Delay-Doppler (DD) signal processing has emerged as a powerful tool for analyzing multipath and time-varying channel effects. Due to the inherent sparsity of the wireless channel in the DD domain, compressed sensing (CS) based techniques, such as orthogonal matching pursuit (OMP), are commonly used for channel estimation. However, many of these methods assume integer Doppler shifts, which can lead to performance degradation in the presence of fractional Doppler. In this paper, we propose a windowed dictionary design technique while we develop a delay-aware orthogonal matching pursuit (DA-OMP) algorithm that mitigates the impact of fractional Doppler shifts on DD domain channel estimation. First, we apply receiver windowing to reduce the correlation between the columns of our proposed dictionary matrix. Second, we introduce a delay-aware interference block to quantify the interference caused by fractional Doppler. This approach removes the need for a predetermined stopping criterion, which is typically based on the number of propagation paths, in conventional OMP algorithm. Our simulation results confirm the effective performance of our proposed DA-OMP algorithm using the proposed windowed dictionary in terms of normalized mean square error (NMSE) of the channel estimate. In particular, our proposed DA-OMP algorithm demonstrates substantial gains compared to standard OMP algorithm in terms of channel estimation NMSE with and without windowed dictionary.

In federated learning (FL), data heterogeneity is the main reason that existing theoretical analyses are pessimistic about the convergence rate. In particular, for many FL algorithms, the convergence rate grows dramatically when the number of local updates becomes large, especially when the product of the gradient divergence and local Lipschitz constant is large. However, empirical studies can show that more local updates can improve the convergence rate even when these two parameters are large, which is inconsistent with the theoretical findings. This paper aims to bridge this gap between theoretical understanding and practical performance by providing a theoretical analysis from a new perspective on data heterogeneity. In particular, we propose a new and weaker assumption compared to the local Lipschitz gradient assumption, named the heterogeneity-driven pseudo-Lipschitz assumption. We show that this and the gradient divergence assumptions can jointly characterize the effect of data heterogeneity. By deriving a convergence upper bound for FedAvg and its extensions, we show that, compared to the existing works, local Lipschitz constant is replaced by the much smaller heterogeneity-driven pseudo-Lipschitz constant and the corresponding convergence upper bound can be significantly reduced for the same number of local updates, although its order stays the same. In addition, when the local objective function is quadratic, more insights on the impact of data heterogeneity can be obtained using the heterogeneity-driven pseudo-Lipschitz constant. For example, we can identify a region where FedAvg can outperform mini-batch SGD even when the gradient divergence can be arbitrarily large. Our findings are validated using experiments.

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24× speedup compared to conventional uncoded MapReduce, a 12 to 52 percent reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes.

Hierarchical SGD (H-SGD) has emerged as a new distributed SGD algorithm for multi-level communication networks. In H-SGD, before each global aggregation, workers send their updated local models to local servers for aggregations. Despite recent research efforts, the effect of local aggregation on global convergence still lacks theoretical understanding. In this work, we first introduce a new notion of "upward" and "downward" divergences. We then use it to conduct a novel analysis to obtain a worst-case convergence upper bound for two-level H-SGD with non-IID data, non-convex objective function, and stochastic gradient. By extending this result to the case with random grouping, we observe that this convergence upper bound of H-SGD is between the upper bounds of two single-level local SGD settings, with the number of local iterations equal to the local and global update periods in H-SGD, respectively. We refer to this as the "sandwich behavior". Furthermore, we extend our analytical approach based on "upward" and "downward" divergences to study the convergence for the general case of H-SGD with more than two levels, where the "sandwich behavior" still holds. Our theoretical results provide key insights of why local aggregation can be beneficial in improving the convergence of H-SGD.

In this work, we develop a two time-scale deep learning approach for beamforming and phase shift (BF-PS) design in time-varying RIS-aided networks. In contrast to most existing works that assume perfect CSI for BF-PS design, we take into account the cost of channel estimation and utilize Long Short-Term Memory (LSTM) networks to design BF-PS from limited samples of estimated channel CSI. An LSTM channel extrapolator is designed first to generate high resolution estimates of the cascaded BS-RIS-user channel from sampled signals acquired at a slow time scale. Subsequently, the outputs of the channel extrapolator are fed into an LSTM-based two stage neural network for the joint design of BF-PS at a fast time scale of per coherence time. To address the critical issue that training overhead increases linearly with the number of RIS elements, we consider various pilot structures and sampling patterns in time and space to evaluate the efficiency and sum-rate performance of the proposed two time-scale design. Our results show that the proposed two time-scale design can achieve good spectral efficiency when taking into account the pilot overhead required for training. The proposed design also outperforms a direct BF-PS design that does not employ a channel extrapolator. These demonstrate the feasibility of applying RIS in time-varying channels with reasonable pilot overhead.

Actor-critic (AC) algorithms have been widely used in decentralized multi-agent systems to learn the optimal joint control policy. However, existing decentralized AC algorithms either need to share agents’ sensitive information or lack communication-efficiency. In this work, we develop decentralized AC and natural AC (NAC) algorithms that avoid sharing agents’ local information and are sample and communication-efficient. In both algorithms, agents share only noisy rewards and use mini-batch local policy gradient updates to ensure high sample and communication efficiency. Particularly for decentralized NAC, we develop a decentralized Markovian SGD algorithm with an adaptive mini-batch size to efficiently compute the natural policy gradient. Under Markovian sampling and linear function approximation, we prove that the proposed decentralized AC and NAC algorithms achieve the state-of-the-art sample complexities $\mathcal{O}(\epsilon^{-2}\ln\epsilon^{-1})$ and $\mathcal{O}(\epsilon^{-3}\ln\epsilon^{-1})$, respectively, and achieve an improved communication complexity $\mathcal{O}(\epsilon^{-1}\ln\epsilon^{-1})$. Numerical experiments demonstrate that the proposed algorithms achieve lower sample and communication complexities than the existing decentralized AC algorithms.

Our extensive real measurements over Amazon EC2 show that the virtual instances often have different computing speeds even if they share the same configurations. This motivates us to study heterogeneous Coded Storage Elastic Computing (CSEC) systems where machines, with different computing speeds, join and leave the network arbitrarily over different computing steps. In CSEC systems, a Maximum Distance Separable (MDS) code is used for coded storage such that the file placement does not have to be re-defined with each elastic event. Computation assignment algorithms are used to minimize the computation time given computation speeds of different machines. While previous studies of heterogeneous CSEC do not include stragglers - the slow machines during the computation, we develop a new framework in heterogeneous CSEC that introduces straggler tolerance. Based on this framework, we design a novel algorithm using our previously proposed approach for heterogeneous CSEC such that the system can handle any subset of stragglers of a specified size while minimizing the computation time. Furthermore, we establish a trade-off in computation time and straggler tolerance. Another major limitation of existing CSEC designs is the lack of practical evaluations using real applications. In this paper, we evaluate the performance of our designs on Amazon EC2 for applications of the power iteration and linear regression. Evaluation results show that the proposed heterogeneous CSEC algorithms outperform the state-of-the-art designs by more than 30%.

In this paper, we propose a novel extrinsic neural network equalizer (ExNE) for joint iterative equalization and decoding. The proposed ExNE takes the received signal sequence and a priori probabilities from the channel decoder as inputs to directly generate output extrinsic probabilities. This approach improves the performance of iterative equalization and decoding by making explicit use of extrinsic information. A three-step, open-loop neural network (NN) training procedure is developed for the ExNE, independent of the choice of channel code. We propose a new NN architecture termed deep concatenated convolutional blocks with skip connections (DCCB-SC) for ExNE which attains excellent performance with only a moderate number of network parameters. For challenging linear and non-linear inter-symbol-interference (ISI) channels considered in this work, the proposed ExNE approaches the performance of the maximum a posteriori probability (MAP) equalizer without assuming prior knowledge of the channel model.

We study the optimal design of heterogeneous Coded Elastic Computing (CEC) where machines have varying computation speeds and storage. CEC introduced by Yang et al. in 2018 is a framework that mitigates the impact of elastic events, where machines can join and leave at arbitrary times. In CEC, data is distributed among machines using a Maximum Distance Separable (MDS) code such that subsets of machines can perform the desired computations. However, state-of-the-art CEC designs only operate on homogeneous networks where machines have the same speeds and storage. This may not be practical. In this work, based on an MDS storage assignment, we develop a novel computation assignment approach for heterogeneous CEC networks to minimize the overall computation time. We first consider the scenario where machines have heterogeneous computing speeds but same storage and then the scenario where both heterogeneities are present. We propose a novel combinatorial optimization formulation and solve it exactly by decomposing it into a convex optimization problem to find the optimal computation load and a filling problem to find the exact computation assignment. A low-complexity filling algorithm is adapted and can be completed within a number of iterations equal to at most the number of available machines.

Coded Distributed Computing (CDC) introduced by Li et al. in 2015 offers an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce and Spark. In particular, increasing the computation load in the Map phase by a factor of r can create coded multicasting opportunities to reduce the communication load in the Shuffle phase by the same factor. However, the CDC scheme is designed for the homogeneous settings, where each node maps the same number of files and is assigned the same number of reduce functions. It requires an exponentially large number of input files (data batches), reduce functions and multicasting groups relative to the number of nodes to achieve the promised gain. We address the CDC limitations by proposing a novel CDC approach based on a combinatorial design, which accommodates heterogeneous networks and maintains a multiplicative computation-communication trade-off. In addition, the proposed approach requires an exponentially less number of input files compared to the original CDC scheme proposed by Li et al. Finally, we derive a new information theoretic converse for general heterogeneous CDC and show that the communication load of the proposed design is optimal within a constant factor.

Coding theoretic approaches have been developed to significantly reduce the communication load in modern distributed computing system. In particular, coded distributed computing (CDC) introduced by Li et al. can efficiently trade computation resources to reduce the communication load in MapReduce like computing systems. For the more general cascaded CDC, Map computations are repeated at r nodes to significantly reduce the communication load among nodes tasked with computing Q Reduce functions s times. In this paper, we propose a novel low-complexity combinatorial design for cascaded CDC which 1) determines both input file and output function assignments, 2) requires significantly less number of input files and output functions, and 3) operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication tradeoff, from which we show the proposed scheme can outperform the state-of-the-art scheme proposed by Li et al. for the homogeneous networks. Further, when the network is heterogeneous, we show that the performance of the proposed scheme can be better than its homogeneous counterpart. In addition, the proposed scheme is optimal within a constant factor of the information theoretic converse bound while fixing the input file and the output function assignments.

Coded distributed computing (CDC) introduced by Li et. at. is an effective technique to trade computation load for communication load in a MapReduce framework. CDC achieves an optimal trade-off by duplicating map computations at r computing nodes to yield multicasting opportunities such that r nodes are served simultaneously in the Shuffle phase. However, in general, the state-of-the-art CDC scheme is mainly designed only for homogeneous networks, where the computing nodes are assumed to have the same storage, computation and communication capabilities. In this work, we explore two approaches of heterogeneous CDC design. First, we study CDC schemes which operate on multiple, collaborating homogeneous computing networks. Second, we allow heterogeneous function assignment in the CDC design, where nodes are assigned a varying number of reduce functions. We propose an expandable heterogeneous CDC scheme where r-1 nodes are served simultaneously in the Shuffle phase. In comparison to the state-of-the-art homogeneous CDC scheme with an equivalent computation load, we find our newly proposed heterogeneous CDC scheme has a smaller communication load in some cases.

We study the optimal design of a heterogeneous coded elastic computing (CEC) network where machines have varying relative computation speeds. CEC introduced by Yang et al. is a framework which mitigates the impact of elastic events, where machines join and leave the network. A set of data is distributed among storage constrained machines using a Maximum Distance Separable (MDS) code such that any subset of machines of a specific size can perform the desired computations. This design eliminates the need to re-distribute the data after each elastic event. In this work, we develop a process for an arbitrary heterogeneous computing network to minimize the overall computation time by defining an optimal computation load, or number of computations assigned to each machine. We then present an algorithm to define a specific computation assignment among the machines that makes use of the MDS code and meets the optimal computation load.

We consider wireless device-to-device (D2D) caching networks with single-hop transmissions. Previous work has demonstrated that caching and coded multicasting can significantly increase per user throughput. However, the state-of-the-art coded caching schemes for D2D networks are generally impractical because content files are partitioned into an exponential number of packets with respect to the number of users if both library and memory sizes are fixed. In this paper, we present two combinatorial approaches of D2D coded caching network design with reduced packetizations and desired throughput gain compared to the conventional uncoded unicasting. The first approach uses a “hypercube” design, where each user caches a “hyperplane” in this hypercube and the intersections of “hyperplanes” represent coded multicasting codewords. In addition, we extend the hypercube approach to a decentralized design. The second approach uses the Ruzsa-Szeméredi graph to define the cache placement. Disjoint matchings on this graph represent coded multicasting codewords. Both approaches yield an exponential reduction of packetizations while providing a per-user throughput that is comparable to the state-of-the-art designs in the literature. Furthermore, we apply spatial reuse to the new D2D network designs to further reduce the required packetizations and significantly improve per user throughput for some parameter regimes.

We propose capacity-achieving schemes for private information retrieval (PIR) from uncoded databases (DBs) with both homogeneous and heterogeneous storage constraints. In the PIR setting, a user queries a set of DBs to privately download a message, where privacy implies that no one DB can infer which message the user desires. In general, a PIR scheme is comprised of storage placement and delivery designs. Previous works have derived the capacity, or infimum download cost, of PIR with uncoded storage placement and sufficient conditions of storage placement to meet capacity. However, the currently proposed storage placement designs require splitting each message into an exponential number of sub-messages with respect to the number of DBs. In this work, when DBs have the same storage constraint, we propose two simple storage placement designs that satisfy the capacity conditions. Then, for more general heterogeneous storage constraints, we translate the storage placement design process into a “filling problem”. We design an iterative algorithm to solve the filling problem where, in each iteration, messages are partitioned into sub-messages and stored at subsets of DBs. All of our proposed storage placement designs require a number of sub-messages per message at most equal to the number of DBs.

We propose a capacity-achieving scheme for private information retrieval (PIR) from databases (DBs) with heterogeneous storage constraints. In the PIR setting, a user queries a set of DBs to privately download a message, where privacy implies that no one DB can infer which message the user desires. Our PIR scheme uses an uncoded storage placement and we derive sufficient conditions to meet capacity in this design architecture. We translate the storage placement design to a "filling problem" where messages are partitioned into sub- messages and stored at subsets of DBs. We prove a set of necessary and sufficient conditions for the existence of the filling problem solution and design an iterative algorithm to find a filling problem solution. Our proposed algorithm requires at most a number of iterations equal to the number of DBs. Furthermore, we significantly reduce the number of sub-messages compared to the state-of- the-art PIR scheme, as our proposed PIR scheme requires that each message is split into a polynomial number of sub-messages with respect to the number of DBs.

Private information retrieval (PIR) allows a user to download one of K messages from N databases without revealing to any database which of the K messages is being downloaded. In general, the databases can be storage constrained where each database can only store up to μKL bits where 1/N ≤ μ ≤ 1 and L is the size of each message in bits. Let t = μN, a recent work showed that the capacity of Storage Constrained PIR (SC-PIR) is (1 + 1/t + 1/t2 + ··· +1)-1, which is achieved by a storage placement scheme inspired by the content placement scheme in the literature of coded caching and the original PIR scheme. Not surprisingly, this achievable scheme requires that each message is L = (Nt)tKbits in length, which can be impractical. In this t paper, without trying to make the connection between SC-PIR and coded caching problems, based on a general connection between the Full Storage PIR (FS-PIR) problem (μ = 1) and SCPIR problem, we propose a new SC-PIR design idea using novel storage placement schemes. The proposed schemes significantly reduce the message size requirement while still meeting the capacity of SC-PIR. In particular, the proposed SC-PIR schemes require the size of each file to be only L = NtK-1compared to the state-of-the-art L = (Nt)tK.

Coded distributed computing (CDC) introduced by Li et al. in 2015 offers an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. For the more general cascaded CDC, Map computations are repeated at r nodes to significantly reduce the communication load among nodes tasked with computing Q Reduce functions s times. While an achievable cascaded CDC scheme was proposed, it only operates on homogeneous networks, where the storage, computation load and communication load of each computing node is the same. In this paper, we address this limitation by proposing a novel combinatorial design which operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication trade-off and show that it is optimal within a constant factor and could outperform the state-of-the-art homogeneous schemes.

Orthogonal Time Frequency Space (OTFS) is a novel modulation scheme designed in the Doppler-delay domain to fully exploit time and frequency diversity of general time-varying channels. In this paper, we present a novel discrete-time analysis of OFDM-based OTFS transceiver with a concise and vectorized input-output relationship that clearly characterizes the contribution of each underlying signal processing block in such systems. When adopting cyclic prefix in the time domain, our analysis reveals that the proposed MIMO OTFS and OFDM systems have the same ergodic capacity despite the well-known fact that the former has great advantages in low-complexity receiver design for high Doppler channels. The proposed discrete-time vectorized formulation is applicable to general fast fading channels with arbitrary window functions. It also enables practical low-complexity receiver design for which such a concise formulation of the input-output relationship is of great benefits.

We consider a wireless distributed computing network, where all computing nodes (workers) are connected via wireless medium obeying the seminal protocol channel model. In particular, we focus on the MapReduce-type platform, where each worker is assigned to compute some arbitrary output functions from F input files, which are distributively cached in all workers. The overall computation is decomposed into computing a set of “Map” and “Reduce” functions across all workers. The goal is to characterize the minimum computing latency as a function of the computation load. Unlike other related works, which consider either wireline settings or restrict the communication among workers to single-hop, here we focus on the wireless scenario and do not constrain any communication schemes. We propose a data set cache strategy based on a deterministic assignment of Maximum Distance Separable (MDS)-coded date sets over all input files, and a coded multicast transmission strategy where the workers send linearly coded computing results to each other in order to collectively satisfy their assigned tasks. We show that our approach can achieve a scalable communication latency, outperform the state of the art schemes in the order sense, and achieve the information theoretic outer bound within a multiplicative constant factor in practical parameter regimes.

Coded distributed computing introduced by Li et al. in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. In particular, Li et al. show that increasing the computation load in the Map phase by a factor of r can create coded multicasting opportunities to reduce the communication load in the Reduce phase by the same factor. However, there are two major limitations in practice. First, it requires an exponentially large number of input files (data batches) when the number of computing nodes gets large. Second, it forces every s computing nodes to compute one Map function, which leads to a large number of Map functions required to achieve the promised gain. In this paper, we make an attempt to overcome these two limitations by proposing a novel coded distributed computing approach based on a combinatorial design. We demonstrate that when the number of computing nodes becomes large, 1) the proposed approach requires an exponentially less number of input files; 2) the required number of Map functions is also reduced exponentially. Meanwhile, the resulting computation-communication trade-off maintains the multiplicative gain compared to conventional uncoded unicast and achieves the information theoretic lower bound asymmetrically for some system parameters.

We consider wireless device-to-device (D2D) caching networks with single-hop transmissions. Previous work in the literature has shown that caching and coded multicasting can be strategically used to significantly increase the per user throughput. However, these schemes require partitioning files into a large number of packets which grows exponentially as the number of users increases. This makes these schemes impractical to implement. In this paper, we address this issue by designing cache placement, coded multicasting and scheduling schemes based on disjoint matchings in Ruzsa-Szeméredi Graphs, which has been applied to design a coded caching scheme in the shared link caching networks. We demonstrate that by using the proposed approach, the per user throughput is not much worse than that proposed in the literature with the requirement of exponential file subpacketization in terms of the number of users. Nevertheless, by using the proposed scheme, the requirement of file subpacketization is at most sub-quadratic in terms of the number of users if no spatial reuse is allowed. In addition, both per user throughput and file subpacketization can be improved significantly when spatial reuse is allowed.

The development of low complexity, high performance spatial-multiplexing MIMO detectors continues to be an important area of research capable of increasing the spectral efficiency and capacity of wireless networks. The Markov Chain Monte Carlo (MCMC) detector has shown promise as a high performance method with low complexity growth. We present a solution to the high SNR stalling problems of previous MCMC detectors. Near-MAP performance is verified in simulation and in real-world measurements on an 8-antenna MIMO testbed using the 802.11ac WiFi protocol. This demonstration shows that the channel models predominantly used in the MCMC literature are too well-conditioned to provide an understanding of performance and complexity for indoor channels. Additional information is provided on the methods and techniques to match simulation to measurement and to construct a low cost and effective 8-antenna MIMO testbed.

We consider a wireless Device-to-Device (D2D) caching network, where users make arbitrary requests from a library of files and have pre-fetched (cached) information on their devices, subject to a per-node storage capacity constraint. The network is assumed to obey the “protocol model”, widely considered in the wireless network literature. Unlike other related works, which either restrict the communication to single-hop, or assume entire file caching, here we consider both multi-hop transmission and fully general caching strategies, including file subpacketization. We propose a caching strategy based on deterministic assignment of MDS-coded packets of the library files, and a coded multicast delivery strategy where the users send linearly coded messages to each other in order to collectively satisfy their demands. We show that our approach can achieve the information theoretic outer bound within a multiplicative constant factor in practical parameter regimes.

We study the delay-outage tradeoff in a shared link caching network formed by one source node and n users over a wireless slow fading channel. Each user requests an arbitrary file from a library of m files, each of entropy F bits. The users can locally cache up to MF information bits. Under a single-input single-output (SISO) multi-user system, we present a closed form expression of the delay-outage tradeoff under any i.i.d. channel distributions by using the classical caching and coded multicasting scheme presented by Maddah-Ali and Niesen in [1], where outage probability is regarded as the probability that a user cannot decode the requested file. In addition, when the channel gains follow a Gaussian distribution, we show that for a large range of vanishing outage probability p, the average delay scales as Ω (min {n2-ε1, n1-ε2/p}), where ε1, ε2> 0 are some arbitrarily small constants. This means that the multiplicative caching gain introduced by Maddah-Ali and Niesen is lost. To overcome this problem, we modify the network to a single-input multiple-output system (SIMO) system and find the required number of receive antennas per user such that the promised multiplicative caching gain can be preserved.

In this paper, we study soft decision directed channel estimation algorithms for joint data detection and channel estimation over time-varying multiple-input multiple-output (MIMO) channels. The optimal Wiener filter (OWF) channel estimator is data dependent, and requires high complexity due to the computation of matrix inversion at each time instance. We develop a low-complexity, dual-layer channel estimation algorithm aiming to achieve the performance of OWF with a significantly reduced complexity. Excellent performance of the proposed design is achieved for both the soft minimum mean square error (soft-MMSE) MIMO detector and the Markov Chain Monte Carlo (MCMC) MIMO detector. The MCMC detector is shown to be significantly more robust to channel estimation error than the soft- MMSE detector.

This work describes a motion-sensing enabled personalized exercise system for cardiac rehabilitation called CAROLS, capable of assisting healthcarers to find evidence-based effective exercise program for individual, and then motivating cardiac outpatients to improve their health recovery throughout a series of effective personalized rehabilitation exercises. In addition to making home-based cardiac rehabilitation exercise more effective and efficient, the CAROLS system can provide an interactive and user-friendly virtual gaming-environment for rehabilitation exercise in terms of monitoring real-time vital signs, increasing outpatient compliance for doctor prescription, as well as evaluating exercise efficiency and patient safety via a fusion function between a motion sensing technology and a wearable sensor device. The experimental system is in service trials with preliminary verification results including heart rate variability (HRV) analyses and motion similarity analysis of the 3-minute step test for normal users. Therefore, in addition to monitoring the exercise intensity and posture accuracy of outpatients, the CAROLS system provides clinical experts with a valuable reference for adjusting the exercise prescription in future scheduled return.

In this paper, we analyze the delay performance of a secondary user (SU) under dynamic spectrum access. We design simple time-threshold policies for the SU to minimize the average delay while satisfying the collision probability constraint of the primary user (PU). Such policies perform closely to an optimized policy found by a Markov Decision Process (MDP) formulation, while facilitating analytical analysis of the delay and collision probability. For general PU busy and idle period distributions, we analyze the performance of threshold policies through a one-dimensional Markov chain, and develop analytical expressions to approximate the delay and collision probability. The accuracy of the Markov chain analysis and the analytical approximations is examined under various busy and idle distributions. We investigate the impact of busy and idle distributions on system performance. We find that while the idle distribution determines the time capacity of SU access, the busy distribution significantly affects the delay performance of the threshold policies. The effect of imperfect sensing is also studied.

In this paper, we develop a novel statistical detection algorithm following similar principles to that of expectation maximization (EM) algorithm. Our goal is to develop an iterative algorithm for joint channel estimation and data detection in channels that have a long memory and are fast varying in time. At each iteration, starting with an estimate of the channel, we combine a Markov Chain Monte Carlo (MCMC) algorithm for data detection, and an adaptive algorithm for channel tracking, to develop a statistical search procedure that finds joint important samples of possible transmitted data and channel impulse responses. The result of this step, which may be thought as E-step of the proposed algorithm, is used in an M-step that refines the channel estimate, for the next iteration. Excellent behavior of the proposed algorithm is presented by examining it on real data from underwater acoustic communication channels.

In this paper, we study joint iterative data detection and channel decoding under imperfect channel state information (CSI). We apply the Markov Chain Monte Carlo technique to generate a list of channel estimates (LCE) that maximizes the a posteriori probabilities of the transmitted data, given the received signal and the soft feedback from the channel decoder. The LCE is refined over each iteration of data detection and decoding to facilitate improved channel estimation and thus yields superior detection performance. It is shown that, even with a small list size, the proposed MCMC-LCE detector outperforms the coherent detector in which data detection is performed based on a single channel estimate (SCE). As opposed to the noncoherent detectors which impose stringent constraints on the fading distribution, the MCMC-LCE detector is applicable to general fading distributions. It also offers a low complexity that is linear in the coherent length of the channel and the list size.

This paper examines near capacity performance of Markov Chain Monte Carlo (MCMC) detectors for multiple-input and multiple-output (MIMO) channels. The proposed MCMC detector (Log-MAP-tb b-MCMC) operates in a strictly bit-wise fashion and adopts Log-MAP algorithm with table look-up. When concatenated with an optimized low-density parity-check (LDPC) code, Log-MAP-tb b-MCMC can operate within 1.2-1.8 dB of the capacity of MIMO systems with 8 transmit/receive antennas at spectral efficiencies up to ¿ = 24 bits/channel use (b/ch). This result improves upon best performance achieved by turbo coded systems using list sphere decoding (LSD) detector by 2.3-3.8 dB, leading to nearly 50% reduction in the capacity gap. Detailed comparisons of the Log-MAP-tb b-MCMC with LSD based detectors demonstrate that MCMC detector is indeed the detector of choice for achieving channel capacity both in terms of performance and complexity.

We address the problem of collaborative sensing in cognitive radios. In a cognitive radio network, all the nodes may sense the spectrum simultaneously. They should then exchange their sensing results in order to improve the reliability of the detection. This exchange of information has to be done efficiently to improve on the bandwidth efficiency of the network. We propose a medium access control (MAC) signaling protocol and study its performance behavior. For the case of a single-band channel, we present a thorough analysis of the proposed protocol and use the results to pick the protocol parameters that minimizes the signaling time for a given probability of detection. Analysis of the proposed protocol for multiband channels is solved by introducing a matrix formulation of the proposed protocol that allows its evaluation numerically.

Statistical detectors that are based on Markov chain Monte Carlo (MCMC) simulators have emerged as promising low-complexity solutions to both multiple-input multiple-output (MIMO) and code division multiple access (CDMA) communication systems. While these types of detectors achieve unprecedented near capacity performance, i.e., when operated in low signal-to-noise ratio (SNR) regime, they exhibit a serious problem at medium to high SNR regimes, referred to as the "stalling" problem. In this paper, we investigate the sources of this degradation and propose a new search strategy called constrained MCMC to remedy the issue of stalling.

In this paper, we propose a novel low complexity soft-in soft-out (SISO) equalizer using the Markov chain Monte Carlo (MCMC) technique. Direct application of MCMC to SISO equalization (reported in a previous work) results in a sequential processing algorithm that leads to a long processing delay in the communication link. Using the tool of factor graph, we propose a novel parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that, both the sequential and parallel processing SISO equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum SISO equalizer. The optimum SISO equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed SISO equalizers grows linearly.

This paper investigates performance of channel coded noncoherent systems over block fading channels. We consider an iterative system where an outer channel code is serially concatenated with an inner modulation code amenable to noncoherent detection. We emphasize that, in order to obtain near-capacity performance, the information rates of modulation codes should be close to the channel capacity. For certain modulation codes, a single-input single-output (SISO) system with only one transmit antenna may outperform a dual-input and single-output (DISO) system with two transmit antennas. This is due to the intrinsic information rate loss of these modulation codes compared to the DISO channel capacity. We also propose a novel noncoherent detector based on Markov Chain Monte Carlo (MCMC). Compared to existing detectors, the MCMC detector achieves comparable or superior performance at reduced complexity. The MCMC detector does not require explicit amplitude or phase estimation of the channel fading coefficient, which makes it an attractive candidate for high rate communication employing quadrature amplitude modulation (QAM) and for multiple antenna channels. At transmission rates of 1 ~ 1.667 bits/sec/Hz, the proposed SISO systems employing 16QAM and MCMC detection perform within 1.6-2.3 dB of the noncoherent channel capacity achieved by optimal input.

In this paper, we propose a novel hybrid QRD- MCMC MIMO detector that combines the features of a QRD-M detector and a Markov chain Monte Carlo (MCMC) detector. The QRD-M algorithm is applied first to obtain initial estimates of the transmitted signal vector. Subsequently, the QRD-M estimate is used to initialize one of the Gibbs samplers for MCMC detection. The MCMC detection reduces the M parameter required by the QRD-M detector, while the QRD-M initialization effectively alleviates the well-known high-SNR problem in existing MCMC detectors. Performance of the QRD-M/MCMC detector is examined under both an idealized MIMO channel with perfect channel side information (CSI) and a practical IEEE 802.16e MIMO- OFDMA system with imperfect CSI. Numerical results show that, compared to the stand-alone QRD-M or MCMC detectors, the QRD-MCMC detector achieves superior performance at a reduced complexity.

In this paper, we consider the optimality of the beamforming scheme for both the multiple-input multiple-output (MIMO) point-to-point channel and the MIMO multiple access channel (MAC), where all communication terminals are assumed to be equipped with multiple antennas. For both channels, the channel matrices have correlated elements and are modelled by virtual representation. For the point-to-point channel, i.e., the single user case, we show that the optimal beamforming angle is unique and is independent of the signal-to-noise ratio (SNR). We further show that there exists a certain SNR threshold below which beamforming is optimal and above which beamforming is strictly suboptimal. For the MIMO MAC, we show that to achieve sum capacity, the inputs from different users are independent and their covariance matrices are diagonal. We also derive a necessary and sufficient condition for the optimal input distribution to achieve the sum capacity. Based on these results, we investigate the conditions under which beamforming achieves the sum capacity. We show that the optimal beamforming angles are not unique, and are dependent on both the value of SNR and beamforming angles of other users. We further provide explicit conditions to determine the optimal beamforming angles for a special class of correlated MIMO MACs.

In this paper, we investigate the application of nonbinary low-density parity-check (LDPC) cycle codes over Galois field GF(q) to multiple-input multiple-output (MIMO) channels. Two types of LDPC coded systems that employ either joint or separate MIMO detection and channel decoding are considered, depending on the size of the Galois field and the modulation choice. We construct a special class of nonbinary LDPC cycle codes called the parallel sparse encodable (PSE) codes. The PSE code, consisting of a quasi-cyclic (QC) LDPC cycle code and a simple tree code, has the attractive feature that it is not only linearly encodable, but also allows parallel encoding which can reduce the encoding time significantly. We provide a systematic comparison between nonbinary coded systems and binary coded systems in both performance and complexity. Our results show that the proposed nonbinary system employing the PSE code outperforms not only the binary LDPC code specified in the 802.16e standard, but also the optimized binary LDPC code obtained using the EXIT chart methods. Through a detailed complexity analysis, we conclude that for the MIMO channel considered, the nonbinary coded systems achieve a superior performance at a receiver complexity that is comparable to that of the binary systems.

In this paper, we investigate the application of non- binary low density parity check (LDPC) codes over Galois field GF(q) for both single-input single-output (SISO) and multiple- input multiple-output (MIMO) fading channels using higher order modulations. As opposed to the widely studied binary systems that employ joint detection and channel decoding, we propose a nonbinary system where optimal signal detection is performed only once followed by channel decoding. To reduce the complexity of proposed system, we first develop a low complexity LDPC decoding algorithm over GF(q) in the logarithmic domain. We then provide a quasi-cyclic construction of nonbinary LDPC codes which not only allows linear-time encoding, but also gives comparable performance to the best known progressive edge growth (PEG) codes. Our results show that the proposed system that employs regular nonbinary LDPC codes outperforms systems using the best optimized binary irregular LDPC codes in both performance and complexity.

In this work we study joint channel decoding and noncoherent detection for block fading channels. We propose a novel, low-complexity noncoherent detection method based on Markov Chain Monte Carlo (MCMC). The MCMC noncoherent detector makes it possible to use large constellations such as 16 QAM and transmit at higher rates of 1 or 1.6 bits/channel use. By employing joint channel decoding and noncoherent detection, the proposed schemes achieve within 1.2-1.4 dB of the noncoherent channel capacity. Moreover, for the same transmission rates, the proposed single transmit antenna system performs 4-6 dB better than published results of the two transmit antenna systems that employ unitary space-time codes or orthogonal space-time codes.

In a recent work, it has been found that the suboptimum detectors that are based on Markov chain Monte Carlo (MCMC) simulation techniques perform significantly better than their sphere decoding (SD) counterparts. In this letter, we explore the sources of this difference and show that a modification to existing sphere decoders can result in some improvement in their performance, even though they still fall short when compared with the MCMC detector. We also present a novel SD detector that is an exact realization of max-log-MAP detector. We call this exact max-log SD detector. Comparison of the results of this detector with those of the max-log version of the MCMC detector reveals that the latter is near optimal.

In this paper a MIMO system with transmit and receive antenna and the channel model with two methods of pilot-aided (PA) schemes are considered. The first scheme is pilot insertion (PI) scheme, where pilots are time multiplexed with data and the second scheme pilot embedding (PE) scheme, where pilots are added and transmitted concurrently with data. The study shows that PI scheme is better than PE scheme. To obtain the capacity the maximum-likelihood channel estimation is used. Then the discrete and continuous capacity of a MIMO channel with imperfect channel estimates is evaluated.

It is well known that independent and identically distributed Gaussian inputs, scaled appropriately based on the signal-to-noise ratio (SNR), achieve capacity on the additive white Gaussian noise (AWGN) channel at all values of SNR. In this correspondence, we consider the question of whether such good input distributions exist for frequency-nonselective Rayleigh-fading channels, assuming that neither the transmitter nor the receiver has a priori knowledge of the fading coefficients. In this noncoherent regime, for a Gauss-Markov model of the fading channel, we obtain explicit mutual information bounds for the Gaussian input distribution. The fact that Gaussian input generates bounded mutual information motivates the search for better choices of fixed input distributions for high-rate transmission over rapidly varying channels. Necessary and sufficient conditions are derived for characterizing such distributions for the worst case scenario of memoryless fading, using the criterion that the mutual information is unbounded as the SNR gets large. Examples of both discrete and continuous distributions that satisfy these conditions are given. A family of fixed input distributions with mutual information growth rate of O((loglogSNR)/sup 1-u/), u>0 are constructed. It is also proved that there does not exist a single fixed-input distribution that achieves the optimal mutual information growth rate of loglogSNR.

The paper contains a systematic investigation of practical coding strategies for noncoherent communication over fading channels, guided by explicit comparisons with information-theoretic benchmarks. Noncoherent reception is interpreted as joint data and channel estimation, assuming that the channel is time varying and a priori unknown. We consider iterative decoding for a serial concatenation of a standard binary outer channel code with an inner modulation code amenable to noncoherent detection. For an information rate of about 1/2 bit per channel use, the proposed scheme, using a quaternary phase-shift keying (QPSK) alphabet, provides performance within 1.6-1.7 dB of Shannon capacity for the block fading channel, and is about 2.5-3 dB superior to standard differential demodulation in conjunction with an outer channel code. We also provide capacity computations for noncoherent communication using standard phase-shift keying (PSK) and quadrature amplitude modulation (QAM) alphabets; comparing these with the capacity with unconstrained input provides guidance as to the choice of constellation as a function of the signal-to-noise ratio. These results imply that QPSK suffices to approach the unconstrained capacity for the relatively low information and fading rates considered in our performance evaluations, but that QAM is superior to PSK for higher information or fading rates, motivating further research into efficient noncoherent coded modulation with QAM alphabets.