MIMO-OFDM is a key physical-layer architecture for current and next-generation wireless communication. This paper proposes a receive beamforming design scheme for MIMO-OFDM radars configured with partially-connected hybrid linear arrays, in an attempt to improve target parameter (i.e., angle, range, and velocity) estimation. Unlike existing joint estimation methods, our approach first adopts a subspace algorithm to acquire the angle information at the analog beamformer output. Then, maximum signal-to-interference ratio (MSINR) digital beamforming is conducted for target signal alignment to achieve (i) accurate range-velocity estimation thanks to suppressed interference, and (ii) automatic pairing of the identified parameter triple. Moreover, the received signal structure at the analog beamformer output is exploited for obtaining side information of the target range, which can be used to accelerate the search of range and velocity. Simulation results are used to illustrate the effectiveness of the proposed scheme.

Angle-of-arrival (AoA) estimation via hybrid uniform linear arrays is subject to inherent ambiguity incurred by mixing many subarray measurements into just few RF chains. With the aid of non-uniform subarray placement, this paper proposes a low-complexity beam-space MUSIC algorithm capable of achieving ambiguity-free multiple AoA estimation. An ambiguity-free condition, specified by inter-subarray spacings, is derived, leading to various array configurations guaranteeing unique AoA recovery. Simulation results show that our proposed approach compares favorably with an existing temporal-domain based MUSIC method at reduced computational complexity.

Conventional sparse phase retrieval schemes can recover sparse signals from the magnitude of linear measurements only up to a global phase ambiguity. This work proposes a novel approach that instead utilizes the magnitude of affine measurements to achieve ambiguity-free signal reconstruction. The proposed method relies on two-stage approach that consists of support identification followed by the exact recovery of nonzero signal entries. In the noise-free case, perfect support identification using a simple counting rule is guaranteed subject to a mild condition on the signal sparsity, and subsequent exact recovery of the nonzero signal entries can be obtained in closed-form. The proposed approach is then extended to two noisy scenarios, namely, sparse noise (or outliers) and non-sparse bounded noise. For both cases, perfect support identification is still ensured under mild conditions on the noise model, namely, the support size for sparse outliers and the power of the bounded noise. Under perfect support identification, exact signal recovery can be achieved using a simple majority rule for the sparse noise scenario, and reconstruction up to a bounded error can be achieved using linear least-squares (LS) estimation for the non-sparse bounded noise scenario. The obtained analytic performance guarantee for the latter case also sheds light on the construction of the sensing matrix and bias vector. In fact, we show that a near optimal performance can be achieved with high probability by the random generation of the nonzero entries of the sparse sensing matrix and bias vector according to the uniform distribution over a circle. Computer simulations using both synthetic and real-world data sets are provided to demonstrate the effectiveness of the proposed scheme.

Sparse subspace clustering (SSC) relies on sparse regression for accurate neighbor identification. Inspired by recent progress in compressive sensing, this paper proposes a new sparse regression scheme for SSC via two-step reweighted$\ell _{1} $-minimization, which also generalizes a two-step$\ell _{1} $-minimization algorithm introduced by E. J. Candèset al.in [The Annals of Statistics, vol. 42, no. 2, pp. 669–699, 2014] without incurring extra algorithmic complexity. To fully exploit the prior information offered by the computed sparse representation vector in the first step, our approach places a weight on each component of the regression vector, and solves a weighted LASSO in the second step. We propose a data weighting rule suitable for enhancing neighbor identification accuracy. Then, under the formulation of the dual problem of weighted LASSO, we study in depth the theoretical neighbor recovery rates of the proposed scheme. Specifically, an interesting connection between the locations of nonzeros of the optimal sparse solution to the weighted LASSO and the indexes of the active constraints of the dual problem is established. Afterwards, under the semi-random model, analytic probability lower/upper bounds for various neighbor recovery events are derived. Our analytic results confirm that, with the aid of data weighting and if the prior neighbor information is accurate enough, the proposed scheme with a higher probability can produce many correct neighbors and few incorrect neighbors as compared to the solution without data weighting. Computer simulations are provided to validate our analytic study and evidence the effectiveness of the proposed approach.

This work proposes a collaborative sensor caching and data reconstruction method based on the sequential compressed sensing framework. Here, multiple caches are assumed to exist in the wireless sensor network to store the most recent data gathered from sensors within their respective coverage areas. To reduce the cache size and the data-acquisition overhead, each cache accesses measurements only from a small subset of sensors. This work proposes a collaborative sparse-signal reconstruction method that exploits the presence of sensors simultaneously accessible by multiple caches as anchor nodes to introduce dependency in the reconstruction. The reconstruction is based on the use of the alternating direction method of multipliers (ADMM), which enables distributed implementation of the algorithm. Simulations are provided to demonstrate the effectiveness of the proposed scheme.

This work proposes efficient methods for the detection of malicious crowdsourcing workers using only privacy-aware group queries. In the proposed system, the crowdsourcing platform first issues a series of standard tasks to the workers, and allows users (i.e., data owners) to access aggregate responses from the workers through group queries that can be described by sparse encoding vectors. The identities of workers associated with individual responses are not explicitly revealed. By exploiting the sparse nature of the encoding vectors, we first propose an approximate maximum a posteriori probability (Approx. MAP) detector to perform the detection. Then, to further reduce computational complexity, we devise a generalized likelihood ratio test (GLRT) where probable malicious workers are first identified before a simple hypothesis test is performed. The identification of malicious workers is performed by a low-complexity probability-based rule that exploits a certain sparse structure inherent in the crowd data as well as the associated statistical assumptions. Computer simulations show that the proposed methods outperform the conventional energy detector.

Built on the sparse representation framework, sparse subspace clustering (SSC) received considerable attention in the recent years. Conventional SSC employs ℓ1-minimization based sparse regression for neighbor identification on a sample-by-sample basis, and is unaware of the neighbor information revealed by those already computed sparse representation vectors. To rid this drawback, this paper proposes a weighted ℓ1-minimization based sparse regression method, and an associated data ordering rule able to reflect the reliability of neighbor information for further enhancing the clustering accuracy. The selection of weighting coefficients for SSC is also discussed. Computer simulations using both the synthesis and real data are provided to evidence the effectiveness of the proposed method.

This paper aims to exploit the fundamental limits on the downlink coverage and spatial throughput performances of a cellular network comprised of a tier of unmanned aerial vehicle (UAV) base stations (BSs) using the millimeter wave (mmWave) band and a tier of ground BSs using the ultra high frequency (UHF) band. To reduce handover signaling overhead, the ground BSs take charge of control signaling delivery whereas the UAVs are in charge of payload data transmission so that users need to be simultaneously associated with a ground BS and a UAV in this network with a control-data plane-split architecture. We first propose a three-dimensional (3D) location distribution model of the UAVs using stochastic geometry which is able to generally characterize the positions of the UAVs in the sky. Using this 3D distribution model of UAVs, two performance metrics, i.e., multi-cell coverage probability and volume spectral efficiency, are proposed. Their explicit low-complexity expressions are derived and their upper limits are found when each of the UAVs and ground BSs is equipped with a massive antenna array. We further show that the multi-cell coverage probability and the volume spectral efficiency can be maximized by optimally deploying and positioning the UAVs in the sky and thereby their fundamental maximal limits are found. These important analytical findings are validated by numerical simulations.

Cooperative beamforming is a potentially useful technique for enhancing link reliability in modern wireless relay communications. To aid the beamforming design, the signal-to-noise ratio (SNR) of the source-to-relay (S-R) channel links must be known at the destination node. Abdallah and Papadopoulos in IEEE Trans. Signal Processing, vol. 56, no. 10, 2008, proposed a beamforming system with relay-assisted SNR acquisition; that is, the S-R link SNR is first quantized at each relay and is then forwarded to the destination. An optimal quantizer design problem was proposed in that paper, aiming at reducing the system bit error rate; however, only the binary quantization case was considered therein, with the quantization threshold computed by a suggested rule of thumb. In this paper, we study the aforementioned quantizer design problem in the general multiple-bit setting. We show that the solutions to the first-order necessary condition for optimality can be obtained as a fixed point of a certain nonlinear map over the feasible threshold set. The existence and uniqueness of the fixed point are established by using, respectively, the Brouwer's and Schauder's fixed point theorems. Finally, we show that the fixed point thus obtained yields the globally optimal set of quantization thresholds. To the best of our knowledge, this paper is the first which leverages fixed point theories to rigorously solve SNR quantization problems in the context of wireless relay communication.

Pilot contamination (PC) is known as a dominant factor for the performance of time-division duplex massive MIMO systems. Most of the existing studies of PC assumed perfect inter-cell synchronization, which is too costly to achieve or even impossible in practice. In this paper, we consider the unsynchronized scenario, in which the reused pilot signals from other cells are subject to timing misalignment, and propose a method for pilot sequence design for reducing the mean square error (MSE) of the linear least squares (LS) channel estimation. An analytic MSE formula is first derived, which is a very complicated function of the pilot coefficients. To ease analysis, an upper bound on the MSE is then derived. Through minimization of this upper bound, an analytic solution is obtained. Computer simulations are used to illustrate the performance of the proposed pilot sequence.

This paper proposes a compressive sensing (CS) based active user identification scheme over frequency-selective fading channels. Unlike the conventional cyclic prefix (CP) based preamble transmission, our approach does not utilize CP in order to conserve signaling overhead, in turn reducing the system-wide processing latency. Our approach first estimates the multi-user channel impulse response vectors by solving a mixed ℓ2/ℓ1-norm optimization problem; then, the active users are identified via a sorting of the norms of the estimated channel vectors. By exploiting the Toeplitz channel matrix structure resulting from CP-free preamble transmission, analytic performance guarantee in term of the block restricted isometry property of the preamble matrix is given. Computer simulations are used to illustrate the performance of the proposed method.

To strike a balance between energy efficiency and data quality control, this paper proposes a sensor censoring scheme for distributed sparse signal recovery via compressive-sensing-based wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor: 1) directly transmits the real-valued compressed data, if the sensing vector support is detected to be overlapped with the signal support; 2) sends a one-bit hard decision if empty support overlap is inferred; and 3) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. In addition, the average communication cost is analyzed. To aid global signal reconstruction under the proposed censoring framework, we propose a modified ℓ1-minimization based algorithm, which exploits certain sparse nature of the hard decision vector received at the fusion center. Analytic performance guarantees, characterized in terms of the restricted isometry property, are also derived. Computer simulations are used to illustrate the performance of the proposed scheme.

This paper proposes a new compressive sensing based downlink channel state information (CSI) estimation scheme for FDD massive MIMO systems. The proposed approach, which involves two-stage weighted block ℓ1-minimization, exploits the block sparse nature of the angular domain representation of the MIMO channel matrices, as well as the existence of common scattering paths in the realistic propagation environment. In the first stage of our method, a conventional block ℓ1-minimization program is solved to extract the information about the common/individual supports of the multi-user channel matrices. In the second stage, a weighted block ℓ1-minimization algorithm, with the weighting coefficients suitably chosen to exploit the acquired support knowledge, is then performed for channel matrix estimation. Analytic performance guarantees of the proposed method are specified using the block restricted isometry property of the sensing matrix; specifically, the I-norm reconstruction error upper bounds achieved by our approach are derived. The analytic results allow us to discuss the selection of weighting coefficients for enhancing CSI estimation performance. Computer simulations show that our method achieves better estimation accuracy as compared to an existing greedy-based algorithm.

Collaborative detection for continuously-varying event-region scenarios using wireless sensor networks (WSNs) has attracted much attention recently. However, most existing works adopt either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive data exchange takes place among the sensors by means of real-value data communications. By contrast, the present study proposes a distributed dynamic event-region detection scheme for WSNs. The proposed scheme is based on a two-phase cooperative space-time decision fusion protocol. In Phase I, each sensor makes an initial decision in accordance with its Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions in accordance with the decision information received from their neighboring nodes. The performance of the proposed scheme is compared with that of a semi-centralized detection scheme by means of computer simulations.

This paper proposes a new compressive sensing-based downlink channel state information (CSI) estimation scheme for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The proposed scheme, which involves two-stage weighted block ℓ1-minimization, exploits the block sparse nature of the angular domain representation of the MIMO channel matrices and the existence of common scattering paths in the realistic propagation environment. In the first stage of the implemented scheme, a conventional block ℓ1-minimization program is solved to extract the information about the common/individual supports of the multiuser channel matrices. In the second stage, a weighted block ℓ1-minimization algorithm, the weighting coefficients of which are suitably chosen to exploit the acquired support information, is used to estimate the channel matrices. The analytic performance guarantees of the proposed scheme are specified based on the block restricted isometry property of the sensing matrix. Specifically, the upper bounds of the ℓ2-norm reconstruction error are derived using various assumptions regarding the weighting. The obtained analytical results enable a discussion of the selection of weighting coefficients to enhance the CSI estimation performance, and the determination of a sufficient condition under which the proposed scheme outperforms the unweighted naive solution. Computer simulations show that the proposed method achieves higher estimation accuracy as compared to an existing greedy-based algorithm.

Various methods have been proposed for monitoring continuously varying event-region scenarios using wireless sensor networks (WSNs). However, these methods use either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive real-value data exchange takes place among the sensors. By contrast, this paper proposes a two-phase distributed dynamic event-region detection scheme for WSNs characterized by a space-time Markov random field with a particular Gibbs distribution. In Phase I of the proposed scheme, each sensor makes an initial decision in accordance with its current Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions based on the decision information received from their neighbors. Notably, the proposed scheme has a low-communication-rate characteristic, and is thus ideally suited to WSN applications. The performance of the proposed scheme is compared with that of a semicentralized detection scheme by means of computer simulations.

A sufficient condition reported very recently for perfect recovery of a K-sparse vector via orthogonal matching pursuit (OMP) in K iterations (when there is no noise) is that the restricted isometry constant (RIC) of the sensing matrix satisfies δK+1K+1K+1K+1= (1/(K)). Our approach relies on a newly established near orthogonality condition, characterized via the achievable angles between two orthogonal sparse vectors upon compression, and, thus, better exploits the knowledge about the geometry of the compressed space. The proposed near orthogonality condition can be also exploited to derive less restricted sufficient conditions for signal reconstruction in two other compressive sensing problems, namely, compressive domain interference cancellation and support identification via the subspace pursuit algorithm.

The theory of compressive sensing has shown that with a small number of samples from random projections of a sparse signal, one can recover the original signal under certain conditions. In this paper, we use compressive sensing to design a random access protocol for requesting uplink data channels. A wireless node transmits a pseudo-random sequence to an access point (AP) when it requires an uplink channel. The AP receives multiple sequences in a random access shared channel. Due to different propagation delays, the received signals from different wireless nodes are not synchronized at the receiver. Assume that the number of sequence transmissions is substantially less than the number of wireless nodes in the system. Under such circumstances, we design an asynchronous compressive sensing based decoder to recover the original signals in a random access setting. The key difference between our proposed decoder and those presented in the literature is that we do not require any synchronization before sequence transmission which makes our approach practical. Simulation results show the throughput improvement of our proposed scheme compared to two other random access protocols.

This study presents a new relay strategy and associated diversity combining technique for improving the communication performance in semi-blind cooperative networks. In constructing the network model, it is assumed that each relay can obtain the perfect channel state information (CSI) from the source to itself, and the destination can acquire the perfect CSI from all the relays to itself, but does not require the CSI from the source to the participating relays. In performing the considered semi-blind cooperative communications, the relays forward their reliability to the destination using a quantized reliability-relaying (QRR) scheme. Specifically, the relays partition their reliability into three levels in accordance with the log-likelihood ratio (LLR) value of the received signal, and forward a regenerative symbol to the destination if the quantized reliability falls within the "send +1" or "send -1" region; and remain silent otherwise. It is shown theoretically that the QRR scheme achieves a higher deflection coefficient than the regular decode-and-forward (DF) scheme, in which all the participating relays forward their regenerative messages to the destination irrespective of their reliability. Moreover, the simulation results show that given the same semi-blind model, the QRR scheme achieves a lower bit error rate (BER) than existing relay selection DF schemes for all considered values of the average input signal-to-noise ratio (SNR).

This paper discusess the reply to "Corrections on Cooperative Communications Using Reliability-Forwarding Relays".

The angle between two compressed sparse vectors subject to the norm/distance constraints imposed by the restricted isometry property (RIP) of the sensing matrix plays a crucial role in the studies of many compressive sensing (CS) problems. Assuming that (i) u and v are two sparse vectors separated by an angle thetha, and (ii) the sensing matrix Phi satisfies RIP, this paper is aimed at analytically characterizing the achievable angles between Phi*u and Phi*v. Motivated by geometric interpretations of RIP and with the aid of the well-known law of cosines, we propose a plane geometry based formulation for the study of the considered problem. It is shown that all the RIP-induced norm/distance constraints on Phi*u and Phi*v can be jointly depicted via a simple geometric diagram in the two-dimensional plane. This allows for a joint analysis of all the considered algebraic constraints from a geometric perspective. By conducting plane geometry analyses based on the constructed diagram, closed-form formulae for the maximal and minimal achievable angles are derived. Computer simulations confirm that the proposed solution is tighter than an existing algebraic-based estimate derived using the polarization identity. The obtained results are used to derive a tighter restricted isometry constant of structured sensing matrices of a certain kind, to wit, those in the form of a product of an orthogonal projection matrix and a random sensing matrix. Follow-up applications to three CS problems, namely, compressed-domain interference cancellation, RIP-based analysis of the orthogonal matching pursuit algorithm, and the study of democratic nature of random sensing matrices are investigated.

Motivated by the fact that system parameter mismatch occurs in real-world sensing environments, this paper proposes power allocation schemes for robust distributed bestlinear-unbiased estimation (BLUE) that take account of the uncertainty in the local sensing noise levels. Assuming that (i) the sensing noise variance follows a statistical distribution widely used in the literature and (ii) the link channel gains between sensor nodes and the fusion center (FC) are i.i.d. Rayleigh fading, we propose to use the average reciprocal mean square error (ARMSE), averaged with respect to the distributions of sensing noise variance and fading channels, as the distortion measure. A fundamental inequality characterizing the relation between ARMSE and the average mean square error (AMSE) is established to justify the proposed design metric. While the exact formula for ARMSE is difficult to find, we derive an associated closed-form lower bound which involves the incomplete gamma function. To further ease analysis, we further derive a key inequality that specifies the range of the ARMSE lower bound. Particularly, it is shown that the boundary points of this inequality are characterized by a common function, which involves the Gaussian-tail Q(·) and is thus more analytically appealing. By conducting optimization on the basis of such a function, we obtain closed-form robust solutions for two power allocation problems: (i) optimizing distortion metric under a total power constraint, and (ii) minimizing total power under a target distortion requirement. In case that instantaneous channel state information (CSI) is available to the FC, the proposed approach can be easily modified to derive analytic robust power allocation factors best matched to the CSI realizations. Computer simulations evidence the effectiveness of the proposed schemes.

Energy consumption in wireless sensor networks is dominated by intra-network communication dedicated to coordination and information exchange between sensor nodes and the fusion center. The design of distributed estimation algorithms with reduced communication overheads is thus rather crucial. For amplify-and-forward sensor networks over flat fading channels, this paper proposes a new distributed best-linear-unbiased-estimation (BLUE) scheme by exploiting the statistical characterizations of the sensing noise variance and channel gains. The performance measure is the reciprocal of the mean square error averaged over the considered statistical distributions. We derive a closed-form lower bound for the adopted design metric. By means of this result, we further derive a closed-form universal sensor power amplification factor capable of maintaining a target estimation performance. The proposed scheme has the advantage that repeated power scheduling and message feedback are no longer needed in the parameter estimation phase and, hence, the in-network communication cost is further reduced. Some key features regarding the proposed method are discussed. Computer simulations are conducted to evidence our analytic study.

Signaling overhead reduction has been a key approach to realizing energy-efficient wireless cooperative communication systems. Motivated by the fact that consecutive temporal samples of real-world wireless channels are typically correlated, this paper proposes to exploit such time-domain correlation for relay training overhead reduction in cooperative networks. Specifically, we consider a cooperative transmit beamforming system, in which relay terminals employing the decode-and-forward protocol collaboratively transmit the source message according to the pre maximal-ratio-combining (pre-MRC) principle. During the training phase, the relays send training signals to aid channel estimation at the destination. Based on the acquired record of the channel state information (CSI), the destination then employs a linear minimum-mean-square-errors (LMMSE) channel predictor to update the CSI; in this way, training overhead dedicated by relays can then be reduced. We derive a closed-form expression for the receive SNR at the destination when the pre-MRC beamforming factors are computed in accordance with the predicted CSI. Our analytic results can be used for characterizing the performance degradation of channel prediction as the duration of the prediction phase is enlarged. The proposed analytic studies are corroborated by numerical simulations.

Knowledge of the achievable restricted isometry constant (RIC) of the sensing matrix is crucial for assessing the signal reconstruction performance of compressive sensing systems. In this paper we consider compressive-domain interference cancellation via orthogonal projection, and study the achievable RIC of the effective sensing matrix, namely, the product of the orthogonal projection matrix and the original sensing matrix. While existing algebraic based methods resorted to the polarization identity to find an upper bound of the considered RIC, motivated by geometric interpretations of the orthogonal projection and the restricted isometry property we derive an improved RIC in a closed form. The proposed solution is shown to be tighter than the existing upper bound. Our analytical results, and the asserted performance advantages, are further evidenced via computer simulations.

This paper examines the impact of sensor censoring on the decision fusion performance in networks with an unknown number of sensors. In performing the decision fusion process, the fusion center applies the Chair-Varshney test; suitably modified to take account of the unknown network size. A closed-form analytical expression is derived for the error probability of the modified fusion rule. It is shown that reducing the censoring probability, i.e., allowing a greater number of sensors to transmit their decisions, does not necessarily improve the decision fusion performance. Rather, there exists a certain censoring probability threshold below which increasing the number of transmitting sensors simply incurs a greater intra-network communication overhead but without improving the global decision performance. Our findings establish that the design of energy-efficient local detection rules should commence with the censoring rate threshold. Hence, it is desirable that the value of this censoring probability threshold be known in advance. Accordingly, the present study proposes an efficient method for identifying the censoring probability threshold value and determining the corresponding local censoring rule.

While the zero-forcing (ZF) transmit beamforming is a widely used technique for realizing multi-input multi-output (MIMO) broadcast transmissions, the ZF receiver combined with multiuser scheduling is an effective alternative that yields improved robustness against CSI mismatch caused by feedback link errors. In this paper we consider the practical scenario that channels are imperfectly estimated at the receiver. Our goal is to characterize the sum rate performance of the receive ZF MIMO broadcast systems under channel estimation errors. We derive analytic sum-rate expressions for both the uniform transmit power and transmit water-filling cases. Our analytic results characterize the sum-rate floor incurred by channel estimation errors. Numerical simulations are used to confirmed the analytic study.

Spectrum sensing in next-generation wireless cognitive systems, such as overlay femtocell networks, is typically subject to timing misalignment between the primary transmitter and the secondary receiver. In this paper, we investigate the performance of the energy detector (ED) when the arrival time of the primary signal is modeled as a uniform random variable over the observation interval. The exact formula for the detection probability is derived and corroborated via numerical simulation. To further improve the detection performance, we propose a robust ED based on the Bayesian principle. Computer simulation confirms the effectiveness of the Bayesian based solution when compared with the conventional ED.

Achieving high system throughput for limited- feedback communications against the time-varying channel effect is rather crucial in modern mobile system designs. Within the beamforming setup, this paper derives analytic throughput results for both the "more feedback less often" and "less feedback more often" scenarios. More specifically, under the assumptions that (i) the channels over two consecutive time slots follow the first-order Markov model and (ii) in each time slot, reliable feedback of a fixed amount of bits is allowed, the achievable system throughput over two consecutive time slots in both scenarios are characterized. In particular, while the exact throughput is in an integral form, we derive the associated closed-form approximate formulae which facilitate throughput evaluation without resorting to numerical integration. The analytic results also lead to a very low-complexity throughput-based mode selection scheme. Simulation study shows that: (1) the derived closed-form approximation is quite accurate; (2) with the aid of the proposed mode selection method, the throughput performance is robust against the channel temporal variation.

Transmit precoding is a key technique for facilitating blind channel estimation at the receiver but the impact due to precoding on the channel capacity is scarcely addressed in the literature. In this paper we consider the single-carrier block transmission with cyclic prefix, in which a recently proposed diagonal-precoding assisted blind channel estimation scheme via covariance matching is adopted to acquire the channel information. It is shown that, when perfect channel knowledge is available at the receiver, the optimal noise resistant precoder proposed in the literature incurs the worst-case capacity penalty. When the coherent interval is finite, channel mismatch occurs due to finite-sample covariance matrix estimation. Thus, we aim to determine how much of the coherent interval should be dedicated to precoding in order to trade channel estimation accuracy for the maximal capacity. Toward this end, we leverage the matrix perturbation theory to derive a closed-form capacity measure which explicitly takes account of the channel uncertainty in the considered blind estimation setup. Such a capacity metric is seen to be a complicated function of the precoding interval. To facilitate analysis, an approximate formula for the derived capacity measure is further given. This allows us to find a closed-form estimate of the capacity-maximizing precoding time fraction, and can also provide insights into the optimal tradeoff between channel estimation accuracy and achievable capacity. Numerical simulations are used for evidencing the proposed analytic study.

We propose a BER improved power allocation scheme for D-STTD systems over i.i.d. Rayleigh fading channels under the QR-based successive detection framework. Instead of relying on BER under a fixed channel realization, the adopted design criterion is the mean BER (assuming there is no inter-layer error propagation) averaged with respect to the channel distribution. Such a design metric has two-fold advantages: (i) It is analytically tractable and is closely related to a block error probability upper bound when inter-layer error propagation occurs, and (ii) There is no need for repeated feedback of the instantaneous channel information. By exploiting a distinctive channel matrix structure unique to D-STTD systems we derive a closed-form approximate upper bound of the considered BER metric; through minimization of this bound an optimal power allocation scheme is obtained. Numerical simulation is used to illustrate the performance of the proposed method.

We propose a statistical covariance-matching based blind channel estimation scheme for zero-padding (ZP) MIMO-OFDM systems. By exploiting the block Toeplitz channel matrix structure, it is shown that the linear equations relating the entries of the received covariance matrix and the outer product of the MIMO channel matrix taps can be rearranged into a set of decoupled groups. The decoupled nature reduces computations, and more importantly guarantees unique recovery of the channel matrix outer product under a quite mild condition. Then the channel impulse response matrix is identified, up to a Hermitian matrix ambiguity, through an eigen-decomposition of the outer product matrix. Simulation results are used to evidence the advantages of the proposed method over a recently reported subspace algorithm applicable to the ZP-based MIMO-OFDM scheme.

Medium access control (MAC) protocol design for cooperative networks over multi-packet reception (MPR) channels is a challenging topic, but has not been addressed in the literature yet. In this paper, we propose a MAC protocol to exploit the cooperation diversity for throughput enhancement over MPR channels. The proposed approach can efficiently utilize the idle periods for packet relaying, and can thus effectively limit the throughput loss resulting from the relay phase. By means of a Markov chain model, the worst-case throughput analysis is conducted. Specifically, we derive (i) a closed-form upper bound for the throughput penalty of the direct link that is caused by the interference of concurrent packet relay transmission; (ii) a closed-form lower bound for the throughput gain that a user with packet transmission failure can benefit thanks to cooperative packet relaying. The results allow us to investigate the throughput performance of the proposed protocol directly in terms of the MPR channel coefficients. Simulation results confirm the system-wide throughput advantage achieved by the proposed scheme, and also validate the analytic results.

Bit assignment for local sensor data quantization in the decentralized best-linear-unbiased-estimation (BLUE) scenario is widely addressed in the signal processing research for wireless sensor networks. When the timely knowledge of the instantaneous sensor noise variance (for implementing the BLUE fusion rule) is too costly to obtain, one plausible alternative is to exploit the associated statistical characterization. Related such proposals, however, do not explicitly take into account the communication link impairments such as channel fading. In this paper we extend the current results to the more realistic case when signal transmission is subject to the fading effect. We show that the optimal bit allocation problem can be reformulated in the form of convex optimization, and then derive an analytical solution. Through numerical simulation the proposed solution is seen to outperform the uniform energy allocation scheme.

This paper addresses the joint source/relay precoder design problem in amplify-and-forward (AF) cooperative communication systems where multiple antennas are equipped at the source, the relay, and the destination. Existing solutions to the problem only consider the relay link and, thus, do not fully exploit all the available link resource. Using a minimum-mean- squared-error (MMSE) criterion, we propose a joint precoder design method, taking both the direct and relay links into account. It is shown that the MMSE is a highly nonlinear function of the precoder matrices, and a direct minimization is not feasible. To facilitate analysis, we propose to design the precoders toward first diagonalizing the MSE matrix of the relay link. This imposes certain structural constraints on both precoders that allow us to derive an analytically tractable MSE upper bound. By conducting minimization with respect to this upper bound, the solution can be obtained by an iterative water- filling technique. Simulations show that the proposed design can significantly enhance the performance of MIMO AF cooperative systems.

This paper considers transmitter precoding in an amplify-and-forward cooperative system where multiple antennas are equipped at the source, the relay, and the destination. Existing methods for the problem only consider the design of the relay precoder. To further improve the performance, we include the source precoder into the design. Using a minimum-meansquare- error (MMSE) criterion, we propose a joint source/relay precoder design method, taking both the direct and relay links into account. It is shown that the MMSE is a highly nonlinear function of the precoding matrices, and a direct minimization is not feasible. To facilitate analysis, we propose to design the precoders toward first diagonalizing the MSE matrix of the relay link. This imposes certain structural constraints on both precoders that allow us to derive an analytically tractable MSE upper bound. By conducting minimization with respect to this bound, the solution can be obtained by an iterative water-filling technique.

Medium access control (MAC) protocol design for cooperative networks over multi-packet reception (MPR) channels is a challenging topic, but has not been addressed in the literature yet. In this paper, we propose a cooperative multi-group priority (CMGP) based MAC protocol to exploit the cooperation diversity for throughput enhancement over MPR channels. The proposed approach can bypass the computationally-intensive active user identification process. Moreover, our method can efficiently utilize the idle periods for packet relaying, and can thus effectively limit the throughput loss resulting from the relay phase. By means of a Markov chain model, the worst-case throughput analysis is conducted. The results allow us to investigate the throughput performance of the proposed CMGP protocol directly in terms of the MPR channel coefficients. Simulation results confirm the system-wide throughput advantage achieved by the proposed scheme, and also validate the analytic results.

This paper studies the energy-constrained MMSE decentralized estimation problem with the best-linear-unbiased-estimator fusion rule, under the assumptions that i) each sensor can only send a quantized version of its raw measurement to the fusion center (FC), and ii) exact knowledge of the sensor noise variance is unknown at the FC but only an associated statistical description is available. The problem setup relies on maximizing the reciprocal of the MSE averaged with respect to the prescribed noise variance distribution. While the considered design metric is shown to be highly nonlinear in the local sensor transmit energy (or bit loads), we leverage several analytic approximation relations to derive a associated tractable lower bound; through maximizing this bound a closed-form solution is then obtained. Our analytical results reveal that sensors with bad link quality are shut off to conserve energy, whereas the energy allocated to those active nodes is proportional to the individual channel gain. Simulation results are used to illustrate the performance of the proposed scheme.

This brief considers blind signal source separation via algebraic known modulus algorithm. It is shown that through proper signal modulus design the estimation accuracy of the beamforming vector, as well as the performance of signal separation, can be improved. Specifically, based on a matrix perturbation analysis we propose a criterion, in the form of minimizing the maximal singular value of the modulus code matrix, for enhancing robustness of the beamforming vector against measurement noise. A closed-form solution is then derived and its performance is tested through numerical simulation.

We consider MIMO single-carrier block transmission over time-varying multipath channels, under the assumption that the channel parameters are not exactly known but are estimated via the least-squares training technique. While the channel temporal variation is known to negate the tone-by-tone frequency-domain equalization facility, it is otherwise shown that in the time domain the signal signatures can be arranged into groups of orthogonal components, leading to a very natural yet efficient group-by-group symbol recovery scheme. To realize this figure of merit we propose a constrained-optimization based receiver which also takes into account the mitigation of channel mismatch effects caused by time variation and imperfect estimation. The optimization problem is formulated in an equivalent unconstrained generalized-sidelobe-canceller setup. This enables us to directly model the channel mismatch effect into the system equations through the perturbation technique and, in turn, to further exploit the statistical assumptions on channel temporal variation and estimation errors for deriving a closed-form solution. Within the considered framework the proposed robust equalizer can be further combined with the successive interference cancellation mechanism for further performance enhancement.

Relying on a simple flag-assisted mechanism, a multi- group priority queueing (MGPQ) medium access control (MAC) protocol is proposed for the multipacket reception (MPR) channel. The proposed MGPQ scheme is capable of overcoming two major performance bottlenecks inherent in the existing MPR MAC protocols. First, the proposed solution can automatically produce the list of active users by observing the network traffic conditions, removes the need of active user estimation algorithm, and thus can largely reduce the algorithm complexity. Second, the packet blocking constraint imposed on the active users for keeping compliant with prediction is relaxed. As a result, the proposed MGPQ is not only applicable to both homogeneous and heterogeneous cases, but also outperforms the existing MPR MAC protocols. Simulation results show that the network throughput can be improved by 40% maximum and 14% average as compared with the well known dynamic queue (DQ) MAC protocol.

This letter studies the energy-constrained MMSE decentralized estimation problem with the best-linear-unbiased-estimator fusion rule, under the assumptions that 1. Each sensor can only send a quantized version of its raw measurement to the fusion center (FC), and 2. Exact knowledge of the sensor noise variance is unknown at the FC but only an associated statistical description is available. The problem setup relies on maximizing the reciprocal of the MSE averaged with respect to the prescribed noise variance distribution. While the considered design metric is shown to be highly nonlinear in the local sensor bit loads, we leverage several analytic approximation relations to derive an associated tractable lower bound; through maximizing this bound, a closed-form solution is then obtained. Our analytical results reveal that sensors with bad link quality are shut off to conserve energy, whereas the energy allocated to those active nodes is proportional to the individual channel gain. Simulation results are used to illustrate the performance of the proposed scheme.

We consider MIMO single-carrier block transmission over time-varying multipath channels, under the assumption that the channel parameters are not exactly known but are estimated via the least-squares training technique. While the channel temporal variation is known to negate the tone-by-tone frequency-domain equalization facility, it is otherwise shown that in the time domain the signal signatures can be arranged into groups of orthogonal components, leading to a very natural yet efficient group-by-group symbol recovery scheme. To realize this figure of merit we propose a constrained-optimization based receiver which also takes into account the mitigation of channel mismatch effects caused by time variation and imperfect estimation. The optimization problem is formulated in an equivalent unconstrained generalized-sidelobe-canceller setup. This enables us to directly model the channel mismatch effect into the system equations through the perturbation technique and, in turn, to further exploit the statistical assumptions on channel temporal variation and estimation errors for deriving a closedform solution. Within the considered framework the proposed robust equalizer can be combined with the successive interference cancellation mechanism for further performance enhancement. Flop count evaluation and numerical simulation are used to evidence the advantages of the proposed scheme.

This paper studies minimal-energy decentralized estimation in sensor networks under best-linear-unbiased-estimator fusion rule. While most of the existing related works require the knowledge of instantaneous noise variances for energy allocation, the proposed approach instead relies on an associated statistical model. The minimization of total energy is subject to certain performance constraint in terms of mean square error (MSE) averaged over the noise variance distribution. A closed-form formula for the overall MSE metric is derived, based on which the problem can be reformulated in the form of convex optimization and is shown to yield an analytic solution. The proposed method shares several attractive features of the existing designs via instantaneous noise variances; through simulations it is seen to significantly improve the energy efficiency against the uniform allocation scheme.

We consider MIMO-OFDM transmission, in a scenario that the adopted cyclic-prefix (CP) length is shorter than the channel delay spread for boosting data rate and, moreover, the channel parameters are not exactly known but are estimated using the least-squares (LS) training technique. By exploiting the receiver spatial resource, we propose a constrained-optimization based linear equalizer which can mitigate inter- symbol interference and inter-carrier interference incurred by insufficient CP interval, and is robust against the net detrimental effects caused by channel estimation errors. The optimization problem is formulated in an equivalent unconstrained generalized-sidelobe- canceller (GSC) setup. The channel parameter error is explicitly incorporated into the constraint-free GSC system model through the perturbation technique; this allows us to exploit the presumed LS channel error property for deriving a closed-form solution. Simulation results confirm the effectiveness of the proposed method.

This paper investigates the least-squares (LS) estimation of unknown deterministic parameters from a standard linear model characterized by a class of block-circulant-with-circulant-block (BCCB) matrix. We propose a method for designing the BCCB system matrix coefficients to minimize the mean square error incurred by the LS estimate, under certain equality and inequality constraints. By exploiting the eigenvalue characteristic of BCCB matrices, precise analysis is undertaken to derive a closed-form solution. The considered optimization problem arises in the study of blind channel estimation for single-carrier block transmission with cyclic prefix; the presented analysis reveals several key features associated with the BCCB family, and shows an original investigation of the BCCB matrix structure for facilitating linear optimal parameter estimation

This paper studies the V-BLAST detection in a general multiuser space-time wireless system, in which each user's data stream is either (orthogonal) space-time block coded (OSTBC) for transmit diversity or spatially multiplexed (SM) for high spectral efficiency. The motivation behind this work is that each user adopting a signaling scheme better matched to his own channel condition proves to improve the individual link performance but the resultant co-channel interference mitigation problem is scarcely addressed thus far. By exploiting the algebraic structure of orthogonal code, it is shown that the V-BLAST detector in the considered dual-signaling environment allows for an attractive group-wise implementation: at each iteration a group of symbols, transmitted either from an OSTBC station or from an antenna of an SM terminal, are jointly detected. The group detection property, resulting uniquely from the use of orthogonal codes, potentially improves the dual-mode signal separation efficiency, especially when the OSTBC terminals are dense in the cell. The embedded structure of the channel matrix is also exploited for deriving a computationally efficient detector implementation. Flop count evaluations and numerical examples are used for illustrating the performance of the proposed V-BLAST based solution

This paper investigates multiuser orthogonal space-time block coded signal detection within the ordered successive interference cancellation (OSIC) framework. Both the zero-forcing and minimum-mean-square-error ordering criteria are considered. When each user terminal is equipped with no more than four transmit antennas, it is shown that orthogonal transmit redundancy leads to an appealing signal ordering property: in each processing layer the transmitted symbols of an arbitrary user are associated with an identical ordering metric. This guarantees the feasibility of (user based) group-wise symbol recovery through the OSIC mechanism. Analytic bit-error-rate performance is given. Computer simulations and flop count evaluations are also provided for comparing the OSIC based solution with existing multiuser detection schemes reported for the considered system

A multi-antenna generalized sidelobe canceller (GSC) based equalizer for inter-symbol interference (ISI) suppression is proposed for high-rate single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) systems without cyclic prefix (CP). The proposed method relies on the block representation of the OFDM transmission and exploits the ISI subspace structure in the associated multi-antenna system model. A computationally efficient partial adaptivity (PA) implementation of the GSC equalizer is also provided for reducing the receiver complexity. Simulation results show that the proposed GSC-based solution yields an equalization performance almost identical to that obtained by the conventional CP-based OFDM system and is highly resistant to the increase of channel delay spread.

We propose a method for constructing the FIR approximate inverse for discrete-time causal FIR periodic filters in the presence of measurement noise. The objective function to be minimized is the sum of the error variance over one period. The optimization problem is formulated based on the matrix impulse response of the multi-input multi-output (MIMO) time-invariant representation of the periodic filter as one that minimizes the sum of equation errors of a set of overdetermined linear equations. It is shown that the problem is equivalent to a set of least squares problems and a simple closed-form solution is obtained. Numerical examples are used to illustrate the performance of the proposed FIR approximate inverse.

A new MIMO transceiver is proposed for the downlink of high data rate multicarrier CDMA (MC-CDMA) systems over frequency-selective multipath channels. The design of the transceiver involves the following procedure. First, the data stream is demultiplexed into multiple substreams, which are then encoded by a set of space time block codes (STBC) for achieving spatial diversity. Second, the coded substreams are spread by a set of space-path spreading (SPS) codes which is designed to achieve path multiplexing by exploiting independent multipath channels and pre-suppress the multiple access interference (MAI) without the use of channel state information (CSI). These substreams are then transmitted simultaneously from multiple antennas. At each mobile station, a simple matched filter (MF) is used to despread the received data. Linearly combining the MF outputs with the aid of CSI and employing a multi-user detection scheme can then separate the mutually interfering signals from the multiple transmit antennas and restore the diversity gain due to SPS and STBC. An increased spectral efficiency and diversity gain can thus be achieved at the same time. Simulation results confirm that the proposed transceiver offers better performance than the conventional BLAST transceiver, and achieves nearly the performance of STBC and space-time spreading BLAST transceiver.