The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation, which is a delay-Doppler (DD) domain multi-carrier (DDMC) modulation scheme based on the DD domain orthogonal pulse (DDOP), is studied. We first revisit the linear time-varying (LTV) channel model for the wireless channel, and review the conventional multi-carrier (MC) modulation schemes and their design guidelines for both linear time-invariant (LTI) and LTV channels. We then focus on the representation of the LTV channel in an equivalent sampled DD (ESDD) domain, and propose an impulse-function-based transmission strategy for the ESDD channel. Next, we take an in-depth look into the DDOP and show that it achieves orthogonality with respect to the fine time and frequency resolutions in the ESDD domain thusbehaves likean impulse function. This allows us to unveil the unique input-output relation of the resultant ODDM modulation over the ESDD channel. We point out that the conventional MC modulation design guidelines based on the Weyl-Heisenberg (WH) frame theory can be relaxed without compromising its orthogonality or violating the WH frame theory. More specifically, for a practical communication system with bandwidth and duration constraints, MC modulation signals can be designed considering so-calledlocal or sufficient (bi)orthogonality,which refers to the (bi)orthogonality among a WH subset for the MC signal within a specific bandwidth and duration. This is different from the conventional MC modulation waveform design guidelines (such as for orthogonal frequency division multiplexing and orthogonal time frequency space) based on the global (bi)orthogonality, which is the (bi)orthogonality among a WHfull setcorresponding to the MC signal occupying the entire TF domain. This novel design guideline could potentially open up opportunities for developing future waveforms required by new applications such as communication systems associated with high delay and/or Doppler shifts, as well as integrated sensing and communications.

This paper proposes a new orthogonal delay-Doppler division multiplexing (ODDM) system, which is designed to operate without the need of using a cyclic prefix (CP) in the transmitted signals. The CP-free ODDM is enabled by exploiting the unique structures of the ODDM prototype pulses, which are constructed through repetitions of finite-duration elementary pulses. The system eliminates the need of CP by designing a new receiving filter through temporally extending the transmission prototype pulse. The elementary pulse repetition at the transmitter along with the temporal filter extension at the receiver introduce a wrap-around effect in the equivalent channel spreading function in the dealy-Doppler (DD) domain. The wrap-around effect leads to a block-circulant-like structure of the DD-domain channel matrix that is the same as conventional ODDM systems with CP. Thus CP-free ODDM can employ the same receiver as conventional ODDM systems, yet it yields a higher energy efficiency as no energy needs to be allocated for CP symbols. The wrap-around effects are theoretically demonstrated by deriving the exact analytical expressions of the cross-ambiguity functions of practical transmission and receiving prototype pulses. In addition, theoretical analysis of the cross-ambiguity function verifies that the CP-free ODDM waveforms satisfy local bi-orthogonality in the delay and Doppler domains. This bi-orthogonality property along with the wrap-around effects of the channel spreading function enables the design of a low complexity iterative receiver, which performs interference cancellation and coherent matrix combining (CMC) in the delay domain and minimum mean squared error (MMSE) detection in the Doppler domain. The proposed receiver can collect the diversity in both the delay and Doppler domains while simultaneously suppressing the negative impacts of DD-domain intersymbol interference (ISI) introduced by the doubly-selective fading channels.

A low complexity receiver is developed for orthogonal time frequency space (OTFS) systems by exploring the special structure of the delay-Doppler (DD) domain channel matrix. Based on the system architecture of OTFS, we propose to shuffle the received samples in the DD domain such that samples experiencing the same delay but different Doppler spreads are grouped together. It is shown through theoretical analysis that the proposed shuffling operation yields a special block-circulantlike structure of the DD domain channel matrix. Enabled by the special structure, we propose to develop an iterative receiver that performs coherent matrix combining (CMC) with minimum mean squared error (MMSE) detection in the DD domain. The proposed receiver can collect the diversity in both the delay and Doppler domains while simultaneously suppress the negative impacts of DD domain intersymbol interference (ICI). Simulation results show that the proposed DD-CMC-MMSE receiver achieves significant performance gain over the commonly used message passing (MP) receiver for OTFS systems.

In this paper, we develop a unified dynamic intelligent reflecting surface (IRS) beamforming framework to boost the sum computation rate of an IRS-aided mobile edge computing (MEC) system, where each device follows a binary offloading policy. Specifically, the task of each device has to be either executed locally or offloaded to MEC servers as a whole with the aid of given number of IRS beamforming vectors available. By flexibly controlling the number of times for IRS reconfiguring phase-shifts, the system can achieve a balance between the performance and associated signalling overhead. We aim to maximize the sum computation rate by jointly optimizing the computational mode selection for each device, offloading time allocation, and IRS beamforming vectors across time. Since the resulting optimization problem is non-convex and NP-hard, there are generally no standard methods to solve it optimally. To tackle this problem, we first propose a penalty-based successive convex approximation algorithm, where all the associated variables in the inner-layer iterations are optimized simultaneously and the obtained solution is guaranteed to be locally optimal. Then, we further derive the offloading activation condition for each device by deeply exploiting the intrinsic structure of the original optimization problem. According to the offloading activation condition, a low-complexity algorithm based on the successive refinement method is proposed to obtain high-quality suboptimal solutions, which are more appealing for practical systems with a large number of devices and IRS elements. Moreover, the optimal condition for the proposed low-complexity algorithm is revealed. The effectiveness of the proposed algorithms is demonstrated through numerical examples. In addition, the results illustrate the practical significance of the IRS in MEC systems for achieving coverage extension and supporting multiple energy-limited devices for task offloading, and also unveil the fundamental performance-cost tradeoff embedded in the proposed dynamic IRS beamforming framework.

We study the problem of dynamic computation offloading and resource allocation in mobile edge computing (MEC) systems consisting of multiple mobile users (MUs) with stochastic task arrivals and wireless channels. Each MU can execute its task either locally or remotely in an MEC server. The objective is to identify the optimum scheduling scheme that can minimize the long-term average weighted sum of energy consumption and delay of all MUs, under the constraints of limited transmission power per MU and limited computation resources at the MEC server. The optimum design is performed with respect to three decision parameters: whether to offload a given task, how much transmission power to be allocated for offloading, and how much MEC resources to be allocated for an offloaded task. We propose to solve the problem by developing a dynamic scheduling strategy based on deep reinforcement learning (DRL) with deep deterministic policy gradient (DDPG). Simulation results show that the proposed algorithm outperforms other existing strategies such as deep Q-network (DQN).

In this paper, we coin a new concept of tensor-computing, which is based on tensor theory and designed for future sixth generation (6G) wireless communication systems. Two different types of tensors, namely spectrum-tensor and system-tensor, are defined and analysed to develop a new spectrum usage framework for 6G. The spectrum-tensor encapsulates high dimensional spectrum big data into the format of a compact tensor. The system-tensor summarizes key system performance, including data rate, bandwidth, delay, spectral efficiency, and energy efficiency, into a multi-dimension tensor. The concepts of spectrum-tensor and system-tensor enable unique tensor-based computing and analysis with the help of high efficiency tensor-computing tools, such as tensor completion and tensor decomposition. In the new spectrum usage framework, a value-based spectrum fusion scheme is designed. The maximum system value is achieved under the constraint that the individual value of single user should be guaranteed. The proposed tensor-computing framework builds a bridge between 6G wireless functions with real-world high dimension data processing tools, such as TensorFlow and Tensor Processing Unit (TPU). The authors hope this paper will shine a beam of tensor theory in and open a new research field of tensor-computing for future 6G wireless communications.

We study the problem of computation offloading and resourceallocation in multi-user multi-channel mobile edge computing (MEC) systems. Each user equipment (UE) in the system has a computation-intensive and time-sensitive task that needs to be executed either locally or remotely in an MEC server. All UE tasks have individual deadline constraints that are treated as soft constraints. The MEC server has limited computational resources, which impose hard constraints on the overall offloading computation capacity. The objective is to minimize a cost function that is expressed as a weighted sum of energy consumption, delay, and deadline penalty of all UEs. The optimum design is performed with respect to three decision parameters: whether to offload a given task, which wireless channel to use during offloading, and how much MEC resources should be allocated for an offloaded task. We apply a Deep Reinforcement Learning approach known as Deep Deterministic Policy Gradient to solve the problem. Simulation results demonstrate that the proposed algorithm outperforms other existing schemes such as Deep Q-Network (DQN).

The objective of this paper is to theoretically identify the optimum tradeoff between energy efficiency (EE) and area spectral efficiency (ASE) of a user-centric ultra-dense network (UC-UDN). In a UC-UDN, there are more remote access points (RAPs) than user equipments (UEs). Each UE is served by its closest RAP, and the RAPs that are not associated with any UE are put in sleeping mode. The RAPs and UEs are assumed to form independent homogeneous Poisson point process (PPP). Increasing RAP density can in general improve ASE due to shorter transmission distance, but usually at the cost of EE due to stronger mutual interference and higher energy consumption. Thus it is critical to identify the optimum RAP density that can achieve the optimum EE-ASE tradeoff. We tackle this problem by analytically solving an optimization problem that aims at maximizing the EE under the constraint of minimum achievable ASE. The solution to the problem provides a closed-form expression of the RAP density that can achieve the optimum EE-ASE tradeoff.

In this paper, we study the optimum multicast scheduling of cache-enabled content-centric wireless networks, where multiple users requesting multiple contents with different quality of service (QoS) constraints. The QoS of a content is measured by using the worst-case delay between a request and transmission. Each content is assigned a unique delay threshold based on its delay tolerance. To ensure the timely delivery of the contents, a large delay penalty is imposed if the worst case delay for a content exceeds its delay threshold. In case two or more contents reach their respective delay thresholds simultaneously, the content with a higher priory is assigned a larger penalty in the cost function to prioritize its transmission. Delaying the transmission of a content can potentially increase the number of requests served by a single transmission, given that more requests for the same content might arrive during the waiting period, thus reducing the average power consumption per request. The objective of this paper is to identify the optimum multicast scheduling policy that can jointly minimize the weighted combination of average power, delay penalty, and the fetching cost associated with fetching uncached contents from a remote server. The problem is formulated as an infinite horizon average cost Markov decision process (MDP), and it is optimally solved by applying the relative value iteration algorithm. Simulation results demonstrate that the proposed multicast scheduling outperforms existing scheduling algorithms, and it can achieve flexible tradeoff between power and delay in a multicast system by adjusting the weight coefficient in the cost function.

We study the optimum designs of the downlink of user-centric ultra-dense Internet of Things (IoT) networks with fiber-wireless communications (FWCs). A large number of low power radio access points (RAPs) are densely deployed in the network to provide service to spatially distributed IoT physical devices (PDs). The RAPs are connected to a central unit (CU) through optical fiber (OF) front-hauls. Radio-frequency-over-fiber (RFoF) is employed in the optical front-hauls to reduce RAP complexity, cost, and energy consumption. With RFoF front-hauls, wireless signals received by PDs are subject to distortions accumulated through the optical and wireless links, including optical loss, optical chromatic distortion, optical and thermal noises, wireless pathloss, and small scale fading. The optimum designs are performed across the optical and wireless domains with the help of a newly developed model that quantifies the combined effects of the optical and wireless links. One of the main challenges faced by the design of an ultra-dense IoT network is the high energy consumption due to dense RAP deployment. The objective of this paper is to minimize the total energy consumption of the entire IoT network, including both optical and wireless links, by jointly optimize RAP power allocation and RAP-PD association, subject to quality-of-service (QoS) constraints for each PD. We propose a low complexity suboptimum binary forcing gradient search (BFGS) algorithm, which performs a gradient-based search based on the unique structure of the problem. Simulation results show that the optical front-hauls have significant impacts on the performance and design of ultra-dense IoT networks.

Finite-time distributed data diffusion in a decentralized network is studied in this paper. The objective of distributed data diffusion is to disseminate local data at each node to all other nodes in the network without a central controller. We propose to achieve distributed data diffusion with a linear iterative information propagation algorithm. In the proposed algorithm, each node maintains and updates a state vector with size much less than the number of nodes in the network. In each iteration a node broadcasts its current state vector to all its neighbors. The state vector at a node is iteratively updated by using a linear combination of its own state vector and those from all its neighbors in the previous iteration. The algorithm converges when all nodes have a copy of the data from all other nodes in the network. We analytically identify the design parameters that guarantee the fastest convergence of the algorithm in diameter time, that is, the number of iterations required for convergence equals the network diameter. The optimum designs of square grid networks are studied by using the analytical results. The algorithm is efficient with guaranteed diameter time convergence, and it is scalable in that the total amount of data transmitted by each node throughout the iterative process scales linearly with the number of nodes in the network.

We study the optimum scheduling of a multicast system, where a server transmits information to multiple users via multicasting based on requests from the users. To improve energy efficiency, the server can queue and bundle the requests from different users based on the requested contents, and serve all users requesting the same contents in a later one-time transmission. A longer waiting time can increase the average number of users served in each multicasting transmission, thus improve the energy efficiency. The higher energy efficiency is achieved at the cost of the timeliness of the information, which can be measured by using age-of-information (AoI). The goal of this paper is to identify the multicast scheduling strategy that can optimize the tradeoff between energy efficiency and AoI. Using optimum stopping theories, we develop optimum stopping rules that can minimize a cost function expressed as a weighted combination of AoI penalty function and energy efficiency, where the weight coefficient is used to adjust the tradeoff between the two. Specifically, we consider the case that the AoI penalty grows exponentially with time, and show that the optimum scheduling can be formulated as a simple threshold test with a low complexity one-step look ahead stopping rule. The proposed multicast scheduling strategy can achieve the optimum tradeoff between energy efficiency and AoI penalty.

We study the optimum designs of a wireless ad hoc network that employs a random multiple access protocol and operates in an interference-limited environment. The nodes are assumed to form a homogeneous Poisson point process (PPP). With the random multiple access protocol, each node transmits with a certain probability, the value of which is critical to system performance. A higher transmission probability means more transmission opportunities for a node, but at the cost of more interference from other nodes in the network due to more simultaneous transmissions. For half-duplex nodes that cannot transmit and receive at the same time, a higher transmission probability will also decrease the probability that a node is in the receiving mode. The objective of this paper is to identify the optimum transmission probabilities of the ad hoc network. Two performance metrics are considered, the connectivity probability and the random access transport capacity (RATC), which are derived as closed-form expressions of system parameters by considering the effects of channel propagations, interference, and the stochastic geometric of node distributions. The optimum transmission probabilities that can maximize the connectivity probability or RATC are then obtained for half-duplex and full-duplex systems, respectively.

We study the distributed estimations of a spatially correlated random field with decentralized wireless sensor networks (WSNs). Nodes in the WSN take spatial samples of the random field, then each node estimates the values of arbitrary points on the random field by iteratively exchanging information with each other, without the need of a central controller. The objective is to minimize the time (or number of iterations) required for all nodes in the network to reach a distributed consensus on the estimation result, with mean squared error (MSE) below a certain threshold. We find the sufficient conditions for this optimization problem, and identify the asymptotically optimum solutions when time is large and the MSE threshold is small. Specifically, we propose a distributed iterative estimation algorithm that defines the procedures for both information propagation and information estimation in each iteration. The key parameters of the algorithm, including an edge weight matrix and a sample weight matrix, are designed by following the asymptotically optimum criteria. It is shown that the asymptotically optimum performance can be achieved by distributively projecting the measurement samples into a subspace related to the covariance matrices of data and noise samples. Simulation results show that all nodes in a large network can obtain accurate estimation results with only a few iterations.

In this paper, we consider a scenario where an energy harvesting sensor continuously monitors a system and sends time-stamped status updates to a destination. The destination keeps track of the system status through the received updates. We use the metric Age of Information (AoI), the time that has elapsed since the last received update was generated, to measure the “freshness” of the status information available at the destination. We assume energy arrives randomly at the sensor according to a Poisson process, and each status update consumes one unit of energy. Our objective is to design optimal online status update policies to minimize the long-term average Aol, subject to the energy causality constraint at the sensor. We consider three scenarios, i.e., the battery size is infinite, finite, and one unit only, respectively. For the infinite battery scenario, we adopt a best-effort uniform status update policy and and show that it minimizes the long-term average AoI. For the finite battery scenario, we adopt an energy-aware adaptive status update policy, and prove that it is asymptotically optimal when the battery size goes to infinity. For the last scenario where the battery size is one, we propose a threshold based status update policy. We analytically characterize the long-term average AoI under this policy, and prove it is optimal. Simulation results corroborate the theoretical bounds.

In this paper, we study the optimal energy management policy of an energy harvesting transmitter by taking both battery degradation and finite battery constraints into consideration. We consider a scenario where the sensor is able to harvest energy from the ambient environment and use it to power its transmission. The harvested energy can be used for transmission immediately without entering the equipped battery, or charged into the battery and discharged later for transmission. When the battery is charged or discharged, a cost will be incurred to account for its impact on battery degradation. We impose a long-term average cost constraint on the battery, which is translated to the average number of charge/discharge operations per unit time. At the same time, we assume the capacity of the battery is finite, and the total amount of energy stored in the battery cannot exceed its capacity. Our objective is to develop an online energy management policy to maximize the long-term average throughput of the transmitter under both the battery usage constraint and finite battery constraint. We propose an energy-aware adaptive transmission policy, which is a modified version of the optimal policy for the infinite battery case. Our analysis indicates that the energy-aware adaptive transmission policy is asymptotically optimal when the battery size is sufficiently large. Simulation results corroborate the theoretical analysis.

In this paper, we study the optimum sensing of a time-varying random event with a sensor powered by energy harvesting devices. The system aims at reconstructing a band-unlimited continuous-time random process by using discrete-time samples collected by a sensor. Due to the random nature of the harvested energy, the sensor might not have sufficient energy to perform a sensing operation at a desired time instant. We propose a best- effort Poisson sensing policy. The sensing policy defines a set of random candidate sampling instants following a Poisson point process (PPP) in the time domain. At a given candidate sampling instant, the sensor collects a sample if there is sufficient energy to do so, and does nothing otherwise. By analyzing the statistical properties of the best-effort Poisson sensing policy, we develop an optimum estimator of the underlying random event. The asymptotic mean-squared error (MSE) of the estimation are expressed as an explicit closed-form expression of several key system parameters, such as the ratio between the average energy harvesting rate and consumption rate, the time correlation of the random event of interests, and the energy allocation between sensing and transmission. %The analytical results are used to identify optimum system operation parameters. Numerical results show that the proposed best-effort Poisson sensing policy outperforms existing uniform sensing policies.

In this paper, we study the connectivity probability of linear multi-hop ad hoc networks operating in an interference-limited environment. The nodes in the network are assumed to form a Poisson point process (PPP), and they generate mutual interference during communications. A node communicates to other nodes in the network by using its intermediate neighbors as relays. The average node density plays a crucial role on the communication quality. A higher node density means shorter transmission distance for each hop, but also a stronger interference from surrounding nodes. We characterize this tradeoff relationship by deriving the analytical probability that all nodes in a finite section of the network are connected, that is, the signal-to-interference ratios (SIRs) of all adjacent node pairs in the finite network section are no less than a threshold. The connectivity probability is expressed as an explicit closed-form function of various system parameters, such as average node density, the length of the network section, pathlosss exponent, and the SIR threshold. It is shown through theoretical analysis that the connectivity probability is a quasi-concave function of the node density, and the optimum node density that can maximize the connectivity probability is identified.

In this paper, we study the end-to-end delay of a linear multihop network with mobile relays located on the line connecting the source-destination pair. The network operates in an interference-limited environment, where the interfering signals are from nodes with positions following the Poisson point process (PPP) on a two-dimensional plane. Due to node mobilities, the relay nodes are assumed to move randomly on the source-destination line by following a random waypoint mobility (RWPM) model. Based on the statistical properties of RWPM, we first analyze the probability distributions of the inter-node distance of the mobile relays. The results are then used to derive the exact analytical expressions of the end-to-end delay between a given source-destination pair. The analytical results explicitly quantify the impacts of several system parameters on the end-to-end delay, including the source-destination distance, the number of relays, the propagation parameters, and the density of interferers, etc. The optimum number of relays that can minimize the end-to-end delay is obtained. Both analytical and simulation results show that the mobility of the relay nodes can decrease the end-to-end delay.

Level set estimation (LSE) is the process of classifying the region(s) that the values of an unknown function exceed a certain threshold. It has a wide range of applications such as spectrum sensing or environment monitoring. In this paper, we study the the optimal LSE of a linear random field that changes with respect to time. A linear sensor network is used to take discrete samples of the spatially-temporally correlated random field in both the space and time domain, and the sensors operate under a total power constraint. The samples are congregated at a fusion center (FC), which performs LSE of the random field by using the noisy observation of the samples. Under the Gaussian process (GP) framework, we first develop an optimal LSE algorithm that can minimize the LSE error probability. The results are then used to derive the exact LSE error probability with the assistance of frequency domain analysis. The analytical LSE error probability is expressed as an explicit function of a number of system parameters, such as the distance between two adjacent nodes, the sampling period in the time domain, the signal-to-noise ratio (SNR), and the spatial-temporal correlation of the random field. With the analytical results, we can identify the optimum node distance and sampling period that can minimize the LSE error probability.

This paper takes the impact of charging and discharging operations on battery degradation into consideration, and studies the optimal energy management policy for an energy harvesting communication system under a battery usage constraint. Specifically, in each time slot, we assume the harvested energy can be used to power the transmitter immediately without entering into the battery, or stored into the battery for now and retrieved later for transmission. Whenever the battery is charged or discharged, a cost will be incurred to account for its impact on battery degradation. We impose an long-term average cost constraint on the battery, which is translated to the average number of charge/discharge operations per unit time. The objective is to develop an online policy to maximize the long-term average throughput of the transmitter under energy causality constraint and the battery usage constraint. We first relax the energy causality constraint on the system, and impose an energy flow conservation constraint instead. We show that the optimal energy management policy has a double-threshold structure: if the amount of energy arrives in each time slot lies in between the two thresholds, it will be used immediately without involving the battery; otherwise, the battery will be charged or discharged accordingly to maintain a constant transmit power. We then modify the double-threshold policy slightly to accommodate the energy causality constraint, and analyze its long-term performance. We show that the system achieves the same long-term average performance, thus it is optimal.

A pilot contamination elimination (PCE) precoding was recently proposed for multi-cell massive MIMO systems with distributed antenna subarrays, where the number of subarrays is required to be not less than the number of users. In this paper, we consider practical scenario of insufficient number of subarrays, and propose a corresponding PCE precoding scheme. The key idea of the proposed scheme is to utilize multiple pilots in one cell rather than single pilot in the original scheme. It is shown that by separating users into multiple groups according to the associated pilots, multiple downsized equivalent MIMO channels can be obtained, which linearly reduces the required number of subarrays and therefore enable the PCE precoding. This linear relation based interpretation also introduces a new optimization problem of finding subarray power normalization factors to maximize signal-to-noise-plus-interference ratio, where an optimized solution is proposed. Finally, simulation results confirm the validity of the proposed PCE precoding scheme.

In this paper, we study the optimal sensing scheduling problem for an energy harvesting sensor. The objective is to strategically select the sensing time such that the long-term time-average sensing performance is optimized. In the sensing system, it is assumed that the sensing performance depends on the time durations between two consecutive sensing epochs. Example applications include reconstructing a wide-sense stationary random process by using discrete-time samples collected by a sensor. We consider both scenarios where the battery size is infinite and finite, assuming the energy harvesting process is a Poisson random process. We first study the infinite battery case and identify a performance limit on the long-term time average sensing performance of the system. Motivated by the structure of the performance limit, we propose a best-effort uniform sensing policy, and prove that it achieves the limit asymptotically, thus it is optimal. We then study the finite battery case, and propose an energy-aware adaptive sensing scheduling policy. The policy dynamically chooses the next sensing epoch based on the battery level at the current sensing epoch. We show that as the battery size increases, the sensing performance under the adaptive sensing policy asymptotically converges to the limit achievable by the system with infinite battery, thus it is asymptotically optimal. The convergence rate is also analytically characterized.

In this paper, we study the stochastic optimization of cloud radio access networks (C-RANs) by joint remote radio head (RRH) activation and beamforming in the downlink. Unlike most previous works that only consider a static optimization framework with full traffic buffers, we formulate a dynamic optimization problem by explicitly considering the effects of random traffic arrivals and time-varying channel fading. The stochastic formulation can quantify the tradeoff between power consumption and queuing delay. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed into a per-slot penalized weighted sum rate maximization problem, which is shown to be nondeterministic polynomial-time hard. Based on the equivalence between the penalized weighted sum rate maximization problem and the penalized weighted minimum mean square error (WMMSE) problem, the group sparse beamforming optimization-based WMMSE algorithm and the relaxed integer programming-based WMMSE algorithm are proposed to efficiently obtain the joint RRH activation and beamforming policy. Both algorithms can converge to a stationary solution with low-complexity and can be implemented in a parallel manner, thus they are highly scalable to large-scale C-RANs. In addition, these two proposed algorithms provide a flexible and efficient means to adjust the power-delay tradeoff on demand.

In this paper, we study the impact of clock timing mismatch of analog-to-digital converter in a receiver that employs quadrature bandpass sampling of the radio frequency (RF) signal. It is shown that the clock timing mismatch introduces not only symbol timing offset but also image interference, that is, the interference between in-phase and quadrature signals. Then, a mismatch compensation scheme and a pilot-aided mismatch estimation method are proposed. We also propose a blind estimation method, where the clock timing mismatch can be estimated from the second order statistics of the sampled sequences. Numerical simulations confirm the validity of the proposed methods, even in the presence of unknown channel.

The cloud radio access network (C-RAN) is an emerging network architecture that holds the promise of coping with the explosive growth of mobile wireless data. In this paper, by considering the stochastic traffic arrivals and time-varying channel conditions, we address the stochastic optimization of joint remote radio head (RRH) activation and linear beamforming to maintain delay performance and minimize average network power consumption in downlink slotted C-RAN. We first formulate the joint optimization as a group sparse beamforming problem. Based on Lyapunov optimization technique, the stochastic optimization problem is then transformed into a queue-aware joint RRH activation and beamforming problem, which can be greedily solved at each slot. Finally, a low-complexity stationary algorithm with guaranteed convergence and closed-form expressions is proposed. The proposed algorithm can be implemented in a parallel manner, thus it is highly scalable to a large-sized C-RAN. Extensive numerical simulations validate the effectiveness of the proposed algorithm.

Level set estimation (LSE) is the process of using noisy observations of an unknown function to estimate the region(s) where the function values lie above a given threshold. It has a wide range of applications in many scientific and engineering areas, such as spectrum sensing or environment monitoring. In this paper, we study the optimum LSE of a time-varying random field under a total power constraint. A sensor performs uniform sampling of the random field and sends the samples to a fusion center, which estimates the level set by using distorted observations of the samples. Under a total power constraint, a higher sampling rate means less energy per sample, which may negatively impact the estimation performance, but also a stronger correlation between adjacent samples, which can improve the estimation accuracy. Thus it is critical to identify the optimum sampling rate that can minimize the LSE error provability. With the help of a Gaussian process (GP) prior model, we first develop an optimum LSE algorithm based on GP regression. The exact analytical LSE error probability of the LSE algorithm is then derived by considering a number of factors, such as the power consumptions of both sensing and transmission, the power constraint of the sensor, the sampling rate, and the probability distributions of the random field. To simplify analysis, we also obtain a closed-form upper bound of the LSE error probability. The optimum sampling rate is identified by using the analytical error probabilities.

In this paper, a circular-shift division multiple access (CSDMA) scheme with oversampling receivers is proposed for multi-carrier multi-user wireless networks. The CSDMA scheme differentiates signals from different users by requiring each user to circularly shift their respective signals by a unique number of time domain samples. At the transmitter, each modulated data symbol is spread onto a subset of orthogonal subcarriers in the frequency domain, and the signals are then circularly shifted in the time domain. At the receiver, the received signals are oversampled in the time domain and then converted into the frequency domain for processing. The combination of circularly shifting at the transmitter and oversampling at the receiver ensures full multipath diversity, and it results in a special signal structure that allows the receiver to extract the signals from different users with zero or a small amount of multiple access interference (MAI). Both analytical and simulation results show that the proposed oversampled CSDMA scheme can achieve significant performance gains over conventional orthogonal frequency division multiple access (OFDMA) with the same spectral efficiency.

In this paper, we study the optimal sensing scheduling policy for an energy harvesting sensing system equipped with a finite battery. The objective is to strategically select the sensing epochs such that the long-term average sensing performance is optimized. In the sensing system, it is assumed that the sensing performance depends on the time duration between two consecutive sensing epochs. Example applications include reconstructing a wide-sense stationary random process by using discrete-time samples collected by a sensor. The randomness of the energy harvesting process and the finite battery constraint at the sensor make the optimal sensing scheduling very challenging. Assuming the energy harvesting process is a Poisson random process, we first identify a performance limit on the long-term average sensing performance of the system without the finite battery constraint. We then propose an energy-aware adaptive sensing scheduling policy, which dynamically chooses the next sensing epoch based on the battery level at the current sensing epoch. We show that as the battery size increases, the sensing performance under the adaptive sensing policy asymptotically converges to the performance limit of the system with an infinite battery, thus it is asymptotically optimal. The convergence rate is also analytically characterized.

This paper studies the maximum Doppler diversity order of high mobility systems operating in the presence of imperfect channel state information (CSI). High mobility of wireless terminals causes large Doppler spread, which provides Doppler diversity. However, channel estimation errors are usually inevitable in such systems due to fast time-varying fading caused by large Doppler spread, and they will negatively impact system performance. We study the fundamental tradeoff between Doppler diversity and channel estimation errors by identifying the exact analytical expression of the maximum Doppler diversity order achievable in the presence of imperfect CSI. The analytical study is enabled by a simple repetition code at the transmitter and a new optimum diversity receiver, which is developed by analyzing the statistical properties of channel estimation errors. The analytical results show that if the energy of pilot symbols scales linearly with that of data symbols, then systems with imperfect CSI can achieve the same Doppler diversity order as those with perfect CSI. A non-linear scaling between the energy of pilot and data symbols always results in a loss of Doppler diversity for systems with imperfect CSI.

In this paper, we study the optimum estimation of a continuous-time random process by using discrete-time samples taken by a sensor powered by energy harvesting power sources. The system employs a best-effort sensing scheme to cope with the stochastic nature of the energy harvesting sources. The best-effort sensing scheme defines a set of equally-spaced candidate sensing instants, and the sensor performs sensing at a given candidate sensing instant if there is sufficient energy available, and remains silent otherwise. It is shown through asymptotic analysis that when the energy harvesting rate is strictly less than the energy consumption rate, there is a non-negligible percentage of silent symbols due to energy outage. For a given average energy harvesting rate, a larger sampling period means a smaller energy outage probability and/or more energy per sample, but a weaker temporal correlation between two adjacent samples. Such a tradeoff relationship is captured by developing a closed-form expression of the estimation MSE, which analytically identifies the interactions among the various system parameters, such as the ratio between the energy harvesting rate and energy consumption rate, the sampling period, and the energy allocation between sensing and transmission. It is shown through theoretical analysis that the optimum performance can be achieved by adjusting the sampling period and sampling energy such that the average energy harvesting rate is equal to the average consumption rate.

In this paper, we consider an energy harvesting multiple access channel (MAC) where the transmitters are powered by energy harvested from the ambient environment. We assume that the energy harvesting processes at the transmitters can be modeled as independent Bernoulli processes with parameters λis, and the channel states between the transmitters and the receiver are independent Bernoulli processes with parameter µis. An active transmitter always transmits with a fixed power and consumes one unit amount of energy in a time slot. Under the assumption that µi≥ λi, ∀i, our objective is to schedule the transmissions adaptively according to the instantaneous channel and battery states of transmitters, so that the long-term average sum-throughput of the MAC is maximized in expectation. We first show that for a general asymmetric scenario where λis and µis are not identical across the transmitters, the expected long-term average sum-throughput has an upper bound for any transmission scheduling policy satisfying the energy causality constraints. We then consider a special symmetric scenario where λis and µis are uniform among transmitters. We propose a randomized longest-connected-queue transmission scheduling policy and show that it achieves the upper bound almost surely as time T approaches infinity, thus it is optimal.

This paper studies spectral efficient Doppler diversity transmissions in the presence of imperfect channel state information (CSI). Fast time-varying fading in high mobility communication systems introduces Doppler diversity that can benefit system performance. On the other hand, it is more difficult to estimate and track fast time-varying channel, thus channel estimation errors might seriously degrade system performance in high mobility systems. We propose a practical pilot- assisted system design that can balance the tradeoff between Doppler diversity and channel estimation errors. At the transmitter, the data symbols are precoded with a rate-1 Doppler domain multiplexing scheme to achieve maximum Doppler diversity without sacrificing spectral efficiency. At the receiver, the information is detected by using imperfect CSI and a block decision feedback equalizer (BDFE), which is developed by exploiting the statistical properties of channel estimation errors. The analytical pairwise error probability (PEP) and a bit error rate (BER) lower bound are developed by considering a large number of design parameters, such as the percentage of pilot symbols in the transmitted symbols, the energy allocation between pilot and data symbols, and the maximum Doppler spread of the channel. Both simulation and analytical results show that maximum Doppler diversity can be achieved through optimizations of the various design parameters in the presence of imperfect CSI.

This research studies the optimum system designs of high mobility wireless communication systems with imperfect channel state information. In high mobility systems, the channel estimation error is usually inevitable and might have significant impacts on system performance. The impacts of channel estimation errors on system performance are quantified through the development of a spectral efficiency lower bound. With the help of the asymptotic analysis of the channel estimation mean squared error, the spectral efficiency lower bound is expressed as explicit functions of a number of system parameters, such as the maximum Doppler frequency, the percentage of pilots in the transmitted symbols, and the ratio of energy allocated between data symbols and pilot symbols. The optimum pilot percentage and energy allocation factor that jointly maximise the spectral efficiency lower bound are identified through analytical studies. It is discovered that, in term of maximising spectral efficiency, the optimum performance can be achieved by optimising the energy allocation factor when the pilots sample the fast time‐varying fading at its Nyquist rate.

This study presents a low‐complexity soft‐interference cancellation equaliser (SICE) for the turbo detection of multiple‐input–multiple‐output systems operating in time dispersive channels. The SICE contains three time‐invariant linear filters: a feedforward filter, a causal feedback filter and an anti‐causal feedback filter. The feedforward filter is designed to suppress the intersymbol interference because of channel time dispersion and the multiplexing interference from multiple transmit antennas. The causal (or anti‐causal) feedback filter is developed to remove the residual interference caused by the symbols transmitted before (or after) the symbol under detection. The filters are designed by analysing the statistical properties of soft decisions. The performance of the proposed SICE is verified through both extrinsic information transfer (EXIT) chart analysis and computer simulations. The EXIT chart analysis shows that, because of the inclusion of the anti‐causal soft decision, the SICE performance approaches the ideal matched filter bound as the iteration progresses. Consequently, the proposed SICE achieves significant performance gains over conventional equalisers.

In this paper, we consider a collaborative sensing scenario where sensing nodes are powered by energy harvested from the ambient environment. In each time slot, an active sensor consumes one unit amount of energy to take an observation and transmit it back to a fusion center (FC). After receiving observations from all of the active sensors in a time slot, the FC aims to extract information from them. We assume that the sensing utility generated by the observations is a concave function of the number of the active sensing nodes in that slot. Our objective is to develop a sensing scheduling policy so that the time average utility generated by the sensors is maximized. We first consider an offline setting, where the energy harvesting profile over duration$[0,T-1]$for each sensor is known beforehand. Assuming infinite battery capacity at sensors, we show that the optimal scheduling structure has a “majorization” property, and propose a procedure to construct a collaborative sensing policy with the identified structure explicitly. We then consider an online setting, under which the energy harvesting profile is available causally. Assuming the energy harvesting processes at individual sensors are independent but not necessarily identical Bernoulli processes, we show that the expected long-term time average sensing utility has an upper bound under any feasible scheduling policy satisfying the energy causality constraints. We then propose a randomized myopic policy, which aims to select a number of sensors with the highest energy levels to perform the sensing task in each slot. We show that the time average utility generated under the proposed policy converges to the upper bound almost surely as time$T$approaches infinity, thus it is optimal. The corresponding convergence rate is also explicitly characterized.

The objective of this paper is to quantify the fundamental tradeoff between Doppler diversity and channel estimation errors in high mobility systems. Fast fading variation in high mobility systems introduces Doppler diversity that can benefit system performance. On the other hand, it is more difficult to estimate and track a fast changing channel, thus channel estimation errors are non-negligible and they may seriously degrade system performance. Such a tradeoff relationship is studied by identifying the exact analytical expressions of two performance metrics: the maximum Doppler diversity order achievable with imperfect channel state information (CSI), and the loss in signal-to-noise ratio (SNR) caused by channel estimation errors. The analytical study is enabled by a simple repetition code at the transmitter, and a new optimum diversity receiver developed by analyzing the statistical properties of channel estimation errors. With the analytical results, the optimum allocation of transmission energy between pilot symbols and data symbols are obtained to simultaneously maximize the Doppler diversity order and minimize the SNR loss, thus achieve the optimum tradeoff between the two. The results reveal the fundamental performance limits of Doppler diversity systems operating in the presence of imperfect CSI, and they can be used to guide the design of practical systems.

In this paper, a multi-carrier circular-shift division multiple access (MC-CSDMA) scheme is proposed for multiuser wireless communication systems. At the transmitter, each modulated symbol is first spread onto a subset of M subcarriers in the frequency domain through simple repetition codes. The obtained signals are then converted to the time domain, where users circularly shift their respective time domain signals by different numbers of samples. Signals from multiple users are transmitted over the same frequency at the same time. The time domain circular shifting operations render a special signal structure that allows the receiver to extract the signals from different users with zero or a small amount of multiple access interference (MAI). If the number of users is no more than M, which is the number of subcarriers bearing the same modulated symbol, then there are at most L mutually interfering users at any given subcarrier, where L is the length of the equivalent discrete-time frequency selective fading channel. Furthermore, if the number of users is no more than M/L, then MAI-free communications can be achieved. Both analytical and simulation results show that full multipath diversity gain can be achieved by the proposed MC-CSDMA scheme regardless of the presence of MAI, thus it achieves a significant performance gains over conventional orthogonal frequency division multiple access (OFDMA) with the same spectral efficiency.

In this paper, we seek answer to the question: can a wireless sensing system with energy harvesting power supplies perform as well as one with conventional power supplies? Due to the stochastic nature of the energy harvested from the ambient environment, uniform sampling employed by conventional sensing systems is usually infeasible for energy harvesting sensing systems. We propose a simple best-effort sensing scheme, which defines a set of equally-spaced candidate sensing instants. At a given candidate sensing instant, the sensor will perform sensing if there is sufficient energy available, and it will remain silent otherwise. It is analytically shown that the percentage of silent candidate sensing instants diminishes as time increases, if and only if the average energy harvesting rate is no less than the average energy consumption rate. The theoretical results are then used to guide the design of a practical sensing system that monitors a time-varying event. Both analysis and simulations show that the energy harvesting system with the best-effort sensing scheme can asymptotically achieve the same mean squared error (MSE) performance as one with uniform sensing and deterministic energy sources. Therefore, we provide a positive answer to the question from both theoretical and practical aspects.

In this paper, we study the optimal sampling policy for an energy harvesting sensing system, which is designed to estimate a wide-sense stationary random process by using discrete-time samples collected by a sensor. The energy in the sensor is consumed by taking observations and is replenished randomly with energy harvested from the ambient environment. Our goal is to identify the optimal sampling policy that minimizes the estimation mean squared error (MSE) under stochastic energy constraints. The problem can be formulated as a stochastic programming problem, which is generally difficult to solve. We identify an asymptotically optimal solution to the problem by exploiting the properties of random processes with power-law decaying covariance. Specifically, with the help of a newly derived inverse covariance matrix of the random process, it is discovered that the linear minimum MSE (MMSE) estimation of the random process demonstrates a Markovian property. That is, the optimal estimation of any point in a time segment bounded by two consecutive samples can be achieved by using the knowledge of only the two bounding samples while ignoring all other samples. Such a Markovian property enables us to identify a lower bound of the long term average MSE. Motivated by the structure of the MSE lower bound, we then propose a simple best-effort sampling scheme by considering the stochastic energy constraints. It is shown that the best-effort sampling scheme is asymptotically optimal in the sense that, for almost every energy harvesting sample path, it achieves the MSE lower bound as time becomes large.

A low complexity detection algorithm based on list sphere decoding (LSD) is proposed for under-determined multiple-input multiple-output (UD-MIMO) systems with N transmit antennas and M <; N receive antennas. The proposed algorithm utilizes the unique structure of UD-MIMO systems by dividing the N detection layers into two groups. Group 1 contains layers 1 to M that have similar structures as a symmetric MIMO system; while Group 2 contains layers M + 1 to N that contribute to the rank deficiency of the channel Gram matrix. Tree search algorithms are used for both groups, but with different search radii. A new method is proposed to adaptively adjust the tree search radius of Group 2 based on the statistical properties of the received signals. The employment of the adaptive tree search can significantly reduce the computational complexity. Simulation results show that the proposed algorithm can reduce the complexity by one orders of magnitude with less than 0.01 dB degradation in the Bit-Error-Rate (BER) performance.

This paper studies the optimum system designs of high mobility wireless communication systems with channel estimation errors. In high mobility systems, the accurate estimation and tracking of the fast time-varying fading channel is nontrivial, and the residual channel estimation errors might have significant impacts on the system performance. The impacts of channel estimation error on system performance are quantified through the development of the analytical symbol error rate (SER) by analyzing the statistical properties of the estimated channel coefficients. With the help of the asymptotic analysis of the channel estimation mean squared error, the SER is expressed as an explicit function of a number of system parameters, such as the maximum Doppler frequency, the percentage of pilots in the transmitted symbols, and the ratio of energy allocated between data symbols and pilot symbols. The optimum pilot percentage and energy allocation factor that minimize the SER are identified through analytical and numerical studies.

This paper presents an accurate approximation of the frame error rate (FER) of coded wireless communication systems with receiver diversity, such as single-input multiple-output (SIMO) systems with maximum ratio combining (MRC) or hybrid automatic repeat request (HARQ) systems with Chase combining. The signals at different diversity branches experience independent but non-identically distributed Rayleigh fading. The FER approximation is obtained with a threshold-based method. Specifically, the threshold value, which is critical to the FER approximation accuracy, is modeled as a linear function of the frame length in the log-domain, with the slope and intercept of the linear function determined by the underlying modulation and channel coding schemes. The analytical FER approximation is expressed as an explicit function of parameters related to modulation, coding, frame length, number of diversity branches, and the power distribution across the diversity branches. Such an FER approximation summarizes the complex physical layer operations into a few parameters, and it provides the parametric flexibility that is not available in most existing FER approximations. Simulation results show that the proposed FER approximation can accurately predict the FER performance of a wide range of receiver diversity systems.

This paper studies the maximum Doppler diversity transmissions in the presence of imperfect channel state information (CSI) in high mobility systems. Due to fast time-varying fading in high mobility systems, channel estimation error is usually inevitable and they might have significant impacts on system performance. On the other hand, Doppler spread caused by fast time-varying fading introduces Doppler diversity that can benefit system performance. The fundamental tradeoff between channel estimation errors and Doppler diversity are studied by deriving the analytical symbol error rate (SER), which captures both effects in a single expression. It is shown that conventional maximal ratio combining (MRC) receiver is no longer optimum with channel estimation errors. A new optimum receiver that can collect the maximum Doppler diversity with imperfect CSI is developed by analyzing the statistical properties of the estimated channel coefficients. The SER of the new receiver is then derived and evaluated. The analytical and simulation results are used to identify the system parameters that can render the optimum tradeoff between Doppler diversity and channel estimation error.

This paper studies the performance and design of high speed railway communications that employ massive MIMO systems. The users onboard the train communicate with the BS by using antennas mounted on top of the train as gateways. Two antenna layouts on the train are considered, one has co-located antennas (CA) at the center of the train, and the other has distributed antennas (DA) uniformly placed along the train. The analytical uplink ergodic capacities for both layouts are derived by averaging over all the locations in a cell. For the CA layout, the average uplink cell capacity is concave in the number of antennas on the train, and the optimum number of antennas on the train is identified by maximizing the average uplink capacity. For the DA layout, the average cell capacity is upper and lower bounded by two concave functions, which are then used to find a sub-optimum value of the number of antennas on the train. Both analytical and numerical results demonstrate that the CA layout has a higher average cell capacity while the DA layout has less variations in terms of uplink capacity at different locations inside a cell.

This paper studies the theoretical performance of channel estimations in high mobility wireless communication systems operating with doubly-selective (both time-selective and frequency-selective) fading. High mobility communications result in fast time-selective fading with a Doppler spread as high as a few kilo-Hertz. The fast fading variation can be estimated and tracked with pilot-assisted minimum mean squared error (MMSE) channel estimation. The percentage of pilot symbols among the transmitted symbols plays a critical role. A higher pilot percentage can lead to a better channel estimation, but also more overhead. The impacts of the pilot percentage on channel estimations are identified through the derivation of the asymptotic mean squared error (MSE) of channel estimations. The results are expressed as a closed-form expression of various system parameters, including the pilot percentage, the maximum Doppler spread, the signal-to-noise ratio (SNR), etc.

The optimum energy efficient and spectral efficient designs for type-I automatic-repeat-request (ARQ) systems in Rayleigh flat fading channels are studied in this paper. Three optimum designs are considered: the first scheme maximizes the energy efficiency (EE), or equivalently, minimizes the total energy per information bit without considering the spectral efficiency (SE); the second scheme minimizes a new metric, the energy per information bit normalized by the SE; and the third scheme maximizes the EE under the constraint of a minimum SE. For given physical and link layer parameters, such as hardware power consumption, coding rate, modulation, bit rate, number of overhead bits, and communication distance, the three schemes are optimized with respect to the average transmission energy and the number of information bits per frame. The fundamental EE-SE tradeoff curve is analytically identified via the optimization of the third scheme, and it is shown that the optimum EE is quansiconcave in SE in a type-I ARQ system. The optimum solutions to the first two schemes are special operating points on the EE-SE tradeoff curve. Computer simulation results verify the analytical solutions.

This paper studies the optimum system design that can maximize the spectral efficiency of high mobility wireless communication systems with imperfect channel state information (CSI). The fast time-varying fading in high mobility systems can be tracked with pilot-assisted channel estimation. The percentage of pilot symbols in the transmitted symbols plays a critical role on the system performance: a higher pilot percentage yields a more accurate channel estimation, but also more overhead. The effects of pilot percentage are quantified through the derivation of the channel estimation mean squared error (MSE), which is expressed as a closed-form expression of various system parameters through asymptotic analysis. It is discovered that, if the pilots sample the channel above its Nyquist rate, then the estimation of the channel coefficients of data symbols through temporal interpolation yields the same asymptotic MSE as the direct estimation of the channel coefficients of the pilot symbols. Based on the statistical properties of the channel estimation error, we quantify the impacts of the imperfect CSI on the system performance by developing the analytical symbol error rate (SER) and a spectral efficiency lower bound of the communication system. The optimum pilot percentage that can maximize the spectral efficiency lower bound is identified through both analytical and simulation results.

A new graph modulation scheme is proposed for massive multiple-input multiple-output (MIMO) systems with a large number of transmit antennas. Each data symbol is transmitted in the form of multiple weighted replicas. The replicas from different symbols are multiplexed onto a two-dimensional (2D) space-time grid, which is formed by multiple transmit antennas in the space domain and different transmission instants in the time domain. Multiplexing repeated symbols onto a 2D space-time grid achieves performance gains in terms of both diversity gain and high degree-of-freedom of the received signals. In addition, it enables a bipartite graph representation of the system, with one set of nodes being the transmitted symbols and the other set containing the received space-time samples. A modified message passing algorithm is proposed to iteratively detect the transmitted symbols by exploiting the structure of the bipartite graph. The proposed graph modulation scheme can flexibly adjust the tradeoff among energy efficiency, spectral efficiency, space diversity, and detection complexity by choosing different design parameters based on application requirements. Simulation results demonstrate that the proposed approach can achieve significant performance gains over the conventional spatial multiplexing scheme used in MIMO systems.

This paper studies the optimum system design that can maximize the spectral efficiency of a high mobility wireless communication system with imperfect channel state information (CSI). In a high mobility system, the percentage of pilot symbols in the transmitted symbols plays a critical role on system performance: a higher pilot percentage yields a more accurate channel estimation at the cost of more overhead. The channel estimation mean square error is quantified as a closed-form expression of the pilot percentage through asymptotic analysis. The effects of both estimation at pilot locations and channel interpolation at non-pilot locations are studied. The results are then used to derive an analytical spectral efficiency lower bound for systems operating with imperfect CSI. The spectral efficiency lower bound is expressed as an explicit function of a number of parameters, such as pilot percentage, maximum Doppler spread, and the signal-to-noise ratio, etc. The optimum pilot percentage that can maximize the spectral efficiency lower bound is analytically identified, and the impacts of imperfect CSI on system performance are studied through both analytical and simulation results.

This paper presents a low complexity soft-interference cancellation equalizer (SICE) for the turbo detection of multiple-input multiple-output (MIMO) systems operating in time dispersive channels. The SICE contains three time-invariant linear filters: a feedforward filter, a causal feedback filter and an anti-causal feedback filter. The feedforward filter is designed to suppress the intersymbol interference (ISI) due to time dispersive channels and the multiplexing interference from multiple transmit antennas. The causal (or anti-causal) feedback filter is developed to remove the residual interference caused by the symbols transmitted before (or after) the symbol under detection. The performance of the proposed SICE is verified through both extrinsic information transfer chart (EXIT) analysis and computer simulations. The analytical and simulation results demonstrated that the inclusion of the anti-causal soft decision during SICE is critical to the system performance. The EXIT chart analysis shows that the SICE performance approaches the ideal matched filter bound as the iteration progresses.

A new distributed joint source-channel code (DJSCC) is proposed for a communication network with spatial-temporally correlated Markov sources. The DJSCC is performed by puncturing the information bits of a systematic linear block code but leaving the parity bits intact, and transmitting the information and parity bits with unequal energy allocations. At the receiver, the spatial data correlation is exploited with a new multi-codeword message passing (MCMP) decoding algorithm. The MCMP decoder performs decoding by exchanging information between codewords from correlated sources, whereas conventional message passing (MP) algorithms exchanges soft information only inside a codeword. The inter-codeword soft information exchange of MCMP yields additional performance gains over the MP algorithm. In recognition that the signals at the receiver are distorted observations of the Markov source and thus can be modeled by a hidden Markov model (HMM), we propose to exploit the temporal data correlation by adding a HMM decoding module to the MCMP decoder. The HMM decoder iteratively exchanges soft information with the MCMP decoder, and this results in significant performance gains over conventional systems.

Energy efficiency (EE) and spectral efficiency (SE) feature one of the most fundamental tradeoffs in communication systems. In this paper, we propose an optimum transmission scheme for coded Type-I automatic repeat request (ARQ) systems to balance the EE-SE tradeoff. The optimization is performed with respect to a new design metric, the normalized EE (NEE), which is defined as the energy per bit normalized by the SE. Minimizing NEE means either reducing the energy per bit or increasing the SE, it thus yields a balanced tradeoff between the two. The system design incorporates a wide range of practical system parameters, such as circuit power, modulation, coding, and detection errors in the physical layer, and frame length and protocol overhead in the media access control layer. Under the constraints of fixed modulation level and data rate, the optimum transmission energy and frame length are identified as closed-form expressions of the system parameters. Simulation results show that minimizing the NEE instead of energy per bit almost doubles the SE with less than 1 dB loss in EE.

A two-stage list sphere decoding (LSD) algorithm is proposed for under-determined multiple-input multiple-output (UD-MIMO) systems that employ N transmit antennas and M<;N receive antennas. The two-stage LSD algorithm exploits the unique structure of UD-MIMO systems by dividing the N detection layers into two groups. Group 1 contains layers 1 to M that have similar structures as a symmetric MIMO system; while Group 2 contains layers M+1 to N that contribute to the rank deficiency of the channel Gram matrix. Tree search algorithms are used for both groups, but with different search radii. A new method is proposed to adaptively adjust the tree search radius of Group 2 based on the statistical properties of the received signals. The employment of the adaptive tree search can significantly reduce the computation complexity. We also propose a modified channel Gram matrix to combat the rank deficiency problem, and it provides better performance than the generalized Gram matrix used in the Generalized Sphere-Decoding (GSD) algorithm. Simulation results show that the proposed two-stage LSD algorithm can reduce the complexity by one to two orders of magnitude with less than 0.1 dB degradation in the Bit-Error-Rate (BER) performance.

In this paper, a frequency-domain cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed for the up-links of broadband wireless networks with asynchronous users. The collision tolerance is achieved with a frequency-domain on-off accumulative transmission (FD-OOAT) scheme, where the spectrum is divided into a large number of orthogonal sub-channels, and each symbol is transmitted over a small subset of the sub-channels to reduce collisions. Such a radio resource management scheme renders a special signal structure that enables multi-user detection (MUD) in the physical layer to resolve the collisions at the MAC layer. Most existing MUDs require precise symbol level synchronization among users. The proposed scheme, however, can operate with asynchronous users. A new theoretical framework is provided to study the impacts of time-domain user delays on system performance. Both analytical and simulation results demonstrate that the proposed FD-OOAT structure with time-domain oversampling is robust to user delays and the timing phase offset caused by the sampling clock difference between the transmitter and the receiver. It is shown that the proposed scheme can achieve significant performance gains, in terms of both the number of users supported and the normalized throughput.

In this paper, a cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed for wireless networks. Unlike conventional MAC schemes that discard and retransmit signals colliding at a receiver, the CT-MAC extracts the salient information from the colliding signals with a new on-off accumulative transmission (OOAT) scheme in the physical layer. Users employing OOAT deliver information to the base station (BS) through uncoordinated on-off transmissions of multiple identical sub-symbols (accumulative transmission). Silence periods are inserted between sub-symbols inside a frame to reduce collision probability and render a special signal structure for physical layer detection. Algebraic properties of the on-off transmission patterns, which are represented as cyclic-shifted binary vectors, are analyzed, and the results provide guidelines on the design of OOAT systems and other systems that rely on cyclic-shifted binary vectors. Then, we demonstrate that the structure of the on-off transmission patterns enables a sub-optimum iterative detection method, which improves performance by iteratively exchanging extrinsic soft information between a forward and a backward soft interference cancellation (SIC). Both analytical and simulation results show that the new CT-MAC with OOAT scheme significantly outperforms many existing cross-layer MAC schemes in terms of the number of users supported and the normalized throughput.

A modified FSD algorithm is proposed for under-determined (UD) multiple-input multiple-output (MIMO) systems with N transmit antennas and M < N receive antennas. This paper focuses on the low-complexity detection of coded UD-MIMO systems with iterative turbo detection, where a soft-input soft-output (SISO) MIMO detector exchanges soft information with a SISO decoder. In the first iteration, a modified fixed complexity sphere decoding (FSD) method is developed by utilizing the structure of a UD-MIMO system. The modified FSD employs a new detection ordering scheme that has a lower complexity but a better performance compared to the conventional ordering scheme. From the second iteration and beyond, the MIMO detector is implemented with a generalized serial interference cancelation (GSIC) scheme and a block decision feedback equalizer (BDFE) to further reduce the complexity. Simulation results show that the newly proposed FSD-GSIC-BDFE structure can achieve significant performance gains over existing schemes, especially for systems with high level modulations.

The energy efficient design of coded automatic-repeat-request (ARQ) systems is studied in this paper. The optimization aims to minimize the energy required for the successfully delivery of one information bit from a transmitter to a receiver. The design is performed by incorporating a wide range of practical system parameters and metrics, such as hardware power consumption, modulation, channel coding, and frame error rate (FER) in the physical layer, and frame length and protocol overhead in the media access control layer. A new log-domain threshold approximation method is proposed to analytically quantify the impacts of the various system parameters on the FER, and the results are used to facilitate the system design. The optimum transmission energy and frame length that minimize the energy per information bit are identified in closed-form expressions as functions of the various practical system parameters. The analytical and simulation results demonstrate that the total energy consumption in a coded ARQ system can be reduced by increasing the transmission energy during one transmission attempt, and significant energy saving as high as 9.5 dB is achieved with the optimum system.

A cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed in this paper. In the MAC layer, each user transmits multiple weighted replicas of a packet at randomly selected data slots in a frame, and the indices of the selected slots are transmitted in a special collision-free position slot at the beginning of each frame. Collisions of the data slots in the MAC layer are resolved by using multiuser detection (MUD) in the physical (PHY) layer. The MUD is performed by employing a modified message passing (MP) algorithm, which treats the MAC structure as a bipartite graph, with each unique packet denoted as a message node (MN), and each slot denoted as a slot node (SN). The graph is simplified by removing the nodes with 0 or 1 connection to reduce the complexity of the MP algorithm. Simulation results demonstrate that the proposed CT-MAC achieves significant performance gains over existing cross-layer MAC schemes. It can support as many as N = 2.4M simultaneous users for a system with M slots per frame, yet most existing schemes can only operate with N ≤ M.

A new Slepian-Wolf coded cooperation scheme is proposed for a cognitive radio network with two secondary users (SUs) performing cooperative spectrum sensing through a fusion center (FC). The SUs sense the spectrum by measuring the energy statistics of the received signals. The measured energy statistics are quantized with a Lloyd-Max quantizer at the SUs and forwarded to the FC, which then performs soft combining over the quantized information. This is different from most previous works that forward hard decisions based on local sensing results at the SUs. Due to the wireless nature of the channel, signals transmitted by one SU to the FC will also be observed by the other SU, which can cooperate with the transmitting SU by relaying the observed signals to the FC. In recognition of the strong correlation between the signals observed at the FC and the relay SU, a new asymmetric Slepian-Wolf code is employed at the relay SU to reduce the amount of cooperation information, thus to improve the cooperation efficiency. In addition, we propose to unequally allocate energy among the coded bits to compensate the energy loss due to the redundancy introduced by the coded cooperation, and this yields better performance compared to conventional equal energy coding schemes. Simulation results demonstrate that the proposed cooperative spectrum sensing scheme operating in practical fading channels can achieve a performance that is almost identical to the ideal case with soft combining performed over unquantized energy statistics transmitted through distortion-free channels.

In this paper, we propose a new cross-layer technique that utilizes frequency-domain on-off accumulative transmission (OOAT) in the physical layer to achieve collision-tolerance in the media access control (MAC) layer. The frequency-domain OOAT is developed for wideband systems operating in frequency-selective fading channels. The available spectrum is divided into a large number of orthogonal non-overlapping sub-channels. To achieve collision tolerance, each symbol is transmitted over a set of randomly chosen sub-channels to reduce the probability of collision. Spreading the signal over the frequency-domain also enables frequency diversity, and further improves system performance. Performance of the proposed scheme is analyzed and a performance bound, matched filter bound, is derived. Simulation results show that the proposed scheme can support more active users simultaneously than sub-channels, and it achieves a higher spectral efficiency compared to conventional MAC schemes.

This paper presents optimum multi-hop transmission strategies (MHTS) for energy constrained wireless sensor networks (WSNs). Nodes in a multi-hop WSN need to transmit their own information and to relay each other's information to a base station (BS), and there are usually multiple available paths between a node and the BS. The optimum MHTS derived in this paper answers three questions: 1) how should a node divide its limited energy between the transmission of the self-information and the relay-information? 2) whether a single path or a combination of multiple paths should be used to route the information from a node to the BS? and 3) if multi-path routing is used, how should a single data stream be divided among the multiple paths? The answers to these questions are obtained by minimizing the energy per bit, or equivalently, by maximizing the amount of information delivered to the BS under certain energy constraints. Two different scheduling strategies are considered, the fair equal information strategy that requires all the nodes deliver the same amount of information to the BS, and the unfair maximum information strategy that maximizes the total amount of information delivered to the BS. The optimum MHTS for these two strategies are derived, with either convex optimization or analytical expressions, under a per node energy constraint and a total energy constraint, respectively.

The optimum sensor node density for one- and two-dimensional (1-D and 2-D) wireless sensor networks (WSNs) with spatial source correlation is studied in this paper. The WSN attempts to reconstruct a spatially correlated signal field by collecting the location-dependent measurements from the distributed sensor nodes. The WSN is designed to minimize the mean square error (MSE) distortion between the original and the reconstructed signals under the constraint of a fixed power per unit area. The impacts of node density and spatial data correlation on the network performance are investigated for both small networks with finite number of nodes, and large networks with infinite area, infinite number of nodes, but finite node density through asymptotic analysis. The interactions among the various network parameters and their impacts on the system performance are quantitatively identified with exact analytical expressions, many of which are in closed-forms. The results provide guidelines on the design of practical WSNs.

The optimum space and time sampling in a wireless sensor network (WSN) with spatial-temporally correlated data is studied in this paper. The impacts of the node density in the space domain and the sampling rate in the time domain on the network performance are investigated asymptotically by considering a large network with infinite area, infinite time period, but finite node density and finite temporal sampling rate. Two cases are studied under the constraint of fixed power per unit area. The first case investigates the estimations of the space-time samples collected by the sensors, and the samples are discrete in both the space and time domains. The second case estimates an arbitrary data point on the space-time plane, by interpolating the discrete samples collected by the sensors. The interactions among the various network parameters and their impacts on the system performance are quantitatively identified with exact analytical expressions.

Random on-off accumulative transmission (R-OOAT) is a cross-layer technique that can achieve collision-tolerance in the media access control (MAC) layer by leveraging on the signal processing capability in the physical (PHY) layer. In this paper, a new PHY/MAC cross-layer design is proposed for the R-OOAT framework. In the PHY layer, we propose an iterative method for the detection of R-OOAT signals colliding at the receiver, such that the transmitted information can be recovered with a low complexity in the presence of severe signal collisions. The iterative detection is enabled by the unique signal structure of the R-OOAT, and it can operate in both coded and uncoded systems. In the MAC layer, the R-OOAT scheme uses silence periods inside a frame to achieve collision-tolerance, which is different from most conventional MAC schemes that rely on random intervals between frames to reduce collision. The theoretical spectral efficiency of R-OOAT is analyzed with the PHY/MAC operations. Analytical and simulation results show that the proposed cross-layer design can support more users and achieve a much higher spectral efficiency compared to conventional MAC schemes.

In this paper, a new cooperative spectrum sensing algorithm is proposed for a cognitive radio network with multiple secondary users (SUs) sharing spectrum with one or more primary users (PUs). Unlike most previous spectrum sensing algorithms that do not consider the time domain traffic statistics of the PU, the algorithm in this paper is developed by exploiting the statistical properties of the PU's transmission pattern, which is modeled with a Markov chain with two states: busy (1) and idle (0). Each SU performs energy detection based on an observation of the Markov chain, and the detection results are forwarded to a fusion center (FC) through a noisy channel. The FC recovers the decisions of the SUs by using a new progressive maximum a posteriori (MAP) algorithm, where the a priori probability essential to the MAP detection is obtained by progressively estimating the transition probabilities of the Markov chain. Analytical expressions are derived for the probabilities of false alarm and missing detection, with both the majority data fusion rule and the OR data fusion rule. Both theoretical analysis and simulation results indicate that the proposed algorithm can provide reliable and efficient spectrum sensing over a large range of system configurations.

The turbo detection of a coded under-determined multiple-input multiple-output (UD-MIMO) system is studied in this paper. A UD-MIMO system with N transmit antennas and M <; N receive antennas has more unknowns than the observations. However, most existing low complexity detectors, such as the vertical Bell Laboratories Layered Space-Time (V BLAST) or the block decision feedback equalizer (BDFE), work only when M ≥ N. We propose a new turbo detector structure that combines the existing generalized parallel interference cancellation (GPIC) technique and a new generalized serial interference cancellation (GSIC) technique, with a soft-input soft output (SISO) BDFE. In the first iteration, the GPIC-BDFE equalization is adopted. From the second iteration and beyond, due to the availability of the a priori soft information, the UD MIMO system can be partitioned into multiple sub-systems by using a reliability-based partition scheme. Then, the GSIC-BDFE equalization, which has a much lower complexity than the GPIC BDFE equalization, can be used. Simulation results show that the new detection scheme achieves significant performance gain as the iteration progresses with a reasonable complexity.

A new ultra-low power (ULP) wireless sensor network (WSN) structure is proposed to monitor the vibration properties of civil structures, such as buildings and bridges. The new scheme integrates energy harvesting, data sensing, and wireless communication into a unified process, and it is fundamentally different from all the existing WSNs. In the new WSN, piezoelectric sensors are employed to harvest vibration energy and measure vibration intensity simultaneously, by utilizing the fact that the harvested energy accumulated through time is proportional to the vibration amplitude and frequency. Once the harvested energy reaches a threshold, it is released as an impulse with a wireless transmitter. An estimate of the structure vibration intensity can then be obtained by measuring the intervals between the binary impulses. Such an approach does not require complicated analog-to-digital conversion or signal processing, and it can achieve an ULP performance unrivaled by existing technologies. Optimum and sub-optimum impulse density estimation algorithms are proposed for the FC to take advantage of the spatial correlation among the sensors. Exact analytical expressions of the optimum estimation mean square error (MSE) are derived. Simulation and analytical results demonstrate that the proposed scheme can achieve a MSE of 5× 10-5at a signal-to-noise-ratio of -8 dB for a 10-node WSN.

Spectral efficiency is a key characteristic of cellular communications systems, as it quantifies how well the scarce spectrum resource is utilized. It is influenced by the scheduling algorithm as well as the signal and interference statistics, which, in turn, depend on the propagation characteristics. In this paper we derive analytical expressions for the short-term and long-term channel-averaged spectral efficiencies of the round robin, greedy Max-SINR, and proportional fair schedulers, which are popular and cover a wide range of system performance and fairness trade-offs. A unified spectral efficiency analysis is developed to highlight the differences among these schedulers. The analysis is different from previous work in the literature in the following aspects: (i) it does not assume the co-channel interferers to be identically distributed, as is typical in realistic cellular layouts, (ii) it avoids the loose spectral efficiency bounds used in the literature, which only considered the worst case and best case locations of identical co-channel interferers, (iii) it explicitly includes the effect of multi-tier interferers in the cellular layout and uses a more accurate model for handling the total co-channel interference, and (iv) it captures the impact of using small modulation constellation sizes, which are typical of cellular standards. The analytical results are verified using extensive Monte Carlo simulations.

A new oversampled orthogonal frequency division multiplexing (OOFDM) scheme is proposed for doubly selective fading environment. The proposed OOFDM scheme employs oversampling in the time domain and linear processing in the frequency domain, both at the receiver without changing the structure of conventional orthogonal frequency division multiplexing (OFDM) transmitters. The time-frequency processing enables a two dimensional Doppler-frequency grid of the fading channel, such that each data symbol is modeled equivalently as being transmitted at multiple subcarriers and various Doppler spreads simultaneously, while retaining the same spectral efficiency as conventional OFDM systems. Optimum combining ensures coherent combining of the data samples spread over the Doppler-frequency grid and non-coherent combining of inter-carrier interference (ICI) components caused by time varying fading. Theoretical error probabilities of the OOFDM systems are derived by analyzing the statistical properties of Doppler-frequency fading coefficients and noise sample correlations. Both theoretical analysis and computer simulation show that the new system with an oversampling factor of two outperforms the conventional OFDM system by as much as 7 dB.

This paper proposes a reliability-based turbo detection scheme for multiple-input multiple-output (MIMO) systems with frequency-selective fading. The proposed scheme performs iterative successive soft interference cancellation (SSIC) with a block decision-feedback equalizer (BDFE). To minimize the negative impacts of error propagation in SSIC, we propose a new group-wise reliability-based ordering scheme, where neighboring symbols that severely interfere with each other are clustered into the same group, and for each group, more "reliable" symbols are detected before less "reliable" ones. The symbol reliability is measured by using the symbol a priori probability, which is a unique byproduct of the turbo detection and can be obtained with little overhead. The reliability information is updated iteratively as the turbo detection progresses, and this leads to a dynamic ordering scheme that is unavailable in conventional ordered successive interference cancellation (OSIC) schemes. Simulation results show that extra performance gain is obtained at a very small ordering cost, and the reliability-based turbo detection can achieve a performance that is only 0.5 dB away from the optimum maximum a posteriori probability (MAP) detection.

In this paper, a random on-off accumulative transmission (R-OOAT) scheme is proposed to achieve collision-tolerant (CT) media access control (MAC) for asynchronous wireless sensor networks. Unlike conventional MAC schemes that discard packages with collisions at receivers, the CT-MAC extracts the salient information from the colliding signals by using the R-OOAT scheme in the physical layer. Nodes employing the R-OOAT deliver information to a base station through asynchronous on-off transmission of multiple identical sub-symbols at random positions. The R-OOAT improves the deterministic OOAT (D-OOAT) proposed in [1], where sub-symbols are transmitted at deterministic positions. Compared to the D-OOAT, the R-OOAT scheme achieves a smaller collision probability and supports more simultaneous users, while it inherits all the advantages of the D-OOAT. Design guidelines of the R-OOAT system are presented for given parameters, such as the probability of collisions and the maximum number of supported users. It is demonstrated by simulation results that the new CT-MAC with R-OOAT scheme can operate at the presence of severe signal collision.

The optimum sensor node density in a large linear wireless sensor network with spatial source correlation is studied. Unlike most previous works that rely on the design metric of network capacity with an error-free communication assumption, this paper performs analysis under a distortion-tolerant communication framework, where controlled distortion in the recovered information is allowed as long as the information can be recovered beyond a certain fidelity. The impacts of node density and spatial data correlation on the information distortion are investigated asymptotically by considering a large network with infinite area, infinite node numbers, but finite node density. Under fixed energy per unit area, it is discovered that: 1) for applications that only need to recover data at discrete locations, placing exact one sensor at the desired measurement locations will generate the optimum performance; 2) for applications that need to recover data at arbitrary locations in the measurement field, the optimum node density is a function of the spatial data correlation.

This paper proposes a low-complexity turbo block decision feedback equalizer (BDFE) with reliability-based successive soft interference cancellation (SSIC) for multiple-input multiple-output (MIMO) systems. Turbo equalization in MIMO systems needs to combat both intersymbol interference (ISI) due to frequency-selective fading and multiplexing interference (MI) among data streams transmitted by different antennas. The proposed MIMO BDFE performs SSIC on both ISI in the time-domain and MI in the space-domain. For an SSIC receiver, the order in which the symbols are detected and cancelled is critical to the system performance, yet it is often overlooked in the literature. We propose a new reliability-based detection ordering scheme, where symbols with higher a priori reliability are detected before those with lower a priori reliability. The reliability information can be obtained from the a priori probability at the input of the soft-decision equalizer. The reliability-based ordering scheme is enabled by the turbo equalization structure, and it yields an extra equalization gain that is unavailable in a conventional equalization system. Furthermore, the adoption of non-linear BDFE in MIMO systems enables a low-complexity sequence-based log-likelihood ratio (LLR) evaluation, which is superior to the symbol-based LLR evaluation method employed by most other low-complexity turbo equalizers.

In this paper, a cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed to achieve reliable low power communications for one-hop asynchronous wireless sensor networks (WSNs). Unlike conventional MAC schemes that discard and retransmit signals colliding at a receiver, the CT-MAC extracts the salient information from the colliding signals by leveraging on the signal processing capability in the physical layer with a new on-off accumulative transmission (OOAT) scheme. Nodes employing OOAT delivers information to a base station (BS) through asynchronous duty-cycled transmission (on-off transmission) of multiple identical sub-symbols (accumulative transmission). The on-off transmission reduces collision probability at the BS, and the accumulative transmission in the time-domain enables the simultaneous detection of colliding signals in the spatial-domain. An optimum maximum likelihood sequence estimation detector with time-varying trellis structure is employed by the BS to perform detection over the colliding signals.

A new layered frequency-domain turbo equalization (LFDTE) scheme is proposed for single carrier (SC) multiple-input multiple-output (MIMO) systems. The proposed scheme combines the respective advantages of layered detection and turbo equalization to further lower the bit error rate (BER) for high-data-rate communications. Different from the traditional frequency-domain turbo equalization (FDTE) method for MIMO systems, our new detection algorithm employs a layered structure in each turbo iteration, and at each layer a group of best data streams is detected in the form of soft symbols and then canceled from the received signals in frequency domain. The extrinsic information for the detected coded bits gleaned by the current layer is fed back to the previous layers as a-priori information utilized for equalization in the next iteration. Since the frequency-domain equalization (FDE) is performed in each frequency bin, the proposed receiver scheme has similar computational complexity compared to the traditional FDTE, but achieves better performance of BER. Simulation results demonstrate that the new proposed scheme outperforms the traditional FDTE without layered detection at the same number of iterations, and the performance becomes better if multiple layers are used in the detection.

We propose an enhanced linear minimum mean square error (LMMSE) turbo equalization scheme for multiple-input multiple-output (MIMO) communication systems. The turbo equalizer employs soft interference cancellation (SIC), where tentative soft decisions of the interfering symbols are subtracted from the received samples before the LMMSE filtering. The enhanced LMMSE turbo equalization with SIC introduces two improvements over existing methods. First, unlike existing methods that completely rely on the a priori soft decisions at the equalizer input for SIC, the new scheme performs SIC by continuously updating the soft decisions with the a posteriori information of the symbols that have just been equalized. The a posteriori soft decision has a better quality than the a priori soft decision, thus it leads to a better SIC performance. Second, since the a posteriori soft decision of an equalized symbol will affect the SIC operation of all the subsequent symbols to be equalized, the order in which the symbols are equalized and detected plays a critical role on the overall performance. We propose a reliability-based detection ordering scheme, where symbols with more "reliable" soft input will be equalized before those less "reliable" symbols. The reliability information is extracted from the symbol a priori probability, which is a unique byproduct of turbo equalization and can be obtained with minimum extra cost. Simulation results demonstrate that the proposed scheme achieves considerable performance gain over the conventional turbo equalization methods.

The connectivity properties of a mobile linear network with high speed mobile nodes, dynamic node population, and a strict delay constraint are investigated. A new mobility model is developed to represent the steady state node distributions in terms of random node location and random node population. The model accurately captures the statistical properties of random node arrival, time-varying node speed, and the distinct behaviors of nodes following different traffic patterns. With the new mobility model, the statistical properties of network connectivity are studied and identified. Unlike most previous works that do not consider the impacts of transmission latency, which is critical for real time applications, this paper identifies the quantitative relationship between network connectivity and delay constraint by placing a bound on the maximum transmission distance. The connectivity analysis is performed with a novel geometry-assisted analytical method. Exact connectivity probability expressions are developed by using the volumes of a hypercube intersected by a hyperplane. Results presented in this paper provide insight on the design and operations of mobile linear networks, such as a vehicular ad hoc network (VANET) that has rapidly changing network topology and node density.

A new soft-decode-and-forward (SDF) relay strategy for asynchronous wireless networks is presented in this paper. Unlike most previous works that assume simplified or idealized channel conditions, the new SDF relay strategy is designed to operate in practical asynchronous wireless networks with doubly-selective (both time-selective and frequency selective) fading. To combat the impairments caused by doubly-selective fading, as well as to preserve the correlation between signals delivered by the source and the relay, the relay nodes perform soft decoding, and forward reliability information to destination. A block decision feedback equalizer (BDFE) is employed at relay nodes to extract the soft information from the distorted signals. Distributed orthogonal frequency division multiplexing (OFDM) is adopted to cope with node asynchronism and fading time dispersion. Distributed OFDM systems in the literature were usually developed based on the assumption of perfect decoding at relay nodes, and such unrealistic assumption is not required by the new SDF relay strategy. Simulation results show that the new SDF method can flexibly adapt to the dynamic signal qualities at relay nodes, and it always outperforms the decode-and-forward relay strategy with hard decoding at relay.

The connectivity properties of a mobile linear network with high speed mobile nodes and strict delay constraint are investigated. A new mobility model is developed to represent the steady state node distribution, and it accurately captures the statistical properties of random node arrival, time-varying node speed, and the distinct behaviors of nodes following different traffic patterns.With the mobility model, the statistical properties of network connectivity are studied and identified. Unlike most previous works that do not consider the impacts of transmission latency, which is critical for real time applications, this paper identifies the quantitative relationship between network connectivity and delay constraint. The results are applicable to both delay constrained networks and delay tolerant networks. The connectivity analysis is performed with a novel geometry-assisted analytical method. Exact connectivity probability expressions are developed by using the volumes of a hypercube intersected by a hyperplane, and a hyperpyramid. The geometry-assisted analytical method significantly simplifies the connectivity analysis.

Channel estimation for orthogonal frequency division multiplexing (OFDM) system at the presence of carrier frequency offset (CFO) and phase noise is discussed in this paper. A CFO estimation algorithm is developed by exploiting the time-frequency structure of training symbols, and it provides a very accurate estimation of CFO at the presence of both unknown frequency selective fading and phase noise. Based on the estimated CFO, the phase noise and frequency selective fading are jointly estimated by employing the maximum a posteriori (MAP) criterion. Specifically, the fading channel is estimated in the form of frequency domain channel transfer function (CTF). The estimation of CTF eliminates the requirement of the priori knowledge of channel length, and it is simpler compared to the time domain channel impulse response (CIR) estimation method in the literature. Theoretical analysis with Cramer-Rao lower bound demonstrates that the joint phase noise and CTF estimation can achieve near optimum performance.

A new proportionally sampled multicarrier modulation (PSMCM) system with optimum receiver is presented in this paper. By adopting different sampling rates at transmitter and receiver, the PSMCM system can achieve multipath diversity with simple linear operations. It has the same spectral efficiency as conventional orthogonal frequency division multiplexing (OFDM) system, while doesn't require complex precoder/decoder as used by most other multicarrier systems for the fulfillment of multipath diversity. In addition, it is insensitive to timing phase offset caused by the phase difference between transmitter clock and receiver clock, thus eliminates the need of precise sample level synchronization as required by conventional OFDM system. Exact bit error rate expression of the new system is derived. Both analytical and simulation results show that PSMCM system outperforms OFDM system with the same spectral efficiency by as much as 10 dB.

A new oversampled orthogonal frequency division multiplexing (OFDM) system operating in doubly selective fading environment is proposed in this paper. The oversampled OFDM structure employs oversampling in time domain and linear signal processing in frequency domain. The time-frequency processing enables a two dimensional Doppler-frequency grid that allows each data symbol be transmitted at multiple subcarriers and various Doppler spreads simultaneously, without sacrificing spectral efficiency. As a result, both Doppler diversity and multipath diversity inherent in doubly selective fading are achieved with simple linear operations. A Doppler domain block decision feedback equalizer is designed to suppress the intercarrier interference (ICI) caused by time varying fading, as well as to collect the diversity benefits enabled by the oversampled OFDM system. Simulation results show that the new system outperforms conventional OFDM system with the same spectral efficiency by as much as 8 dB.

A low complexity soft-input soft-output (SISO) block decision feedback equalizer (BDFE) is presented for turbo equalization. The proposed method employs a sub-optimum sequence-based detection, where the soft-output of the equalizer is calculated by evaluating an approximation of the sequence-based a posteriori probability (APP) of the data symbol. The sequence-based APP approximation is enabled by the adoption of both soft a priori information and soft decision feedback, and it leads to better performance and faster convergence compared to symbol-based detection methods as used by most other low complexity equalizers. The performance and convergence property of the proposed algorithm is analyzed by using extrinsic information transfer (EXIT) chart. Both analytical and simulation results show that the new equalizer can achieve a performance similar to that of trellis-based equalization algorithms, with a complexity similar to linear SISO minimum mean square error equalizers.

Optimal receiver diversity combining employing linear channel estimation is examined. Based on the statistical properties of least-squares (LS) and minimum mean square error (MMSE) channel estimation, an optimal diversity receiver for wireless systems employing practical linear channel estimation on Rician fading channels is proposed. The new receiver structure includes the conventional maximal ratio combining receiver as a special case. Exact analytical expressions for the symbol error rates (SERs) of LS and MMSE channel estimation aided optimal diversity combining are derived. It is shown that, if an optimal detector is used, an MPSK wireless system with MMSE channel estimation has the same SER when the MMSE channel estimation is replaced by LS estimation. This is an interesting counterexample to the common perception that channel estimation with smaller mean square error leads to smaller SER. Extensive simulation results validate the theoretical results.

Turbo equalization improves communication system performance by iteratively exchanging information between soft-input soft-output (SISO) equalizer and SISO channel decoder. The trellis-based maximum a posteriori probability (MAP) algorithm serves as the optimum SISO equalizer for turbo equalization. However, MAP algorithm is unsuitable for systems with large modulation constellation size and severe inter-symbol interference (ISI) due to its prohibitively high computational complexity. In this paper, an improved SISO block decision feedback equalizer (BDFE) is proposed for low complexity turbo equalization. Unlike other sub-optimum equalizers which perform symbol by symbol detection, the proposed equalizer generates the soft output for each data bit by collecting information from a sequence of samples as in MAP algorithm. The sequence-based equalization is enabled by using not only soft a priori input from channel decoder, but also hard a priori information obtained from BDFE in previous iteration. The combination of soft a priori information and hard a priori information renders better performance with less iterations compared to other sub-optimum algorithms. In addition, the computational complexity of the proposed algorithm is on the same order as conventional SISO BDFE algorithm, and is much lower compared to the trellis-based MAP algorithm.

In this paper, we present a new Doppler spread estimation algorithm for broadband wireless orthogonal frequency division multiplexing (OFDM) systems with time-varying and frequency-selective Rayleigh fading. The algorithm is developed by analyzing the statistical properties of the power of received signals in the time domain, thus it excludes the influence of inter- carrier interference introduced by channel variation within one OFDM symbol. The operation of the algorithm doesn't require the knowledge of fading coefficients, transmitted data symbols, or signal-to-noise ratio (SNR). It works well under time-selective and frequency-selective Rayleigh fading channel with SNR as low as 0 dB. Moreover, unlike existing algorithms, the proposed algorithm takes into considerations of the discrete-time channel inter-tap correlation, as the case in practical systems. Simulation results demonstrate that this new algorithm can accurately estimate a wide range of Doppler spread with low estimation latency and high computational efficiency.

A low complexity soft-input soft-output block decision feedback equalization (BDFE) algorithm is presented for Turbo equalization. Based on minimum mean square error criterion, the feedforward filter and feedback filter of BDFE are adaptively formulated by extracting symbol statistics from soft a priori information. The adoption of a priori information during filter design greatly benefit system performance. Unlike most other low complexity equalization algorithms with symbol-based detection, the proposed algorithm adopts a sub-optimum sequence-based method to evaluate an approximation of data symbol a posteriori probability (APP). The sequence-based APP approximation is enabled by hard a priori information from previous iteration, and it outperforms symbol-based detection method adopted by most other low complexity algorithms.

Accurate system planning and performance evaluation requires knowledge of the joint impact of scheduling, interference, and fading. However, current analyses either require costly numerical simulations or make simplifying assumptions that limit the applicability of the results. In this paper, we derive analytical expressions for the spectral efficiency of cellular systems that use either the channel-unaware but fair round robin scheduler or the greedy, channel-aware but unfair maximum signal to interference ratio scheduler. As is the case in real deployments, non-identical co-channel interference at each user, both Rayleigh fading and lognormal shadowing, and limited modulation constellation sizes are accounted for in the analysis. We show that using a simple moment generating function-based lognormal approximation technique and an accurate Gaussian-Q function approximation leads to results that match simulations well. These results are more accurate than erstwhile results that instead used the moment-matching Fenton-Wilkinson approximation method and bounds on the Q function. The spectral efficiency of cellular systems is strongly influenced by the channel scheduler and the small constellation size that is typically used in third generation cellular systems.

Optimal receiver diversity combining employing linear channel estimation is examined. Based on the statistical properties of pilot-assisted least-squares (LS) and minimum mean square error (MMSE) channel estimation, an optimal diversity receiver for wireless systems employing practical linear channel estimation on Rician fading channels is proposed. Exact analytical expressions for the symbol error rates of LS and MMSE channel estimation aided optimal diversity combining are derived. It is shown that an MPSK wireless system with MMSE channel estimation has the same SER when the MMSE channel estimation is replaced by LS estimation. This is an interesting counter-example to the common perception that channel estimation with smaller mean square error leads to smaller SER. Extensive simulation results validate the theoretical results.

A simple, novel, and general method is presented in this paper for approximating the sum of independent or arbitrarily correlated lognormal random variables (RV) by a single lognormal RV. The method is also shown to be applicable for approximating the sum of lognormal-Rice and Suzuki RVs by a single lognormal RV. A sum consisting of a mixture of the above distributions can also be easily handled. The method uses the moment generating function (MGF) as a tool in the approximation and does so without the extremely precise numerical computations at a large number of points that were required by the previously proposed methods in the literature. Unlike popular approximation methods such as the Fenton-Wilkinson method and the Schwartz-Yeh method, which have their own respective short-comings, the proposed method provides the parametric flexibility to accurately approximate different portions of the lognormal sum distribution. The accuracy of the method is measured both visually, as has been done in the literature, as well as quantitatively, using curve-fitting metrics. An upper bound on the sensitivity of the method is also provided.

The theoretical error performance of double space time transmit diversity (DSTTD) system with optimum combining receiver is analyzed in this paper. By employing both spatial multiplexing and transmit diversity in one system, DSTTD provides practical tradeoff between system spectral efficiency and diversity gain. We derive exact analytical expressions to describe the symbol error rate for DSTTD systems. The effects of both diversity gain and antenna interference introduced by spatial multiplexing are quantified in the results. In addition, the performance of DSTTD system with successive interference cancellation is also investigated. Simulation results are in excellent agreement with the theoretical results obtained in this paper.

Receiver timing phase is one of the essential factors defining the performance of wireless communication systems. In this paper, we investigate the effects of timing phase offset, which is introduced by the phase difference between the transmitter clock and receiver clock, on the performance of wireless systems over frequency selective fading. With frequency domain analysis, the instantaneous signal-to-noise ratio (SNR) observed by the communication receiver is expressed as an explicit function of system timing phase offset and receiver oversampling factor. A tight performance lower bound, which corresponds to the best possible system performance under particular system configuration, is then derived by examining the statistical properties of the receiver SNR. From the analytical results, it is observed that, if the receiver sampling rate is less than the Nyquist rate of the received signal, then the system performance lower bound is a periodic function of the timing phase offset. On the other hand, the best possible performance of the oversampled system is independent of timing phase offset. Moreover, the oversampled system can use a receive filter matched to the time-invariant transmit filter instead of a statistical filter matched to the joint response of channel and transmit filter without affecting the best possible system performance. Simulation results show that the theoretical bound derived in this paper can accurately predict the performance of practical communication systems suffering from both frequency selective fading and timing phase offset

Doppler effect induced by temporal fading variation provides Doppler diversity, which can be exploited to combat the impairments caused by multipath fading. In this paper, a Doppler domain multiplexing (DDM) communication structure is presented to explore the potential Doppler diversity. Data streams in DDM system are encoded and multiplexed at different Doppler shifts to achieve both Doppler diversity and high spectral efficiency. Theoretical performances of both uncoded and coded DDM systems are analyzed. The analytical results show that DDM structure can effectively exploit the potential Doppler diversity inherent in time- varying fading. A sub-optimum decoder with Doppler domain equalization and soft maximum a posteriori decoding is employed to recover the data at the communication receiver. Simulation results show that significant performance gains can be achieved by DDM system over conventional time domain system.

A simple and novel method is presented to approximate by the lognormal distribution the probability density function of the sum of correlated lognormal random variables. The method is also shown to work well for approximating the distribution of the sum of lognormal-Rice or Suzuki random variables by the lognormal distribution. The method is based on matching a low-order Gauss-Hermite approximation of the moment-generating function of the sum of random variables with that of a lognormal distribution at a small number of points. Compared with methods available in the literature such as the Fenton-Wilkinson method, Schwartz-Yeh method, and their extensions, the proposed method provides the parametric flexibility to address the inevitable trade-off that needs to be made in approximating different regions of the probability distribution function.

We derive exact closed-form expressions for the system-level theoretical spectral efficiency of cellular radio systems that use channel-aware schedulers and operate in the presence of co-channel interference and noise. The co-channel interferers are not identically distributed, as is the case in typical cellular layouts. Accounting for non-identical interferers avoids the loose spectral efficiency bounds in the literature that only look at the worst case and best case locations of identical co-channel interferers. It also enables including the effect of second-tier interferers in the cellular layout, and leads to analytical results that are in excellent agreement with the simulation results. The spectral efficiencies of the greedy Max-SINR and the fair round-robin scheduler are compared. The detrimental effect of using small modulation alphabet sizes, as is the case in second and third generation cellular standards, is also quantified.

A simple and novel method is presented to approximate the distribution of the sum of independent, but not necessarily identical, lognormal random variables, by the lognormal distribution. It is shown that matching a short Gauss-Hermite approximation of the moment generating function of the lognormal sum with that of the lognormal distribution leads to an accurate lognormal sum approximation. The advantage of the proposed method over the ones in the literature, such as the Fenton-Wilkinson method, Schwartz-Yeh method, and the recently proposed Beaulieu-Xie method, is that it provides the parametric flexibility to handle the inevitable trade-off that needs to be made in approximating different regions of the probability distribution function. The accuracy is verified using extensive simulations based on a cellular layout

The sampler timing (phase) sensitivity of wireless communication systems is discussed in this paper. Based on the matched filter bound technique, a tight error performance bound is derived for systems experiencing frequency selective Rayleigh fading, with the receiver timing offset being quantified in the error performance expressions. With the error performance bound, the timing phase sensitivity of systems with both symbol spaced receivers and fractionally spaced receivers is analyzed. Simulation results show that the new bound can accurately predict the performance of practical communication systems suffering both frequency selective fading and timing phase offset

An adaptive space time transmit diversity scheme with simple feedback is proposed for the next generation wireless communication systems. By combining orthogonal space time block codes with adaptive sub-group antenna encoding, this new diversity scheme can effectively exploit the diversity potential provided by multiple antenna arrays without introducing interference among the signals transmitted at different antennas. In order to reduce the amount of feedback information as well as the computational complexity, a new quadrant phase constraining method is introduced for the computation of the feedback information. With simple operations at both the transmitter and the receiver, the new adaptive diversity scheme outperforms not only open loop space time block encoding techniques, but also some closed loop transmit diversity techniques with the same amount of feedback.

Theoretical error performances of communication systems with doubly selective (time-varying and frequency-selective) fadings and fractionally spaced (oversampled) receivers are analyzed. Closed-form error probability expressions of MPSK, MASK and MQAM systems are derived as tight lower bounds of the symbol error probabilities. The effects of receiver oversampling, Doppler spread and fading power delay profile are quantified in the error probability expressions. Simulation results show that the new analytical results can accurately predict the error performances of MLSE and MAP equalizers in a wide range of SNR. Moreover, it is discovered that significant performance gain can be achieved by fractionally spaced receivers over symbol spaced receivers for systems experiencing fast time-varying fading, whereas the effects of Doppler spread are overlooked by most previous works.

The performances of coherent diversity receivers with noisy channel estimation are examined. Fading channel gain estimates are modeled as sums of the true fading channel gain values plus independent Gaussian distributed estimation errors. The optimal diversity receiver for coherent reception with noisy channel state information and independent and identically distributed fading channels is derived. Exact expressions for the average error probability of optimal diversity MPSK with noisy channel estimation are derived for Rayleigh and Ricean fading channels; closed-form expressions are obtained for some special cases. Some interesting observations regarding practical diversity receiver design for higher-order modulation formats are drawn.

An adaptive transmit diversity scheme with quadrant phase constraining feedback is proposed in this paper. With simple linear operations at both transmitter and receiver, the proposed algorithm can achieve better system performances with only 2M-2 bits of feedback information for systems with M transmit antennas. Theoretical performance bounds of the proposed transmit diversity scheme are derived. Simulation examples and theoretical analyses show that the proposed transmit diversity scheme outperforms not only the conventional open-loop transmit diversity techniques, but also some closed-loop transmit diversity techniques with more information transmitted in the feedback channel.

A statistical discrete-time model is proposed for simulating wideband multiple-input multiple-output (MIMO) fading channels which are triply selective due to angle spread, Doppler spread, and delay spread. The new discrete-time MIMO channel model includes the combined effects of the transmit filter, physical MIMO multipath channel fading, and receive filter, and it has the same sampling period as that of the MIMO receiver. This leads to very efficient simulation of physical continuous-time MIMO channels. A new method is also presented to efficiently generate the MIMO channel stochastic coefficients. The statistical accuracy of the discrete-time MIMO channel model is rigorously verified through theoretical analysis and extensive simulations in different conditions. The high computational efficiency of the discrete-time MIMO channel model is illustrated by comparing it to that of the continuous-time MIMO channel model. The new model is further employed to evaluate the channel capacity of MIMO systems in a triply selective Rayleigh fading environment. The simulation results reveal some interesting effects of spatial correlations, multipaths, and number of antennas on the MIMO channel capacity.

The channel estimation and equalization for EDGE system with time-varying and frequency-selective fading channels are discussed. It is shown that the fast fading channel during a selected slot in the EDGE system can be modeled as a linear function of time, and a linear least-squares algorithm is proposed to estimate the fading channel. For typical channel profiles of the EDGE system, the channel impulse response is not in its minimum phase form, thus cannot be directly used in computationally efficient equalizers, such as delayed decision feedback sequence estimation or reduced state sequence estimation. To overcome this problem, a Cholesky decomposition-based method is introduced to transform the estimated channel impulse response into its minimum phase form. The simulation results show that the proposed algorithms can effectively combat the time-varying and frequency-selective channel fading with Doppler frequency being in a wide range up to 300 Hz.

In this paper, an accurate and computationally efficient algorithm is proposed for estimating a time-varying and frequency-selective fading channel with unequally spaced pilot symbols. By employing the time-varying coefficient polynomial interpolation method, it is proved that the time-varying channel impulse response can be estimated by the product of a constant interpolation matrix and the fading information at pilot symbol positions. Furthermore, a least square off-line training algorithm is presented to optimally calculate the constant matrix, taking into consideration the statistics of channel fading and noise. Simulation results indicate that the bit error rate performance of our new estimation algorithm is close to that of the perfect channel estimation.

In this paper, a statistical discrete-time model is proposed for simulating wideband MIMO channels which experience spatially and temporally correlated, widesense stationary uncorrelated scattering (WSSUS) multipath Rayleigh fading. A new method is also presented to efficiently generate the correlated MIMO channel coefficients, which can be used for accurate simulation of physical continuous-time MIMO channel. The statistic accuracy of the discrete-time MIMO channel model is rigorously verified through theoretical analysis and extensive simulations in different criteria.