approximate message passing. We further extend the semi-blind detection method to systems with low-resolution analog-to-digital converters. Simulation results show that the proposed methods achieve significant performance gain over the pilot-only method with reduced training overhead for hybrid massive MIMO systems in various settings.

This paper studies the multigroup multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio requirement for each user, the sum power constraint at each gateway, as well as the per feed power constraints at the satellite. Both transparent payload and payload with on-board processing are considered. In the former case, we propose a centralized algorithm by employing the successive convex approximation (SCA) approach, which iteratively approximates the original nonconvex problem to a second-order cone program. Moreover, in order, for each gateway, to compute its precoding vector locally with local channel state information, we devise a decentralized algorithm by incorporating consensus alternating direction method of multipliers (ADMM) into the SCA framework. For the latter case, we devise a two-stage precoding scheme where, in the first stage, a leakage-based minimum mean-square-error scheme is employed to control the feeder link interference efficiently. In the subsequent second stage, we use the SCA-ADMM approach to deal with the user link interference while maximizing the sum rate. Finally, numerical results are presented to demonstrate the performance of the proposed schemes.

In this paper, we study wireless power transfer (WPT) using the discrete lens array-based beamspace large-scale multiple-input multiple-output (MIMO) system. The channel matrix of beamspace MIMO exhibits sparse property, which enables the transmitter to employ only a small number of active antennas while maintaining the full MIMO performance. Hence, the number of radio frequency (RF) chains can be significantly reduced, cutting the cost of hardware implementation and circuit power consumption. We consider two WPT design problems in the beamspace MIMO system with constraints on the number of RF chains: the sum power transfer and the max-min power transfer problems; and for each problem, we consider both multi-stream and uni-stream transmissions. For the sum power transfer problem, we show that the uni-stream power transfer achieves the same performance as the multi-stream case, and we propose two algorithms for the uni-stream transmission, namely, an eigendecomposition-based greedy algorithm and a truncated power iteration algorithm. For the max-min power transfer problem, we propose a semidefinite relaxation-based greedy algorithm for the multi-stream power transfer and a Riemannian conjugate gradient algorithm for the uni-stream case. The simulation results show that with a small number of RF chains, the beamspace MIMO system significantly outperforms the conventional MIMO system in terms of WPT efficiency.

Grant-free non-orthogonal multiple access (NOMA) has recently gained significant attention for reducing signaling overhead in machine-type communications (MTC). In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame based multi-user uplink scenarios where users are (in)active for the duration of a frame, namely frame-wise joint sparsity model. Firstly, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and consider that the user sparsity level should be unknown for multiuser detection, two enhanced BCS- based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross validation aided block sparsity adaptive subspace pursuit (CVA- BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance, by reasonably setting the threshold based on the AWGN noise floor. And the proposed CVA-BSASP algorithm is a highly practical algorithm design that does not require any prior knowledge, by adopting the statistical and machine learning mechanism cross validation (CV) to determine the stopping condition of the algorithm. Superior performance of the proposed algorithms is demonstrated by numerical experiments.

Interferences due to non-orthogonality of training sequences usually exist in cellular networks when the number of all users is relatively large compared to the coherence time, such as the case in massive MIMO systems. In this paper, we address this effect from the perspective of non-orthogonal training sequence design in two-cell interference networks with K users per cell. We relax the general assumption in which the cross-correlations of sequences are restricted to be 0 or 1, and target at designing the training sequences to minimize training phase interference with a given pilot length τ, which is no larger than the total number of users, i.e., τ ∈ [K, 2K]. We note that when large scale fading between different cells β ≠ 1, the strengths of interferences arising from non-orthogonal training sequences within a cell or from the adjacent cell become asymmetric, and optimal design needs to treat the intra-cell sequence correlation and inter-cell correlation differently. To this end, by incorporating β into the design, we extend the Welch bound (Welch 1974 [1]) to the two-cell scenario with asymmetric intra-cell and inter-cell interference, and characterize the lower bound of the interference precisely. Specifically, we obtain the result that the sum of the squares of β -weighted cross-correlations of the training sequences is lower-bounded by [(2K2(1+β2))/(K+(τ-K)β2)], which can be achieved by the proposed training sequence design in closed-form. Particularly, when β = 1, this bound reduces to [((2K)2)/(τ)] which is exactly the Welch bound. This result is applicable for the uplink design of general interference networks such as the pilot design in massive MIMO and the signature sequence design in multicell CDMA systems.

Recent development in wireless power transfer enables a new paradigm of energy harvesting communications that can significantly impact in Internet of Things (IoT) applications. In this paper, we study the wirelessly powered IoT networks, in which a central node transmits radio frequency (RF) energy to power the IoT sensors, and the sensors harvest RF power to transmit data back to the central node. In this IoT network, the sensors transmit data according to their energy utilization policies, and the central node allocates charging powers to all sensors. We design the sensor energy utilization policies and the power allocation among sensors by maximizing the total data throughput of the system. In particular, the subproblem of energy utilization policy design is formulated as a Markov decision process and the power allocation subproblem is formulated as discrete optimization. By showing several key properties of these two subproblems, we propose low-complexity algorithms to solve the subproblems optimally. We demonstrate the performance gains of the proposed algorithms over some simple heuristics via simulations in terms of the total data throughput in wireleslly powered IoT networks.

In this paper, we consider a scenario where the satellite-terrestrial network is overlaid over the legacy cellular network. The established communication system is operated in the millimeter wave (mmWave) frequencies, which enables the massive antennas arrays to be equipped on the satellite and terrestrial base stations (BSs). The secure communication in this coexistence system of the satellite-terrestrial network and cellular network through the physical-layer security techniques is studied in this paper. To maximize the achievable secrecy rate of the eavesdropped fixed satellite service, we design a cooperative secure transmission beamforming scheme, which is realized through the satellite's adaptive beamforming, artificial noise, and BSs' cooperative beamforming implemented by terrestrial BSs. A non-cooperative beamforming scheme is also designed, according to which BSs implement the maximum ratio transmission beamforming strategy. Applying the designed secure beamforming schemes to the coexistence system established, we formulate the secrecy rate maximization problems subjected to the power and transmission quality constraints. To solve the nonconvex optimization problems, we design an approximation and iteration-based genetic algorithm, through which the original problems can be transformed into a series of convex quadratic problems. Simulation results show the impact of multiple antenna arrays at the mmWave on improving the secure communication. Our results also indicate that through the cooperative and adaptive beamforming, the secrecy rate can be greatly increased. In addition, the convergence and efficiency of the proposed iteration-based approximation algorithm are verified by the simulations.

In this paper, we investigate the transceiver design for multiple-input multiple-output visible light communication (VLC) systems that employ the integer-forcing (IF) lattice decoding technique. To facilitate the joint design of the transmitter precoder, the integer matrix and the receiver equalizer, we first give a necessary and sufficient condition for the integer matrix to be invertible over 1-D lattice. Based on this condition, we then propose a new method for choosing the integer matrix which achieves better performance than the existing lattice reduction method. Moreover, taking into account two typical constraints in VLC, we optimize transmit and receive matrices using the conditional gradient method or the projected gradient method. Finally, the integer matrix and the transmit and receive matrices are jointly optimized in an iterative manner. Simulation results are provided to demonstrate the performance improvement achieved by the proposed framework.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based two-way relay network with secrecy considerations, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of single and multiple eavesdroppers. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to degrade the performance of any potential eavesdropper. Moreover, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. We propose different decoding schemes based on the successive interference cancellation for the legitimate users, relay, and eavesdroppers. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols under both single- and multiple-eavesdropper cases are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

In this letter, we study the deterministic sampling patterns for the completion of low-rank matrix, when corrupted with a sparse noise, also known as robust matrix completion. We extend the recent results on the deterministic sampling patterns in the absence of noise based on the geometric analysis on the Grassmannian manifold. A special case where each column has a certain number of noisy entries is considered, where our probabilistic analysis performs very efficiently. Furthermore, assuming that the rank of the original matrix is not given, we provide an analysis to determine if the rank of a valid completion is indeed the actual rank of the data corrupted with sparse noise by verifying some conditions.

In this letter, we propose new sub-Nyquist sampling schemes for multiple sinusoids, which require fewer number of samples than previous works. Since it is impossible to resolve the frequency ambiguity using a single sub-Nyquist sample sequence, an additional sampling channel is used to determine the correct frequencies. First, a time-staggered sampling system, with the staggered time less than or equal to the Nyquist sampling interval, is proposed. This approach requires only 3K samples to estimate the K frequency components in the signal. However, aliasing can occur when the differences between some frequencies are integer multiples of the sampling rate. Then, another sampling strategy that makes use of feedback is proposed to prevent aliasing. We demonstrate that using two sampling channels and with feedback, 4K samples suffice to resolve both frequency ambiguity and image frequency aliasing. Simulation results are provided to demonstrate the effectiveness of the proposed systems.

This letter considers the decentralized detection of a noncooperative moving target by employing a wireless sensor network. Suppose that, if present, the target moves along a direction with a constant velocity, and it emits an unknown signal experiencing distance-dependent attenuation that is periodically sampled by sensors. The sensor observations are quantized into one-bit data individually and then sequentially transmitted to a fusion center, which is in charge of making a global decision. We first derive the generalized Rao test statistic as a more computationally efficient alternative when compared to the typical generalized likelihood ratio test statistic. Then, we propose a truncated one-sided sequential (TOS) test rule by imposing a finite maximum stopping time (namely the deadline) on typical one-sided sequential tests. With a deadline slightly larger than the sample size of a benchmarked fixed-sample-size (FSS) test, the proposed TOS test rule provides the same detection performance and significantly accelerates the target-detection process on average, which is corroborated by simulation results.

The emerging Internet of Things (IoT) systems are fueling an exponential explosion of sensory data. The major challenge to effective implementation of IoT systems is the presence of massive missing data entries, measurement noise, and anomaly readings, which motivates us to investigate the robust recovery of sensory big data. In this paper, we propose an LS-decomposition approach that decomposes a sensory reading matrix as the superposition of a Low-rank matrix and a Sparse anomaly matrix. First, based on data sets from three representative real-world IoT projects, i.e., the IntelLab project (indoor environment), the GreenOrbs project (mountain environment), and the NBDC-CTD project (ocean environment), we observe that anomaly readings are ubiquitous and cannot be ignored. Second, we prove that the convex surrogate of the LS-decomposition problem guarantees bounded recovery error under proper conditions. Third, we propose an accelerated proximal gradient algorithm that converges to the optimal solution at a rate that is inversely proportional to the square of the number of iterations. Evaluations on the above three data sets show that the proposed scheme achieves (relative) recovery error ≤ 0.05 for missing data rate ≤ 50 percent and almost exact recovery for missing data rate ≤ 40 percent, while previous methods have (relative) recovery error 0.04 ~0.15 even at only 10 percent missing data rate.

The millimeter-wave (mmWave) full-dimensional (FD) MIMO system employs planar arrays at both the base station and the user equipment and can simultaneously support both azimuth and elevation beamforming. In this paper, we propose atomic-norm-based methods for mm-wave FD-MIMO channel estimation under both uniform planar arrays (UPA) and non-uniform planar arrays (NUPA). Unlike existing algorithms, such as compressive sensing (CS) or subspace methods, the atomic-norm-based algorithms do not require to discretize the angle spaces of the angle of arrival and angle of departure into grids, thus provide much better accuracy in estimation. In the UPA case, to reduce the computational complexity, the original large-scale atomic norm minimization problem is approximately reformulated as a semi-definite program (SDP) containing two decoupled two-level Toeplitz matrices. The SDP is then solved via the alternating direction method of multipliers where each iteration involves only closed-form computations. In the NUPA case, the atomic-norm-based formulation for channel estimation becomes nonconvex and a gradient-decent-based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithms achieve better performance than the CS-based and subspace-based algorithms.

We consider a cache- and SIC-enabled wireless network, where the locations of the base stations (BSs) and users are modeled by the Poisson point process and Poisson cluster process, respectively. The BS serves two users simultaneously by transmitting a superimposed signal, and each user employs a joint scheme of cache-assisted interference cancellation (CAIC) and successive interference cancellation (SIC). Each user is classified as a caching or non-caching user depending on whether or not it caches data sent to its interferer in the same cell. The receiver operation of each user is classified into three possible cases: CAIC only, both CAIC and SIC, and SIC only. Then, the successful packet transmission probabilities of the caching and non-caching users are derived, based on which the average spectral efficiency (SE) and the average area SE (ASE) are obtained. The average SE and ASE are further optimized. Moreover, extensions to the scenario where the superimposed transmissions are within a finite radius around the BS are provided. Finally, the result for the general scenario of the BS serving multiple users simultaneously is obtained. The optimization problem of the general scenario is solved by converting it to the convex or the difference of convex problems in different subregions. Simulation results verify the analytical accuracy and the advantages of the proposed schemes.

In this paper, we consider a multicell system with cache-equipped base stations (BSs) and users. A coded caching scheme is employed at user ends such that multiple requested file contents can be combined and encoded together at the BS, and decoded successfully at the users. The BSs can then multicast the coded cached content and unicast the uncached content to users. Two different system models are proposed based on different levels of BS cooperation. We formulate the joint multicast and unicast beamformer design problem to maximize the weighted user fairness rate subject to the per BS power constraint. We propose a Riemannian conjugate gradient algorithm to solve the problem, which operates on the multi-sphere manifold of the parameter space and has a low computational complexity but better performance compared with the existing semi-definite relaxation methods. Extensive simulation results are provided to demonstrate the performance of the proposed systems.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

In this paper, online detection of false data injection attacks and denial of service attacks in the smart grid is studied. The system is modeled as a discrete-time linear dynamic system and state estimation is performed using the Kalman filter. The generalized cumulative sum algorithm is employed for quickest detection of the cyber-attacks. Detectors are proposed in both centralized and distributed settings. The proposed detectors are robust to time-varying states, attacks, and set of attacked meters. Online estimates of the unknown attack variables are provided, that can be crucial for a quick system recovery. In the distributed setting, due to bandwidth constraints, local centers can only transmit quantized messages to the global center, and a novel event-based sampling scheme called level-crossing sampling with hysteresis is proposed that is shown to exhibit significant advantages compared with the conventional uniform-in-time sampling scheme. Moreover, a distributed dynamic state estimator is proposed based on information filters. Numerical examples illustrate the fast and accurate response of the proposed detectors in detecting both structured and random attacks and their advantages over existing methods.

This paper analyzes the asymptotic performances of fully distributed sequential hypothesis testing procedures as the type-I and type-II error rates approach zero, in the context of a sensor network without a fusion center. In particular, the sensor network is defined by an undirected graph, where each sensor can observe samples over time, access the information from the adjacent sensors, and perform the sequential test based on its own decision statistic. Different from most literature, the sampling process and the information exchange process in our framework take place simultaneously (or, at least in comparable time-scales), thus cannot be decoupled from one another. Our goal is to achieve second-order asymptotically optimal performance at every sensor, i.e., the average detection delay is within a constant gap from the centralized optimal sequential test as the error rates approach zero for the fixed number of sensors. To that end, a type of test procedure that resembles the well-known sequential probability ratio test (SPRT), termed as distributed SPRT (DSPRT) in this paper, is studied based on two message-passing schemes, respectively. The first scheme features the dissemination of the raw samples. In specific, every sample propagates over the network by being relayed from one sensor to another until it reaches all the sensors in the network. Although the sample propagation-based DSPRT is shown to yield the asymptotically optimal performance at each sensor, it incurs excessive intersensor communication overhead due to the exchange of raw samples with index information. The second scheme adopts the consensus algorithm, where the local decision statistic is exchanged between sensors instead of the raw samples, thus significantly lowering the communication requirement compared with the first scheme. In particular, the decision statistic for DSPRT at each sensor is updated by the weighted average of the decision statistics in the neighborhood at every message-passing step. We show that, under certain regularity conditions, the consensus algorithm-based DSPRT also yields the secondorder asymptotically optimal performance at all sensors given a fixed number of sensors. Our asymptotic analyses of the two message-passing-based DSPRTs are then corroborated by simulations using the Gaussian and Laplacian samples.

Public vehicle (PV) systems are promising transportation systems for future smart cities which provide dynamic ride-sharing services according to passengers' requests. PVs are driverless/self-driving electric vehicles which require frequent recharging from smart grids. For such systems, the challenge lies in both the efficient scheduling scheme to satisfy transportation demands with service guarantee and the cost-effective charging strategy under the real-time electricity pricing. In this paper, we study the joint transportation and charging scheduling for PV systems to balance the transportation and charging demands, ensuring the long-term operation. We adopt a cake cutting game model to capture the interactions among PV groups, the cloud and smart grids. The cloud announces strategies to coordinate the allocation of transportation and energy resources among PV groups. All the PV groups try to maximize their joint transportation and charging utilities. We propose an algorithm to obtain the unique normalized Nash equilibrium point for this problem. Simulations are performed to confirm the effects of our scheme under the real taxi and power grid data sets of New York City. Our results show that our scheme achieves almost the same transportation performance compared with a heuristic scheme, namely, transportation with greedy charging; however, the average energy price of the proposed scheme is 10.86% lower than the latter one.

Grant-free non-orthogonal multiple access has recently gained significant attention for reducing signaling overhead in machine-type communications. In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame-based multi-user uplink scenarios where users are (in)-active for the duration of a frame, namely, the frame-wise joint sparsity model. First, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and considering the user sparsity level to be unknown for multiuser detection, two enhanced BCS-based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross-validation aided block sparsity adaptive subspace pursuit (CVA-BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance by reasonably setting the threshold based on the additive white Gaussian noise floor. Moreover, the proposed CVA-BSASP algorithm is a highly practical algorithm design that adopts the statistical and machine learning mechanism cross-validation to determine the stopping condition of the algorithm and this does not require prior knowledge. Furthermore, the convergence and the computational complexity of the proposed algorithms are derived and the superior performance of the proposed algorithms is demonstrated by numerical experiments.

We address full-duplex relaying for optical wireless communication with stochastic and non-negligible self-interference at the relay node with on-off keying modulation. Assume a relay communication system with a source node, a relaying node, and a destination node, where there is no interference at the source and destination nodes, since they merely transmit or receive. We propose two full-duplex relay protocols, namely, the detect-and-forward relaying protocol and the decode-and-forward relay protocol. For the former, the relay node performs the symbol detection and forwards the detected symbols to the destination node, and for the latter, the relay node performs block decoding and forwards the re-encoded block to the destination node. We prove that for sufficiently high self-interference intensity, the achievable transmission rate of the full-duplex relay system can be lower than that of the half-duplex relay system without self-interference for the detect-and-forward relay protocol. We also prove that for the decode-and-forward relay protocol, the full-duplex relay system always has a larger transmission rate compared with the half-duplex relay system. This can be justified by the fact that, for the decode-and-forward protocol, the source-relay link adopts two codebooks in the scenarios with and without self-interference. The performance of the two proposed protocols is evaluated by the numerical results.

In this paper, the interference statistics in a clustered device-to-device network is studied and the cluster centers are modeled as a Poisson point process. For such, each active receiver device chooses its transmitter randomly following the uniform distribution. We first characterize the statistics of the number of intra/inter-cluster interferers and develop the simplified expressions for several special cases. With the aid of these derived probability distributions of the number of interferers, the Laplace transforms of intra/inter-cluster interference are formulated and simplified approximations for the special cases are also attained. Finally, several applications of the derived interference statistics are shown to illustrate the performance difference between the proposed interference model and the assumed one in the literature.

We consider a cache- and SIC-enabled wireless network where the locations of the base stations (BSs) and users are modeled by the Poisson point process (PPP) and Poisson cluster process, respectively. The BS serves two users simultaneously by transmitting a superimposed signal and each user employs a joint scheme of cache-assisted interference cancellation (CAIC) and successive interference cancellation (SIC). Each user is classified as a caching or non-caching user depending on whether or not it caches data sent to its interferer in the same cell. The receiver operation of each user is classified into three possible cases: CAIC only, both CAIC and SIC, and SIC only. Then the successful packet transmission probabilities of the caching and non-caching users are derived, based on which the spectral efficiency (SE) are obtained. The power allocation problems are further formulated to maximize the SE, which prove to be convex.

This work considers the sequential hypothesis testing problem in the fully distributed sensor network. In specific, each sensor can observe samples over time, exchange information with adjacent sensors, and perform testing based on its own locally available decision statistic. Under such setting, we study the sequential probability ratio test based on the statistic that is obtained by running consensus algorithm. It is shown that, under certain regularity conditions on the data distribution and network topology, this distributed sequential test procedure yields the order-2 asymptotically optimal performance at all sensors.

In this paper, we consider the energy-bandwidth allocation for a network of multiple users, where the transmitters each powered by both an energy harvester and conventional grid, access the network orthogonally on the assigned frequency band. The tradeoff among the weighted sum throughput, the use of grid energy, and the amount of energy cooperation is studied through an optimization objective which is a linear combination of these quantities. To solve the problem efficiently, an iterative algorithm is proposed using the Proximal Jacobian ADMM. We show that this algorithm converges to the optimal solution with an overall complexity of O(N2K2). Numerical results show that the proposed algorithms can make efficient use of the harvested energy, grid energy, energy cooperation, and the available bandwidth.

In this paper, we develop optimal energy scheduling algorithms for optical Poisson channels with energy harvesting devices. The objective is to maximize the channel sum-rate, assuming that the side information of energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O(K) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver is exponential in the number of constraints. This paper gives an efficient energy scheduling algorithm that has a computational complexity of O(K2). The proposed algorithm is also shown to be optimal. The proposed energy schedule is piece-wise constant, which changes when the battery overflows or depletes. Numerical results depict significant improvement of the optimal strategy over benchmark strategies.

In this paper, we characterize the deterministic conditions on the locations of the sampled entries, which are equivalent (necessary and sufficient) to finite completability of a tensor given some components of its Tucker rank. In order to derive this characterization, we propose an algebraic geometric analysis on the Tucker manifold, which allows us to incorporate multiple rank components in the proposed analysis in contrast with the conventional geometric approaches on the Grassmannian manifold. Then, using the developed tools for this analysis, we also derive a sufficient condition on the sampling pattern that ensures there exists only one completion for the sampled tensor (unique completability).

In this paper, we consider the problem of completing a sampled matrix U = [U1|U2] given the ranks of U, U1, and U2which is known as the multi-view data completion problem. We characterize the deterministic conditions on the locations of the sampled entries that is equivalent (necessary and sufficient) to finite completability of the sampled matrix. To this end, in contrast with the existing analysis on Grassmannian manifold for a single-view matrix, i.e., conventional matrix completion, we propose a geometric analysis on the manifold structure for multi-view data to incorporate more than one rank constraint. Then, using the proposed geometric analysis, we propose sufficient conditions on the sampling pattern, under which there exists only one completion (unique completability) given the three rank constraints.

Using multi-antennas can improve the received energy efficiency of the radio-frequency (RF) enabled wireless power transfer (WPT) system. However, for the resource constrained internet of things (IoT) devices, only partial information which is received signal strength (RSS) value instead of channel state information (CSI) can be fed back. In this paper, we propose an adaptive random beamforming algorithm based on Monte-Carlo method to achieve the maximum received power efficiency. The proposed algorithm does not require any complicated channel estimation and it adapts the beamforming scheme only according to the RSS values. Gibbs sampling is used to generate the random beamforming weight vectors and re-sample them according to the feedback RSS values in an iterative manner. In addition, we employ a simulated annealing algorithm to control the convergence rate. The simulation results indicate that this algorithm can fast converge to an optimal value and achieve the maximum received power.

The recently proposed concept of beamspace MIMO can significantly reduce the number of power- hungry radio frequency (RF) chains in millimeter- wave (mmWave) massive MIMO systems. However, most existing studies ignore the power leakage problem in beamspace MIMO systems, which results in an obvious loss in the achievable sum rate. In this paper, a phase shifter network (PSN)-based precoding structure is proposed to solve this problem. Its key idea is to employ multiple phase shifters from each RF chain to select multiple instead of only one beam to collect most of the leaked power. Based on the proposed structure, a rotation-based precoding algorithm is further designed to maximize the signal-to-noise-ratio (SNR) of each user by rotating the channel gains of the selected beams to the same direction. Simulation results show that the proposed PSN- based precoding can effectively collect the leaked power to achieve the near-optimal sum rate, and enjoys a higher energy efficiency than the conventional precoding solutions.

Samples of molecular sequence data of a locus obtained from random individuals in a population are often related by an unknown genealogy. More importantly, population genetics parameters, for instance, the scaled population mutation rate Θ=4N e μ for diploids or Θ=2N e μ for haploids (where N e is the effective population size and μ is the mutation rate per site per generation), which explains some of the evolutionary history and past qualities of the population that the samples are obtained from, is of significant interest. In this paper, we present the evolution of sequence data in a Bayesian framework and the approximation of the posterior distributions of the unknown parameters of the model, which include Θ via the sequential Monte Carlo (SMC) samplers for static models. Specifically, we approximate the posterior distributions of the unknown parameters with a set of weighted samples i.e., the set of highly probable genealogies out of the infinite set of possible genealogies that describe the sampled sequences. The proposed SMC algorithm is evaluated on simulated DNA sequence datasets under different mutational models and real biological sequences. In terms of the accuracy of the estimates, the proposed SMC method shows a comparable and sometimes, better performance than the state-of-the-art MCMC algorithms. We showed that the SMC algorithm for static model is a promising alternative to the state-of-the-art approach for simulating from the posterior distributions of population genetics parameters.

Two‐way relay is potentially an effective approach to spectrum sharing and aggregation by allowing simultaneous bidirectional transmissions between source–destinations pairs. In this study, the two‐way relay network, a class of four‐unicast networks, where there are four source/destination nodes and two relay nodes, with each source sending a message to its destination, is studied. They show that without relay caching the total degrees of freedom (DoF) is bounded from above by , indicating that bidirectional links do not double the DoF (it is known that the total DoF of one‐way relay network is 2). Further, they show that the DoF of is achievable for the two‐way relay network with relay caching. Finally, even though the DoF of this network is no more than for generic channel gains, DoF of 4 can be achieved for a symmetric configuration of channel gains.

We consider the problem of low canonical polyadic (CP) rank tensor completion. A completion is a tensor whose entries agree with the observed entries and its rank matches the given CP rank. We analyze the manifold structure corresponding to the tensors with the given rank and define a set of polynomials based on the sampling pattern and CP decomposition. Then, we show that finite completability of the sampled tensor is equivalent to having a certain number of algebraically independent polynomials among the defined polynomials. Our proposed approach results in characterizing the maximum number of algebraically independent polynomials in terms of a simple geometric structure of the sampling pattern, and therefore we obtain the deterministic necessary and sufficient condition on the sampling pattern for finite completability of the sampled tensor. Moreover, assuming that the entries of the tensor are sampled independently with probability $p$ and using the mentioned deterministic analysis, we propose a combinatorial method to derive a lower bound on the sampling probability $p$, or equivalently, the number of sampled entries that guarantees finite completability with high probability. We also show that the existing result for the matrix completion problem can be used to obtain a loose lower bound on the sampling probability $p$. In addition, we obtain deterministic and probabilistic conditions for unique completability. It is seen that the number of samples required for finite or unique completability obtained by the proposed analysis on the CP manifold is orders-of- magnitude lower than that is obtained by the existing analysis on the Grassmannian manifold.

Recently, fundamental conditions on the sampling patterns have been obtained for finite completability of low-rank matrices or tensors given the corresponding ranks. In this paper, we consider the scenario where the rank is not given and we aim to approximate the unknown rank based on the location of sampled entries and some given completion. We consider a number of data models, including single-view matrix, multi-view matrix, CP tensor, tensor-train tensor and Tucker tensor. For each of these data models, we provide an upper bound on the rank when an arbitrary low-rank completion is given. We characterize these bounds both deterministically, i.e., with probability one given that the sampling pattern satisfies certain combinatorial properties, and probabilistically, i.e., with high probability given that the sampling probability is above some threshold. Moreover, for both single-view matrix and CP tensor, we are able to show that the obtained upper bound is exactly equal to the unknown rank if the lowest-rank completion is given. Furthermore, we provide numerical experiments for the case of single-view matrix, where we use nuclear norm minimization to find a low-rank completion of the sampled data and we observe that in most of the cases the proposed upper bound on the rank is equal to the true rank.

Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth generation wireless communication networks, which is expected to support massive connectivity for massive machine-type communications (mMTC). Due to the sporadic communication nature of mMTC, the grant-free transmission methodology is highly expected in uplink NOMA systems, to drastically reduce the transmission latency and signaling overhead. Exploiting the inherent sparsity nature of user activity, compressive sensing (CS) techniques have been applied for efficient multi-user detection in the uplink grant-free NOMA. In this paper, we propose a prior-information-aided adaptive subspace pursuit (PIA-ASP) algorithm to improve the multi-user detection performance. In this algorithm, a parameter evaluating the quality of the prior-information support set is introduced, in order to exploit the intrinsically temporal correlation of active user support sets in several continuous time slots adaptively. Then, to mitigate the incorrect estimation effect of the prior support quality information, a robust PIA-ASP algorithm is further proposed, which adaptively exploits the prior support based on the corresponding support quality information in a conservative way. It is noted that both of the two proposed algorithms do not require the knowledge of the user sparsity level, while most of the state-of-the-art CS-based multi-user detection algorithms usually need. Moreover, for the two proposed algorithms, the upper bound of the signal detection error and the computational complexity is derived. Simulation results demonstrate that the two proposed algorithms are capable of achieving much better performance than that of the existing CS-based multi-user detection algorithms with a similar computational complexity.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based multi-pair two-way relay (TWR) network, in which a rate splitting scheme and a successive group decoding strategy are employed. By exploiting the interference signals received from neighbor users with the leverage of the full-duplex technique, we enhance the decoding ability of each user and further achieve an effective multiuser interference management for the network. We propose different decoding strategies for different types of nodes by processing the received signals with only local incoming channel state information in different manners. Moreover, under the limited group decoding size, each individual node decodes its own desired messages along with a fraction of the interference successively. We further investigate the joint uplink and downlink fair rate allocation problem based on the max-min criterion, and the solution to which also contains the optimal group decoding schedule. Simulation results in terms of ergodic rate and outrage probability corroborate the superiority of our NOMA-based multi-pair TWR network over the OMA-based counterpart.

Detection of unknown signals is considered under complex elliptically symmetric (CES) distributions, a wide family that includes several well-known distributions as special cases. We study the detection problem for unknown deterministic signals and random CES signals, both corrupted by CES noise. For detection of unknown deterministic signals, we form the generalized likelihood ratio test and reduce it to a sufficient test statistic, which is either the norm of the received vector or a function of it. Performance analysis is provided under both known and estimated noise scatter matrix. For detection of random CES signals, we form the likelihood ratio test, provide performance analysis, and establish conditions under which the sufficient test statistic is given by the norm of the received vector. Interestingly, this norm test statistic is proven to be the uniformly most powerful test for the detection of all CES distributed signals.

Considering a dual-hop energy-harvesting (EH) relaying system, this paper advocates novel relaying protocols based on adaptive time-switching (TS) for amplify-and-forward and decode-and-forward modes, respectively. The optimal TS factor is first studied, which is adaptively adjusted based on the dual-hop channel state information (CSI), accumulated energy, and threshold signal-to-noise ratio (SNR), to achieve the maximum throughput efficiency per block. To reduce the CSI overhead at the EH relay, a low-complexity TS factor design is presented, which only needs single-hop CSI to determine the TS factor. Theoretical results show that, in comparison with the conventional solutions, the proposed optimal/low-complexity TS factor can achieve higher limiting throughput efficiency for sufficiently small threshold SNR. As the threshold SNR approaches infinity, the throughput efficiency of the proposed optimal/low-complexity TS factor tends to zero in a much slower pace than that of the conventional solutions. Simulation results are presented to corroborate the proposed methodology.

We consider content delivery to users in a system consisting of a macro base station (BS), several energy-harvesting small cells (SCs), and many users. Each SC has a large cache and stores a copy of all contents in the BS. A user's content request can be either handled by the SC for free if it has enough energy, or by the BS that has a cost. Each user has a finite cache and can store some most popular contents. We propose an efficient coded content caching schemes and an optimal transmission schemes for this system to maximally off-load the data traffic from the macro BS to the energy-harvesting SCs, and therefore minimize the power consumption from the grid. Specifically, the proposed coded caching scheme stores fractions of some most popular contents, such that contents requested from multiple users can be simultaneously delivered by the BS or SC. Moreover, the optimal transmission policy is formulated and solved as a Markov decision process. Extensive simulation results are provided to demonstrate that the proposed coded caching and transmission schemes can provide significantly higher traffic off-loading capability compared with systems with no caching or with uncoded caching, as well as systems that employ heuristic-based transmission schemes.

Wireless power transfer (WPT) provides a novel solution to the painstaking power-charging issue in wireless sensor networks. However, due to the propagation loss, the fast attenuation in energy transfer efficiency over the transmission distance is the main impediment to the WPT application. In this paper, we apply the simultaneous wireless information and power transfer (SWIPT) to a wirelessly powered sensor network, where each node has two circuits, which operate on energy harvesting mode and information decoding mode separately. We propose a novel cooperative SWIPT scheme (CSS) for this system. First, we present a conflict-free schedule initialization algorithm for CSS. For a given conflict-free schedule, we formulate a resource allocation problem to maximize the network energy efficiency, which is then transformed to an equivalent convex optimization problem and resolved via dual decomposition. Finally, a heuristic algorithm is presented to achieve the transmission schedule with the maximum energy efficiency and the corresponding resource assignment policy. Simulation results indicate that the CSS can significantly improve the energy efficiency of the wirelessly powered sensor network.

This paper considers the sequential composite hypothesis test with multiple sensors. The sensors observe random samples in parallel and communicate with a fusion center, who makes the global decision based on the sensor inputs. On the one hand, in the centralized scenario, where local samples are precisely transmitted to the fusion center, the generalized sequential likelihood ratio test (GSPRT) is shown to be asymptotically optimal in terms of the expected sample size as error rates tend to zero. On the other hand, for systems with limited power and bandwidth resources, decentralized solutions that only send a summary of local samples (we particularly focus on a one-bit communication protocol) to the fusion center is of great importance. To this end, we first consider a decentralized scheme where sensors send their one-bit quantized statistics every fixed period of time to the fusion center. We show that such a uniform sampling and quantization scheme is strictly suboptimal and its suboptimality can be quantified by the KL divergence of the distributions of the quantized statistics under both the hypotheses. We then propose a decentralized GSPRT based on level-triggered sampling. That is, each sensor runs its own GSPRT repeatedly and reports its local decision to the fusion center asynchronously. We show that this scheme is asymptotically optimal as the local thresholds and global thresholds grow large at different rates. Finally, two particular models and their associated applications are studied to compare the centralized and decentralized approaches. Numerical results are provided to demonstrate that the proposed level-triggered sampling based decentralized scheme aligns closely with the centralized scheme with substantially lower communication overhead, and significantly outperforms the uniform sampling and quantization-based decentralized scheme.

In many application areas ranging from bioinformatics to imaging, we are faced with the following question: can we recover a sparse vector xo∈ ℝNfrom its undersampled set of noisy observations y ∈ ℝn, y = Axo+w. The last decade has witnessed a surge of algorithms and theoretical results to address this question. One of the most popular schemes is the ℓp-regularized least squares given by the following formulation:x̂(y, p) ∈ arg minx(1/2)∥y - Ax∥22+ γ∥x∥pp, where p ∈ [0, 1]. Among these optimization problems, the case p = 1, also known as LASSO, is the best accepted in practice, for the following two reasons. First, thanks to the extensive studies performed in the fields of high-dimensional statistics and compressed sensing, we have a clear picture of LASSO's performance. Second, it is convex and efficient algorithms exist for finding its global minima. Unfortunately, neither of the above two properties hold for 0 ≤ pothan x̂(γ, 1). Second, if we employ iterative methods that aim to converge to a local minima of arg minx(1/2)∥y - Ax∥22+ γ∥x∥pp, then under good initialization, these algorithms converge to a solution that is still closer to xothan x̂(γ, 1). In spite of the existence of plenty of empirical results that support these folklore theorems, the theoretical progress to establish them has been very limited. This paper aims to study the above-mentioned folklore theorems and establish their scope of validity. Starting with approximate message passing (AMP) algorithm as a heuristic method for solving ℓp-regularized least squares, we study the following questions. First, what is the impact of initialization on the performance of the algorithm? Second, when does the algorithm recover the sparse signal xounder a “good” initialization? Third, when does the algorithm converge to the sparse signal regardless of the initialization? Studying these questions will not only shed light on the second folklore theorem, but also lead us to the answer the first one, i.e., the performance of the global optima x̂(γ, p). For that purpose, we employ the replica analysis1to show the connection between the solution of AMP and x̂(γ, p) in the asymptotic settings. This enables us to compare the accuracy of x̂(γ, p) and x̂(γ, 1). In particular, we will present an accurate characterization of the phase transition and noise sensitivity of ℓp-regularized least squares for every 0 ≤ pp-regularized least squares (if γ is tuned optimally) exhibits the same phase transition for every 0 ≤ pp-regularized least squares with different values of p. For instance, we will show that for very small and very large measurement noises, p = 0 and p = 1 outperform the other values of p, respectively.

We consider a generic problem of multicast beamforming design in multicell networks where each base station (BS) has multiple independent messages to multicast and each user intends to decode an arbitrary subset of messages sent from all BSs using successive group decoding (SGD). We first formulate the total transmit power minimization problem subject to the constraints that a target rate vector is achievable by the SGDs at all receivers. This problem is a non-convex quadratically constrained quadratic program and NP-hard. We propose a new method based on solving a sequence of linearly regularized semi-definite programming (SDP) relaxation of the original problem that yields feasible and near-optimal solutions with high probability. Moreover, we propose a decentralized algorithm based on the alternating direction method of multipliers to solve each linearly regularized SDP, which consists of solving a quadratic program at the central controller, and closed-form analytic computations at each BS. Finally, we propose an iterative procedure for joint beamformer and rate optimization under the SGD framework. Numerical results confirm the superiority of the proposed beamformer design in both performance and complexity. It is also demonstrated that, compared with the traditional linear receivers, the SGD receivers achieve both significant rate improvement and energy savings.

This paper investigates the interference statistics in a clustered device-to-device network, where the cluster centers form a Poisson point process and each active receiver device selects the transmitter randomly following the uniform distribution. The statistics of the number of intra/inter-cluster interferers is first analyzed for both finite and infinite number of receivers per cluster. In addition, for the special cases where the numbers of transmitters and receivers differ significantly, simplified expressions are given. Then, using these derived probability distributions of the number of interferers, the Laplace transforms of intra/inter-cluster interference are established and simple approximations for the special cases are obtained as well. Our results show the following: 1) with the concurrent uniform transmitter selection, the distribution of the number of intra/inter-cluster interfering devices differs greatly from the truncated Poisson distribution assumed in the literature; 2) the model assumed in the literature underestimates the coverage probability specially for a small number of transmitters/receivers; and 3) unlike the model assumed in the literature, the ASE of the proposed model increases with the number of transmitters/receivers when the average number of active receivers is relatively small, while it does not monotonically vary with the number of transmitters/receivers when the average number of active receivers is large.

Millimeter-wave (mm-wave) massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task as the size of beamspace channel is large, while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mm-wave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beamspace channel can be exploited.

Due to the spatial sparsity caused by severe propagation loss, mm-wave channel estimation can be performed by estimating the directions and gains of the paths that have significant power. In this paper, we apply the beamspace 2-D multiple signal classification (MUSIC) method to estimate the path directions (the angles of departure and arrival) and use the least-squares method to estimate the path gains. Different from its element-space counterpart, the beamspace MUSIC method may exhibit spectrum ambiguity caused by the beamformers. In this paper, we therefore analyze the sufficient conditions on the beamformers under which the MUSIC spectrum has no ambiguity which also leads to the maximum number of resolvable path directions. Moreover, based on the uniform linear array with half-wavelength spacing, we show that the discrete Fourier transform beamformers, which are naturally analog and often employed in the mm-wave communication systems with hybrid precoding structure, can avoid the spectrum ambiguity and maximize the number of resolvable path directions. Simulation results demonstrate that the proposed 2-D beamspace MUSIC mm-wave channel estimator significantly outperforms existing estimators that are based on beam training and sparse recovery; and in the meantime, it requires much less training slots than these existing methods.

This paper considers the problem of opportunistically accessing a wide range of frequency band in which multiple subbands may be occupied. A major obstacle to utilizing such wideband spectrum is that performing Nyquist sampling on the wideband signal is either infeasible or too expensive. We propose an adaptive energy-constrained sensing scheme based on sub-Nyquist sampling and stochastic control theory. In contrast to the existing sub-Nyquist approaches, we select the subband that has high probability to be idle based on the sub-Nyquist samples and spectrum prediction, without reconstructing the wideband signal. The sensing process is formulated as a constrained partially observable Markov decision process to exploit the statistical characteristics of the wideband signal, and a simulation-based gradient algorithm is proposed to compute the optimal adaptive sensing policy. The algorithm is shown to converge to the optimal solution with probability one. Simulation results show that with low computational complexity, the adaptive sensing policy performs well even in the crowded spectrum with low SNR.

Massive multiple-input multiple-output (MIMO) systems are expected to operate in the frequency-division duplex (FDD) mode, which is feasible in the channel environment with limited scattering. Since accurate channel estimation is critical for gaining unprecedented capacity, we investigate beam-domain channel estimation and feedback for FDD massive MIMO systems. In particular, we focus on the threshold-based method for channel estimation, where an enhanced estimator is proposed to exploit the common support among all beam-domain channels. For threshold-based estimation, we derive its closed-form mean-squared error (MSE) expression, and obtain an optimal threshold as a function of sparsity, noise variance, and channel variance, and a simplified threshold, which is a function of noise variance only. For the enhanced estimator, we present a threshold to identify the common support, with which an algorithm is designed to improve the estimation accuracy. As for channel feedback, we suggest to feed back only significant elements (above the given threshold) in the beam domain. Numerical results validate our derived MSE expression and demonstrate the superior performance of proposed threshold-based estimators.

We consider a system where a source node communicates with a destination node with the assistance of a wireless energy-powered full-duplex relay node. The relay node splits its received signal into two components for energy harvesting and information decoding, respectively, and forwards the decoded information using a portion of the harvested energy. To maximize the end-to-end transmission rate, the power splitting factor and energy consumption proportion are jointly optimized for the relay with a single transmit antenna in the presence of self-interference. Furthermore, for the relay with multiple transmit antennas, a suboptimal relay beamformer is first designed and the power splitting factor and energy consumption proportion are then jointly optimized. Finally, the asymptotic transmission rate is analyzed with a large number of transmit antennas. Simulation results are provided to demonstrate that the proposed schemes offer significant rate gain compared with some typical reference schemes, irrespective of the residual self-interference level. Especially, by employing a large number of source or relay transmit antennas, the wireless energy-powered relay system is capable of cutting its energy consumption significantly.

Generalized spatial modulation (GSM) is a relatively new multi-input multi-output transmission technique that enables a flexible trade-off between the achievable transmission rate and the cost of radio frequency chains. However, due to the constraint of transmit antenna combination and the variation of interchannel interference, the efficient low-complexity demodulation of GSM signals is challenging, especially when soft demodulation is needed. In this paper, we propose two low-complexity algorithms based on the deterministic sequential Monte Carlo (SMC) technique for the demodulation of GSM. The type-I SMC demodulator, which uses the conventional successive interference cancellation as the kernel and draws antenna-wise samples from the extended constellation, is proposed for the overdetermined GSM system. The type-II SMC demodulator, which consists of two stages and uses the orthogonal matching pursuit as the kernel in the first stage, is proposed for the underdetermined GSM system. A key component in both algorithms is an efficient online scheme to eliminate the illegal samples during the sampling process. Both proposed algorithms achieve near-optimal performances with complexity linear in terms of the antenna size. Moreover, owing to their soft-input soft-output nature, they can be employed in a turbo receiver for a coded GSM system.

With 5G communication systems on the horizon, efficient interference management in heterogeneous multicell networks is more vital than ever. This paper investigates the linear precoder design for downlink multicell multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, where base stations (BSs) coordinate to reduce the interference across space and frequency. In order to minimize the overall feedback overhead in next-generation systems, we consider precoding schemes that require statistical channel state information (CSI) only. We apply the random matrix theory to approximate the ergodic weighted sum rate of the system with a closed form expression. After formulating the approximation for general channels, we reduce the results to a more compact form using the Kronecker channel model for which several multicarrier concepts such as frequency selectivity, channel tap correlations, and intercarrier interference (ICI) are rigorously represented. We find the local optimal solution for the maximization of the approximate rate using a gradient method that requires only the covariance structure of the MIMO-OFDM channels. Within this covariance structure are the channel tap correlations and ICI information, both of which are taken into consideration in the precoder design. Simulation results show that the rate approximation is very accurate even for very small MIMO-OFDM systems and the proposed method converges rapidly to a near-optimal solution that competes with networked MIMO and precoders based on instantaneous full CSI.

Channel estimation is a major overhead factor in large-scale multiple-input multiple-output (MIMO) systems, especially under the high-speed railway scenarios. This paper proposes a position-aided channel estimation scheme for high-speed railway communication systems, where both the transmitter and the receiver are equipped with large-scale antenna linear arrays. By joint spatio-temporal correlation, the pilot overhead can be significantly reduced. Furthermore, the optimal design of transmit power and time interval partition between the training and data phases as well as the antenna size are presented accordingly. Both analytical and simulation results show that the system throughput with position-aided channel estimation does not deteriorate significantly as the mobility increases, which is sharply in contrast with the conventional one that intends to re- estimate the entire channel matrix each block.

In this paper, we consider tensor completion under adaptive sampling of tensor (a multidimensional array) fibers. Tensor fibers or tubes are vectors obtained by fixing all but one index of the array. This sampling is in contrast to the cases considered so far where one performs an adaptive element-wise sampling. In this context we exploit a recently proposed algebraic framework to model tensor data [1] and model the underlying data as a tensor with low tensor tubal-rank. Under this model we then present an algorithm for adaptive sampling and recovery, which is shown to be nearly optimal in terms of sampling complexity. We apply this algorithm for robust estimation of RF fingerprints for accurate indoor localization. We show the performance on real and synthetic data sets. Compared to existing methods, that are primarily based on non-adaptive matrix completion methods, adaptive tensor completion achieves significantly better performance.

This work considers the online sensor selection for the finite-horizon sequential hypothesis testing. In particular, at each step of the sequential test, the “most informative” sensor is selected based on all the previous samples so that the expected sample size is minimized. In addition, certain sensors cannot be used more than their prescribed budgets on average. Under this setup, we show that the optimal sensor selection strategy is a time-variant function of the running hypothesis posterior, and the optimal test takes the form of a truncated sequential probability ratio test. Both of these operations can be obtained through a simplified version of dynamic programming. Numerical results demonstrate that the proposed online approach outperforms the existing offline approach to the order of magnitude.

Recovering a sparse vector x0∈ ℝNfrom its noisy linear observations, y ∈ ℝnwith y = Ax0+ w, has been the central problem of compressed sensing. One of the classes of recovery algorithms that has attracted attention is the class of ℓp-regularized least squares (LPLS) that seeks the minimum of 1/2 ∥y - Ax∥22+ λ∥x∥ppfor p ∈ [0, 1]. In this paper we employ the Replica method1from statistical physics to analyze the global minima of LPLS. Our paper reveals several surprising asymptotic properties of LPLS: (i) The phase transition curve of LPLS is the same for every 0 ≤ p0. (iii) Despite the equality of the phase transition curves, different values of p show different performances once a small amount of measurement noise, w, is added.

In this paper, considering a mixed Rician and Rayleigh fading environment, we investigate the impact of line-of-sight (LOS) path component on the outage performance of dual-hop energy harvesting (EH) amplify-and-forward (AF) relay systems. To this end, a tight asymptotic outage lower bound expression is derived, which reveals that the system diversity order is limited to one and the outage behavior decays as log(SNR)/SNR, with SNR denoting the transmit signal-to-noise ratio (SNR) at the source. In contrast, when the LOS path component in the first-hop link becomes very strong (i.e., a large Rician K factor), our analytical results show that the easing-off factor log(SNR) can be effectively eliminated, becoming the decaying rate of outage curves steeper in this case and, consequently, improving the end-to-end transmission robustness since the outage curves scale as 1/SNR at high SNR regions.

Wi-Fi fingerprint-based localization has attracted significant research interest recently. Previous works in this area mainly focus on locating an individual user, whereas the additional assistance from peer-to-peer interactions has not been fully exploited. In this paper, we propose a cooperative localization method which not only utilizes the initial results by the fingerprint-based algorithm but also takes into account the physical constraint of pairwise distances to refine the localization estimates for multiple users simultaneously. The experimental results demonstrate that our algorithm is robust against the ranging error and the outdated fingerprint database. With the proposed peer selection scheme, it considerably improves localization accuracy. We further extend our framework to single-user motion tracking and localization based only on access-point-connectivity data.

A passive ultra-high frequency (UHF) RF identification (RFID) system is proposed that employs multiple antennas at the reader and single antenna at each tag. The gain due to multiple antennas in terms of the maximum reader interrogation range is first quantified. Then, a blind adaptive beamforming (BABF) algorithm that does not require the channel state information (CSI) is presented to improve both the interrogation range and the data transmission performance of the system in both the full- and half-duplex configurations. Moreover, an estimator for the number of tags within the reader interrogation range is proposed to enhance the system throughput. Simulation results show that both the interrogation range and the packet error rate (PER) performance for data transmission can be improved significantly using multiple antennas at the reader. A Universal Software Radio Peripheral (USRP)-based prototype system is implemented to validate the interrogation range improvement by employing multiple-antenna readers with several beamforming schemes. The proposed multiantenna RFID reader with the BABF complies with the existing RFID standard and can be readily implemented in existing systems.

Wireless energy transfer is a promising solution to provide convenient and steady energy supplies for low-power relays. This paper investigates the simultaneous information and energy transfer in fading relay channels, where the relay has no fixed energy supply and replenishes energy from radio frequency signals transmitted by the source. Assume that the relay can switch among energy harvesting, information decoding, and information retransmission in each channel fading state. Our objective is to maximize the ergodic throughput by optimizing the mode switching rule and transmit power jointly under the data and energy causality constraints. When the source knows channel state information (CSI) of all links, to make the problem tractable, for the relay, we neglect the causality constraints during the transmission, and only consider the total data and energy constraints. We thus obtain an upper bound on the ergodic throughput by solving a convex optimization problem. Numerical results show that the achievable rate is very close to the upper bound when we apply the optimized parameters to a practical system. When the source only knows CSI of partial links, the whole transmission process is divided into two phases: the source transmits in the first phase and the relay decodes and forwards received bits using the harvested energy in the second phase. The throughput maximization problem is solved by combing convex optimization, fractional programming, and linear search. We also consider the simplified network topology when a direct link between the source and destination is unavailable. In this network, we propose algorithms based on bisection method to obtain the optimal parameters in information/energy transfer scheduling and power control when the source knows full or partial CSI. The simulation results reveal that the throughput gain brought by wireless powered relaying in different system configurations when the source knows full or partial CSI. Moreover, the effect of the relay position is discussed.

In this paper, we develop optimal energy-bandwidth allocation algorithms in fading channels for multiple energy harvesting transmitters. We first assume that the side information of both the channel states and the energy harvesting states is known for K time slots a priori, and the battery capacity and the maximum transmission power in each time slot are bounded. The network consists of N transmitter-receiver pairs and the objective is to maximize the sum-rate of all communication links over the K time slots by assigning the transmission power and bandwidth for each transmitter in each slot. The problem is formulated as a convex optimization problem with O(N K) constraints, where N is the number of the receivers, making it hard to solve with a generic convex solver. An iterative algorithm is proposed based on efficiently solving two subproblems in each iteration, that has an overall complexity of O(N K2). The convergence and the optimality of this algorithm are also shown. Moreover, a heuristic algorithm is also proposed for energy-bandwidth allocation based on causal information of channel and energy harvesting states. Simulation results show that the proposed causal and noncausal algorithms can make efficient use of the harvested energy and the available bandwidth. And they achieve significantly higher rates than some heuristic policies for energy and bandwidth allocation.

The small wavelength at millimeter wave (mmWave) allows to pack antenna arrays in a very small area enabling practical large-scale MIMO. However, wideband and high-precision analog-to-digital converters (ADCs) are very expensive and power-hungry. In this paper, we propose a mmWave MIMO communication system that employs simple one-bit ADCs at the receiver and MIMO precoding at the transmitter. One significant challenge associated with this system is efficient channel estimation based on one-bit quantized channel output. We propose a novel continuous-time channel estimator based on level-triggered sampling, a nonuniform sampling scheme that offers estimation performance similar to that of the estimator based on full-precision channel output. We also develop the transmitter precoding scheme that reduces the transmit peak-to-average power ratio (PAPR) and, at the same time, equalizes the antenna cross-talks so that receiver demodulation can be implemented simply by symbol-rate slicing. Extensive simulation results are provided to demonstrate the effectiveness of the proposed channel estimation and precoding techniques for mmWave systems.

This paper considers the real-time voltage quality monitoring in smart grid systems. The goal is to detect the occurrence of disturbances in the nominal sinusoidal voltage signal as quickly as possible such that protection measures can be taken in time. Based on an autoregressive model for the disturbance, we propose a generalized local likelihood ratio detector, which processes meter readings sequentially and alarms as soon as the test statistic exceeds a prescribed threshold. The proposed detector not only reacts to a wide range of disturbances, but also achieves lower detection delay compared with the conventional block processing method. Then, we further propose to deploy multiple meters to monitor the voltage signal cooperatively. The distributed meters communicate wirelessly to a central meter, where the data fusion and detection are performed. In light of the limited bandwidth of wireless channels, we develop a level-triggered sampling scheme, where each meter transmits only one-bit each time asynchronously. The proposed multi-meter scheme features substantially low communication overhead, while its performance is close to that of the ideal case where distributed meter readings are perfectly available at the central meter.

In this paper, we develop achievability schemes for symmetric K-user interference channels with a rate-limited feedback from each receiver to the corresponding transmitter. We study this problem under two different channel models: the linear deterministic model, and the Gaussian model. For the deterministic model, the proposed scheme achieves a symmetric rate that is the minimum of the symmetric capacity with infinite feedback, and the sum of the symmetric capacity without feedback and the symmetric amount of feedback. For the Gaussian interference channel, we use lattice codes to propose a transmission strategy that incorporates the techniques of Han-Kobayashi message splitting, interference decoding, and decode and forward. This strategy achieves a symmetric rate, which is within a constant number of bits to the minimum of the symmetric capacity with infinite feedback, and the sum of the symmetric capacity without feedback and the amount of symmetric feedback. This constant is obtained as a function of the number of users, K. We note that for the special case of Gaussian IC with K = 2, our proposed achievability scheme results in a symmetric rate that is within at most 21.085 bits/s/Hz of the outer bound, which is the first constant gap bound despite the constant gap claim in [1]. The symmetric achievable rate is used to characterize the achievable generalized degrees of freedom, which exhibits a gradual increase from no feedback to perfect feedback in the presence of feedback links with limited capacity.

Effective interference management in the multiuser interference channel necessitates that the users form their transmission and interference management decisions in coordination, and adapt them to the state of the channel. Establishing such coordination, often facilitated through information exchange, is prohibitive in fast-varying channels, especially when the network size grows. This paper focuses on the multiuser Gaussian interference channel and offers a receiver-centric approach to interference management. In this approach, the transmitters deploy rate-splitting and superposition coding to generate their messages according to independent Gaussian codebooks. The receivers can freely decode any arbitrary set of interfering messages along with their designated messages in any desired joint or ordered fashion, and treat the rest of the interferers as Gaussian noise. The proposed receiver-centric interference management approach is applied to two class of problems (outage optimization and fairness-constrained rate allocation), and constructive proofs are provided to establish the following properties for the proposed approach: 1) the optimal set of codebooks to be decoded by each receiver is a local decision made by each receiver based on its local channel state information (CSI); 2) the globally optimal transmission rates are related to locally optimal rates computed by the receivers based on their local information, which implies that the transmitters do not require explicit knowledge of the CSI and can determine their rates via limited feedback from the receivers; and 3) obtaining the optimal interference management strategy at each receiver has controlled complexity.

Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promising approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint databasea priorifor localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as atensorand use tensor algebraic methods for anadaptivetubal-sampling of this fingerprint space. In particular, using a recently proposed tensor algebraic framework in[1], we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation, the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions, we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by$71$percent for the high SNR case and$55$percent for the low SNR case.

In this paper, we investigate the effects of line-of-sight (LoS) path and opportunistic scheduling (OS) on dualhop energy harvesting (EH) relay systems, where the LoS path exists between source and EH relay. We first study a single-destination case and show that the system diversity order is one and the outage behavior decays as log(SNR)/SNR, with SNR denoting the transmit signal-to-noise ratio (SNR) at the source. In the presence of a strong LoS path, asymptotic results reveal that the easing-off factor log(SNR) can be eliminated and a faster decay rate, 1/SNR, is achieved. Afterward, the OS of the second hops (N destinations) is considered, and its separate as well as joint effects on the system outage behavior are examined. In addition, the choice of power-splitting factor is discussed and the impacts of EH constraint on the performance limit compared with conventional relay system are investigated, showing that under the same transmit-power consumption from the fixed energy supply, the EH relay system could achieve the same performance with that of conventional relay system if the wireless power transfer in the first hop is lossless and the energy conversion efficiency at EH relay tends to be unity.

In multicell multiuser massive multi-input multi-output (MIMO) systems, pilot contamination degrades the uplink (UL) channel estimation performance. To mitigate the effect of pilot contamination, we propose a semiblind channel estimation method that does not require cell cooperation or statistical information of the channels. In the proposed method, we first sequentially estimate the UL data from different users in the target cell. To do that, for each user, we solve a constrained minimization problem to obtain an extracting vector and then use it to extract the desired data source from the observed mixture signal. An efficient algorithm is presented to solve the optimization problem. After the ambiguities in the extracted source are corrected with the aid of the pilot sequence, the estimates of the user UL data can be obtained. Based on the demodulated UL data of all users in the target cell, we finally obtain the least squares (LS) estimate of the channel. The pilot contamination effect is shown to be reduced as the UL data length grows. Simulation results demonstrate that the proposed method significantly outperforms some existing channel estimation methods that do not require cell cooperation or channel statistics.

The replica method originated from statistical mechanics has been successfully applied to analyzing performance of the global maximum-likelihood (GML) MIMO detector in the large-system limit. In this paper, the analysis is extended to the local maximum-likelihood (LML) detectors. A bit error rate (BER) formula for the LML detectors with a fixed neighborhood size is obtained by the replica method and interestingly by the method of Gaussian approximation as well. It is shown that the LML BER is always one of the solutions to the GML BER in any system configuration. Furthermore, the LML BER is the only solution of the GML BER in a broad range of system parameters of practical interest. In the high signal-to-noise ratio regime, both LML and GML detectors achieve the AWGN channel performance when the channel load is up to 1.51 bits/dimension with an equal-energy distribution, and the load can be higher with an unequal-energy distribution. This analytical result is verified by simulation that the sequential likelihood ascent search detector, which is a linear-complexity LML detector, can approach the BER of the NP-hard GML detector predicted by the analysis. This result might be practically useful in large MIMO systems.

We consider multiple transmission links in a frequency-selective fading channel, where the transmitters are powered by renewable energy sources that provide variable amount of energy at different times. We formulate the problem of joint energy and subchannel allocation for all transmitters over a scheduling period, as a mixed integer program. Assuming that the harvested energy and subchannel gains can be predicted, we propose an algorithm to efficiently obtain the energy-subchannel allocations for all links over the scheduling period based on controlled water-filling. The proposed algorithm is shown to be asymptotically optimal when the bandwidth of the subchannel goes to zero. A causal algorithm is also proposed based on the Q-learning technique that makes use of the statistics of the energy harvesting and channel fading processes. Simulation results demonstrate that the performance of the proposed noncausal algorithm is close to the upper-bound on the optimal performance and the proposed causal algorithm outperforms various heuristic allocation policies.

The large-scale multiple-input multiple-output (MIMO) system with full-duplex relay is a promising candidate for future mobile communication systems, where accurate channel estimation is very challenging due to interference. Here, we address two channel estimation problems for such systems: individual estimation, where both the base station (BS) and the relay estimate their respective channels, and cascaded estimation, where only the BS estimates the cascaded two-hop channel. For the BS, we propose an estimator that exploits the sparsity and slowly varying nature of the channel in the beam domain. Then, we analyze the probability of correctly distinguishing the desired and interfering direct-link channels. For the relay, we present an estimator that simultaneously estimates both the source-to-relay and self-interference channels based on the expectation–maximization algorithm. The performance of this estimator is also analyzed. Numerical results demonstrate the excellent performance of the proposed channel estimators and corroborate the analysis results

We consider a downlink massive MIMO system, where the base station simultaneously sends information and energy to information users and energy users, respectively. The aim is to maximize the minimum harvested energy among the energy users while meeting the rate requirements of information users. With perfect channel state information (CSI), the problem is solved by obtaining the asymptotically optimal power allocation of information users and the combination coefficients of the energy precoder. For the CSI estimation in time-division duplex systems, orthogonal pilot sequences are employed by information users during the uplink, and one common pilot sequence is shared by all energy users. It is shown that the energy-harvesting performance of such a shared pilot scheme is always better than that of the orthogonal pilot scheme. Further, exploiting the intercell interference in multicell systems, a joint precoder is proposed for cooperative energy transfer, for which both the centralized and distributed implementations are given. Results indicate that the cooperative energy transfer always outperforms the noncooperative scheme with either perfect or estimated CSI.

The air-based information network of the Space-Air-Ground Integrated Network has the distinct characteristics of heterogeneous, time-varying, distributed and self-organized, which bring not only new challenges of node mobility model, dynamic network grouping and interconnection and intercommunication of heterogeneous networks, but also higher requirements of network security. Therefore, selecting an efficient and safe communication link is critical when there may not be fixed rules. In this article, we build a model of the air-based information network, and use the method of dynamic programming to select the most efficient communication link by minimizing the energy consumption in the communication model. In the communication model, energy consumption is associated with the security level of the communication link. The experiments show that the presented algorithms can effectively reduce the total cost while satisfying security constraints.

Load balancing (LB) is important in heterogeneous networks (HetNet). This paper investigates effective cell association schemes for LB. Considering the centralized cellular network architecture with Super Base Stations (SBS), a centralized cell association scheme is proposed, where the calculations are all performed in the SBS and information exchange within the SBS is convenient. There is also no need for the users to feed back information. Thus the signaling overhead is reduced a lot. Moreover, to alleviate the sensitiveness to load changes, the load fairness index (LFI) is defined. The proposed scheme is not performed until the LFI is lower than a given threshold. It is verified by simulations that the proposed centralized cell association scheme can adjust the cell-specific bias according to the cell load and provide better geometry mean rate (GMR) and outage probability (OP) over the comparing schemes. Moreover, with the introduction of LFI, the number of LB performed can be effectively reduced with a lower LFI threshold, however, the system performance such as GMR and OP also degrades. Therefore, an appropriate LFI threshold is needed to balance the system performance and complexity.

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in an orthogonal frequency-division multiplexing (OFDM) relaying system, where a source node transfers information and a fraction of power simultaneously over OFDM to a relay node, and the relay node uses the harvested power from the source node to forward the source information to the destination. To support such simultaneous information and energy cooperation, we propose two transmission protocols, namely power splitting (PS) relaying protocol and the transmission mode adaptation (TMA) protocol for without/with the source-destination link, respectively. For both transmission protocols, joint resource allocation problems are formulated to maximize the system throughput. By using the Lagrange dual method, we develop efficient algorithms to find the optimal polices of the nonconvex optimization problems.

Device-to-device (D2D) cooperation can improve both the system performance and Quality of Services (QoS) of users with bad channel qualities. However, cooperation consumes valuable power of a terminal to help others and thus is not preferred by the terminal. So it is important to design schemes to stimulate selfish terminals to cooperate. Defining energy efficiency as incentive parameters, this paper proposes a novel resource allocation scheme to encourage users to relay data for others with the reward of transmitting resource including time and power. Since the optimal algorithm is highly complicated, the proposed energy efficient incentive resource allocation scheme is a suboptimal solution which is composed of three steps. Firstly, D2D relay users are selected for others in bad channel condition. Secondly, the two-user case resource allocation problem is formulated to simplify the original problem in cellular networks. Finally, a two-dimensional search method is designed to solve the two-user case problem based on monotonicity analysis. Simulation results demonstrate that the proposed scheme can stimulate users to implement D2D relay to achieve better performance in energy efficiency and throughput. It is also shown that the performance of the proposed scheme is close to that of the optimal one.

In this paper, we study the capacity regions of two-way linear deterministic diamond channels. We show that the capacity of the diamond channel in each direction can be simultaneously achieved for all values of channel parameters, where the forward and backward channel parameters are not necessarily the same. We propose a relay strategy called `reverse amplify-and-forward' strategy and show that this strategy and its variants combined with proper transmission strategies achieve the capacity of linear deterministic diamond channel.

In this paper, we consider the energy-bandwidth allocation for a network with multiple orthogonal broadcast channels, where each transmitter communicates with multiple receivers orthogonally. We assume that the harvested energy and channel gain of each transmitter can be predicted for K slots a priori. To maximize the weighted throughput of the network, we formulate an optimization problem with O(MK) constraints, where M is the number of the receivers, making it hard to solve using a generic convex solver since the computational complexity of the solver becomes impractically high when the number of constraints is large. In order to use the iterative algorithm proposed in [1] to solve the problem efficiently, we decompose the problem into the energy and bandwidth allocation subproblems and propose algorithms to solve the two corresponding subproblems, so that the optimal energy-bandwidth allocation can be obtained with an overall complexity of O(MK2).

We consider an energy harvesting network with multiple transmission links in a frequency-selective fading channel. We formulate the problem of joint energy and subchannel allocation for all transmitters over a scheduling period, as a mixed integer program. With the predictions of the harvested energy and subchannel gains, we propose an algorithm to efficiently obtain the energy-subchannel allocations for all links over the scheduling period based on controlled water-filling. The proposed algorithm is shown to be asymptotically optimal when the bandwidth of the subchannel goes to zero. Simulation results demonstrate that the performance of the proposed algorithm is close to the upper-bound on the optimal performance, which is also outperforms various heuristic allocation policies.

The centralized radio access cellular network architecture with Super BS (CSBS) has been proposed to reduce high construction cost and energy consumption. In CSBS, the computing resource is centralized and can be flexibly allocated to different virtual BSs (VBS). In the general purpose platforms, the protocol processing of multiple VBS can be carried out in a single processor due to its high processing capability. However, using this organization, it is difficult to guarantee the real-time protocol processing in TDD systems. This paper firstly analyzes the requirement of real-time processing of TDD protocols. Then, a real-time guaranteed TDD protocol processing mechanism, dynamic adaptive organization mechanism (DAOM), is proposed, whose main idea is to carry out downlink protocol processing consecutively. Simulation results show that DAOM can guarantee the real-time protocol processing and keep a high computing resource efficiency at the same time.

BACKGROUND: In most sequenced organisms the number of known regulatory genes (e.g., transcription factors (TFs)) vastly exceeds the number of experimentally-verified regulons that could be associated with them. At present, identification of TF regulons is mostly done through comparative genomics approaches. Such methods could miss organism-specific regulatory interactions and often require expensive and time-consuming experimental techniques to generate the underlying data. RESULTS: In this work, we present an efficient algorithm that aims to identify a given transcription factor's regulon through inference of its unknown binding sites, based on the discovery of its binding motif. The proposed approach relies on computational methods that utilize gene expression data sets and knockout fitness data sets which are available or may be straightforwardly obtained for many organisms. We computationally constructed the profiles of putative regulons for the TFs LexA, PurR and Fur in E. coli K12 and identified their binding motifs. Comparisons with an experimentally-verified database showed high recovery rates of the known regulon members, and indicated good predictions for the newly found genes with high biological significance. The proposed approach is also applicable to novel organisms for predicting unknown regulons of the transcriptional regulators. Results for the hypothetical protein D d e0289 in D. alaskensis include the discovery of a Fis-type TF binding motif. CONCLUSIONS: The proposed motif-based regulon inference approach can discover the organism-specific regulatory interactions on a single genome, which may be missed by current comparative genomics techniques due to their limitations.

We consider an energy harvesting point-to-point communication system where the transmitter is powered by an energy arrival process and is equipped with a battery of finite capacity Bmax, which could be used for saving energy for future use. We assume a discrete i.i.d. energy arrival process where at each time step, energy of amount Ai is harvested with probability pi Vi ∈ {1, 2, .. ., K} independent of the other time steps. We provide upper and lower bounds on the capacity of this channel. These bounds are shown to be within a constant gap for K ≤ 3 for all parameters, and for K > 3 when the battery capacity Bmax is small or large enough, where this constant does not depend on any energy or battery parameters.

In this paper, we develop optimal energy scheduling algorithms for N-user fading multiple-access channels with energy harvesting to maximize the channel sum-rate, assuming that the side information of both the channel states and energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O (NK) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver becomes impractically high when the number of constraints is large. This paper gives an efficient energy scheduling algorithm, called the iterative dynamic water-filling algorithm, that has a computational complexity of O(NK2) per iteration. For the single-user case, a dynamic water-filling method is shown to be optimal. Unlike the traditional water-filling algorithm, in dynamic water-filling, the water level is not constant but changes when the battery overflows or depletes. An iterative version of the dynamic water-filling algorithm is shown to be optimal for the case of multiple users. Even though in principle the optimality is achieved under large number of iterations, in practice convergence is reached in only a few iterations. Moreover, a single iteration of the dynamic water-filling algorithm achieves a sum-rate that is within (N-1)K nats of the optimal sum-rate.

In this paper, we consider the energy-bandwidth allocation for a network with multiple broadcast channels, where the transmitters each powered by an energy harvester, access the network orthogonally on the assigned frequency band and each transmitter communicates with multiple receivers orthogonally or non-orthogonally. We assume that the energy harvesting state and channel gain of each transmitter can be predicted for K time slots a priori. To maximize the weighted throughput, we formulate an optimization problem with O(MK) constraints, where M is the total number of receivers, and optimize over the energy and bandwidth allocation variables. To solve the problem efficiently, an iterative algorithm is proposed that alternatively solves the two subproblems of energy allocation and bandwidth allocation in each iteration. We show that this algorithm converges to the optimal solution. Also, we propose efficient algorithms to solve the two subproblems, so that the optimal energy-bandwidth allocation can be obtained with an overall complexity of O(MK2), even though the problem is non-convex when the broadcast channel is non-orthogonal. Simulation results show that the proposed algorithms can make efficient use of the harvested energy and the available bandwidth, and achieve significantly better performance as compared to some heuristic policies for energy and bandwidth allocation. Moreover, it is seen that with energy-harvesting transmitters, the non-orthogonal broadcast channel offers limited gain over the orthogonal broadcast channel.

We investigate the sum-rate-optimal precoding for the uplink of the multi-cell large-scale MIMO systems. Specifically, we focus on transmitter precoder design based only on the statistical channel state information (CSI). We first consider the partial cooperation system, where only the CSI is shared among cells, and its base station (BS) decodes its in-cell users by treating out-cell signals as colored Gaussian noise. We derive the ergodic sum rate and its deterministic approximation for the regime where both the number of total user antennas and the number of BS antennas are large. We obtain the necessary conditions to maximize the deterministic approximation of the sum rate under the transmit power constraint, based on which a gradient search algorithm is proposed to find the local optimal precoders. Furthermore, a super cell system is also considered, where users from all cells are jointly decoded. The deterministic approximation to the sum rate and optimal precoder design for such systems are given. Numerical experiments show that the proposed precoders achieve significant performance gains over systems with no precoding. Compared with the existing precoding methods that require perfect CSI, the proposed precoders achieve similar sum rates and require only the statistical CSI.

In this paper, we study the capacity regions of two-way diamond channels. We show that for a linear deterministic model the capacity of the diamond channel in each direction can be simultaneously achieved for all values of channel parameters, where the forward and backward channel parameters are not necessarily the same. We divide the achievability scheme into three cases, depending on the forward and backward channel parameters. For the first case, we use a reverse amplify-and-forward strategy in the relays. For the second case, we use four relay strategies based on the reverse amplify-and-forward with some modifications in terms of replacement and repetition of some stream levels. For the third case, we use two relay strategies based on performing two rounds of repetitions in a relay. The proposed schemes for deterministic channels are used to find the capacity regions within constant gaps for two special cases of the Gaussian two-way diamond channel. First, for the general Gaussian two-way relay channel, the capacity within a constant gap is achieved with a simpler coding scheme as compared with the prior works. Then, a special symmetric Gaussian two-way diamond model is considered and the capacity region is achieved within four bits.

We propose a new scheme to mitigate the uplink co-tier interference in dense femtocell networks, assuming that advanced receivers are employed by the femtocell base stations (FBSs). The conventional solution where interfering cells are assigned orthogonal spectrum resources leads to the inefficient spectrum usage. We exploit resource reuse by taking advantage of the successive group decoder (SGD) such that users can opportunistically access the entitled resources of nearby cells. The SGD decodes some interference signals in order to best decode the useful signal. Multi-cell uplink resource allocation with SGDs is formulated as a joint channel, rate and decoding group (CRG) allocation problem to maximize the weighted sum rates of the variable bit rate (VBR) users while meeting the rate requirements of the guaranteed bit rate (GBR) users. Due to the provable NP-hardness of the problem, we propose a greedy algorithm where the idea is to let GBR users opportunistically transmit on the channels of nearby cells and release more interference-free channels for high-rate transmission of VBR users. A semi-analytical framework is developed to provide a rough estimate of the potential throughput gain by the proposed technique. Simulation results show that the throughput gain over the conventional orthogonal resource allocation ranges from 10%–140% with different traffic proportion, number of users and rate requirement.

Recently, utilizing renewable energy for wireless system has attracted extensive attention. However, due to the instable energy supply and the limited battery capacity, renewable energy cannot guarantee to provide the perpetual operation for wireless sensor networks (WSN). The coexistence of renewable energy and electricity grid is expected as a promising energy supply manner to remain function of WSN for a potentially infinite lifetime. In this paper, we propose a new system model suitable for WSN, taking into account multiple energy consumptions due to sensing, transmission and reception, heterogeneous energy supplies from renewable energy, electricity grid and mixed energy, and multi-dimension stochastic natures due to energy harvesting profile, electricity price and channel condition. A discrete-time stochastic cross-layer optimization problem is formulated to achieve the optimal trade-off between the time-average rate utility and electricity cost subject to the data and energy queuing stability constraints. The Lyapunov drift-plus-penalty with perturbation technique and block coordinate descent method is applied to obtain a fully distributed and low-complexity cross-layer algorithm only requiring knowledge of the instantaneous system state. The explicit trade-off between the optimization objective and queue backlog is theoretically proven. Finally, through extensive simulations, the theoretic claims are verified, and the impacts of a variety of system parameters on overall objective, rate utility and electricity cost are investigated.

We study the massive MIMO (multiple-input multiple-output) multicast transmission in multicell networks, where each base station (BS) is equipped with a large-scale antenna array and transmits a common message using a single beamformer to multiple mobile users. We first consider the case when each BS knows the perfect channel state information (CSI) of all its served users. We show that the asymptotically optimal beamformer structure at each BS is a linear combination of the channel vectors of its multicast users. The optimal combination coefficients are also obtained in closed form. Then we consider the imperfect CSI scenario where each BS obtains the CSI through uplink channel estimation. We propose a novel pilot scheme that estimates the compound channel rather than the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The optimal power control of pilot transmission is also derived. Numerical results show that the performance of the proposed pilot scheme with pilot power control is close to that of the perfect CSI case.

We develop optimal energy-bandwidth allocation algorithm for the energy harvesting transmitters in interference networks. We assume that both the channel gain and the harvested energy are known for K slots as a priori, and the battery capacity is finite. The problem is formulated as a convex optimization problem with O(NK) constraints, making it hard to solve efficiently with a generic convex solver. To efficiently obtain the optimal energy-bandwidth allocation for each transmitter in each time slot, an iterative algorithm is proposed based on solving two subproblems with efficient algorithms, that has an overall complexity of O(NK2). Moreover, the numerical results show that the proposed iterative algorithm achieves the optimal performance, providing a significant improvement as compared to some naive allocation policies.

This study analyses power spectral density (PSD) of a pulse train where the pulses take from a prototype pulse but randomly take an independently and identically distributed time scaling and an independent stationary amplitude. A closed‐form expression of PSD is obtained, which is an implicit function of the Fourier transform of the prototype pulse without time scaling, the probability distribution of time scaling, and the first and the second moment means of amplitude. In the special case when the time scaling and amplitude are fixed with probability one, the PSD is degenerated to the well‐known PSD of a periodic signal. Results of numerical evaluation and simulation for pulse trains with three rates as well as with Gaussian rates demonstrate that the analytical formula well predicts the data PSD.

We study physical layer multicasting in cellular networks where each base station (BS) is equipped with a very large number of antennas and transmits a common message using a single beamformer to multiple mobile users. The messages sent by different BSs are independent, and the BSs do not cooperate. We first show that when each BS knows the perfect channel state information (CSI) of its own served users, the asymptotically optimal beamformer at each BS is a linear combination of the channel vectors of its multicast users. Moreover, the optimal and explicit combining coefficients are obtained. Then we consider the imperfect CSI scenario where the CSI is obtained through uplink channel estimation in time-division duplex systems. We propose a new pilot scheme that estimates the composite channel, which is a linear combination of the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The pilot power control for optimizing the multicast beamformer at each BS is also derived. Numerical results show that the asymptotic performance of the proposed scheme is close to the ideal case with perfect CSI. Simulation also verifies the effectiveness of the proposed scheme with finite number of antennas at each BS.

Dynamic spectrum access under channel uncertainties is considered. With the goal of maximizing the secondary user (SU) throughput subject to constraints on the primary user (PU) outage probability we formulate a joint problem of spectrum sensing and channel state estimation. The problem is cast into a sequential framework since sensing time minimization is crucial for throughput maximization. In the optimum solution, the sensing decision rule is coupled with the channel estimator, making the separate treatment of the sensing and channel estimation strictly suboptimal. Using such a joint structure for spectrum sensing and channel estimation we propose a distributed (cooperative) dynamic spectrum access scheme under statistical channel state information (CSI). In the proposed scheme, the SUs report their sufficient statistics to a fusion center (FC) via level-triggered sampling, a nonuniform sampling technique that is known to be bandwidth-and-energy efficient. Then, the FC makes a sequential spectrum sensing decision using local statistics and channel estimates, and selects the SU with the best transmission opportunity. The selected SU, using the sensing decision and its channel estimates, computes the transmit power and starts data transmission. Simulation results demonstrate that the proposed scheme significantly outperforms its conventional counterparts, under the same PU outage constraints, in terms of the achievable SU throughput.

We consider power allocation for an access-controlled transmitter with energy harvesting capability based on causal observations of the channel fading state. We assume that the system operates in a time-slotted fashion and the channel gain in each slot is a random variable which is independent across slots. Further, we assume that the transmitter is solely powered by a renewable energy source and the energy harvesting process can practically be predicted. With the additional access control for the transmitter and the maximum power constraint, we formulate the stochastic optimization problem of maximizing the achievable rate as a Markov decision process (MDP) with continuous state. To efficiently solve the problem, we define an approximate value function based on a piecewise linear fit in terms of the battery state. We show that with the approximate value function, the update in each iteration consists of a group of convex problems with a continuous parameter. Moreover, we derive the optimal solution to these convex problems in closed-form. Further, we propose power allocation algorithms for both the finite- and infinite-horizon cases, whose computational complexity is significantly lower than that of the standard discrete MDP method but with improved performance. Extension to the case of a general payoff function and imperfect energy prediction is also considered. Finally, simulation results demonstrate that the proposed algorithms closely approach the optimal performance.

This paper gives the approximate capacity region of a two-user multiple-input multiple-output (MIMO) interference channel with limited receiver cooperation, where the gap between the inner and outer bounds is in terms of the total number of receive antennas at the two receivers and is independent of the actual channel values. The approximate capacity region is then used to find the degrees of freedom region. For the special case of symmetric interference channels, we also find the amount of receiver cooperation in terms of the backhaul capacity beyond, which the degrees of freedom do not improve. Further, the generalized degrees of freedom is found for MIMO interference channels with equal number of antennas at all nodes. It is shown that the generalized degrees of freedom improves gradually from a W curve to a V curve with increase in cooperation in terms of the backhaul capacity.

We consider the problem of decentralized scalar parameter estimation using wireless sensor networks with Gaussian noise. Specifically, we propose a novel framework based on level-triggered sampling, a non-uniform sampling strategy, and sequential estimation. The proposed estimator can be used as an asymptotically optimal fixed-sample-size decentralized estimator when the observed Fisher information, i.e., Fisher information without expectation, is deterministic, as an alternative to the one-shot estimators commonly found in the literature. It can also be used as an asymptotically optimal sequential decentralized estimator when the observed Fisher information is random. We show that the optimal centralized estimator under Gaussian noise, which is the maximum likelihood estimator, is characterized by two processes, namely the observed Fisher information Ut and the observed correlation Vt. It is noted that Vt is always random even when Ut is not. In the proposed scheme, each sensor computes its local random processes, and sends a single bit to the fusion center (FC) whenever the local random processes passes certain predefined levels. The FC, upon receiving a bit from a sensor, updates its approximation to the corresponding global random process and, accordingly, its estimate. The sequential estimation process terminates when Ut (or the approximation to it) reaches a target value. We provide an asymptotic analysis for the proposed estimator and the one based on conventional uniform-in-time sampling under both deterministic and random Ut, and determine the conditions under which they are asymptotically optimal, consistent, and asymptotically unbiased. Analytical results, together with simulation results, demonstrate the superiority of the proposed estimator based on level-triggered sampling over the traditional decentralized estimator based on uniform sampling.

We consider a point-to-point delay-stringent video communication system. To reduce the fluctuation of the coded data packet sizes for efficient wireless transmission, we adopt the intra-refreshment and adaptive slice partitioning for video encoding. We model the delay-constrained video transmission with the proposed video coding and slicing scheme using an MDP model, and compute the optimal coding and modulation scheme corresponding to each channel state and the encoding and transmission delay using the value iteration algorithm. Extensive simulations based on real videos show that the MDP-based transmission scheme significantly reduces the transmission cost and enhances the quality of the reconstructed video sequences compared with the non-adaptive transmission scheme, which transmits constant number of transmitted symbols for all slices. Simulation results also show that the intra-refreshment and adaptive slice partitioning significantly enhances the quality of the reconstructed video sequences compared with the video coding without intra-refreshment.

We consider transmit rate allocation and the associated cooperative decoding strategies in uplink multi-cell networks where base stations (BSs) are connected by capacity-limited backhaul (BH) links. In particular, we propose two cooperative group decoding (CGD) schemes for the BSs with partial decoding results shared via the BH. In parallel CGD, at each stage, each BS locally selects and jointly decodes a group of users by treating the remaining users as noise; then it forwards some partial decoding results to other BSs via the BH. After subtracting the decoded users and the received decoding results from other BSs, each BS repeats the same procedure until all its designated users are decoded. On the other hand, in sequential CGD, each user can be decoded at any BS. At each stage only one BS is selected to decode a group of users, which then forwards the decoding results to other BSs for interference cancellation. The process is repeated until all users are decoded. Numerical results are provided to demonstrate that the proposed CGD schemes offer significant gain in terms of the achievable rates.

This paper considers multiuser interference channels in which the transmitters have imperfect channel state information (CSI) where CSI perturbations are modeled stochastically. Transmitters are assumed to be equipped with multiple antennas serving single-antenna receivers. Transmitters use pre-designed discrete codebooks for beamforming directions and dynamically (based on the available CSI) select the best set of beamformers from the given codebook. The objective is to perform optimal power allocation to different users while certain quality of service (QoS) guarantees are ensured for the users. Imposed by stochastic CSI uncertainties, guarantees provided for the QoS measures have a stochastic nature too. The primary focus is placed on the interference channels for which two power allocation problems are considered. The first problem minimizes power consumption subject to serving users at certain data rates and the second problem considers max-min rate allocation subject to given power budgets for the transmitters. The core step in formalizing these problems in mathematically tractable forms relies on using Bernstein approximation, which approximates and convexifies the non-convex stochastic guarantees by conservative convex and deterministic counterparts. For solving this resulting convex and deterministic optimization problem, a specialized version of the long-step logarithmic barrier cutting plane (LLBCP) algorithm is used. Effectiveness of the proposed solutions and comparisons with other existing methods are assessed via extensive simulation results.

We propose a green femto-station (FS) assignment algorithm for a hierarchical cellular network. It is assumed that the cellular network consists of one macro station, several femto stations equipped with the renewable energy source, and several battery-driven mobile stations. We also assume that the FS has limited incoming energy, and the occurrence and the duration of the mobile station's downlink transmission request is a stochastic process. To improve the service quality, i.e., reduce the probability of the FSs' service outage, a stationary FS assignment schedule is obtained for certain period by solving a combinatorial optimization problem. First we construct a Markov chain model to estimate the service outage probability, i.e., rejection probability, for given FS assignment schedule; based on the estimation model, we then propose a low-complexity local search algorithm, which balances the computation complexity and the performance, to obtain the local optimal FS assignment schedule efficiently. Simulation results are provided to demonstrate the superior performance of the proposed techniques over the traditional methods.

We consider SINR-constrained power minimization in MISO interference channel with imperfect channel state information (CSI). The problems is formulated as probability-constrained optimization problems. We make use of the Bernstein approximation to conservatively transform the probabilistic constraints into deterministic ones, and consequently convert the original stochastic optimization problems into convex optimization problems. Extensive simulation results are provided to demonstrate the effectiveness of the proposed method.

Recently, a multiple-input multiple-output (MIMO) two-way X relay channel, where two groups of source nodes each having 2 nodes exchange independent messages via a common relay node, was studied in [1]. In this paper, we extend it to the generalized MIMO two-way X relay channel, where m ≥ 2 and n ≥ 2 source nodes are contained in two groups, respectively. Based on signal space alignment, a new beamforming scheme is proposed to maximize the minimum effective signal to interference plus noise ratios (SINRs) among all data streams. The beamforming vectors are designed by an iterative algorithm in which a closed-form solution is obtained in each step. Moreover, we show that the power allocation problem given the shape of the beamformers can be transformed as a linear programming problem. Simulation results show that the proposed beamforming scheme can achieve significantly better error performance than random beamforming schemes subject to signal space alignment only.

In this paper, we investigate the effect of feedback on two-user MIMO interference channels. At first, the capacity region of MIMO interference channels with feedback is characterized within a constant number of bits, where this constant is independent of the channel matrices. Further, the generalized degrees of freedom region for the MIMO interference channel with feedback is characterized.

We develop optimal centralized sequential estimators under different formulations of the problem. Decentralized sequential estimation is also considered for wireless sensor networks. We propose an asymptotically optimal decentralized scheme based on level-triggered sampling, a non-uniform sampling technique. Performance of the proposed scheme is analyzed.

We consider decentralized detection for wireless sensor networks. A sequential scheme based on level-triggered sampling is proposed. In the proposed scheme, sensors compute log-likelihood ratio (LLR) of their local observations, sample local LLR using level-triggered sampling and transmit a single bit at each sampling time to a fusion center (FC). At each sampling time excess LLR over (below) sampling threshold is linearly encoded in time. The FC, upon receiving a bit from a sensor, decodes excess LLR and updates approximate global LLR, which it uses as test statistic. An SPRT-like test is used by the FC to reach a final decision. We show that the proposed scheme achieves order-2 asymptotic optimality by using only a single bit for each sample, thanks to time-encoding the overshoot.

MOTIVATION: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. RESULTS: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state-space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR-LiuR network in bacteria Shewanella oneidensis. AVAILABILITY: MATLAB code and datasets are available to download at http://www.duke.edu/∼lw174/Fitness.zip and http://genomics.lbl.gov/supplemental/fitness-bioinf/

BACKGROUND: DNA pooling constitutes a cost effective alternative in genome wide association studies. In DNA pooling, equimolar amounts of DNA from different individuals are mixed into one sample and the frequency of each allele in each position is observed in a single genotype experiment. The identification of haplotype frequencies from pooled data in addition to single locus analysis is of separate interest within these studies as haplotypes could increase statistical power and provide additional insight. RESULTS: We developed a method for maximum-parsimony haplotype frequency estimation from pooled DNA data based on the sparse representation of the DNA pools in a dictionary of haplotypes. Extensions to scenarios where data is noisy or even missing are also presented. The resulting method is first applied to simulated data based on the haplotypes and their associated frequencies of the AGT gene. We further evaluate our methodology on datasets consisting of SNPs from the first 7Mb of the HapMap CEU population. Noise and missing data were further introduced in the datasets in order to test the extensions of the proposed method. Both HIPPO and HAPLOPOOL were also applied to these datasets to compare performances. CONCLUSIONS: We evaluate our methodology on scenarios where pooling is more efficient relative to individual genotyping; that is, in datasets that contain pools with a small number of individuals. We show that in such scenarios our methodology outperforms state-of-the-art methods such as HIPPO and HAPLOPOOL.

In this study, the authors propose a transceiver system for large‐scale multiple‐input–multiple‐output (MIMO) (LSM) wireless communications. The authors present the main challenges facing such LSM system, also, they find solutions for those problems. The transmitters in this proposed downlink system uses a simple fair user scheduling based on limited‐feedback algorithm with basic random precodeing algorithm. On the other side, receivers employ constrained partial group decoder to detect their desired signals. Simulation is used to evaluate sum‐rate performance of this LSM downlink system against the total number of users and signal‐to‐noise ratio, using different number of scheduled users and with various group sizes of jointly decoded users. Numerical results interestingly show an encouraging performance for the proposed transceiver system in this study to be considered as a candidate scheme for LSM communication systems.

We propose continuous power allocation strategies for secondary users (SUs) based on sensing the primary user (PU) channels in a multiband cognitive radio (CR) network. Unlike the conventional sensing-based spectrum sharing, where there are two transmit power levels corresponding to whether the PU is sensed present or not, in the proposed strategy, the power levels are continuous functions of the sensing statistics, and optimized with respect to the achievable rate of the SU. The power control process consists of two phases: in the first phase, the SU listens to the multiple bands licensed to the PU and obtains the received signal energies on these bands; in the second phase, the SU adjusts its transmit power levels on these bands based on the sensing results. Simulation results demonstrate that the proposed strategies can significantly improve the achievable throughput of the SU compared to the conventional methods.

We propose a robust close-to-capacity dirty-paper coding (DPC) design framework in which multi-level low density parity check (LDPC) codes and trellis coded quantization (TCQ) are employed as the channel and source coding components, respectively. The proposed design framework is robust in the sense that it yields close to capacity solutions in the high-, medium-, and low-rate regimes. This is in contrast to existing practical DPC schemes that perform well only in one or two of these regimes, but not all three. We design codes for transmission rates of 0.5, 1.0, 1.5, and 2.0 bits/sample (b/s) using one, two, three, and four LDPC levels; at a block length of 2×105, the codes perform 0.95, 0.58, 0.55, and 0.54 dB from the corresponding information theoretic limits, respectively. We also propose a low-complexity decoding scheme that does not involve iterative message passing between the source and channel decoders; the low-complexity scheme performs only 1.08, 0.85, and 0.79 dB away from the theoretical limits at transmission rates of 1.0, 1.5, and 2.0 b/s, respectively.

We propose a cross-layer design framework for efficient broadcasting scalable H.264 videos over the downlink multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing systems. The objective is to maximize the average peak signal-to-noise ratio of the received video streams by jointly optimizing video layer extraction, subcarrier allocation, modulation and coding, and transmit precoding, considering the heterogeneity of video sources and channel conditions. Specifically, to exploit the MIMO channel, we employ a codebook-based linear transmit precoding strategy with limited feedback. Given the fact that different quality layers of the video have different importance, we propose an adaptive modulation and coding scheme, where a fixed coding rate is used for each quality layer and unequal error protection is implemented for different layers. We further propose a subcarrier allocation strategy to assign transmission channels for different layers of different users' videos, to satisfy the decoding dependence constraints among the video layers and to maximize the reconstructed video quality. The proposed scalable video broadcast solution has a low complexity and low signaling overhead, which makes it suitable for practical implementations. We provide experimental results to demonstrate the effectiveness of the proposed solution, using both model-based simulations and an end-to-end software simulation testbed.

In this paper, we study the effect of feedback on the two-user MIMO interference channel. The capacity region of the MIMO interference channel with feedback is characterized within a constant number of bits, where this constant is independent of the channel matrices. Further, it is shown that the capacity region of the MIMO interference channel with feedback and its reciprocal interference channel are within a constant number of bits. Finally, the generalized degrees of freedom region for the MIMO interference channel with feedback is characterized.

We study physical layer multicasting in multicell networks where each base station, equipped with multiple antennas, transmits a common message using a single beamformer to multiple users in the same cell. We investigate two coordinated beamforming designs: the quality-of-service (QoS) beamforming and the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The goal of the QoS beamforming is to minimize the total power consumption while guaranteeing that received SINR at each user is above a predetermined threshold. We present a necessary condition for the optimization problem to be feasible. Then, based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. The max-min SINR (MMS) beamforming is to maximize the minimum received SINR among all users under per-base station power constraints. We show that the MMS problem and a weighted peak-power minimization (WPPM) problem are inverse problems. Based on this inversion relationship, we then propose an efficient algorithm to solve the MMS problem in an approximate manner. Simulation results demonstrate significant advantages of the proposed multicast beamforming algorithms over conventional multicasting schemes.

We develop robust power control strategies for cognitive radios in the presence of sensing delay and model parameter uncertainty. We use a discrete-time Markov chain (DTMC) to characterize the primary users' (PU) dynamics as well as the fading channel. The power control problem is formulated as a Markov decision process (MDP) problem, which can be optimally solved by dynamic programming. However, due to the time-varying nature of the wireless channel and the spectrum sensing overhead, typically only the delayed sensing results are available at any time. The delay in spectrum sensing, if not properly accounted for, could significantly deteriorate the power control performance. Furthermore, the false sensing data and limited feedback cause noisy estimate of the transition probability matrix, leading to further performance degradation of the power control and channel outage. We first propose power control schemes based on a delayed MDP formulation, that account for all possible current channel state based on the delayed channel state. In addition, we propose an outage constraint to protect PU transmissions and properly manage channel outage. We then propose a robust power control framework that optimizes the worst-case system performance. Extensive simulation results are provided to demonstrate the effectiveness of the proposed power control algorithms.

In this paper, we study the physical layer multicasting in multicell networks, where each base station equipped with multiple antennas transmits a common message using a single beamformer to multiple users equipped with a single antenna in the same cell. We consider the quality-of-service (QoS) beamforming for minimizing the total power consumption while guaranteeing that the signal-to-interference-plus-noise ratio (SINR) at each user is above a predetermined threshold. Based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. Simulation results demonstrate significant advantages of the proposed coordinated beamforming over conventional beamforming.

Underwater acoustic (UWA) channel is typically sparse. In this paper, a complex Homotopy algorithm is presented and then applied for UWA OFDM channel estimation. Two enhancements that exploit UWA channel temporal correlation for the compressed-sensing(CS)-based channel estimators are proposed. The first one is based on a first-order Gauss-Markov (GM) model which uses the previous channel estimate to assist current one. The other is to use the recursive least-squares (RLS) algorithm together with the CS algorithms to track the time-varying UWA channel. Simulation results show that the Homotopy algorithm offers faster and more accurate UWA channel estimation performance than other sparse recovery methods, and the proposed enhancements offer further performance improvement.

Power control plays a key role in realizing reliable and spectrum-efficient communications in a cognitive radio network. In this paper we study secure power control schemes for cognitive radios via a robust Markov decision process (MDP) approach. We first use the discrete time Markov chain (DTMC) model to characterize the primary user's (PU) activities as well as the dynamics of the fading channel. The resulting power control problem can be optimally solved by a dynamic programming (DP) approach. The presence of malicious users, however, necessitates a cooperative spectrum sensing approach that requires extra signal processing and information exchange efforts to identify false spectrum sensing reports. Such a cooperative approach incurs considerable delay in spectrum sensing that may significantly deteriorate the performance of the DP strategy. Furthermore, the false sensing data generated by malicious users may give rise to erroneous estimation of the transition probabilities. Consequently, the solution obtained based on the estimated transition matrices may exhibit poor performance. To cope with these challenges, we propose a framework of power control schemes based on the robust MDP approach that is capable of achieving reasonably good performance when both spectrum sensing delay and estimation errors are present. The tradeoffs between the robustness, the achievable throughput, and the sensing delay are also discussed. Extensive simulation results are presented to demonstrate the performance of the proposed power control strategies.

The authors consider the robust Tomlinson–Harashima precoding (THP) for downlink multiuser multiple-input multiple-output orthogonal frequency-division multiplexing systems with quantised feedback. The authors discuss vector channel feedback strategies in the frequency and time domains, and develop a robust version of THP that takes into account of error statistics of the channel state information, that consists of the optimal feedforward filters, feedback filters and the receive filters. Feedback techniques are developed to exploit the spatial correlations in realistic 3GPP channel models by applying dimension reduction and scalar-quantisation. Extensive simulations results are provided to demonstrate the performance of the proposed robust THP design as well as the channel feedback scheme.

This paper proposes group decoding and analyzes the associated rate allocation schemes for the relay interference channel where multiple relays assist the transmissions from the sources to destinations. All the relays and destinations employ an advanced decoding strategy called constrained group decoding, where the desired messages are decoded jointly with some interferers' messages when doing so is beneficial. This paper considers two types of relay systems, the hopping relay system with no direct source-destination links, and the inband relay system with direct source-destination links. For each relay type, the objective is to design the relay assignment and the group decoding strategies at the relays and destinations, in order to maximize the minimum information rate among all source-destination pairs. For hopping relays with pre-specified relay assignments, we provide the optimal distributed algorithm for solving the above max-min rate allocation problem. Moreover, for hopping relays with dynamic relay assignments, and for inband relays, the problem becomes intractable and we offer heuristic schemes that perform close to the optimum solutions. Numerical results demonstrate the significant performance improvement provided by the proposed group decoder over the traditional systems that employ the linear minimum mean-square error (MMSE) decoders at both the relays and the destinations, where all interference is treated as noise, as well as the effectiveness of the proposed dynamic relay assignment strategies.

We address the problem of downlink beamforming for mitigating the co-channel interference in multi-cell OFDMA networks. Based on the network utility maximization framework, we formulate the problem as a non-convex optimization problem subject to the per-cell power constraints, in which a general utility function of SINR is used to characterize the network performance. To solve the problem in a distributed fashion, we devise an algorithm based on the non-cooperative game with pricing mechanism. We give a sufficient condition for the convergence of the algorithm to the Nash equilibrium (NE). Moreover, to speed up the optimization of the beam-vectors at each cell, we derive an efficient algorithm to solve the KKT conditions at each cell. We provide extensive simulation results to demonstrate that the proposed distributed multi-cell beamforming algorithm converges to an NE point in just a few iterations with low information exchange overhead. Moreover, it provides significant performance gains, especially under the strong interference scenario, in comparison with several existing multi-cell interference mitigation schemes, such as the distributed interference alignment method.

The 15 papers in this special issue are divided into four categories: challenges in broadband wireless communications; physical layer techniques; radio resource management techniques; and field measurement and channel modeling.

We solve the problem of designing an affordable optimal transmission strategy for the recently proposed system of energy-harvesting active networked tags (EnHANTs), that is adapted to the identification request and the energy harvesting dynamic. We assume that the system operates in a time-slotted fashion, so that the problem is formulated as a Markov decision process (MDP). Both a static exhaustive search method and a modified policy iteration algorithm are employed to obtain the optimal transmission policy. Simulation results are provided to demonstrate that the obtained transmission policy can considerably improve the overall system performance which takes into consideration of both the system activity-time and the communication reliability.

We consider a well-defined joint detection and parameter estimation problem. By combining the Bayesian formulation of the estimation subproblem with suitable constraints on the detection subproblem, we develop optimum one- and two-step test for the joint detection/estimation setup. The proposed combined strategies have the very desirable characteristic to allow for the trade-off between detection power and estimation quality. Our theoretical developments are, then, applied to the problems of retrospective changepoint detection and multiple-input multiple-output (MIMO) radar. In the former case, we are interested in detecting a change in the statistics of a set of available data and provide an estimate for the time of change, while in the latter in detecting a target and estimating its location. Intense simulations in the MIMO radar example demonstrate that by using jointly optimum schemes, we can experience significant improvement in estimation quality, as compared to generalized the likelihood ratio test or the test that treats the two subproblems separately, with only small sacrifices in detection power.

Cognitive radios process their sensed information collectively in order to opportunistically identify and access underutilized spectrum segments (spectrum holes). Due to the transient and rapidly varying nature of the spectrum occupancy, the cognitive radios (secondary users) must be agile in identifying the spectrum holes in order to enhance their spectral efficiency. We propose a novel adaptive procedure to reinforce the agility of the secondary users for identifying multiple spectrum holes simultaneously over a wide spectrum band. This is accomplished by successively exploring the set of potential spectrum holes and progressively allocating the sensing resources to the most promising areas of the spectrum. Such exploration and resource allocation results in conservative spending of the sensing resources and translates into very agile spectrum monitoring. The proposed successive and adaptive sensing procedure is in contrast to the more conventional approaches that distribute the sampling resources equally over the entire spectrum. Besides improved agility, the adaptive procedure requires less-stringent constraints on the power of the primary users to guarantee that they remain distinguishable from the environment noise and renders more reliable spectrum hole detection.

This paper analyzes the gains of opportunistic communication in multiuser interference channels. Consider a fully connected n-user Gaussian interference channel. At each time instance, only K≤n transmitters are allowed to be communicating with their respective receivers and the remaining (n-K) transmitter-receiver pairs remain inactive. For finite n, if the transmitters can acquire the instantaneous channel realizations and if all channel gains are bounded away from zero and infinity, the seminal results on interference alignment establish that for any K arbitrary active pairs the total number of spatial degrees of freedom per orthogonal time and frequency domain is K/2. In dense networks (n → ∞), however, as the size of the network increases, it becomes less likely to sustain the bounding conditions on the channel gains. By exploiting this fact, we show that when n obeys certain scaling laws, by opportunistically and dynamically selecting the K active pairs at each time instance, the number of degrees of freedom can exceed K/2 and in fact can be made arbitrarily close to K. More specifically, for single-antenna transmitters and receivers, the network size scaling as n ∈ ω(SNRd⌈d-1⌉) when power allocation is allowed and scaling as n ∈ ω(SNRd(K-1)) without power allocation are sufficient conditions for achieving d ∈ [1, K] degrees of freedom. Moreover, for achieving these degrees of freedom the transmitters do not require the knowledge of the instantaneous channel realizations. Hence, invoking opportunistic communication in the context of interference channels leads to achieving higher degrees of freedom that are not achievable otherwise. We extend the results for multi-antenna Gaussian interference channels.

Femtocell networks, consisting of a conventional macro cellular deployment and overlaying femtocells, forming a hierarchical cell structure, constitute an attractive solution to improving the macrocell capacity and coverage. However, the inter- and intra-tier interferences in such systems can significantly reduce the capacity and cause an unacceptably high level of outage. This paper treats the uplink interference problem in orthogonal frequency-division multiple-access (OFDMA)-based femtocell networks with partial cochannel deployment. We first propose an inter-tier interference mitigation strategy without the femtocell users power control by forcing the femto-interfering macrocell users to use only some dedicated subcarriers. The non-interfering macrocell users, on the other hand, can use either the dedicated subcarriers, or the shared subcarriers which are also used by the femtocell users. We then propose subcarrier allocation schemes based on the auction algorithm for macrocell users and femtocell users, respectively, to independently mitigate the intra-tier interference. The proposed interference mitigation scheme for femtocell networks offers significant performance improvement over the existing methods by substantially reducing the inter- and intra-tier inferences in the system.

We propose novel coding and decoding methods for a fully connected K-user Gaussian interference channel via assigning multiple codebooks (layers) to each transmitter, such that at each receiver decoding interferers partially becomes feasible. Each receiver first identifies which interferers it should decode and which layers of them should be decoded, and then successively decodes a group of layers with a constraint on its group size treating the remaining layers as Gaussian noise. We provide a distributed algorithm that determines the transmission rate at each transmitter also finds the order of the layers to be successively decoded at each receiver. We also consider practical design of a system that employs the quadrature amplitude modulations (QAM) and rateless codes. Numerical results are provided on the achievable sum-rate under the ideal case of Gaussian signaling with random codes as well as on the system throughput under practical modulations and channel codes. The results show that the proposed multi-layer coding scheme with CPGD offers significant performance gain over the traditional un-layered transmission with single-user decoding.

We propose a close-to-capacity dirty-paper coding framework which employs multi-level low density parity-check (LDPC) and trellis coded quantization. The proposed coding framework is robust in the sense that it performs close to capacity in the high as well as the low rate regimes. This is in contrast to existing practical DPC schemes which perform well at one of these regimes, but never both. In order to evaluate the performance of our scheme, we consider its application to a cognitive radio channel. At a block length of 2 × 105, the designed dirty-paper coding scheme operates within 0.95, 0.58 and 0.6 dB of the theoretical limit at transmission rates of 0.5, 1.0 and 1.5 bits/sample, respectively. As far as the authors are aware, this is the best performance reported in the literature so far.

In this paper, we consider the doped decoder for Luby-Transform (LT) codes, where based on the decoder feedback, the decoding process is revived by retransmission of the in formation packet. We propose several improved doping methods to reduce the average doping rate. We then provide an analysis of the proposed doping methods based on the study of the decoding ripple evolution. Both analytical and simulation results show that the proposed doping approaches provide significant performance gain over the existing random doping strategy. We then apply the doped LT decoding to the wireless broadcast system in which limited feedback is allowed. We propose a majority vote based doping selection and demonstrate the efficiency of the proposed doping selection through simulations.

In order to achieve the Han-Kobayashi rate region for the two-user interference channel each transmitter splits its message into two sub-messages, each drawn from an independent codebook. Generalizing this idea to the K-user interference channel implies that 2K-1codebooks should be allocated to each transmitter, where each of them carries the message that is public to one of the subsets of the K-1 non-designated receivers. While such a rate-splitting scheme yields the best known achievable rate region (with random coding), optimizing a rate-related utility function over this region presents certain challenges stemming from the computational complexities and the distributed nature of interference channels. This paper introduces the notion of partial group decoding which offers a practical rate optimization strategy over this achievable rate region and mitigates these challenges. The merits of partial group decoders are demonstrated through treating the problem of optimal rate allocation with fairness constraints.

The gains of opportunistic communication in multiuser interference channels is analyzed. Consider a network of fully connected n-user Gaussian interference channel that afford activating K ≤ n at-a-time. It is shown that when n obeys certain scaling laws, by opportunistically and dynamically selecting the K active pairs the number of degrees of freedom can exceed K/2 and, in fact, can be made arbitrarily close to K. More specifically the network size scaling as n ∈ ω (SNRd(K-1)) is a sufficient condition for achieving d ∈ [0, K] degrees of freedom.

The authors consider chase-combining hybrid automatic repeat request (HARQ) schemes over multiple antenna multi-user systems. The focus is on a multiple-access channel, where the users as well as the base-station are equipped with multiple antennas. In such chase-combining HARQ systems, the transmitters (users) re-transmit their codewords when requested by the receiver (base-station). The receiver may choose to enforce blanking, that is, it may choose to request only a subset of the transmitters to re-transmit and the remaining ones to be silent. Moreover, subject to the complexity and latency constraints, the receiver may be able to perform codeword cancellation wherein it can re-encode, re-modulate a subset of decoded codewords and subtract them from the received observations. A candidate combining technique is the conventional chase-combining, which in contrast to the optimal combining requires significantly less memory and processing capability at the receiver but can result in substantial performance degradation. The authors propose efficient combining techniques that cater to all the various scenarios that arise in such multi-codeword systems. The proposed techniques impose similar memory and complexity demands as the conventional combining but yield a significant performance improvement. The issue of limited feedback in multi-codeword multi-antenna chase-combining HARQ systems is also addressed and it can be used to further improve the system throughput.

Inter-cell interference mitigation is a key challenge in the next generation wireless networks which are expected to use an aggressive frequency reuse factor and a high-density base station deployment to improve coverage and spectral efficiency. In this work, we consider the problem of maximizing the weighted sum-rate of a wireless cellular network via coordinated scheduling and discrete power control. We present two distributed iterative algorithms which require limited information exchange and data processing at each base station. Both algorithms provably converge to a solution where no base station can unilaterally modify its status (i.e., transmit power and user selection) to improve the weighted sum-rate of the network. Numerical studies are carried out to assess the performance of the proposed schemes in a realistic system based on the IEEE 802.16m specifications. Simulation results show that the proposed algorithms achieve a significant rate gain over uncoordinated transmission strategies for both cell-edge and inner users.

We propose novel coding and decoding methods for a fully connected K-user Gaussian interference channel. Each transmitter encodes its information into multiple layers and transmits the superposition of those layers. Each receiver employs a constrained partial group decoder (CPGD) that decodes its designated message along with a part of the interference. In particular, each receiver performs a twofold task by first identifying which interferers it should decode and then determining which layers of them should be decoded. Determining the layers to be decoded and decoding them are carried out in a successive manner, where in each step a group of layers with a constraint on its group size is identified and jointly decoded while the remaining layers are treated as Gaussian noise. The decoded layers are then subtracted from the received signal and the same procedure is repeated for the remaining layers. We provide a distributed algorithm, tailored to the nature of the interference channels, that determines the transmission rate at each transmitter based on some optimality measure and also finds the order of the layers to be successively decoded at each receiver. We also consider practical design of a system that employs the quadrature amplitude modulations (QAM) and rateless codes. Numerical results are provided on the achievable sum-rate under the ideal case of Gaussian signaling with random codes as well as on the system throughput under practical modulations and channel codes. The results show that the proposed multi-layer coding scheme with CPGD offers significant performance gain over the traditional un-layered transmission with single-user decoding.

We propose a practical message-wise unequal error protection (UEP) scheme using low-density parity-check (LDPC) codes, where one or more special messages are more protected than other ordinary messages. In contrast to the information theoretic cavity coding scheme, which discards the codewords of ordinary messages near those of special messages, the proposed coding scheme performs codeword flipping to separate the codewords of special and ordinary messages without discarding any codewords. To better distinguish the original and flipped codewords, the LDPC codes with all-odd degree check nodes are employed. The decoder performs message type detection and codeword flipping detection based on the unsatisfied check nodes in iterative decoding. We provide performance analysis for both the message type detection and the codeword flipping detection. Moreover, we provide an asymptotic analysis on the detection error exponent to reveal the relationship between the proposed practical coding scheme and the information theoretically optimal cavity coding. Simulation results are provided to show that the proposed practical message-wise UEP schemes offer capacity-approaching protections to both types of messages as if only one type is transmitted.

In this paper, we consider the problem of resource allocation in the DFT-Spread-OFDMA (DFT-S-OFDMA) uplink. We show that the resource allocation problem can be formulated as a set packing problem, which in general is NP-hard. We propose polynomial-time message-passing based algorithms, one of which is guaranteed to yield a solution that is within a constant fraction of the optimal solution and is also asymptotically optimal in the limit as the number of subcarriers in the system goes to infinity. The message-passing based algorithm is also extended to solve the resource allocation problem over a multi-cell uplink in a distributed fashion. Our algorithms account for finite input alphabets and non-ideal practical outer codes. Extensive simulations are performed to assess the performance of the proposed algorithms and it is shown that they yield near-optimal solutions at a low complexity and with a low memory requirement.

We analyze the performance of message-passing decoding of finite-length concatenated codes. We first show that the message-passing decoder is closely related to a dual optimization decoder. The connections between these two decoders are further elucidated by proving that both of them attain the same objective function value of a generalized linear programming decoder in the limit as the signal-to-noise ratio (SNR) goes to infinity. Consequently, the framework of pseudo-weight analysis, which was originally proposed for analyzing the linear programming decoder, can be extended to analyze the performance of the message-passing decoder for finite-length codes. We then derive lower bounds to the pseudo-weights of general concatenated codes by utilizing the special structure of their parity-check matrices. We finally present a method to increase the max-fractional weight by adding redundant parity-check constraints and thereby improving the decoding performance. Simulation studies are carried out to assess the performance of the proposed algorithms and substantiate the theoretic claims.

In orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) systems, the subcarriers already occupied by the primary users cannot be used by the secondary users. This leads to possibly non-contiguous positions of the available subcarriers for the secondary users. The conventional pilot design methods are no longer effective for such systems. In this paper, we propose a new practical pilot design method for OFDM-based CR systems. We first formulate the pilot design as a new optimization problem. Instead of minimizing the mean-square error (MSE) of the least-squares (LS) channel estimator, we minimize an upper bound which is related to this MSE. We then propose an efficient scheme to solve the optimization problem. Specifically, the pilot indices are obtained sequentially by solving a series of one-dimensional optimization problems of significantly lower complexity. The computational complexity of the proposed scheme is low since it only involves real additions. Simulation results show that the pilot index sequences obtained by the proposed method exhibit significantly better performance than those obtained by existing pilot design methods.

In this paper we consider the design of multi-stream demodulators for multiple-input multiple-output (MIMO) systems. Our proposed MIMO demodulator is based on the stack tree-search strategy and provides soft-outputs in the form of exact max-log log-likelihood ratios (max-log LLRs). We prove that our proposed demodulator is optimally efficient in that it visits the least number of nodes among all optimal tree-search based demodulators. We conduct a comprehensive complexity analysis of the optimally efficient demodulators. We also identify key parameters associated with our proposed demodulator that can be tuned to realize near max-log performance with substantially reduced complexity, as demonstrated via simulations.

Vast segments of the frequency spectrum are licensed to specific users for particular applications. These legacy users, however, often under-utilize their designated spectrum segments. Unlicensed (secondary) users can benefit from this fact and opportunistically exploit the vacant spectrum segments (spectral holes). Due to the transient nature of the spectrum occupancy it becomes imperative for secondary users to quickly identify such spectral holes. To accomplish this, we propose a novel sequential and adaptive spectrum sensing procedure. The underlying notion of this procedure is to progressively allocate the sensing resources to only the most promising areas of the spectrum. This translates in a reduction of sensing resources and time needed to accurately identify spectrum holes, in contrast with more conventional approaches that allocate the sensing budget over the entire spectrum uniformly. The proposed method is theoretically sound and further supported by simulation results.

We consider the joint channel and network code design for a half-duplex 4-node multiple-access relay system with two sources, one relay, and one destination. The relay combines the information from both sources and transmits it to the destination together with both sources. We consider two network coding schemes for information combining at the relay, namely, the superposition coding (SC) and the Raptor coding (RC). For both SC and RC, the profiles for joint channel and network coding are optimized based on the extrinsic information transfer (EXIT) function analysis. For both additive white Gaussian noise (AWGN) and block fading channels, the multiple-access relay system with optimized profiles exhibits significant performance gains over that employing the code profile optimized for either the single-user AWGN channel or the 2-user multiple-access channel.

We consider the interference relay channel (IRC) where a single relay assists the communications between multiple source-destination links under the half-duplex (HD) constraint. We assume each source has an independent message and its transmitted signal may cause interference at the other destinations. We assume each node only estimates its backward channels and has no knowledge of its forward channels as well as the other links. The role of the relay is to generate signals to cooperate with the intended signal and mitigate the interference at all destinations. Specifically, we propose coding schemes and transmission strategies for 2-user AWGN IRC with two sourcedestination pairs. We compare the performance of the proposed IRC coding schemes with that of a TDMA strategy where each source-destination pair communicates in alternative time slots with the assistance of the relay. Our simulation results based on practical turbo codes as underlying constituent codes show that the proposed transmission strategies result in a significant performance improvement over TDMA strategy in terms of outage probability and throughput.

This paper studies the performance limits of two-way relay channel (TWRC) at finite signal-to-noise ratio (SNR) in Rayleigh fading environment. A two-phase decode-and-forward (DF) protocol is considered. We first derive closed-form expressions for both outage probability and diversity-multiplexing tradeoff (DMT). Our results are general and suitable for any time sharing and any rate allocation in the two-way relay protocol. It is found that DF outperforms amplify-and-forward (AF) when either multiplexing gain or SNR is small enough, otherwise, DF is inferior to AF in term of outage probability. Meanwhile, finite-SNR DMT of DF is always lower than that of AF regardless of SNR due to the additional sum-rate constraint imposed on the relay node for full decoding. Furthermore, the optimum relay location for any given combination of time sharing and rate allocation is presented.

We present a novel partial decoding method at the relay, called pairwise check decoding (PCD), for two-way relay fading channels. The proposed PCD method forms a so-called check-relationship table for the superimposed Low-Density Parity-Check (LDPC)-coded packet pair during the multiple access phase. Meanwhile, it incorporates adaptive network coding by using closest-neighbor clustering mapping (CNCM) to compensate the phase deviation of the fading channels. The proposed PCD method is a practical and efficient realization of the promising denoise-and-forward relay strategy with advanced channel coding and non-linear network coding. Simulation results show that under the same LDPC-coded two-way relay system, our proposed PCD considerably outperforms the case where the relay performs only adaptive network coding without channel decoding. It also performs better than the case where the relay adopts the belief propagation decoding along with conventional XOR-based network coding under certain regions.

We consider the problem of designing robust linear transceivers for a memoryless narrowband Gaussian interference channel (GIC) where M multi-antenna sources communicate with their respective single-antenna receivers. The design of such linear transceivers heavily depends on the accuracy of the channel state information (CSI) available at the transmitters. In practice, the transmitters can acquire only imperfect or noisy CSI. We adopt a popular noisy CSI model which assumes that the noise terms (i.e., errors in the CSI) lie within known hyper-ellipsoids and design transceivers that optimize a worst-case quality of service measure. In particular, we focus on maximizing the worst-case weighted sum-rate as well as the worst-case minimum rate. For obtaining such transceiver designs, we exploit semidefinite programming methods and offer efficient centralized and distributed algorithms that entail different levels of information exchange among the transmitters.

We implement a dirty-paper coded framework for the cognitive radio channel. We assume that the cognitive user has non-causal knowledge about the primary user's transmissions. Thus the secondary receiver can employ dirty-paper coding to counter the effect of any interference from the primary user. In addition, we consider a situation where the introduction of the cognitive user should not affect the performance of the primary system -- nor should the primary system have to change its encoding/decoding process. For the primary user we use a low-density parity-check code and a 4-ary pulse amplitude modulation format. For the cognitive user, we propose a dirty-paper coding scheme which employs trellis-coded quantization as the source code and an irregular repeat-accumulate code as the channel code. At a transmission rate of 1.0 bits/sample, the designed dirty-paper coding scheme operates within 1.23 dB of the theoretical limit.

We propose a practical message-wise unequal error protection (UEP) scheme using low-density parity-check (LDPC) codes, where one or more special messages are more protected than other ordinary messages, which performs codeword flipping to separate the codewords of special and ordinary messages. To better distinguish the original and flipped codewords, the LDPC codes with all-odd degree check nodes are employed. The decoder performs message type detection and codeword flipping detection by tracking the number of unsatisfied check nodes in iterative decoding. We provide both finite-length and asymptotic performance analysis for the proposed coding scheme. Simulation results are provided to show that the proposed practical message-wise UEP schemes offer capacity-approaching protections to both types of messages as if only one type of message is transmitted.

For coordinated transmissions in multi-cell downlink channels, the base stations are required to acquire and share their channel state information (CSI). Acquiring CSI is often prone to errors and a globally-optimal coordination is not possible when the acquired CSI is imperfect. However, when the errors in the acquired CSI are guaranteed to lie within bounded regions, any quality-of-service (QoS) measure of interest will also lie within a bounded region. Motivated by this premise, by employing the notion of robustness in the worst-case sense, some worst-case guarantees on QoS can be offered. We assume that CSI perturbations belong to known hyper-spheres and aim to design linear transceivers that optimize the minimum worst-case rate of the network. We offer centralized (fully cooperative) and distributed (limited cooperation) procedures imposing different levels of complexity and information exchange among the base stations.

Achievable rate regions of multiuser fading interference channels depend on their fading realizations. Motivated by this premise we consider the problem of adapting the users' rates to fading variations. Channel-dependent rate adjustments are accomplished after each transition of the fading channel from one state to another. Such rate adjustments (increments or decrements) are constrained to meet some notion of fairness among the users and are designed to ensure that all users remain decodable. Here, we employ the notions of symmetric fair and max-min fair rate adaptations and offer algorithms for computing such fair rate adaptations. Besides fairness, the two other major features of these algorithms are that they are amenable to distributed implementation with limited information exchange among the users, and their complexities scale polynomially in the number of users.

We propose a rateless coded protocol for a half-duplex wireless relay channel where all links experience independent quasi-static Rayleigh fading. The protocol utilizes a combination of rateless coded decode-forward and compress-forward relaying schemes. Assuming very limited feedback from the destination, we derive the theoretical performance limits specifically with BPSK modulation. We then implement the rateless coded relaying protocol using carefully designed Raptor codes.

In optically routed networks, information for verifying network configuration is not readily available in electronic form. To address this problem, a low-cost all-digital system was developed in [1], [2] for overlaying a low-rate management data channel on a high-rate payload data channel. In this work, we analyze the performance of this novel multi-user communication system under chip-level asynchronism, extending a previous analysis under chip-level synchronism. Two decoding strategies are investigated: a zero-forcing and a minimum mean-squared error detector. Fundamental tradeoffs among several system parameters are identified.

In this paper, we propose a new signal dimensioning scheme for multi-user MIMO downlink, that adjusts the signal dimensions for different users to maximize the system throughput. By adapting the signal dimensions, we can achieve better tradeoff between multiplexing and diversity, and obtain much better bit rate error performance and higher capacity.

Recently, an all-digital overlay system has been developed in for providing useful management functionalities in optically routed networks. The main feature of the system is to overlay a low-rate stream of management data on a high-rate payload data channel so that the low-rate stream can be recovered by low-cost decoders located at various points within the network. The two key components of the overlay architecture are (i) a constant weight code used for multiplexing payload data streams, and (ii) a CDMA-based protocol used for managing interference among auxiliary data streams. In this work, we provide a general analysis under chip-asynchronous conditions, thereby extending a previous study under chip-synchronous conditions. Our analysis reveals that the bit error rate of the management data channel is limited by the presence of the payload interference. We show that significant performance improvements can be achieved by exploiting the covariance structure of the payload interference and investigate several low-complexity linear detection strategies. Analytical and numerical performance results are provided, and fundamental tradeoffs among several system parameters are identified.

Video transmission on multiuser wireless channels faces manifold challenges in both source- and channel-coding. We propose a framework to analyze three facets of this idea: video distortion, transmit power, and delay. In the scenario considered here, both the transmitter and end-users are equipped with multi-antenna transceivers. The goal is to broadcast video to all users; however, the video is differentiated in quality to match the spatial and temporal variation of the channel. An emerging coding standard that is well-suited to this scenario is the Scalable Video Coding (SVC) extension of H.264. We employ the medium-grain scalability (MGS) feature of SVC, which allows us to generate quality-scalable layers from a single video sequence. For transport, the transmitter employs the block-diagonal zero-forcing (ZF) precoding technique, a well-known technique which yields good performance. With these assumptions, the idea here is to compute a precoder at fixed intervals. Crucially, the cross-layer framework allocates layers of video to the end-users jointly with precoder computation. The framework also ensures that delay and buffer constraints are met, which is necessary for real-time video. In terms of solution approach, the problem turns out to be a difficult mixed-integer nonlinear optimization problem. However, we show that it is tractable via a primal-dual method. We analyze performance in various configurations, gaining insight into the relationship between the main system parameters.

We consider the spectrum sensing problem in cognitive radio networks. We offer a framework for optimal joint detection and parameter estimation when the secondary users have only a small number of signal samples. We discuss the finite-sample optimality of the generalized likelihood ratio test (GLRT) and derive the corresponding GLRT spectrum sensing algorithms by exploiting the statistics of the received signal and the prior information on the channel, noise, as well as the data signal. An iterative GLRT sensing algorithm, and a simple non-iterative GLRT sensing algorithm are developed for slow and fast-fading channels, respectively, with the latter also serving as an approximate sensing method for slow-fading channels. The proposed techniques are also extended for spectrum sensing in orthogonal frequency-division multiple-access (OFDMA) systems and in multiple-input multiple-output (MIMO) systems. It is seen that the proposed simple non-iterative fast-fading GLRT sensing algorithm offers the best performance in all systems under considerations, including slow fading channels, fast fading channels, OFDMA systems, and MIMO systems, and it significantly outperforms several state-of-the-art spectrum sensing methods in these systems when there is noise uncertainty.

A new approach to modulation classification based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs or the ECDFs of the signal under each candidate modulation format. The K-S-based modulation classifiers are developed for various channels, including the AWGN channel, the flat-fading channel, the OFDM channel, and the channel with unknown phase and frequency offsets, as well as the non-Gaussian noise channel, for both QAM and PSK modulations. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.

We introduce an implementation-friendly binary message-passing decoding method for low-density parity-check (LDPC) codes that does not require the degree information of variable nodes or degree dependent parameters. For hard decision decoding, given its low-complexity, the implementation cost for variable node degree information is an important consideration. We develop an estimation method for the extrinsic error probability (EEP) as well as its analysis. The proposed method offers similar performance as the existing methods for time-invariant decoding in most cases, while it facilitates efficient circuit implementations of the LDPC decoder.

A new approach to spectrum sensing in cognitive radio systems based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic obtained from the received signal, and comparing it with the ECDF of the channel noise samples. A sequential version of the K-S-based spectrum sensing technique is also proposed. Extensive simulation results demonstrate that compared with the existing spectrum detection methods, such as the energy detector and the eigenvalue-based detector, the proposed K-S detectors offer superior detection performance and faster detection, and is more robust to channel uncertainty and non-Gaussian noise.

We consider the use of linear constellation precoding and linear detection in a multicarrier OFDM system with multiple receive antennas to obtain improved performance over multipath fading channels at a low complexity. We split the full set of subcarriers into smaller groups and spread the data symbols assigned to each group via precoding matrices. We adopt the diversity-multiplexing tradeoff (DMT) framework and derive the DMT-optimal split of the subcarriers (DMT-optimal grouping) and the DMT-optimal number of symbols assigned to each group (DMT-optimal symbol loading). We determine necessary and sufficient precoder design conditions to achieve DMT optimality and give specific constructions of such precoders. Next, we consider a multiuser OFDMA system and derive an algorithm which divides the available subcarriers among the active users in order to maximize the diversity order of the system (or joint) error probability. We also extend our analysis to OFDM systems equipped with multiple transmit antennas. Finally, we obtain important insights on the role of outer codes in an OFDM system employing linear precoding and linear equalization.

Spectrum access is an important function of cognitive radios for detecting and utilizing spectrum holes without harming the legacy systems. In this paper, we propose novel cooperative communication models and show how deploying such cooperations between a pair of secondary transmitter and receiver assists them in identifying spectrum opportunities more reliably. These cooperations are facilitated by dynamically and opportunistically assigning one of the secondary users as a relay to assist the other one, which results in more efficient spectrum hole detection. Also, we investigate the impact of erroneous detection of spectrum holes and thereof missing communication opportunities on the capacity of the secondary channel. The capacity of the secondary users with interference-avoiding spectrum access is affected by 1) how effectively the availability of vacant spectrum is sensed by the secondary transmitter-receiver pair, and 2) how correlated are the perceptions of the secondary ransmitter-receiver pair about network spectral activity. We show that both factors are improved by using the proposed cooperative protocols. One of the proposed protocols requires explicit information exchange in the network. Such information exchange in practice is prone to wireless channel errors (i.e., is imperfect) and costs bandwidth loss. We analyze the effects of such imperfect information exchange on the capacity as well as the effect of bandwidth cost on the achievable throughput. The protocols are also extended to multiuser secondary networks.

Dynamic allocation of resources to the best link in large multiuser networks offers considerable improvement in spectral efficiency. This gain, often referred to as multiuser diversity gain, can be cast as double-logarithmic growth of the network throughput with the number of users. In this paper, we consider large cognitive networks granted concurrent spectrum access with license-holding users. The primary network affords to share its underutilized spectrum bands with the secondary users. We assess the optimal multiuser diversity gain in the cognitive networks by quantifying how the sum-rate throughput of the network scales with the number of secondary users. For this purpose, we look at the optimal pairing of spectrum bands and secondary users, which is supervised by a central entity fully aware of the instantaneous channel conditions, and show that the throughput of the cognitive network scales double-logarithmically with the number of secondary users$(N)$and linearly with the number of available spectrum bands$(M)$, i.e.,$M\log \log N$. We then propose a distributed spectrum allocation scheme, which does not necessitate a central controller or any information exchange among different secondary users and still obeys the optimal throughput scaling law. This scheme requires that some secondary transmitter–receiver pairs exchange$\log M$information bits among themselves. We also show that the aggregate amount of information exchange between secondary transmitter–receiver pairs is asymptotically equal to$M\log M$. Finally, we show that our distributed scheme guarantees fairness among the secondary users, meaning that they are equally likely to get access to an available spectrum band.

We consider the code design for a half-duplex 4-node joint relay system with two sources, one relay, and one destination. The relay combines the information from both sources and transmits it to the destination together with both sources. We consider two coding schemes for information combining at the relay, namely, the superposition coding (SC) and the Raptor coding (RC). The Raptor codes are employed at the sources as well as the relay. The relay and the destination perform iterative a posteriori probability (APP) detection and soft Raptor decoding. For both SC and RC, the profiles for Raptor codes are optimized based on the extrinsic information transfer (EXIT) function analysis. For both the additive white Gaussian noise (AWGN) and block fading channels, the joint relay system with optimized profiles exhibits significant performance gains over that employing the code profile optimized for either the singleuser AWGN channel or the 2-user multiple-access channel.

Interference alignment (IA) has evolved as a powerful technique in the information theoretic framework for achieving the optimal degrees of freedom of interference channel. In practical systems, the design of specific interference alignment schemes is subject to various criteria and constraints. In this paper, we propose novel transceiver schemes for the MIMO interference channel based on the mean square error (MSE) criterion. Our objective is to optimize the system performance under a given and feasible degree of freedom. Both the total MSE and the maximum per-user MSE are chosen to be the objective functions to minimize. We show that the joint design of transmit precoding matrices and receiving filter matrices with both objectives can be realized through efficient iterative algorithms. The convergence of the proposed algorithms is proven as well. Simulation results show that the proposed schemes outperform the existing IA schemes in terms of BER performance. Considering the imperfection of channel state information (CSI), we also extend the MSE-based transceiver schemes for the MIMO interference channel with CSI estimation error. The robustness of the proposed algorithms is confirmed by simulations.

Frequency-dependent I/Q imbalance is one of the major impairments in the direct-conversion receivers (DCR) for high-speed wideband wireless systems. We propose two new blind methods for compensating frequency-dependent I/Q imbalance. The first one is a time-domain approach. Specifically we develop a blind identifiability condition based on which a cost function and a gradient descent search algorithm are proposed for blind I/Q imbalance compensation. The second blind method is a frequency-domain approach for OFDM systems. Here we provide blind estimators for the frequency-selective I/Q imbalance parameters, which once obtained, the I/Q imbalance can then be compensated by a simple single-tap matrix filter inversion. We provide extensive simulation results to demonstrate the performance of the proposed algorithms.

We consider a multi-cell wireless network with universal frequency reuse and treat the problem of co-channel interference mitigation in the downlink channel. Assuming that each base station serves multiple single-antenna mobiles via space-division multiple-access, we jointly optimize the linear beam-vectors across a set of coordinated cells and resource slots: the objective function to be maximized is the instantaneous weighted sum-rate subject to per-base-station power constraints. After deriving the general structure of the optimal beam-vectors, a novel iterative algorithm is presented which attempts to solve the Karush-Kuhn-Tucker conditions of the non-convex problem at hand. The proposed algorithm admits a distributed implementation which we illustrate. Also, various approaches to choose the initial beam-vectors are considered, one of which maximizes the signal-to-leakage-plus-noise ratio. Finally, simulation results are provided to assess the performance of the proposed algorithm.

We present a new physical layer network coding (PLNC) scheme, called superimposed XOR, for two-way relay channels. The new scheme specifically takes into account the channel as well as information asymmetry in the broadcast phase of two-way relaying. It is based upon both bitwise XOR and symbol-level superposition coding. We first derive its achievable rate regions when integrated with two known time control protocols over Gaussian channels. We then demonstrate its average maximum sum-rate and service delay performance over fading channels. Compared with the existing bitwise XOR and symbol-level superposition coding, the proposed superimposed XOR scheme achieves larger rate region in asymmetric channels. As a result, it performs much better in terms of averaged maximum sum-rate and service delay over fading channels. Numerical results also show that the proposed practical PLNC closely approaches the capacity bound given by the information-theoretic random binning.

In this work we design efficient soft output demodulators (also referred to as fast soft demodulators) for the 2 × 2 Golden code, which is a promising candidate space time block code in the evolving IEEE and 3GPP cellular standards. For this code, the naive approach for maximum likelihood (ML) hard decision as well as soft-output demodulation entails a complexity of O(M4), where M denotes the cardinality of the QAM constellation from which the underlying modulated symbols are drawn. In contrast, our efficient demodulator exploits the structure of the code and yields the ML decision and soft outputs in the form of exact max-log log-likelihood ratios (LLRs) with an O(M2.5) complexity. Moreover, we also design a sub-optimal soft output demodulator that has an O(M0.5) complexity comparable to that of the linear minimum mean square error (LMMSE) based demodulator but results in substantial performance gains.

We propose a framework for multicasting layers of scalable video over a broadcast channel to multiple users simultaneously. The framework ensures that all users receive the content, but at differentiated levels of quality depending on channel conditions. A coding scheme well-suited to this purpose is Scalable Video Coding (SVC). In particular, we employ medium-grain scalability (MGS), which allows us to generate quality-scalable layers from a single video sequence. For transport, we assume that both the transmitter and endusers are equipped with multiple-input multiple-output (MIMO) transceivers. The transmitter employs the zero-forcing (ZF) preceding technique, which yields good performance with low complexity. With these assumptions, the scheme proposed here computes a block-diagonal ZF-precoder at fixed intervals, subject to a power constraint at the transmitter. Crucially, the algorithm assigns quality-scalable layers of video to the end-users jointly with precoder computation. The framework also ensures that delay and buffer constraints are met, which is necessary for realtime video. In terms of solution approach, the problem turns out to be a difficult mixed-integer nonlinear optimization problem.

We present a multilayer space-time-frequency (STF) coding scheme for wideband multiple-input multiple-output (MIMO) systems employing orthogonal frequency-division multiplexing (OFDM). With multiple receive antennas, we employ an iterative demodulator consisting of a low-complexity multilayer detector and a simple soft combiner. The system performance is optimized by efficient power allocation among layers, as well as interleaver design and extrinsic scaling. The results show that the proposed multilayer STF coding scheme performs close to or even better than the optimized linear dispersion (LD) STF codes with maximum likelihood (ML) decoding. Unlike the existing STF codes, the proposed multilayer STF strategy is a universal scheme that exhibit excellent performance under various system configurations and channel statistics.

This paper presents a multi-antenna cognitive radio (CR) system that is capable of operating concurrently with the primary radio (PR) link. The operation of the CR system consists of three stages: environmental learning, CR channel training and CR data transmission. In environmental learning stage, partial channel information between PR and CR are obtained blindly, based on which the transmit beamforming and the receive beamforming strategies are designed at CR to remove/reduce the interference to and from PR, respectively. We characterize all the interference values analytically and study the problem of learning/training tradeoff associated with the proposed scheme. The optimal balancing between learning and training is examined via the minimum mean square error (MSE) of the channel estimation. It is shown that for a given total learning/training time, there indeed exists a optimal learning time that minimizes the MSE of the channel estimation, yet the interference power to the PR is regulated.

In this work, a new framework for channel estimation in MIMO OFDM systems is provided. Sparse channel estimation refers to estimating the time domain channel impulse response by exploiting the fact that the channel has a very few nonzero taps. We formalize the problem and drive necessary and sufficient condition on the number of pilots for perfect channel recovery which leads to a L0 norm optimization problem. A practical suboptimal solution is proposed that is a modified orthogonal matching pursuit (OMP) which exploits the sparsity structure of the MIMO channel. The investigations reveal that the training overhead can be drastically reduced while maintaining the same accuracy as the current state of the art techniques.

We propose a novel cooperative communication protocol for multicasting a common message from one source to two destinations and based on that offer a spectrum access scheme for the cognitive radios that seek to utilizing the spectrum holes within the bands licensed to the legacy systems. The proposed cooperation model has two major traits; first, by opportunistically and dynamically assigning one of the destination nodes as relay for the other one, via a single-time relaying both destinations achieves a second order diversity gain. Secondly, it guarantees performance improvement over all SNR regimes, which is not the case in most cooperation protocols as diversity gain is a high SNR measure and yielding higher diversity orders ensures improvement only over high enough SNRs. Next, we consider cognitive users, to whom the codebook of the primary users is known as side information, and offer a beacon-assisted mechanism for spectrum access. We assume that a primary user multicasts a beacon message upon releasing a spectrum band and adopt the proposed cooperation model to strengthen a cognitive transmitter-receiver pair in decoding the beacon message. Finally, we quantify the effect of such cooperation on the capacity of the channel between the cognitive transmitter-receiver pair, as a meaningful measure to assess how the proposed cooperation model assists the secondary users in exploiting communication opportunities.

We consider decentralized multi-antenna cognitive radio networks where secondary (cognitive) users are granted simultaneous spectrum access along with license-holding (primary) users. We investigate the problem of designing beam- formers for the secondary users by maximizing the minimum rate, subject to a limited sum-power budget and constraints on the interference level imposed on each primary receiver. We consider two scenarios: the first one allows only single-user decoding at each secondary receiver whereas in the second case each secondary receiver is allowed to employ advanced multiuser decoding and is free to decode any subset of secondary users. We provide an optimal distributed algorithm for the first scenario and an explicit formulation of the optimization problem corresponding to the second scenario. This problem however is non-convex and hence cannot be efficiently solved even in a centralized setup. As a remedy, we suggest a two-step approach. In particular, the beamformers are first designed assuming single user decoding at each secondary receiver. An optimal distributed low-complexity algorithm is then proposed to allocate excess rates to the secondary users, which are made possible due to the use of advanced decoders at the secondary receivers. Simulation results demonstrate the gains yielded by the optimal beamformers as well as the rate allocation algorithms.

In this paper, we study a multi-antenna-based cognitive radio (CR) system that is able to operate concurrently with the primary radio (PR) system. We propose a novel CR transmission frame structure consisting of three stages, including a new environment learning stage in addition to the conventional channel training and data transmission stages. During the environment learning stage, the CR terminals blindly learn the spatial knowledge of the PR-CR channels, based on which cognitive beamforming is designed at CR transceivers to restrict the interference to and from the PR, respectively, in the subsequent channel training and data transmission stages. Considering the learning and training errors from the first two stages, we derive a lower bound on the ergodic capacity achievable for the CR link subject to a predefined interference-power constraint at the PR and the CR's own transmit power constraint. We then characterize a general learning/training/throughput (LTT) tradeoff associated with the proposed scheme, pertinent to transmit power allocation between training and transmission stages, as well as time allocation among learning, training, and transmission stages.

This paper studies two-user causal cognitive radio channels, in which the secondary user has causal knowledge about the message being sent by the primary user. An inner bound on the capacity region of such channels is established by employing a coding strategy consisting of block Markov superposition and dirty paper encoding, and backward decoding. An illustrative example in the Gaussian case is provided.

This paper presents the design challenges posed by a new class of ultra-low-power devices referred to as Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and self-reliant (in terms of energy devices that can be attached to objects that are traditionally not networked (e.g., books, clothing, and produce), thereby providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects (items in a store, boxes in transit), and determining locations of disaster survivors. Recent advances in ultra-low-power wireless communications, ultra-wideband (UWB) circuit design, and organic electronic harvesting techniques will enable the realization of EnHANTs in the near future. In order for EnHANTs to rely on harvested energy, they have to spend significantly less energy than Bluetooth, Zigbee, and IEEE 802.15.4a devices. Moreover, the harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using ultra-low-power circuits, the energy required to receive a bit is an order of magnitude higher than the energy required to transmit a bit). These special characteristics pose several new cross-layer research problems. In this paper, we describe the design challenges at the layers above the physical layer, point out relevant research directions, and outline possible starting points for solutions.

We introduce simple yet efficient ARQ protocols in conjunction with two types of anti-jamming coding techniques - rateless coding and piecewise coding - for cognitive radios. For piecewise coding, we propose to employ systematic codes to facilitate efficient selected retransmissions and design short codes to maximize secondary user throughput. For rateless coding, we consider a protocol that transmits new parity packets during retransmission and show that this scheme is stable if the jamming rate of the system is within a certain range. For both anti-jamming coding schemes, the corresponding simple ARQ protocols provide significant improvement in secondary user throughput. Moreover, the piecewise coding together with its ARQ protocol offers similar or better throughput performance compared with the rateless coding counterpart, without incurring the stability issue.

BACKGROUND: Time-course gene expression analysis has become important in recent developments due to the increasingly available experimental data. The detection of genes that are periodically expressed is an important step which allows us to study the regulatory mechanisms associated with the cell cycle. RESULTS: In this work, we present the Laplace periodogram which employs the least absolute deviation criterion to provide a more robust detection of periodic gene expression in the presence of outliers. The Laplace periodogram is shown to perform comparably to existing methods for the Sacharomyces cerevisiae and Arabidopsis time-course datasets, and to outperform existing methods when outliers are present. CONCLUSION: Time-course gene expression data are often noisy due to the limitations of current technology, and may include outliers. These artifacts corrupt the available data and make the detection of periodicity difficult in many cases. The Laplace periodogram is shown to perform well for both data with and without the presence of outliers, and also for data that are non-uniformly sampled.

We propose a low-complexity PAM-based transmission scheme that is well suited for channels constrained by inter-symbol interference (ISI) and colored Gaussian noise. The scheme consists of coset-codes constructed on multi-dimensional lattices with a combination of low-density parity-check (LDPC) codes and classical Reed-Solomon (RS) codes. To approach the capacity of an ISI-constrained channel, the code is easy to employ in conjunction with spectral shaping, Tomlinson- Harashima precoding and decision-feedback equalization (DFE). The scheme performs within 2-2.5 dB of un-shaped channel capacity (the sphere-bound) at very low BER's, even with regular LDPC codes of modest block lengths. We investigate dense multidimensional lattices such as the Schlafli, Gosset, Barnes-Wall, and Leech lattices, besides simple one-dimensional lattices. Via the density evolution technique, we show that the lattices reduce the noise threshold of belief-propagation decoders. We investigate the practical application of the proposed schemes to 10G-Base-T, an emerging Ethernet standard over twisted-pairs at 10 Gbit/sec. A simple 1-dimensional scheme improves upon recent proposals by 0.5-1 dB at BER's approaching 10-11, with half the LDPC coding complexity. Multi-dimensional schemes are seen to reduce the complexity further.

A new framework for efficient exact maximum-likelihood (ML) decoding of spherical lattice codes is developed. It employs a double-tree structure: The first is that which underlies established tree-search decoders; the second plays the crucial role of guiding the primary search by specifying admissible candidates and is our present focus. Lattice codes have long been of interest due to their rich structure, leading to decoding algorithms for unbounded lattices, as well as those with axis-aligned rectangular shaping regions. Recently, spherical Lattice Space-Time (LAST) codes were proposed to realize the optimal diversity-multiplexing tradeoff of MIMO channels. We address the so-called boundary control problem arising from the spherical shaping region defining these codes. This problem is complicated because of the varying number of candidates to consider at each search stage; it is not obvious how to address it effectively within the frameworks of existing decoders. Our proposed strategy is compatible with all sequential tree-search detectors, as well as auxiliary processing such as the MMSEGDFE and lattice reduction. We demonstrate the superior performance and complexity profiles achieved when applying the proposed boundary control in conjunction with two current efficient ML detectors and show an improvement of 1dB over the state-of-the-art at a comparable complexity.

We consider a memoryless Gaussian interference channel (GIC) whereKsingle-antenna users communicate with their respective receivers using Gaussian codebooks. Each receiver employs a successive group decoder with a specified complexity constraint, to decode its designated user. It is aware of the coding schemes employed by all other users and may choose to decode some or all of them only if it deems that doing so will aid the decoding of its desired user. For a GIC with predetermined rates for all transmitters, we obtain the minimum outage probability decoding strategy at each receiver which satisfies the imposed complexity constraint and reveals the optimal subset of interferers that must be decoded along with the desired user. We then consider the rate allocation problem over the GIC under successive group decoding and design a sequential rate allocation algorithm which yields a Pareto-optimal rate allocation, and two parallel rate allocation algorithms which yield the symmetric fair rate allocation and the max-min fair rate allocation, respectively. Remarkably, even though the proposed decoding and rate allocation algorithms use ldquogreedyrdquo or myopic subroutines, they achieve globally optimal solutions. Finally, we also propose rate allocation algorithms for a cognitive radio system.

For the 3GPP LTE uplink transmissions, the DFTspread OFDM technique has been adopted as the air interface in order to reduce the peak-to-average-power ratio (PAPR). In this scheme, each data symbol is spread over many tones by a discrete Fourier transform (DFT) operation at the transmitter before being sent to the orthogonal frequency division multiplexing (OFDM) modulator. Moreover, more than one user can be scheduled over the same frequency and time resource block (RB) via space-division multiple-access (SDMA). The conventional receiver technique for such DFT-spread OFDM systems involves tone-by-tone single-tap equalization followed by an inverse DFT operation. In this paper, we propose a more powerful receiver technique for DFT-spread OFDM systems that consists of an efficient linear pre-filter and a two-symbol soft output demodulator. The proposed method can be applied to both single-user per RB (DFT-S-OFDMA) and multiple users per RB (DFT-S-OFDMSDMA) systems and it offers significant performance gains over the conventional method, especially in the high-rate regime, with little attendant increase in computational complexity.

In this paper, we consider the design of efficient anti-jamming coding techniques for recovering lost packets transmitted through parallel channels. We present two coding schemes with small overhead and low complexity, namely, rateless coding and piecewise coding. For piecewise coding, we propose the optimal as well as several suboptimal design methods to build short block codes with small number of parity checks. One application of the anti-jamming coding techniques is in a cognitive radio system to protect the secondary users from the interference by the primary users. For such application, we consider two types of subchannel selections, i.e., the single uniform and general non-uniform subchannel selections. Throughput and the goodput performance of the secondary users employing either anti-jamming coding technique is analyzed under both subchannel selection strategies. The results show that both coding techniques provide reliable transmissions with high throughput and small redundancy. The piecewise coding using the designed short codes provides better performance with smaller overhead under low to medium jamming rate. For non-uniform subchannel selection, the designed short code improves the throughput and goodput performance of secondary transmission with antijamming piecewise coding while the rateless coding provides similar or worse performance than that in the uniform case.

We present a multilayer space-time-frequency (STF) coding scheme for wideband multiple-input multiple-output (MIMO) systems employing orthogonal frequency-division multiplexing (OFDM). With multiple receive antennas, we employ an iterative demodulator consisting of a low-complexity multilayer detector and a simple soft extrinsic combiner. The system performance is optimized by efficient power allocation among layers, as well as interleaver design and extrinsic scaling to reduce the extrinsic correlation. Simulation results demonstrate that the proposed multilayer STF coding scheme performs close to or even better than the optimized linear dispersion (LD) STF codes with maximum likelihood (ML) decoding. Unlike the existing STF codes, which need to be designed for different system configurations and channel statistics, the proposed multilayer STF strategy is a universal scheme that exhibit excellent performance under various conditions.

Abstract We introduce simple yet efficient automatic retransmission request (ARQ) protocols in conjunction with two types of anti‐jamming coding techniques—rateless coding and piecewise coding—for protecting secondary users' data transmissions in cognitive radio systems. For piecewise coding, we propose to employ systematic codes to facilitate efficient selected retransmissions and design short codes to maximize secondary user throughput. For rateless coding, we consider a protocol that transmits new parity packets during retransmission and show that this scheme is stable if the jamming rate of the system is within a certain range. It is seen that for both anti‐jamming coding schemes, the corresponding simple ARQ protocols provide significant improvement in secondary user throughput. Moreover, the piecewise coding together with its ARQ protocol offers similar or better throughput performance compared with the rateless coding counterpart, without incurring the stability issue. Copyright © 2009 John Wiley & Sons, Ltd.

We consider a memoryless narrowband Gaussian interference channel (GIC) where K single-antenna users communicate with their respective receivers using Gaussian code- books. Each receiver employs a successive group decoder with a specified complexity constraint, to decode its designated user. It is aware of the coding schemes employed by all other users and may choose to decode some or all of them only if it deems that doing so will aid the decoding of its desired user. For a GIC with predetermined rates for all users, we obtain the minimum outage probability decoding strategy at each receiver, which satisfies the imposed complexity constraint and reveals the optimal channel-dependent subset of interferers that must be decoded along with the desired user. We then consider the rate allocation problem over the GIC and design two distributed rate allocation algorithms which yield the symmetric fair rate allocation and the max-min fair rate allocation, respectively.

We propose resource allocation algorithms based on the auction method for uplink OFDMA cellular networks. We consider cellular systems that employ the traditional static frequency reuse as well as the next-generation systems that aim to achieve a universal frequency reuse via base-station coordination. Our algorithms are designed for finite input alphabets and also account for non-ideal practical outer codes, and they can be implemented in a distributed manner, when applied for multi-cell resource allocation. The proposed algorithms have a complexity of O(N) per user per iteration, where N denotes the number of subcarriers in the system, and are also well suited for parallel implementations. We also address power and bandwidth constraints that are motivated by practical concerns. The proposed algorithms exhibit very low complexity and simulation results demonstrate that they offer near-optimal performance.

We consider the design of adaptive hybrid automatic retransmission request (ARQ) with incremental redundancy (IR) in which the transmission rates of different blocks in one hybrid ARQ process can be different. The throughput of adaptive IR hybrid ARQ in block fading channels is formed based on the renewal-reward theorem. Two types of input signals are considered, namely, Gaussian inputs and practical turbo coded modulation. For Gaussian inputs, the error probability after each transmission is obtained from the outage rate. For turbo coded modulation, we obtain the error rate after each transmission block by applying union-Bhattacharyya (UB) bound for parallel channels. The throughput optimization is then formed to seek the optimal transmission rates. The results show that adaptive IR HARQ provides higher throughput than non-adaptive IR HARQ and chase combining in the moderate-to-high SNR region.

Although iterative equalizers are well-known for mitigating inter-symbol interference (ISI), recent results have shown that the schemes perform poorly in the presence of severe ISI, particularly when coding rates are high. Other equalization schemes, such as the non-iterative canonical decision-feedback equalizer (CDFE), have been shown to be asymptotically optimal on any channel. However, the scheme exhibits very high latency. To trade off between performance and latency, we propose an equalizer structure that iterates with both hard- and soft-decisions. In prior works, hard-decision iterative equalizers were seen to perform poorly, chiefly due to error-propagation in the feedback loop. However, the scheme proposed in this paper outperforms the linear turbo equalizer in both strong and weak ISI. On channels with weak ISI, the equalizer outperforms both the CDFE as well as the ideal DFE with perfect feedback. Simulation results on well-known ISI channels support the findings.

A successive interference cancellation (SIC) algorithm based on breadth-first search (BFS) is developed to achieve a soft-input soft-output detector via the tree structure of the MIMO system model in this paper. Instead of visiting all nodes of the tree, the proposed BFS-SIC algorithm only browses and extends those paths with large metrics. If paths are enough, the performance of BFS-SIC algorithm can approach that of sphere decoding but is much more flexible due to its providing a good tradeoff between complexity and performance. Moreover, the BFS-SIC algorithm possesses path metrics including only scalar operations rather than matrix operations. Simulation results demonstrate the effectiveness of the proposed algorithm.

We present a robust non-parametric detection mechanism for CSMA/CA MAC layer denial-of-service attacks that does not require any modification to the existing protocols. This technique, based on the M-truncated sequential Kolmogorov- Smirnov statistics, monitors the successful transmissions and the collisions of the terminals in the network, and determines how 'explainable' the collisions are given such observations. We show that the distribution of explainability of the collisions is very sensitive to abnormal changes in the network, even with a changing number competing terminals. NS-2 simulation results show that the proposed method has a very short detection latency and high detection accuracy.

Robustness of optimization models for networking problems has been an under-explored area. Yet most existing algorithms for solving robust optimization problems are centralized, thus not suitable for many communication networking problems that demand distributed solutions. This paper represents the first step towards building a framework for designing distributed robust optimization algorithms. We first discuss several models for describing parameter uncertainty sets that can lead to decomposable problem structures. These models include general polyhedron, D-norm, and ellipsoid. We then apply these models to solve robust power control in wireless networks and robust rate control in wireline networks. In both applications, we propose distributed algorithms that converge to the optimal robust solution. Various tradeoffs among performance, robustness, and distributiveness are illustrated both analytically and through simulations.

We consider a shared relay channel (SRC) where a single relay assists the communications between multiple source-destination links under the half-duplex (HD) constraint. Specifically, we derive lower and upper bounds on the capacity of 2-user AWGN MIMO SRC with two source-destination pairs. Two different coding strategies are presented based on super-position coding and on dirty paper coding, respectively. We compare the performance of the proposed SRC coding schemes with that of a TDMA strategy where each source-destination pair communicates in alternative time slots with the assistance of the relay. The proposed coding schemes for SRC results in significant performance improvement over TDMA strategy in terms of both ergodic capacity and outage probability.

We consider a block fading multiple access relay channel (MARC) where K users communicate with a single destination in the presence of Q ≥ 1 half-duplex relays. Each relay employs a successive group decoder with a specified complexity constraint and a decode and forward (DF) protocol. We design an outage minimizing relaying strategy for the scenario where no channel dependent feedback is possible between the destination and any user but a limited amount of such feedback is possible between the destination and each relay. We also design a rate allocation algorithm which yields the symmetric fair rate allocation. Remarkably, even though the outage minimization and rate allocation algorithms use low-complexity ‘greedy’ or myopic sub-routines at the relays, they achieve globally optimal solutions.

MOTIVATION: Conserved motifs often represent biological significance, providing insight on biological aspects such as gene transcription regulation, biomolecular secondary structure, presence of non-coding RNAs and evolution history. With the increasing number of sequenced genomic data, faster and more accurate tools are needed to automate the process of motif discovery. RESULTS: We propose a deterministic sequential Monte Carlo (DSMC) motif discovery technique based on the position weight matrix (PWM) model to locate conserved motifs in a given set of nucleotide sequences, and extend our model to search for instances of the motif with insertions/deletions. We show that the proposed method can be used to align the motif where there are insertions and deletions found in different instances of the motif, which cannot be satisfactorily done using other multiple alignment and motif discovery algorithms. AVAILABILITY: MATLAB code is available at http://www.ee.columbia.edu/~kcliang

The authors treat the multiuser scheduling problem for practical power-controlled code division multiple access (CDMA) systems under the opportunistic fair scheduling (OFS) framework. OFS is an important technique in wireless networks to achieve fair and efficient resource allocation. Power control is an effective resource management technique in CDMA systems. Given a certain user subset, the optimal power control scheme can be derived. Then the multiuser scheduling problem refers to the optimal user subset selection at each scheduling interval to maximise certain metric subject to some specific physical-layer constraints. The authors propose discrete stochastic approximation algorithms to adaptively select the user subset to maximise the instantaneous total throughput or a general utility. Both uplink and downlink scenarios are considered. They also consider the time-varying channels where the algorithm can track the time-varying optimal user subset. Simulation results to show the performance of the proposed algorithms in terms of the throughput/utility maximisation, the fairness, the fast convergence and the tracking capability in time-varying environments are presented.

The authors propose a concatenated scheme to reduce the peak-to-average power ratio (PAPR) in coded orthogonal frequency-division multiplexing (OFDM) systems. First, they employ a label-bits-inserted encoder of a random-like code to achieve selected mapping (SLM). Then they set a threshold at the selector to limit the number of candidate sequences. Both analytical and numerical results show that the complexity of the SLM implemented by the label-bit-inserted encoder can be significantly reduced by threshold limited selection. With the same complexity, the performance of PAPR reduction is improved. The proposed concatenated PAPR reduction scheme enjoys many advantages including low-complexity, small overhead, no side information transmission and no performance loss or additional complexity at the receiver.

We consider the sum-rate maximization via linear preceding in downlink MIMO-OFDM systems with quantized feedback. We address the preceding codebook design based on the capacity measure by introducing a new distance metric. We propose a codebook structure and its associated design algorithm that allows for significant reduction in the memory requirement and computational complexity in real-time system implementation. We then provide a system design approach comprising of four main ingredients: (i) a multi-rank beamforming (MRBF) scheme, (ii) an efficient CQI-based precoder selection algorithm, (iii) reduced feedback strategies, and (iv) novel channel quality indicator (CQI) combining. Our simulation results show that the proposed MRBF scheme can approach the precoding upper bounds with relatively few feedback bits. Moreover, with the same number of bits, the proposed scheme simultaneously achieves higher throughput and lower computational complexity in comparison to the other existing precoding schemes.

We derive a novel Bayesian algorithm for multiuser detection in the uplink of a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system employing stacked space-time block codes, such as the stacked Alamouti code with two transmit antennas, and a stacked quasi-orthogonal code with four transmit antennas. The proposed technique accomplishes joint estimation of the carrier frequency offset, phase noise, channel impulse response and data of each active user. Its derivation relies on the specific structure of the transmitted signal and on efficient Markov chain Monte Carlo (MCMC) methods. Simulation results evidence the robustness of the proposed algorithm in both uncoded and coded systems.

We consider the multi-input multi-output (MIMO) downlink channel in the next-generation cellular networks and propose two improved interference suppression receivers for combating out-of-cell interference. The proposed receivers exploit the fact that the co-channel interference seen on the downlink channel (especially the downlink control channel) has a particular structure, in order to obtain significantly improved performance while ensuring low decoding complexity. The first receiver does not require the user to decode the interference or be aware of the particular inner codes employed by the interfering transmitters. The second receiver decodes and subtracts a subset of interferers in a channel-dependent order before processing the desired signal. Each interferer is decoded at most once and the choice of the ordered subset mitigates error propagation. Simulation results are presented to demonstrate the significant gains obtained by the proposed low-complexity receivers over their conventional counterparts.

This paper is concerned with the low-cost iterative equalization/detection principles for coded linear systems with cyclic prefixes. Turbo frequency-domain-equalization (FDE) is applied to systems that may contain the joint effect of multiple-access interference (MAI), cross-antenna interference (CAI) and inter-symbol interference (ISI). We develop an SNR-variance evolution technique for the performance evaluation of the proposed systems. Numerical results in various channel environments demonstrate excellent agreement between the predicted and simulated system performance.

We treat the problem of designing low-density parity-check (LDPC) codes to approach the capacity of relay channels. We consider an efficient analysis framework that decouples the factor graph (FG) of aB-block transmission into successive partial FGs, each of which denotes a two-block transmission. We develop design methods to find the optimum code ensemble for the partial FG. In particular, we formulate the relay operations and the destination operations as equivalent virtual MISO and MIMO systems, and employ a binary symmetric channel (BSC) model for the relay node output. For AWGN channels, we further develop a Gaussian approximation for the detector output at the destination node. Jointly treating the relay and the destination, we analyze the performance of the LDPC-coded relay system using the extrinsic mutual information transfer(EXIT) chart technique. Furthermore, differential evolution is employed to search for the optimum code ensemble. Our results show that the optimized codes always outperform the regular LDPC codes with a significant gain; in the AWGN case, when Protocol-II is employed and the relay is close to the source, the optimized code performs within 0.1dB to the capacity bound.

We propose a OFDM-CDMA system that employs polyphase codes to support variable spreading factors. A systematic approach for constructing the polyphase code sequences of variable spreading factors is developed. Polyphase codes exhibit better auto- and cross-correlation properties than Hadamard codes. When employed in OFDM-CDMA systems, polyphase codes result in certain structured multiple-access interference (MAI) caused by multipath. Analytical and numerical results show that OFDM-CDMA systems employing polyphase codes have better PAPR performance than those using Hadamard codes. The BER performance of the OFDM-CDMA system using polyphase codes is evaluated by numerical results and compared to that of the OFDM-CDMA system using Hadamard codes with and without clipping.

We propose a general network planning framework for multi-radio multi-channel wireless networks. Under this framework, data routing, resource allocation, and scheduling are jointly designed to maximize a network utility function. We first treat such a cross-layer design problem with fixed radio distributions across the nodes and formulate it as a large-scale convex optimization problem. A primal-dual method together with the column-generation technique is proposed to efficiently solve this problem. We then consider the radio allocation problem, i.e., the optimal placement of radios within the network to maximize the network utility function. This problem is formulated as a large- scale combinatorial optimization problem. We derive the necessary conditions that the optimal solution should satisfy, and then develop a sequential optimization scheme to solve this problem. Simulation studies are carried out to assess the performance of the proposed cross-layer network planning framework. It is seen that the proposed approach can significantly enhance the overall network performance.

Carrier-sensing multiple-access with collision avoidance (CSMA/CA)-based networks, such as those using the IEEE 802.11 distributed coordination function protocol, have experienced widespread deployment due to their ease of implementation. The terminals accessing these networks are not owned or controlled by the network operators (such as in the case of cellular networks) and, thus, terminals may not abide by the protocol rules in order to gain unfair access to the network (selfish misbehavior), or simply to disturb the network operations (denial-of-service attack). This paper presents a robust nonparametric detection mechanism for the CSMA/CA media-access control layer denial-of-service attacks that does not require any modification to the existing protocols. This technique, based on the -truncated sequential Kolmogorov-Smirnov statistics, monitors the successful transmissions and the collisions of the terminals in the network, and determines how ldquoexplainablerdquo the collisions are given for such observations. We show that the distribution of the explainability of the collisions is very sensitive to changes in the network, even with a changing number of competing terminals, making it an excellent candidate to serve as a jamming attack indicator. Ns-2 simulation results show that the proposed method has a very short detection latency and high detection accuracy.

The extrinsic information transfer (EXIT) functions of Hadamard codes in the context of repeat-zigzag-Hadamard (RZH) codes with parallel decoding are investigated. EXIT functions over both the binary erasure channel (BEC) and the binary-input additive white Gaussian noise (BIAWGN) channel are derived. The application of these EXIT functions in the design of low-rate capacity-approaching irregular RZH (IRZH) codes is also considered. Using the EXIT functions, the bit error rate (BER) for a given code profile can be easily estimated and the differential evolution (DE) technique can be employed to find the optimal degree profile for given design parameters. The EXIT functions derived in this communication can serve as an effective tool for designing low-rate IRZH codes with parallel decoding in both BEC and BIAWGN channels.

In this paper, we propose a new class of low-rate error correction codes called repeat-zigzag-Hadamard (RZH) codes featuring simple encoder and decoder structures, and flexible coding rate. RZH codes are serially concatenated turbo-like codes where the outer code is a repetition code and the inner code is a punctured zigzag-Hadamard (ZH) code. By analyzing the code structure of RZH codes, we prove that both systematic and nonsystematic RZH codes are good codes, in the sense that for an RZH code ensemble, there exists a positive number gamma0such that for any binary-input memoryless channel whose Bhattacharyya noise parameter is less than , the average block error probability of maximum-likelihood (ML) decoding approaches zero. Two decoding algorithms-serial and parallel decoders for RZH codes-are proposed. We then employ the extrinsic information transfer (EXIT) chart technique to design irregular RZH codes. Results show that the optimized irregular RZH codes exhibit a performance that is very close to capacity in the low-rate regime.

In this paper, we propose a systematic procedure for designing spherical lattice (space–time) codes. By employing stochastic optimization techniques we design lattice codes which are well matched to the fading statistics as well as to the decoder used at the receiver. The decoders we consider here include the optimal albeit of highest decoding complexity maximum-likelihood (ML) decoder, the suboptimal lattice decoders, as well as the suboptimal lattice-reduction-aided (LRA) decoders having the lowest decoding complexity. For each decoder, our design methodology can be tailored to obtain low error-rate lattice codes for arbitrary fading statistics and signal-to-noise ratios (SNRs) of interest. Further, we obtain fundamental lower bounds on the error probabilities yielded by lattice and LRA decoders and characterize their asymptotic behavior.

We consider space-time transceiver architectures for space-division multiple-access (SDMA) fading channels with simultaneous transmissions from multiple users. Each user has up to four transmit antennas and employs a space-time orthogonal or a quasi-orthogonal design as an inner code. At the multiple-antenna receiver, efficient successive group interference cancellation strategies based on zero-forcing or minimum mean-square error (MMSE) filtering are employed in some fixed or channel-dependent order. These strategies are efficient in the sense that they exploit the special structure of the inner codes to yield much higher diversity orders than would be otherwise possible, while at the same time preserving what we call thedecouplingpropertyof the constituent inner codes which enables the use of low-complexity outer encoders/decoders for each user. Motivated by the special structure of the effective channel matrix induced by the inner codes, we obtain several new distribution results on the QR and eigenvalue decompositions of certain structured random matrices. These results are the key to a comprehensive performance analysis of the proposed multiuser transceiver architectures including the characterization of diversity-multiplexing tradeoff (DMT) curves and exact per-user bit-error rates (BERs) without making simplifying assumptions about error propagation.

We consider a slow-fading narrowband multiple-input multiple-output (MIMO) multiple-access channel (MAC) in which multiple users, each equipped with multiple transmit antennas, communicate to a receiver equipped with multiple receive antennas. The users are unaware of the channel state information (CSI) whereas the receiver has perfect CSI and employs a successive group decoder (SGD). We obtain achievable outage probabilities for the case where an outage must be declared simultaneously for all users (common outage) as well as the case where outages can be declared individually for each user (individual outage). We then derive the optimum successive group decoder (OSGD) that simultaneously minimizes the common outage probability and the individual outage probability of each user, over all SGDs of permissible decoding complexity. For each channel realization, the OSGD is also shown to maximize the error exponent of the decodable set of users. An adaptive SGD is derived which not only retains the outage optimality of the OSGD but also minimizes the expected decoding complexity. Asymptotically tight (in the limit of high signal-to-noise ratio (SNR)) affine approximations are then obtained for the weighted sum common and individual outage capacities and the symmetric outage capacity yielded by the OSGD. Limiting expressions for the relevant capacities as the number of users and the number of receive antennas approach infinity are also obtained and it is shown that the OSGD yields symmetric capacity gains commensurate with the decoding complexity allowed. Simulation results with practical low-density parity-check (LDPC) outer codes show that the OSGD offers significantly improved performance at low decoding complexity.

In this paper, we propose a new linear programming formulation for the decoding of general linear block codes. Different from the original formulation given by Feldman, the number of total variables to characterize a parity-check constraint in our formulation is less than twice the degree of the corresponding check node. The equivalence between our new formulation and the original formulation is proven. The new formulation facilitates to characterize the structure of linear block codes, and leads to new decoding algorithms. In particular, we show that any fundamental polytope is simply the intersection of a group of the so-called minimum polytopes, and this simplified formulation allows us to formulate the problem of calculating the minimum Hamming distance of any linear block code as a simple linear integer programming problem with much less auxiliary variables. We then propose a branch-and-bound method to compute a lower bound to the minimum distance of any linear code by solving a corresponding linear integer programming problem. In addition, we prove that, for the family of single parity-check (SPC) product codes, the fractional distance and the pseudodistance are both equal to the minimum distance. Finally, we propose an efficient algorithm for decoding SPC product codes with low complexity and maximum-likelihood (ML) decoding performance.

We propose a framework to address the problem of broadcasting a multiplicity of video sequences over a multiuser broadcast channel. The approach is intended to be general, without assumptions about specific video-coding or modulation techniques. However, we do assume the channel is Gaussian and exhibits quasi-static Rayleigh fading. Under the proposed framework, the algorithms seek to minimize the total distortion of multiple sequences broadcast simultaneously. To suit different applications, both greedy and long-term distortion metrics are considered. A salient aspect of this work is support for real-time video transport, hence delay and buffer constraints need to be accounted for. Under these constraints, the algorithms compute a jointly optimal source-rate and transmit-power allocation for all users under a power constraint. It turns out that problem can be formulated efficiently as a geometric program, which can be solved in different ways. In particular, we investigate a class of primal-dual convex algorithms. The complexity of the optimization is seen to scale well with the number of users. For the purpose of comparison, an orthogonal multiplexing scheme is also considered. Numerical results with H.264-coded video show that significant coding gains can be obtained.

We investigate the optimal design of a multihop wireless mesh network equipped with multiple orthogonal wireless channels and multiple radios. Specifically, we focus on solutions that can efficiently utilize the limited resource to support multiple unicast applications by routing and network coding. We propose a cross-layer optimization framework where the broadcasting feature of the wireless environment, which plays an important role in realizing the achievable gain of network coding, is taken into account. Moreover, we propose a network code construction scheme based on linear programming, with which the possible achievable Coding+MAC gain could be significantly increased. Delay constraints are also included in the network code construction formulation so that the possible impact of the extra decoding delay to the TCP/IP performance can be reduced without changing the upper-layer protocols. The proposed network design based on cross-layer optimization results in significant increase in network throughput.

We treat the problem of outage minimization via power control in MIMO systems with quantized feedback. We formulate the optimal quantized power control design for a general MIMO system and provide the numerical procedure for finding the optimal solution. Our results not only extend but also show the deficiency of the existing quantized power control schemes for MISO systems. We further propose a design based on the pre-evaluation of the packet error rate performance of practical MIMO systems employing short-length LDPC codes and QAM modulations and with possibly unreliable feedback links. It is demonstrated that with only a few bits of feedback, the resulting quantized power control strategy achieves considerable gain over a system without power control.

We consider an optimal resource allocation strategy for cooperative relaying-enabled OFDMA multi-hop wireless networks. A cross-layer optimization problem is formulated that maximizes the balanced end-to-end throughput under the routing and the PHY/MAC constraints. A dual method is employed to solve the problem efficiently and optimally. A cooperative relaying technique is incorporated into the framework by introducing virtual links and nodes. Half-duplex operation of the radios is assumed, and mutual interference between the links is explicitly modeled to allow maximal spatial reuse of the spectral resources. Numerical results are provided to demonstrate how the bottleneck phenomenon typical in multi-hop networks can be alleviated by the proposed technique to yield significant improvement in the throughput.

The authors consider the design of LDPC codes for cooperative relay systems in the half-duplex mode. The capacity of halfduplex relay channels has been studied previously but the design of good channel codes for such channels remains a challenging problem. Employing an efficient relay protocol, we transform the half-duplex relay code design problem into a problem of ratecompatible LDPC code design where different code segments experience different SNRs. The density evolution with conventional Gaussian approximation for single user channels, which assumes invariant SNR within one codeword, is not capable of accurately predicting the code performance for this system. Here we develop a density evolution with a modified Gaussian approximation that takes into account the SNR variation in one received codeword as well as the rate-compatibility constraint. We then optimize the code ensemble using a modified differential evolution procedure. Extensive simulations are carried out to demonstrate that the proposed algorithm offers more accurate prediction of code performance in half-duplex relay channels than the conventional methods, and the optimized codes achieve a significant gain over existing codes.

We treat the problem of designing the optimal transmission scheme that is adapted to the battery state, the channel and buffer conditions, and the incoming traffic rate. We assume that the battery states can be tracked at every time slot, so that the problem is formulated as a large-scale Markov decision process (MDP). An efficient sparse sampling method is employed to obtain a solution. Simulation results are provided to demonstrate that the proposed schemes can considerably increase the lifetime of the battery-powered wireless systems while satisfying QoS (quality of Service) constraints.

Video transmitted over fading wireless channels incurs both encoder-induced distortion and distortion caused by transmission errors. To address these problems, we propose a framework to transmit video reliably over a multi-antenna wireless system. No assumptions are made about specific video-coding standards, except that the encoder employs some variation of motion-compensated block-transform coding. To minimize the total distortion, the scheme optimizes the recently- derived diversity and multiplexing trade-off available on multi-antenna systems. Moreover, this is done jointly with loss-aware rate-distortion optimization (LA-RDO) techniques at the video encoder. Besides minimizing distortion, the algorithm also ensures that delay and buffer constraints are satisfied for real-time transport. We show that problem can be modelled as a short sequence of geometric programs. The overall complexity is linear in the number of antennas. Numerical results with H.264-coded video show that the PSNR at the video-decoder can be improved by various amounts, depending on antenna configuration.

We treat the problem of quantization codebook design for MIMO precoding schemes. We propose a design criterion based on the capacity measure which is different from the maximum mutual-minimum-distance conventionally used to design the codebook. The latter criterion is derived from SNR maximization which is not necessarily the capacity- optimal quantization strategy. While the capacity expression does not directly define a valid distance metric for the design, a bound on the capacity expression can be used to formulate the quantization codebook design. We introduce a new distance metric on the Grassmanian manifold which is used to design the optimal precoder codebook. While the original formulation can be used for the (space) correlated channel model, we propose a differential codebook design in order to track changes in the channel statistics when the channel is correlated in time (or in frequency, e.g., across different tones in OFDM systems). Simulation results has shown considerable improvement through the proposed codebook design both for single user (SU-) and multiple user (MU-) MIMO systems.

An optimal resource allocation strategy for OFDMA multi-hop wireless backhaul networks is considered. A cross-layer optimization problem is formulated that maximizes the balanced end-to-end throughput under the routing and the PHY/MAC constraints. A dual method is employed to solve the problem efficiently and optimally by decoupling it into different layers as well as into different OFDM tones. A cooperative relaying technique is incorporated into the framework to improve the performance. Mutual interference between the links is explicitly modeled to allow maximal spatial reuse of the spectral resources, and half-duplex operation of the radios is assumed. The numerical results exhibit how the bottleneck phenomenon typical in multi-hop backhaul networks can be alleviated by the proposed techniques to yield significant improvement in the throughput.

We consider the use of linear constellation preceding and linear equalization in a multicarrier system to obtain improved performance over multipath fading channels at a low complexity. We split the full set of subcarriers into smaller groups and spread the data symbols assigned to each group via precoding matrices. To obtain diversity order gains at a given data rate, we derive the optimal split of the subcarriers (optimal grouping) and the optimal number of symbols assigned to each group (optimal loading) using a diversity-multiplexing tradeoff analysis. We also derive the necessary and sufficient optimality conditions for the precoder design and provide examples of such optimal precoders.

We consider the maximum-likelihood decoding of single parity-check (SPC) product code. We first prove that, for the family of SPC product code, the fractional distance and the pseudo-distance are both equal to the minimum Hamming distance. We then develop an efficient algorithm for decoding SPC product codes with low complexity and near maximum likelihood decoding performance at practical SNRs.

We consider the design of efficient anti-jamming coding techniques for secondary usage of spectrum in cognitive radio. Specifically, we consider two coding schemes, rateless coding and piecewise coding. Both coding schemes have small overhead, as well as low-complexity encoding and decoding. The proposed piecewise coding also enjoys the advantages of parallel decoding and fast response to packet loss. For piecewise coding, we propose two design methods to build short block codes with small number of parity checks by incorporating the jamming rate. We analyze the throughput performance of the secondary users systems employing either anti-jamming coding technique. The performance results demonstrate that both coding techniques provide reliable transmission with high throughput and small redundancy. The piecewise coding using the designed short codes provides better performance with smaller overhead if the jamming rate is not high.

In this study, we consider the design of LDPC codes for cooperative relay systems in half-duplex mode (namely, "cheap" relay) that are of practical interest. We transform the code design problem into the design of rate-compatible LDPC codes where the SNRs in different parts of one codeword are different. Due to the SNR variation, the conventional density evolution (DE) or extrinsic-mutual-information-transfer (EXIT) is not capable of accurately predicting the code performance. We develop a more refined definition of code ensembles and present a modified DE based algorithm related to the new relay code structure. Our results show that the proposed algorithm is more accurate than the conventional DE or EXIT in this case. We further employ the code optimization based on differential evolution. The optimized "cheap" relay code significantly outperforms existing codes.

We propose a joint power- and rate-control scheme to broadcast a multiplicity of video sequences over a broadcast channel. The formulation is intended to be general in scope, without assumptions about specific coding or modulation schemes. However, we assume the broadcast channel undergoes quasi-static fading, with channel state information available at the transmitters and receivers. With a weighted sum of distortion terms as a metric, the scheme computes a jointly-optimal source-rate and transmit-power allocation under a power constraint. Besides minimizing distortion, the algorithm must also ensure that delay and buffer constraints are satisfied. Although the problem is not convex when formulated directly, it can be reformulated in convex form as a geometric program. We present numerical results via variable bit-rate (VBR) rate-control of H.264-coded video, optimized jointly with power-control of simple idealized broadcast transmitters.

The CSMA/CA protocols are designed under the assumption that all participant nodes would abide to the protocol rules. This is of particular importance in distributed protocols such as the IEEE 802.11 distributed coordinating function (DCF), in which nodes control their own backoff parameters. A selfish node may deliberately modify its random assignment and gain unfair access to the network resources. This would result in an increased observed collision probability for the rest of the nodes, that would increase their backoff windows as a result, further increasing the benefit of the selfish nodes. In this work, we develop of a robust non parametric batch detector based on the Kolmogorov-Smirnov (K-S) statistics that does not require any modification on the existing CSMA/CA protocols, and we apply it to detect misbehaviors in an IEEE 802.11 DCF network using the ns-2 simulator. We show that our method has a performance comparable to the optimum detectors with perfect information for the majority of misbehaviors, and it is able to detect any deviation from the protocol after just a few transmissions from the offending terminal.

We consider the problem of designing space-time- frequency linear dispersion (LD) codes in wideband multiple- input multiple-output (MIMO) antenna systems employing orthogonal-frequency-division-multiplexing (OFDM). Three design methods are presented and discussed, which involve: (1) minimizing the average block error rate, (2) maximizing the ergodic mutual information, and (3) a two-step procedure considering the optimization of the mutual information as well as the average block error rate, respectively. For any set of subcarriers, any number of OFDM symbol intervals, any number of transmit/receive antennas and any statistical fading channel model, the corresponding optimized LD code matrices are numerically computed via a stochastic gradient descent algorithm. Code design examples are provided and discussed for communication systems operating over a realistic 3GPP spatial channel model.

We propose a new linear programming formulation for the decoding of general linear block codes. Different from the original formulation given in [1], the number of total variables to characterize a parity-check constraint in our formulation is less than twice the degree of the corresponding check node. The equivalence between our new formulation and the original formulation is proven. Moreover, we show that any fundamental polytope is simply the intersection of a group of so-called minimum polytopes. Based on this, we propose a branch-and-bound method to compute a non-trivial lower bound to the minimum distance of a linear block code with affordable complexity.

We investigate the extrinsic information transfer (EXIT) functions of Hadamard codes in the context of repeat- zigzag Hadamard (RZH) codes with parallel decoding. The derived EXIT functions can serve as an effective tool for designing low-rate IRZH codes with parallel decoding in BIAWGN channels.

We consider space-time transceiver architectures for multiple access fading channels with K users, each equipped with multiple transmit antennas. Each user employs an orthogonal or a quasi-orthogonal design as an inner code. At the multi-antenna receiver, successive group interference suppression strategies based on the linear zero-forcing or linear MMSE filters are employed in some fixed or channel dependent order. These strategies exploit the specific structure of the inner codes to yield high diversity orders while preserving the decoupling property of the constituent inner codes thereby enabling the use of simple demodulators. Motivated by the special structure of the effective channel matrix induced by the inner codes, we obtain several new results on the QR and eigenvalue decompositions of certain structured random matrices. Using these random-matrix distribution results, we characterize the high-SNR performance limits of the transceiver architectures under consideration by obtaining their diversity-multiplexing tradeoff curves.

A new repetitive synchronization signal structure is proposed for wireless communication systems. A corresponding successive maximum likelihood detection algorithm that uses the proposed synchronization signals is provided. The successive maximum likelihood detection algorithm uses reliably estimated frequency offset in a successive way to refine the estimated timing offset. The performance of our approach is evaluated and compared to other existing algorithms.

Nonlinear precoding schemes for downlink time-division duplex–CDMA systems over multipath fading channels, are considered. First, the capacity results of a downlink CDMA system with either multiuser detection or precoding, were obtained and compared. It is seen that the two schemes exhibit similar capacity regions for both sum rate and maximum equal rate, which motivates the development of efficient nonlinear transmitter precoding techniques to reduce the receiver complexity at the mobile units without degrading the system performance. We then develop both bit-wise and chip-wise Tomlinson–Harashima (TH) multiuser precoding methods for downlink CDMA with multipath, to remove multi-user interference, inter-chip interference and inter-symbol interference. Efficient algorithms for multiuser power loading and ordering are also developed. Implementation of the proposed TH-precoding schemes in time-varying channels based on channel prediction is addressed as well. Simulations results are provided to demonstrate the effectiveness of the proposed techniques in suppressing interference in downlink CDMA.

The problem of throughput maximisation in a wireless multiple-input multiple-output (MIMO) system using a quantised feedback, which is an appropriate model for practical systems with limited feedback capacity, is considered. Unlike the ergodic capacity that can be achieved through power control only, maximising the throughput in the block fading channels is based on appropriate rate control strategy. The optimal quantised rate control design for general MIMO systems is formulated and a gradient descent search algorithm to find the optimal solution is employed. It is seen that the proposed quantised rate control scheme with only a few bits of feedback considerably improves the throughput of a MIMO system. With the same amount of feedback overhead, the proposed quantised rate control with constant power is compared with the optimal quantised power control strategy with an optimised constant rate, and the result demonstrates the importance of rate control in throughput maximisation. The effect of quantised rate control in MIMO systems employing different automatic repeat request schemes is also investigated.

The problem of multiuser scheduling in multicarrier (MC) systems under practical physical-layer constraints and implementations is considered. Subchannel allocation is an important resource assignment issue in multiuser MC systems. The multiuser scheduler is decoupled into a multiuser selector and a subchannel allocator, which result in a sub-optimum multiuser scheduler with significantly reduced computational complexity. Given an active user subset and a channel set, the multiuser scheduling problem then refers to the optimal subchannel allocation to maximise the instantaneous system throughput subject to certain fairness constraints. Efficient adaptive algorithms are developed for optimal subchannel allocation. The extension of the algorithms for tracking the time-varying optimum, which occurs in non-stationary environments, is also addressed. Simulation results are presented to demonstrate the performance of the proposed algorithms in terms of the throughput maximisation, the fast convergence, the excellent tracking capability in time-varying environments, the achievable throughput of the proposed multiuser scheduler as well as the long-term fairness.

Multihop wireless backhual networks are emerging as a cost-effective solution to provide ubiquitous and broadband access to meet the rapidly increasing demands of multimedia applications. In this paper, we consider the joint optimal design of routing, medium access control (MAC) scheduling and physical layer resource allocation for such networks, where beamforming antenna arrays are equipped at the physical layer. The notion of transmission set (TS) is introduced to separate the physical layer operations from those at the upper layers; and a column generation approach is employed to efficiently identify the TSs. We then apply the dual decomposition method to decouple the routing and scheduling subproblems, which are performed at different layers and are coordinated by a pricing mechanism to achieve the optimal overall system objective. To efficiently support multimedia traffic, an admission control criterion is considered for the system objective. The performance of the proposed scheme is verified by simulation results, and the impact of the physical layer capabilities on the network performance is evaluated. We also discuss the implementation issues of the cross-layer scheme based on the IEEE 802.16 mesh mode.

We treat the throughput analyses of parallel ARQ schemes over correlated MIMO channels with adaptive modulation and coding (AMC). To describe the packet transmission over multiple parallel logic channels, we extend the existing burst- error model for single channel to multiple parallel logic channels. Based on such a packet error model, we derive the throughput of different parallel ARQ protocols. Moreover, to describe the temporally correlated physical channel fading, we generalize the existing Markov model for single channel to multiple parallel channels for MIMO systems. Then we develop a method for calculating the packet-level model parameters from the parameters of the physical-layer model and the MIMO transceiver. Using the above hierarchical throughput analysis framework, we investigate the potential throughput gain or throughput loss of parallel ARQ over the conventional serial ARQ in MIMO systems. Our results reveal that as SNR increases, parallel ARQ can achieve higher throughput gain or less throughput loss compared to serial ARQ; parallel SW can achieve throughput gain in most of the MIMO scenarios but increasing the number of antennas does not always bring higher gain; parallel GBN with large number of antennas and independent buffers can achieve throughput gain; parallel SR incurs throughput loss.

The CSMA/CA protocols are designed under the assumption that all participant nodes would abide to the protocol rules. This is of particular importance in distributed protocols such as the IEEE 802.11 distributed coordinating function (DCF), in which nodes control their own backoff parameters. In this work, we propose a method to detect selfish misbehaving terminals that may deliberately modify its backoff window to gain unfair access to the network resources. We develop nonparametric batch and sequential detectors based on the Kolmogorov-Smirnov (K-S) statistics that do not require any modification on the existing CSMA/CA protocols, and we apply it to detect misbehaviors in an IEEE 802.11 DCF network using the ns-2 simulator. We compare the performance of the proposed detectors with the optimum detectors with perfect information about the misbehavior strategy, for both the batch case (based on the Neyman-Pearson test), and the sequential case (based on Wald's sequential probability ratio test). We show that the proposed nonparametric detectors have a performance comparable to the optimum detectors for the majority of misbehaviors (the more severe) without any knowledge of the misbehavior strategies.

It has been shown that electropherograms of DNA sequences can be modeled with hidden Markov models. Basecalling, the procedure that determines the sequence of bases from the given eletropherogram, can then be performed using the Viterbi algorithm. A training step is required prior to basecalling in order to estimate the HMM parameters. In this paper, we propose a Bayesian approach which employs the Markov chain Monte Carlo (MCMC) method to perform basecalling. Such an approach not only allows one to naturally encode the prior biological knowledge into the basecalling algorithm, it also exploits both the training data and the basecalling data in estimating the HMM parameters, leading to more accurate estimates. Using the recently sequenced genome of the organism Legionella pneumophila we show that the MCMC basecaller outperforms the state-of-the-art basecalling algorithm in terms of total errors while requiring much less training than other proposed statistical basecallers.

We consider the design of efficient rate-compatible (RC) irregular repeat accumulate (IRA) codes over a wide code rate range. The goal is to provide a family of RC codes to achieve high throughput in hybrid automatic repeat request (ARQ) scheme for high-speed data packet wireless systems. As a subclass of low-density parity-check codes, IRA codes have an extremely simple encoder and a low-complexity decoder while providing capacity approaching performance. We focus on a hybrid design method which employs both puncturing and extending. We propose a simple puncturing method based on minimizing the maximal recoverable step of the punctured nodes. We also propose a new extending scheme for IRA codes by introducing the degree-1 parity bits for the lower rate codes and obtaining the optimal proportions of extended nodes through density evolution analysis. The throughput performance of the designed RC-IRA codes in hybrid ARQ is evaluated for both AWGN and block fading channels. Simulation results demonstrate that our designed RC codes offer good error correction performance over a wide rate range and provide high throughput, especially in the high and low signal-to-noise ratio regions.

In this paper, we consider the design and analysis of generalized low-density parity-check (GLDPC) codes in AWGN channels. The GLDPC codes are specified by a bipartite Tanner graph, as with standard LDPC codes, but with the single parity-check constraints replaced by general coding constraints. In particular, we consider imposing Hadamard code constraints at the check nodes for a low-rate approach, termed LDPC-Hadamard codes. We introduce a low-complexity message-passing based iterative soft-input soft-output (SISO) decoding algorithm, which employs the a posteriori probability (APP) fast Hadamard transform (FHT) for decoding the Hadamard check codes at each decoding iteration. The achievable capacity with the GLDPC codes is then discussed. A modified LDPC-Hadamard code graph is also proposed. We then optimize the LDPC-Hadamard code ensemble using a low-complexity optimization method based on approximating the density evolution by a one-dimensional dynamic system represented by an extrinsic mutual information transfer (EXIT) chart. Simulation results show that the optimized LDPC-Hadamard codes offer better performance in the low-rate region than low-rate turbo-Hadamard codes, but also enjoy a fast convergence rate. A rate-0.003 LDPC-Hadamard code with large block length can achieve a bit-error-rate (BER) performance of 10-5at -1.44 dB, which is only 0.15 dB away from the ultimate Shannon limit (-1.592 dB) and 0.24 dB better than the best performing low-rate turbo-Hadamard codes

A cross-layer design approach is considered for joint routing and resource allocation for the physical (PHY) and the medium access control (MAC) layers in multihop wireless backhaul networks. The access points (APs) are assumed to be equipped with multiple antennas capable of both transmit and receive beamforming. A nonlinear optimization problem is formulated, which maximizes the fair throughput of the APs in the network under the routing and the PHY/MAC constraints. Dual decomposition is employed to decouple the original problem into smaller subproblems in different layers, which are coordinated by the dual prices. The network layer subproblem can be solved in a distributed manner and the PHY layer subproblem in a semidistributed manner. To solve the PHY layer subproblem, an iterative minimum mean square error (IMMSE) algorithm is used with the target link signal-to-interference-and-noise-ratio (SINR) set dynamically based on the price generated from the upper layers. A scheduling heuristic is also developed, which improves the choice of the transmission sets over time. Simulation results illustrate the efficacy of the proposed cross-layer design.

Current downlink scheduling algorithms in the (enhanced) third-generation (3G) cellular packet systems exploit instantaneous channel status of multiple users, but most of them are blind to traffic information. To improve TCP users' perception of quality-of-services (QoSs), characterized by response delay, goodput, and always-on connectivity, we propose a cross-layer hierarchical scheduler with traffic awareness and channel dependence to properly prioritize buffer and radio resource allocation among different TCP classes. The scheduler has two tiers: at the IP layer, an intrauser scheduler enhances a common practice, i.e., the DiffServ-based buffer management, by dequeuing same-user TCP packets according to per-class specified and measured responsiveness; at the MAC layer, an interuser scheduler transmits the dequeued packets by considering the opportunistic channel states, mean throughput, and class ID of all users. Both tiers consider the online measured throughput, a cross-layer metric, to achieve resource and performance fairness and TCP classification. Experiments show that, compared with (variations of) proportional fairness (PF) and other schemes, our scheduler can notably speed up time-critical interactive TCP services (HTTP and TELNET) or TCP slow-starts with minor cost to bulk file transfer (FTP) or long-lived flows. It offers scalable and low-cost TCP performance enhancement over the emerging cellular systems

We propose low-complexity sequential Monte Carlo (SMC) algorithms for demodulation in MIMO systems that employ large signal constellations. The proposed algorithms exploit the multi-level or hierarchical nature of the signal constellation to reduce the complexity associated with the generation of Monte Carlo samples of the MIMO symbols. The signal space is partitioned into multiple levels and samples are drawn beginning from the highest level space, down to the lowest level, which corresponds to the original symbol space. At each level, we consider only the subspace associated with the sample drawn at the previous level. The advantage of such a strategy is that instead of searching the whole signal space, we restrict our search to the more promising zones of the space, thus saving significant amount of computations. Both stochastic SMC algorithm and deterministic SMC algorithm are considered under such a multi-level framework. For M-QAM signal constellation, the computational complexity of the proposed algorithms is O(log M) in terms of the constellation size, as compared to the O(M) complexity of the existing SMC MIMO detection algorithms (while keeping the number of samples fixed in both the algorithms). We also demonstrate that the performance of these algorithms improves considerably with optimal ordering. The proposed multi-level SMC algorithms are then extended to cope with the case where the number of transmit antennas is larger than the number of receiver antennas, as well as the case of frequency-selective MIMO channels. Extensive simulation results are provided to illustrate the performance of the proposed new MIMO demodulation algorithms in various scenarios

Abstract — In this paper, we compare two classes of linear interference suppression techniques for downlink TDD-CDMA systems, namely, linear multiuser detection methods (receiver processing) and linear precoding methods (transmitter processing). For the linear precoding schemes, we assume that the channel state information (CSI) is available only at the transmitter but not at the receiver (i.e., ultra simple receivers). We propose several precoding techniques and the corresponding power control algorithms. The performance metric used in the comparisons is the total power required at the transmitter to achieve a target SINR at the receiver. Our results reveal that in general multiuser detection and precoding offer similar performance; but in certain scenarios (e.g, low BER requirements or use of random spreading sequences), precoding can bring a substantial performance improvement. These results motivate the use of precoding techniques to reduce the complexity of the system and the mobile terminals (only a matched-filter to the own spreading sequence is required without CSI). Moreover, it is shown that the proposed chip-wise linear MMSE precoding method is optimal in the sense that it requires the minimum total transmitted power to meet a certain receiver SINR performance. Index Terms — Downlink CDMA, linear multiuser detection, linear precoding, power control. I.

The IEEE 802.16 protocol for wireless broadband access (wirelessMAN) has been standardized recently. In order to improve network coverage and scalability, mesh mode is supported in 802.16. In the mesh mode, all nodes are organized in an ad hoc fashion and use a pseudo-random function to compete for their transmission opportunities based on the scheduling information in the two-hop neighborhood. In this paper, we develop a stochastic model for the distributed mesh mode scheduler. With this model, we analyze the scheduler performance under various conditions, and the analytical results match very well with the ns-2 simulation results. The analytical model developed in this paper is instrumental in optimizing the IEEE 802.16 mesh network performance. To the best of our knowledge, this work is the first one theoretically investigating the IEEE 802.16 mesh mode scheduling performance

A distributed joint routing and medium access control (MAC) algorithm is proposed for lifetime maximization of wireless sensor networks. By adopting the flow contention graph model and the resulting MAC constraints, the problem can be formulated into a linear programming problem, which can be solved distributively using dual decomposition. However, the message passing overhead of such a solution is high, since the information exchange must occur among the interfering links as well as the communicating links. In this work, the MAC layer constraints are relaxed in the form of a penalty function, which facilitates distributed optimization using only the collision statistic that each node can accumulate essentially at no extra cost. The resulting algorithm solves a convex optimization problem by a distributed primal-dual approach, where the network layer problem is solved in the dual domain, and the MAC layer problem is solved in the primal domain.

The recently proposed interleave-division multiple-access (IDMA) system is a flexible spread-spectrum air-interface technique featuring low receiver complexity and high spectral efficiency. In IDMA, each user's chip sequence is interleaved by a distinct chip-level interleaver. The receiver employs a simple chip-level iterative multiuser detector to decode the user's data. Here we focus on the analysis and optimization of such an IDMA system. We show that IDMA can be viewed as a limit case of the conventional CDMA employing repetition code and random spreading. Two analytical tools, namely, the large-system performance approximation and the EXIT chart technique are tailored in the context of IDMA chip-level iterative detector to facilitate the system performance analysis and optimization. The spectral efficiencies of a low-rate coded IDMA system with equal power setting in both single-cell and multi-cell scenarios are then analyzed, from which it is seen that the coded IDMA system with turbo receiver is a spectral efficient multiple-access scheme. Finally, we consider optimal power allocation among users in IDMA to maximize the spectral efficiency with finite-alphabet constellation. The differential evolution technique for nonlinear optimization is adopted to solve the power profile optimization problem. With optimized power profiles, the low-rate coded IDMA system can approach the optimal spectral efficiency with finite input constellations

We propose a call (user) admission control (CAC) algorithm and a scheduling framework for real-time services in the enhanced third-generation (3G) cellular systems, e.g., WCDMA HSDPA or cdma2000 HDR systems, where multiple IP users share a time-slotted downlink packet channel in each cell. At the user or flow level, the CAC algorithm maximizes user accommodations under the QoS constraint, e.g., per-user expectation of profile rate, and the constraint of location-dependent resource availability. At the packet level, our scheduling framework, named maximum cost deduction (MCD), derives two algorithms - both are QoS-aware and channel-dependent: One is called real-time MCD (rt-MCD), which minimizes a delay-derived cost function of backlogged packets at the smallest timescale; the other is called non-real-time MCD (nrt-MCD), which balances between the real-time delay deduction and the non-real-time (i.e., large-timescale) per-user fairness. The cross-layer designed CAC and MCD algorithms exploit multi-user diversity based on online measured radio resource allocation. Together they provide high system efficiency and a balance between flow-level QoS (e.g. the aggregate goodput and the blocking rate of newly arrived users) and packet-level QoS (e.g., the packet queueing delay or loss). Extensive simulations and comparisons with the prior art show that our algorithms can deliver efficient real-time services and remain robust to different load scenarios that vary according to system dynamics and/or user mobility

Abstract We propose a 4G system for transmission of video from a server at the base station to numerous wireless clients. We employ the latest technology in scalable video compression (3‐D wavelet video coding) and in channel coding (punctured turbo codes); for the physical layer, we resort to the multicarrier chip‐interleaved system with two‐layer interleaving, which achieves high spectral efficiency and is very suitable for downlink applications. We develop fast algorithms for a cross‐layer resource allocation that minimize the expected distortion of the reconstructed video averaged over all clients. The algorithms find a near‐optimal power, bandwidth, and subcarrier allocation at the physical layer and a source‐channel symbol allocation at the application layer. Our experimental results demonstrate that such a cross‐layer optimization framework leads to higher quality performance of the overall system. Copyright © 2007 John Wiley & Sons, Ltd.

We propose a simple PAM-based coded modulation scheme that combines low-density parity-check (LDPC) codes and classical maximum-distance separable (MDS) codes. To approach the capacity of an ISI-constrained channel, the code is employed in conjunction with spectral shaping, Tomlinson-Harashima preceding and decision- feedback equalization (DFE). The scheme performs within 2-2.5 dB of un-shaped channel capacity (the sphere-bound) at a BER of 10-12, even with regular LDPC codes of modest block lengths (1000-2000 bits). For further improvement, we consider signal sets on dense multi-dimensional lattices. With codes over the well-known Schlafli, Gosset, Barnes-Wall, and Leech lattices, the constructions reduce the gap to the sphere-bound. We consider the practical application of the proposed schemes to 10G- base-T, an emerging Ethernet standard over twisted-pairs at 10 Gbit/sec. A simple 1-dimensional scheme improves upon recent proposals by 0.5 dB at a BER of 10-11, using a (3,6)-regular code of 2000 bits.

We consider the radio allocation problem for multi-radio multi-channel cognitive wireless networks, i.e., the optimal placement of radios within the network to maximize the network utility function. This problem is formulated as a large-scale combinatorial optimization problem. We derive the necessary conditions that the optimal solution should satisfy, and then develop a sequential optimization scheme to solve this problem. Simulation studies are carried out to assess the performance of the proposed radio allocation framework. It is seen that the proposed approach can significantly enhance the overall network performance.

We consider the multiplexed coding design for cooperative communications. The ideal multiplexed coding, which outperforms the superposition coding in theory, is difficult to implement with practical error-correction codes. We therefore introduce a partially multiplexed (PMP) coding scheme to approach the performance of fully multiplexed coding scheme. We then design the PMP coding using irregular repeat accumulate (IRA) codes. We also present a practical superposition coding scheme with two decoding methods. The outage analysis shows that all these schemes perform very close to lower cooperation bound. The simulation results demonstrate that both PMP and practical superposition block Markov coding for two-user cooperation provide significant gain over the non-cooperative system.

High rate transmission over power lines imposes stringent requirements in coding and equalization, the chief impairments being severe signal attenuation and impulsive noise. Yet, transmission requirements over this medium have reached several Mbit/sec to compete with other types of access. In this paper, we propose a novel PAM-based coded modulation scheme that is well suited to meeting such constraints. The proposed scheme combines Low Density Parity Check (LDPC) codes and maximum-distance separable block codes to achieve high spectral efficiency, low decoding complexity, and a high degree of immunity to impulse noise. To achieve better immunity to burst and impulse noise, a novel interleaving scheme is proposed. To achieve good performance in the presence of inter-symbol interference, the proposed coset-coding is combined with Tomlinson-Harashima precoding and spectral shaping at the transmitter.

A joint routing and medium access control (MAC) algorithm is proposed for lifetime maximization of distributed wireless sensor networks. By adopting the flow contention graph model and the resulting MAC constraints, the problem can be formulated into a linear program (LP) with separable structure, which can be solved distributively using dual decomposition. However, the message passing overhead of such a solution is still high, since the information exchange must occur among the interfering links as well as the communicating links. In this work, the MAC layer constraints are relaxed in the form of a penalty function, which facilitates distributed optimization using only the collision statistic that each node can accumulate essentially at no extra cost. The resulting algorithm solves a convex optimization problem by a distributed primal-dual approach, where the network layer problem is solved in the dual domain, and the MAC layer problem is solved in the primal domain.

Cooperative diversity systems have been recently proposed as a solution to provide spatial diversity for terminals where multiple antennas are not feasible to be implemented. As in MIMO systems, space-time codes can be used to efficiently exploit the increase in capacity provided in cooperative diversity systems. In this paper we propose a two-layer linear dispersion (LD) code for cooperative diversity systems and derive a simulation-based optimization algorithm to optimize the LD code and power allocation in terms of block error rate. The proposed code design paradigm can obtain optimal codes under arbitrary fading statistics. The effect that distances between source, relays, and destination terminals have on the energy allocation between the broadcast and cooperative intervals is also studied.

This paper develops a framework for the efficient maximum-likelihood decoding of lattice codes. Specifically we apply it to the spherical Lattice Space-Time (LAST) codes recently put forward by El Gamal et al. that have been proven to achieve the optimal diversity-multiplexing tradeoff of MIMO channels. Our solution addresses the so-called boundary control problem within the same search tree structure as existing suboptimal LAST decoders. We demonstrate its performance and complexity by applying two of the most efficient tree-based ML detectors currently reported in the literature to the spherical LAST code proposed for the 2 × 2 MIMO channel of block length 2. Our optimal decoders exhibit improved performance over the naive lattice decoder with MMSE-GDFE pre-processing at a comparable complexity.

In this paper we describe a general framework for the modelling of the TCP performance over MIMO wireless systems including parameters such as fading, space-time transmission schemes, multiple antenna size, modulation schemes, channel coding and ARQ. We apply the framework to analyze the performance, the optimal channel coding rate and the effect of Doppler on the TCP throughput over the BLAST MIMO system and the orthogonal space-time block coded (STBC) system. We use the network simulator ns-2 to demonstrate the accuracy of the proposed analytical framework in characterizing various parameters of the TCP performance. We apply our framework to study of the buffer occupancy for TCP over MIMO systems and to a system that does not follow the AIMD TCP principle: CBR video transmission over MIMO channels.

We introduce a new class of low-rate error correction codes called concatenated zigzag Hadamard (ZH) codes which are specified by a highly structured zigzag graph with each segment being a Hadamard codeword. The ZH codes enjoy extremely simple encoding and very-low-complexity soft-input soft-output (SISO) decoding. We present an asymptotic performance analysis of the proposed codes using the extrinsic mutual information transfer (EXIT) chart for infinite-length codes. We also provide a union bound analysis of the error performance for finite-length codes.

In this paper, we propose a new multi-rate multiple-access wireless system implemented by variable spreading gain and chip-level random interleaving. Optimization across the physical and network layers in the uplink of such a system is also treated. A multi-criterion reinforcement learning (MCRL)-based adaptive call admission control (CAC) method is proposed which can easily handle multiple average QoS requirements

We propose a new class of low-rate error correction codes called repeat-zigzag-Hadamard (RZH) codes. RZH codes are serially concatenated turbo-like codes where the outer code is a repetition code and the inner code is a punctured zigzag-Hadaniard (ZH) code. We prove that RZH codes are good in the sense that for an RZH code ensemble, there exists a positive number 70 such that for any binary-input memoryless channel whose Bhattacharyya noise parameter is less than 70, the average maximum-likelihood (ML) decoder block error probability approaches zero. EXIT charts are used to design irregular codes

In this paper we propose a systematic procedure for designing optimal lattice (space-time) codes. By employing stochastic optimization techniques we design lattice codes with minimum error rates when lattice decoders are employed at the receiver. Our design methodology can be tailored to obtain optimal lattice (space-time) codes for any fading statistics and SNR of interest. Further, we obtain fundamental lower bounds on the error probabilities yielded by lattice decoders and characterize their asymptotic behavior

We consider the design of efficient rate-compatible (RC) irregular repeat accumulate (IRA) codes, a subclass of LDPC codes, over a wide code rate range. The goal is to provide a family of RC codes to achieve high throughput in hybrid automatic repeat request (ARQ) scheme for high-speed data packet wireless systems. We focus on a hybrid design method which employs both puncturing and extending. We propose a simple puncturing method based on minimizing the maximal recoverable step of the punctured nodes and a new extending scheme by introducing the degree-1 parity bits for the lower rate codes and obtaining the optimal proportions of extended nodes through density evolution analysis. Simulation results show that our designed RC codes offer good error correction performance over a wide rate range and provide high throughput, especially in the high and low SNR regions

We propose a call (user) admission control (CAC) algorithm and a scheduling framework for real-time services in the third-generation (3G) and beyond cellular systems, where multiple users share a downlink packet channel in each cell. At the user or flow level, the CAC algorithm maximizes user accommodations under the QoS constraint, e.g., per-user expectation of profile rate, and the constraint of location-dependent resource availability. At the packet level, our scheduling framework, named maximum cost deduction (MCD), derives QoS-aware and channel-dependent algorithms (rt-MCD and nrt-MCD), which minimize a delay -derived cost function of backlogged packets at different timescales. The cross-layer designed CAC and MCD algorithms exploit multi-user diversity based on online measured radio resource allocation. Together they provide high system efficiency and balance between flow-level QoS, e.g. aggregate goodput and the blocking rate of newly arrived users, and packet- level QoS, e.g., the packet queueing delay or loss.

Novel orthogonal polyphase codes with constant envelope design for OFDM-CDMA system are proposed in the paper. The proposed codes can support large number of users and variable spreading factor like Hadamard codes. Proposed orthogonal polyphase codes not only have better periodic auto- and cross correlations than Hadamard codes but also have constant envelope properties which is preferred for uplink/reverse link transmission. New transmitter and receiver architecture and MMSE receiver are proposed for the OFDM-CDMA system using the proposed polyphase codes. Simulation results show that OFDM-CDMA system using proposed polyphase codes has better PAPR and BER performance than OFDM-CDMA system using Hadamard codes and OFDMA system

We propose a simple pulse-amplitude modulation (PAM)-based coded modulation scheme that overcomes two major constraints of power line channels, viz., severe insertion-loss and impulsive noise. The scheme combines low-density parity-check (LDPC) codes, along with cyclic random-error and burst-error correction codes to achieve high-spectral efficiency, low decoding complexity, and a high degree of immunity to impulse noise. To achieve good performance in the presence of intersymbol interference (ISI) on static or slowly time-varying channels, the proposed coset-coding is employed in conjunction with Tomlinson-Harashima precoding and spectral shaping at the transmitter. In Gaussian noise, the scheme performs within 2 dB of unshaped channel capacity at a bit-error rate (BER) of 10/sup -11/, even with (3,6)-regular LDPC codes of modest length (1000-2000 bits). To mitigate errors due to impulse noise (a combination of synchronous and asynchronous impulses), a multistage interleaver is proposed, each stage tailored to the error-correcting property of each layer of the coset decomposition. In the presence of residual ISI, colored Gaussian noise, as well as severe synchronous and asynchronous impulse noise, the gap to Shannon capacity of the scheme to a Gaussian-noise-only channel is 5.5 dB at a BER of 10/sup -7/.

We consider a code-spread and chip-interleaved time-hopping (TH) multiple-access scheme for multiuser ultra-wideband (UWB) communications. In such a system, each user's chip sequence is interleaved by a user-specific distinct random interleaver, and the receiver is a low-complexity chip-level iterative multiuser detector (MUD) which performs simple Rake-type combining to collect the energy dispersed in multipath UWB channels. To further reduce the receiver complexity, time reversal (TR), a transmitter preprocessing technique, is also considered. When power control is employed along with TR, a single-tap receiver can be utilized which offers a desirable bit error rate (BER) performance with a significantly reduced sampling rate. Furthermore, the zigzag Hadamard (ZH) code is proposed as the low-rate code for both channel coding and spreading in the code-spread TH-UWB system. With its capacity-approaching capability and low encoding/decoding complexity, the parallel concatenated ZH code is a promising coding scheme for UWB applications.

In this paper, we study the design of linear dispersion (LD) codes for uplink multiuser channels with multiple antennas at the base station and each mobile unit. In the considered scheme, each user employs LD codes to transmit the data, i.e., the transmitted codeword is a linear combination over space and time of certain dispersion matrices with the transmitted symbols. The linear space-time spreading can also be utilized to separate multiple users at the base station. We propose a simulation-based optimization method together with gradient estimation to systematically design the multiuser linear space-time coding under either optimal or suboptimal receivers. We perform the gradient estimation through the score function method. The proposed method can also be applied to design codes under different fading statistics. Simulation results show that under the optimal maximum-likelihood (ML) receiver, the codes obtained by the new algorithm provides roughly the same performance as the low-dimensional spread modulation, as well as the interference-resistant modulation. Moreover, the new codes perform significantly better with suboptimal multiuser receiver structures.

We consider the decoding problem for low-density parity-check codes, and apply nonlinear programming methods. This extends previous work using linear programming (LP) to decode linear block codes. First, a multistage LP decoder based on the branch-and-bound method is proposed. This decoder makes use of the maximum-likelihood-certificate property of the LP decoder to refine the results when an error is reported. Second, we transform the original LP decoding formulation into a box-constrained quadratic programming form. Efficient linear-time parallel and serial decoding algorithms are proposed and their convergence properties are investigated. Extensive simulation studies are performed to assess the performance of the proposed decoders. It is seen that the proposed multistage LP decoder outperforms the conventional sum-product (SP) decoder considerably for low-density parity-check (LDPC) codes with short to medium block length. The proposed box-constrained quadratic programming decoder has less complexity than the SP decoder and yields much better performance for LDPC codes with regular structure

The training-based scheme constitutes an efficient method for noncoherent multiantenna communications. In this paper, we propose a superimposed training-based linear dispersion (STLD) transmission scheme, where the transmitted symbols are superposition of the training pilots and the data symbols. The STLD system enjoys simple encoding and efficient suboptimal decoding. Since for noncoherent multiple-input multiple-output systems employing the STLD scheme, analytical expressions of block- or bit-error probabilities do not exist, deterministic optimization methods cannot be used to design the optimal STLD system. We therefore propose to design the optimal STLD systems using simulation-based optimization techniques, together with gradient estimation. Simulation results show that with either maximum-likelihood or suboptimal decoding, the STLD systems obtained by the proposed design algorithm generally outperform the conventional training-based scheme employing space-time codes designed for coherent channels.

In this correspondence, we introduce a new class of low-rate error correction codes called zigzag Hadamard (ZH) codes and their concatenation schemes. Each member of this class of codes is specified by a highly structured zigzag graph with each segment being a Hadamard codeword. The ZH codes enjoy extremely simple encoding and very low-complexity soft-input-soft-output (SISO) decoding based on a posteriori probability (APP) fast Hadamard transform (FHT) technique. We present an asymptotic performance analysis of the proposed concatenated ZH codes using the extrinsic mutual information transfer (EXIT) chart for infinite-length codes. We also provide a union bound analysis of the error performance for finite-length codes. Furthermore, the concatenated ZH codes are shown to be a good class of codes in the low-rate region. Specifically, a rate-0.0107 concatenated code with three ZH components and an interleaver size of 65536 can achieve the bit error rate (BER) performance of 10/sup -5/ at -1.15dB, which is only 0.44 dB away from the ultimate Shannon limit. The proposed concatenated ZH codes offer similar performance as another class of low-rate codes-the turbo-Hadamard codes, and better performance than superorthogonal turbo codes, with much lower encoding and decoding complexities.

The performance of the distributed coordination function (DCF) of the IEEE 802.11 protocol has been shown to heavily depend on the number of terminals accessing the distributed medium. The DCF uses a carrier sense multiple access scheme with collision avoidance (CSMA/CA), where the backoff parameters are fixed and determined by the standard. While those parameters were chosen to provide a good protocol performance, they fail to provide an optimum utilization of the channel in many scenarios. In particular, under heavy load scenarios, the utilization of the medium can drop tenfold. Most of the optimization mechanisms proposed in the literature are based on adapting the DCF backoff parameters to the estimate of the number of competing terminals in the network. However, existing estimation algorithms are either inaccurate or too complex. In this paper, we propose an enhanced version of the IEEE 802.11 DCF that employs an adaptive estimator of the number of competing terminals based on sequential Monte Carlo methods. The algorithm uses a Bayesian approach, optimizing the backoff parameters of the DCF based on the predictive distribution of the number of competing terminals. We show that our algorithm is simple yet highly accurate even at small time scales. We implement our proposed new DCF in the ns-2 simulator and show that it outperforms existing methods. We also show that its accuracy can be used to improve the results of the protocol even when the terminals are not in saturation mode. Moreover, we show that there exists a Nash equilibrium strategy that prevents rogue terminals from changing their parameters for their own benefit, making the algorithm safely applicable in a complete distributed fashion

Multiple-input multiple-output (MIMO) wireless communication systems that employ multiple transmit and receive antennas can provide very high-rate data transmissions without increase in bandwidth or transmit power. For this reason, MIMO technologies are considered as a key ingredient in the next generation wireless systems, where provision of reliable data services for TCP/IP applications such as wireless multimedia or Internet is of extreme importance. However, while the performance of TCP has been extensively studied over different wireless links, little attention has been paid to the impact of MIMO systems on TCP. This paper provides an investigation on the performance of modern TCP systems when used over wireless channels that employ MIMO technologies. In particular, we focus on two representative categories of MIMO systems, namely, the BLAST systems and the space-time block coding (STBC) systems, and how the ARQ and packet combining techniques impact on the overall TCP performance. We show that, from the TCP throughput standpoint, a more reliable channel may be preferred over a higher spectral efficient but less reliable channel, especially under low SNR conditions. We also study the effect of antenna correlation on the TCP throughput under various conditions.

Progressive video delivery over wireless networks is very challenging due to the time-varying nature of wireless channels and limited power in the mobile devices. This paper proposes an end-to-end architecture for multilayer progressive video delivery over space-time differentially coded orthogonal frequency division multiplexing (STDC-OFDM) systems. An input video sequence is compressed by 3D-ESCOT into a layered bitstream. We input multiple layers of the bitstream in series to a STDC-OFDM channel. Different video source layers are protected by different error protection schemes in order to achieve unequal error protection. In progressive transmission, the reconstruction quality is important not only at the target transmission rate but also at the intermediate rates. So, the error protection strategy needs to optimize the average performance over the set of intermediate rates. We propose to use progressive joint source-channel coding to generate operational transmission distortion-rate (TD-R) functions and operational transmission distortion-power (TD-P) functions for multiple layers before forming the operational transmission distortion-power-rate (TD-PR) surfaces. Lagrange multipliers are then employed on the fly to obtain the optimal power allocation and optimal rate allocation among multiple layers, subject to constraints on the total transmission rate and the total power level. Progressive joint source-channel coding offers the scalability feature to handle bandwidth variations and changes in channel conditions. By extending the rate-distortion function in source coding to the TD-PR surface in joint source-channel coding, our work can use the "equal slope" argument to effectively solve the transmission rate allocation problem as well as the transmission power allocation problem for multilayer video transmission. Experiments show that our scheme achieves significant improvement over a nonoptimal system with the same total power level and total transmission rate.

Fast downlink scheduling algorithms play a central role in determining the overall performance of high-speed cellular data systems, characterized by high throughput and fair resource allocation among multiple users. We propose a flexible channel-dependent downlink scheduling scheme, named the (weighted) alpha-rule, based on the system utility maximization that arises from the Internet economy of long-term bandwidth sharing among elastic-service users. We show that the utility as a function of per-user mean throughput naturally derives the alpha-rule scheme and a whole set of channel-dependent instantaneous scheduling schemes following different fairness criteria. We evaluate the alpha-rule in a multiuser CDMA high data rate (HDR) system with space-time block coding (STBC) or Bell Labs layered space-time (BLAST) multiple-input multiple-output (MIMO) channel. Our evaluation shows that it works efficiently by enabling flexible tradeoff between aggregate throughput, per-user throughput, and per-user resource allocation through a single control parameter. In other words the Alpha-rule effectively fills the performance gap between existing scheduling schemes, such as max-C/I and proportional fairness (PF), and provides an important control knob at the media-access-control (MAC) layer to balance between multiuser diversity gain and location-specific per-user performance.

We propose a general framework based in the Gilbert model for cross-layer analysis of TCP and UDP over MIMO wireless systems. Our framework takes into consideration diverse system characteristics often difficult to express as a Gilbert model such as fading, space-time transmission schemes, modulation, channel coding and ARQ. We apply our framework to analyze the TCP performance of two representative MIMO systems, namely, the BLAST system and the orthogonal space-time block coded (STBC) system. In particular, we investigate the optimal information rate that maximizes the TCP throughput, the effect of Doppler on the optimal TCP throughput and the optimal channel coding rate for various modulations. We provide simulations results from the ns-2 network simulator to demonstrate the accuracy of the proposed analytical framework in characterizing the TCP performance. We further apply the framework to two additional cross-layer applications: the analysis of the buffer occupancy on the base station, and the analysis of CBR video transmission over MIMO systems. We show that while the optimal rate for maximum TCP throughput is far from the channel capacity, the optimal rate for error and delay-tolerant video transmission requires much higher rates, and so the physical layer should be aware and adapt to the type of application in order to increase the system performance. We also show that mobility benefits systems with larger buffers, especially for TCP, as the ARQ scheme is able to recover the shorter burst errors. In general, our investigation shows that the type of application plays a crucial role in the optimization of a wireless system, and that our modelling framework is useful for the cross-layer analysis and design of those systems.

The Cayley unitary (CU) codes constitute a systematic way of constructing unitary space-time modulations for noncoherent MIMO communications. For MIMO systems employing CU codes, there is no explicit expression for block (or bit) error probabilities. Hence, deterministic optimization tools cannot be employed to design the optimal CU codes. In this work, we propose to optimize the design of CU codes through simulation-based optimization techniques, in particular, stochastic approximation together with gradient estimation. The proposed methodology can be employed to design optimal CU codes under the maximum likelihood decoding or the suboptimal linearized sphere decoding. Simulation results show that new CU codes obtained by the proposed design significantly outperform those in the literature designed by maximizing the expected distance between codeword pairs. The new CU codes also enjoy comparable performance over training-based designs

We propose a multicarrier interleave-division multiple-access scheme for ultra-wideband wireless communications. For the uplink, a chip-level interleaving method at the transmitter with a simple turbo receiver is proposed to effectively suppress the frequency-selective fading and multiple-access interference. Both hard and soft frequency notching methods are suggested to suppress the narrowband interference. For the downlink, a two-layer interleaving scheme is proposed, in which data from different users are separated in the frequency-domain while the different data streams for the same user are separated in the interleaver-domain. A joint power and subcarrier allocation algorithm is developed to exploit the multiuser diversity and thereby improve the system performance. The performance of the proposed system is evaluated by both theoretic analysis and simulations. It is seen that within a few iterations, the proposed system exhibits single-user performance even under over-loaded conditions

We consider schemes for reducing the peak-to-average power ratio (PAPR) in coded orthogonal frequency-division multiplexing (OFDM) systems. We develop a new PAPR reduction technique using the label-inserted encoder of a random-like code and the soft amplitude limiter (SAL). Using this hybrid scheme provides 5.5 dB PAPR reduction in an OFDM system with 128 subcarriers, 4-bit selection and 3 dB clipping. Besides the significant PAPR reduction, the scheme also enjoys other advantages such as small overhead, low complexity, no side information transmission, and little performance loss. Among various random-like codes, the irregular repeat accumulate (IRA) code is the best choice for its simple encoder and capacity achieving performance. The scheme can be directly applied to multiple-input multiple-output (MIMO) OFDM systems. The capacity of the clipped MIMO-OFDM systems is analyzed based on a Gaussian approximation of the clipping noise. We consider an iterative receiver with soft MAP MIMO-OFDM detector. For both single antenna and multiple antenna systems, the encoder part is independent in the hybrid scheme, thus no additional constraint is applied to the IRA code optimization. The IRA codes are designed for the ergodic MIMO-OFDM systems with different PAPR reduction settings, more specifically different clipping ratios, based on the extrinsic information transfer (EXIT) charts. Simulation results show that the hybrid scheme with 3 dB clipping can achieve as good PAPR reduction performance as the simple clipping with 0 dB ratio but incurs much less performance loss at the receiver

In this paper, we consider progressive image transmission over differentially space-time coded orthogonal frequency-division multiplexing (OFDM) systems and treat the problem as one of optimal joint source-channel coding (JSCC) in the form of unequal error protection (UEP), as necessitated by embedded source coding (e.g., SPIHT and JPEG 2000). We adopt a product channel code structure that is proven to provide powerful error protection and employ low-complexity decision-feedback decoding for differentially space-time coded OFDM without assuming channel state information. For a given SNR, the BER performance of the differentially space-time coded OFDM system is treated as the channel condition in the JSCC/UEP design via a fast product code optimization algorithm so that the end-to-end quality of reconstructed images is optimized in the average minimum MSE sense. Extensive image transmission experiments show that SNR/BER improvements can be translated into quality gains in reconstructed images. Moreover, compared to another non-coherent detection algorithm, i.e., the iterative receiver based on expectation-maximization algorithm for the space-time coded OFDM systems, differentially space-time coded OFDM systems suffer some quality loss in reconstructed images. With the efficiency and simplicity of decision-feedback differential decoding, differentially space-time coded OFDM is thus a feasible modulation scheme for applications such as wireless image over mobile devices (e.g., cell phones). Copyright © 2006 John Wiley & Sons, Ltd.

In this paper we propose a systematic procedure for designing minimum-error-rate lattice (space-time) codes. By employing stochastic optimization techniques we design lattice (space-time) codes with minimum error rate when maximum likelihood (ML) detection is employed. Our design methodology can be tailored to optimize lattice (space-time) codes for any fading statistics and SNR of interest

We consider the problem of multiuser opportunistic fair scheduling (OFS) in downlink MISO systems employing beamforming. OFS is performed on a per scheduling interval basis to achieve fair bandwidth allocation. Transmit beamforming provides TDMA systems with the capability of supporting multiple concurrent transmissions. Since the optimal beamforming scheme can be calculated for a given subset of users, the scheduling problem then refers to the optimal user subset selection to maximize the system throughput subject to certain constraints. We propose two practical multiuser schedulers, and then present discrete stochastic approximation algorithms to select adaptively a better user subset. We also consider scenarios of time-varying channels where the algorithm can track the time-varying optimum. We present results to show the performance of the proposed algorithms in terms of fast convergence, time-varying tracking capability and fairness.

In this paper, we focus on the analysis and optimization of the interleave-division multiple-access (IDMA) system. The spectral efficiencies of the coded IDMA system are analyzed. Optimal power allocation among users in IDMA, to maximize the spectral efficiency with a finite-alphabet constellation, is also considered. Differential evolution is adopted to solve the power profile optimization problem.

The optimization mechanisms proposed in the literature for the distributed coordination function (DCF) of the IEEE 802.11 protocol are often based on adapting the backoff parameters to the estimate of the number of competing terminals in the network. However, existing estimation algorithms are either inaccurate or too complex. In this paper we propose an enhanced version of the IEEE 802.11 DCF that employs an estimator of the number of competing terminals based on a sequential Monte Carlo (SMC) or a approximate maximum a posteriori (MAP) approach. The algorithm uses a Bayesian framework, optimizing the backoff parameters of the DCF based on the predictive distribution of the number of competing terminals. We show that our algorithm is simple yet highly accurate even at small time scales. We implement our proposed new DCF in the ns-2 simulator and show that it outperforms existing methods. We also show that its accuracy can be used to improve the results of the protocol even when the nodes are not in saturation mode.

We develop online Bayesian signal processing algorithms to estimate the state and parameters of a hidden Markov model (HMM) with unknown transition matrix. The first online estimator is based on the sequential Monte Carlo (SMC) technique and uses a set of sufficient statistics to carry the information about the transition matrix. A deterministic variant of the SMC estimator is then developed, which is simpler to implement and offers superior performance. Finally, a novel approximate maximum a posteriori (MAP) algorithm is proposed. These algorithms offer a solution to the problem of estimating the number of competing terminals in an IEEE 802.11 network where better performance can be expected if the backoff parameters are adapted to the number of active users. Realistic simulations using the ns-2 network simulator are provided to demonstrate the excellent performance of the proposed estimators.

We consider the design and analysis of generalized low-density parity-check (GLDPC) codes specified by a bipartite Tanner graph, as with standard LDPC codes, but with the single parity-check constraints replaced by general coding constraints. In particular, we consider imposing Hadamard code constraints at the check nodes for a low-rate approach, termed LDPC-Hadamard codes. The achievable capacity with the GLDPC codes is then discussed. A modified LDPC-Hadamard code graph is also proposed. We then optimize the LDPC-Hadamard code ensemble using a low-complexity optimization method based on approximating the density evolution by a one-dimensional dynamic system represented by an extrinsic mutual information transfer (EXIT) chart. Simulation results show that a rate-0.003 LDPC-Hadamard code with large block length can achieve a bit-error-rate (BER) performance of 10-5at -1.44 dB, only 0.15 dB away from the ultimate Shannon limit (-1.592 dB)

To meet the needs of wireless broadband access, the IEEE 802.16 protocol for wireless metropolitan networks (WirelessMAN) has been recently standardized. The medium access control (MAC) layer of the IEEE 802.16 has point-to multipoint (PMP) mode and mesh mode. Previous works on the IEEE 802.16 have primarily focused on the PMP mode. In the mesh mode, all nodes are organized in an ad hoc fashion and use a pseudorandom function to calculate their transmission time based on the scheduling information of the two-hop neighbors. In this paper, we develop a stochastic model for the distributed scheduler of the mesh mode. With this model, we analyze the scheduler performance under various conditions, and the analytical results match very well with the ns-2 simulation results. The analytical model developed in this paper is instrumental in optimizing the IEEE 802.16 mesh mode system performance. To the best of our knowledge, this work is the first one theoretically investigating the IEEE 802.16 mesh mode scheduling performance.

We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. In order to meet the requirements inherent to sensor networks such as distributed processing and low-power consumption, a collaborative signal processing algorithm is presented. At any time, for a given tracked target, only one sensor is active. This leader node is focused on a single target but takes into account the possible existence of other targets. It is assumed that the motion model of a given target belongs to one of several classes. This class-target dynamic association is the basis of our classification criterion. We propose an algorithm based on the sequential Monte Carlo (SMC) filtering of jump Markov systems to track the dynamic of the system and make the corresponding estimates. A novel class-based resampling scheme is developed in order to get a robust classification of the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC target tracking framework. Simulation results are presented to illustrate the excellent performance of the proposed multitarget tracking and classification scheme in a collaborative sensor network.

The Cayley differential (CD) codes constitute a systematic way to construct differential unitary space-time modulations for noncoherent communication in multiple-antenna systems. The original CD codes are designed based on the criterion of maximizing the expected distance between codeword pairs. However, in practical systems, it is more sensible to design the codes according to the minimum error rate criterion. The main difficulty in such a design is that the error probability does not admit a closed-form expression and, thus, conventional optimization methods cannot be applied. In this paper, we formulate the minimum error-rate CD code design as an unconstrained stochastic optimization problem, and employ the stochastic approximation technique with gradient estimation to solve it. Simulation results show that codes generated by the proposed design procedure generally outperform the CD codes designed by maximizing the expected distance between codeword pairs and the Cayley threaded algebraic space-time codes that satisfy the full rank criterion. The new CD codes also enjoy better or comparable performance over other group based or nongroup designs.

We consider the problem of opportunistic fair scheduling (OFS) of multiple users in downlink time-division multiple-access (TDMA) systems employing multiple transmit antennas and beamforming. OFS is an important technique in wireless networks to achieve fair bandwidth usage among users, which is performed on a per-frame basis at the media access control layer. Multiple-transmit-antenna beamforming provides TDMA systems with the capability of supporting multiple concurrent transmissions, i.e., multiple spatial channels at the physical layer. Given a particular subset of users and their channel conditions, the optimal beamforming scheme can be calculated. The multiuser opportunistic scheduling problem then refers to the selection of the optimal subset of users for transmission at each time instant to maximize the total throughput of the system subject to a certain fairness constraint on each individual user's throughput. We propose discrete stochastic approximation algorithms to adaptively select a better subset of users. We also consider scenarios of time-varying channels for which the scheduling algorithm can track the time-varying optimal user subset. We present simulation results to demonstrate the performance of the proposed scheduling algorithms in terms of both throughput and fairness, their fast convergence, and the excellent tracking capability in time-varying environments.

In this letter, we study the design of expectation-maximization (EM)-based iterative receivers for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing systems with the presence of carrier-frequency offset (CFO). Motivated by the spirit of maximum-likelihood estimation in the EM algorithm, we first present a pilot-aided CFO estimation scheme that allows fast Fourier transform-based fast implementation. Then this CFO estimation is incorporated into the initialization step of the iterative receiver. Experimental results show the effectiveness of our receiver design in combating CFO.

We present a multiple-channel video transmission scheme in wireless CDMA networks over multipath fading channels. We map an embedded video bitstream, which is encoded into multiple independently decodable layers by 3D-ESCOT video coding technique, to multiple CDMA channels. One video source layer is transmitted over one CDMA channel. Each video source layer is protected by a product channel code structure. A product channel code is obtained by the combination of a row code based on rate compatible punctured convolutional code (RCPC) with cyclic redundancy check (CRC) error detection and a source-channel column code, i.e., systematic rate-compatible Reed-Solomon (RS) style erasure code. For a given budget on the available bandwidth and total transmit power, the transmitter determines the optimal power allocations and the optimal transmission rates among multiple CDMA channels, as well as the optimal product channel code rate allocation, i.e., the optimal unequal Reed-Solomon code source/parity rate allocations and the optimal RCPC rate protection for each channel. In formulating such an optimization problem, we make use of results on the large-system CDMA performance for various multiuser receivers in multipath fading channels. The channel is modeled as the concatenation of wireless BER channel and a wireline packet erasure channel with a fixed packet loss probability. By solving the optimization problem, we obtain the optimal power level allocation and the optimal transmission rate allocation over multiple CDMA channels. For each CDMA channel, we also employ a fast joint source-channel coding algorithm to obtain the optimal product channel code structure. Simulation results show that the proposed framework allows the video quality to degrade gracefully as the fading worsens or the bandwidth decreases, and it offers improved video quality at the receiver.

We study the mutual information transfer characteristics of the soft interference cancellation (SIC) multiuser detectors (MUD) for coded code-division multiple-access (CDMA) systems in synchronous additive white Gaussian noise (AWGN) and asynchronous multipath fading channels. Based on the extrinsic information transfer (EXIT) chart technique, we compare the asymptotic (as the block length goes to infinity) performance and the convergence behavior of different joint detection schemes which are highly dependent upon different system parameters and channel conditions. A framework of using the EXIT chart to analyze the coding-spreading tradeoff is also addressed for turbo multiuser detection. Results show that the SIC-minimum mean square error (MMSE) MUD generally provides a higher spectral efficiency than the SIC-match filter (MF) MUD. Moreover, the impact of finite block length on the convergence behavior of turbo MUD is also analyzed using a consistent Gaussian approximation on the distribution of the output extrinsic information.

We treat utility-based joint power control and rate allocation in a downlink code division multiple access system, where either the matched-filter or the blind multiuser detection technique is employed at the mobile receivers. Rate allocation is performed on a per-frame basis; whereas within the frame, power control is performed at the symbol basis. A hierarchical rate allocation scheme is proposed, which together with the utility-based power control and the opportunistic fair scheduling scheme, maximizes the instantaneous weighted system throughput and the number of feasible users, and at the same time, guarantees the fairness among all users. In order to apply such a resource allocation framework to systems employing blind multiuser detection, we make use of the recent analytical results on the signal-to-interference-plus-noise-ratio performance of the blind multiuser detectors, assuming multiple spreading codes are used to realize multiple data rates. Our results show that under the utility-based joint power and rate allocation framework, the system capacity is significantly enhanced by using blind multiuser detection and the fairness can be well guaranteed.

We consider the analysis and design of low-density parity check (LDPC) codes for intersymbol interference (ISI) channels when used with soft interference cancellation plus linear minimum mean-square error filtering (SIC-MMSE) turbo equalization. We discuss techniques to compute the probability density function (pdf) of the extrinsic information at the output of the SIC-MMSE equalizer as a function of pdf of the input extrinsic information, channel impulse response, and the signal-to-noise ratio. For static ISI channels, we show that the output pdf can be modeled as symmetric Gaussian, and show that the mean can be evaluated without simulating the equalizer. For channels with long memory, we propose to use the unscented transform technique to compute the mean, which significantly reduces the computation required. Finally, for fading channels, we model the pdf by a mixture of symmetric Gaussian densities. Using these techniques, we are able to fairly accurately compute the thresholds for LDPC codes and design good irregular LDPC codes. Simulation results are in good agreement with the computed thresholds and the designed irregular LDPC codes outperform regular ones significantly.

In this paper, we propose to design linear-dispersion (LD) codes by minimizing the union bound based on the exact pairwise error probability (PEP). We find that the original programming is not convex, and we present a convex relaxation to the optimization problem. We employ the gradient descent methods to numerically search the optimum dispersion matrices. Simulation results show that codes optimized by the new criterion generally outperform the codes designed based on algebraic number theory. When the knowledge of spatial-fading correlation is available in advance for the design, significant gain relative to the codes designed without knowledge of channel correlation can be achieved by taking into account the correlation structure while performing the optimization. We demonstrate how to exploit knowledge of transmit and receive correlation in designing the LD codes. Numerical and simulation examples show that knowledge of transmit correlation has a large impact on the optimum performance of the LD codes. Furthermore, additional gain can be achieved from the knowledge of receive correlation.

This paper takes into account the design optimization of the random-like ensemble of irregular repeat accumulate (IRA) codes for multiple-input multiple-output (MIMO) communication systems employing iterative receivers. First, the density evolution-based procedure for optimizing the IRA code ensemble is presented. An approximation method based on linear programming is adopted to design an IRA code with the extrinsic information transfer (EXIT) chart matched to that of the soft MIMO demodulator. The authors then reveal the relationship between the IRA codes and the low-density parity-check (LDPC) codes. With a code ensemble mapping relationship between an IRA code and an LDPC code, a quasi-optimal IRA code can be obtained by transforming an optimal LDPC code designed for MIMO systems. Two types of soft MIMO detectors are treated, namely, the maximum a posteriori (MAP) detector and the soft interference canceller with linear MMSE filtering (SIC-MMSE). The results show that with the MAP receiver the designed IRA codes can perform within 1 dB from the ergodic capacities of the MIMO systems under consideration. The authors also treat the short-length IRA code design for block fading MIMO channels. They adopt design techniques for short-length LDPC codes to improve the performance of the short-length IRA code and to reduce the error floor.

We consider the problem of channel equalization in broadband wireless multiple-input multiple-output (MIMO) systems over frequency-selective fading channels, based on the sequential Monte Carlo (SMC) sampling techniques for Bayesian inference. Built on the technique of importance sampling, the stochastic sampler generates weighted random MIMO symbol samples; whereas the deterministic sampler, a heuristic modification of the stochastic counterpart, recursively performs exploration and selection steps in a greedy manner in both space and time domains. Such a space-time sampling scheme is very effective in combating both intersymbol interference and cochannel interference caused by frequency-selective channel and multiple transmit and receiver antennas. Finally, computer simulation results are provided to demonstrate that the proposed sampling-based MIMO equalizers significantly outperform the decision-feedback MIMO equalizers with comparable computational complexity.

We propose a new technique for mobile tracking in wideband code-division multiple-access (WCDMA) systems employing multiple receive antennas. To achieve a high estimation accuracy, the algorithm utilizes the time difference of arrival (TDOA) measurements in the forward link pilot channel, the angle of arrival (AOA) measurements in the reverse link pilot channel, as well as the received signal strength. The mobility dynamic is modelled by a first-order autoregressive (AR) vector process with an additional discrete state variable as the motion offset, which evolves according to a discrete-time Markov chain. It is assumed that the parameters in this model are unknown and must be jointly estimated by the tracking algorithm. By viewing such a nonlinear dynamic system as a jump-Markov model, we develop an efficient auxiliary particle filtering algorithm to track both the discrete and continuous state variables of this system as well as the associated system parameters. Simulation results are provided to demonstrate the excellent performance of the proposed adaptive mobile positioning algorithm in WCDMA networks.

We treat the multiuser scheduling problem in MC-CDMA systems. To reduce the implementation complexity, we present an efficient structure for the multiuser scheduler. Given the active users and the channels, the subchannel allocator then aims at maximizing the instantaneous system throughput. We develop a stochastic-ruler based algorithm to achieve the subchannel allocation, and prove its global convergence. We also extend the algorithm to track the time-varying optimum in non-stationary channels. We present simulation results to show the performance of the proposed schemes in terms of the throughput maximization with fast convergence, the excellent tracking capability in time-varying channels, and the small throughput loss of the multiuser scheduler.

In this paper, we treat the rate control and fairness scheduling problem for downlink UBPC systems. An hierarchical rate allocation scheme is proposed, which is based on the analytical performance analysis for the physical-layer constraints in the UBPC system. Using such a rate control algorithm together with the opportunistic fairness scheduling scheme, the UBPC system achieves rate allocation more fairly in the sense that the instantaneous system throughput and the number of feasible users can be maximized, and meanwhile the long-term fairness can be well guaranteed. We present the simulation results to demonstrate the performance of the proposed algorithms in terms of the throughput and the fairness.

Handoff and cell site selection schemes in multi-cell cellular systems are traditionally developed for mobile stations (MS) of dedicated (circuit-switched) channels to/from base stations (BS). The schemes usually only consider physical-layer signal quality in decision making. In high-speed packet access systems, where a downlink channel is being shared by multiple users within each cell, those schemes are subject to local congestion and asymmetric packet call blocking across multiple cells. A way to reduce the hot-spots and alleviate asymmetry loading in such systems is the target of our study. To do so, we formulate the handoff and cell site selection procedures into a problem of iterative optimal channel assignment in multiple cells, and approximately solve the issue by incorporating a distributed load-awareness scheme into the procedures in order to keep the complexity low but practicality high. Extensive experiments show the effectiveness of our scheme and its advantages over the legacy schemes in load balancing and congestion reduction. With our scheme, a multi-cell wireless system in support of high-speed packet data access can accommodate more satisfied users and be more robust to asymmetric load dynamics across the whole system.

High-speed cellular data systems demand fast downlink scheduling algorithms and multiple-input multiple-output (MIMO) techniques. The associated multiuser diversity and antenna diversity play a central role in achieving high system throughput and fair resource allocation among multiple users. For such systems we evaluate the cross-layer interactions between channel-dependent scheduling schemes and MIMO techniques, such as space-time block coding (STBC) or Bell Laboratories Layered Space-Time (BLAST), and propose a new scheduling algorithm named the alpha-rule. The evaluation shows that the STBC/MIMO provides a reliable channel but at a certain cost of spectral efficiency. Comparatively BLAST/MIMO provides larger capacity and enables higher scheduling throughput. Thus BLAST/MIMO may be a more suitable technique for high-rate packet data transmission at the physical layer. At the medium access control (MAC)-layer, the alpha-rule is shown to be more flexible or efficient to exploit the diversity gains than the exiting max-C/I or proportionally fair (PF) scheduling schemes. It enables online tradeoff between aggregate throughput, per-user throughput, and per-user resource allocation.

We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. A collaborative signal processing algorithm where multiple targets are dynamically associated with leader nodes is presented. It is assumed that each target belongs to one of several classes and that the class information leads to the motion model of a target. We propose an algorithm based on sequential Monte Carlo (SMC) filtering of jump Markov systems to jointly track the system dynamic and classify the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC tracking framework. Simulation results have illustrated the excellent performance of the proposed scheme.

We investigate a wireless system of multiple cells, each having a downlink shared channel in support of high-speed packet data services. In practice, such a system consists of hierarchically organized entities including a central server, Base Stations (BSs), and Mobile Stations (MSs). Our goal is to improve global resource utilization and reduce regional congestion given asymmetric arrivals and departures of mobile users. For this purpose, we propose a scalable cross-layer framework to coordinate packet-level scheduling, call-level cell-site selection and handoff, and system-level loading balancing based on load, throughput, and channel measurements at different layers. In this framework, an opportunistic scheduling algorithm---the weighted Alpha-Rule---exploits multiuser diversity gain in each cell independently, trading aggregate (mean) downlink throughput for fairness and minimum rate guarantees among MSs. Each MS adapts to its channel dynamics and the load fluctuations in neighboring cells, in accordance with MSs' mobility and their arrivals or departures, by initiating load-aware handoff and cell-site selection. The central server adjusts the scheduling parameters of each cell to coordinate cells' coverage, or cell breathing, by prompting distributed MS handoffs. Across the whole system, BSs and MSs constantly monitor their load, throughput, or channel quality in order to facilitate the overall system coordination.Our specific contributions in such a framework are highlighted by the minimum-rate guaranteed Weighted Alpha-Rule scheduling, the load-aware MS handoff/cell-site selection, and the Media Access Control (MAC)-layer cell breathing. Our evaluations show that the proposed framework can improve the global resource utilization and load balancing, which translates into a smaller blocking rate of MS arrivals without extra resources, while the aggregate throughput remains roughly the same or improved around the hot-spots. Our tests also show that the coordinated system is robust to dynamic load fluctuations and is scalable to both system size and MS population.