Secure communications over SIMO and MISO channels between wireless nodes are commonly encountered in applications and widely considered in the literature. This paper investigates such a problem by considering a newly proposed scheme called secrecy-message transmission by echoing encrypted probes (STEEP). We focus on a type of heavily eavesdropped channels where an aggressive eavesdropper (Eve) has multiple antennas and is capable of jamming and eavesdropping in the full-duplex mode. Assuming optimal jamming and eavesdropping by Eve, we present the secrecy rates achievable by STEEP in two forms: using SIMO channel in phase 1 and using MISO channel in phase 2, and vice versa. With any given channel condition between every pair of nodes and any finite jamming power by Eve, the secrecy rate of STEEP in either form is shown to be positive subject to a positive power in phase 1 and a sufficiently large power in phase 2. The connections between STEEP in either form and the conventional SIMO and MISO schemes are provided, and their secrecy rates are compared comprehensively subject to random fading channels and finite transmit powers.

Two or more mobiles users can continuously superimpose sequences of bits chosen from different packets or files already exchanged and authenticated between themselves to continuously renew a secret key for continuous strengthening of their privacy and authentication. This accumulative, adaptable and additive (AAA) method is discussed in this paper. The equivocation to Eve of any bit in the generated key by the AAA method equals to the probability that not all corresponding independent bits exchanged between the users are intercepted by Eve. This performance, achieved without using any knowledge of non-stationary probabilities of bits being intercepted by Eve, is compared to an established capacity achievable using that knowledge. A secrecy robustness of the AAA method against some correlations known to Eve is also discussed.

A scheme for secure communications, called “Secret-message Transmission by Echoing Encrypted Probes (STEEP)”, is revisited. STEEP is a round-trip scheme with a probing phase from one user to another and an echoing phase in the reverse direction. STEEP is shown to be broadly applicable to yield a positive secrecy rate in bits per channel use even if the receive channels at eavesdropper (Eve) are stronger than those between legitimate users in both forward and reverse directions. This paper focuses on STEEP in the following settings: using Gaussian probing signal and Gaussian linear encryption over MIMO Gaussian channel (G-STEEP); using phase-shift-keying probing signal and a nonlinear encryption over SISO channel (P-STEEP); and a variation of G-STEEP for multiple access communication (M-STEEP). In each of the settings, Eve is assumed to have any given number of antennas, and STEEP is shown to yield a positive secrecy rate subject to a sufficiently large power in the echoing phase, as long as Eve’s receive channel in the probing phase is not noiseless. It is also shown that G-STEEP, subject to asymmetric large powers in forward and reverse directions, has its secrecy rate approaching the secret-key capacity based on Gaussian probing signal over MIMO Gaussian channel. STEEP does not require secure feedback channel, collaborative third party, in-band full-duplex or reciprocal channels between users, but only needs a design for echoing encrypted probes, asymmetric power allocation and/or collaborative round-trip coding.

This paper presents further insights into a recently developed round-trip communication scheme called “Secret-message Transmission by Echoing Encrypted Probes (STEEP)”. A legitimate wireless channel between a multi-antenna user (Alice) and a single-antenna user (Bob) in the presence of a multi-antenna eavesdropper (Eve) is focused on. STEEP does not require full-duplex, channel reciprocity or Eve's channel state information, but is able to yield a positive secrecy rate in bits per channel use between Alice and Bob in every channel coherence period as long as Eve's receive channel is not noiseless. This secrecy rate does not diminish as coherence time increases. Various statistical behaviors of STEEP's secrecy capacity due to random channel fading are also illustrated.

Long-range wide area network (LoRaWAN) has been newly designed to exploit the 2.4 GHz unlicensed band instead of the traditional sub-GHz bands. However, this makes LoRaWAN frequently suffer from wireless communication failures caused by the cross-technology interference (CTI) from coexisting Wi-Fi networks using the same 2.4 GHz band. As a physical-layer solution to this problem, this paper presents CoWiL to combat the CTI from Wi-Fi to LoRa (the physical layer of LoRaWAN) in the 2.4 GHz band for better coexistence between LoRaWAN and Wi-Fi networks. Existing approaches address this problem by sacrificing Wi-Fi transmission performance or assuming that Wi-Fi interferes with only a small portion of LoRa signals. Unlike them, CoWiL does not affect normal Wi-Fi communications and is workable regardless of the degree of the CTI. This is achieved by implementing CoWiL at a LoRa receiver to directly extract LoRa data out of the CTI from Wi-Fi. Specifically, CoWiL exploits the correlation of the signal demodulation results between the preamble and the payload of a LoRa signal. A novel frequency bin mask is generated based on the demodulated preamble and then applied to the following payload for data decoding. Experimental results in various real-world environments show that in comparison with the existing solutions, CoWiL can reduce the packet error rate of LoRa transmissions by up to 96% under the CTI from Wi-Fi.

This paper studies an application of “secret-message transmission by echoing encrypted probes (STEEP)” to multiple access (MA) between users' equipment (UEs) and an access point (AP). This method, referred to as MA-STEEP, allows all UEs to take advantage of a common sequence of probes broadcasted by AP, which helps to meet the low-latency requirement. The secrecy capacity of MA-STEEP from each UE to AP is shown to be positive with high probability (subject to a power condition) and robust against the number$M$of UEs. A total secrecy capacity of MA-STEEP increases with$M$, unlike a common-nonce method.

Millimeter-wave (mmWave) radar has become instrumental in diverse consumer applications. Yet current radar architectures face major limitations. While full-MIMO structures are feature-rich, their cost and complexity rise rapidly with more antennas. Phased-MIMO radars promise enhanced scalability by combining large phased arrays with a small number of RF chains. Nevertheless, the phased-MIMO research thus far primarily relies on simulation or theoretical analysis. In this paper, we introduce HybRadar, a novel programmable phased-MIMO radar platform to address this experimental gap. HybRadar repurposes the phased arrays on a low-cost 802.11ad radio to create a scalable low-cost array of phased subarrays. It further incorporates transmit/receive front-end, control channel, and hardware synchronization mechanisms to enable a modular phased-MIMO system. By extending recent MIMO array synthesis models, we optimize the placement of phased subarrays to maximize the spatial resolution. Our prototype validation and case studies confirm the capability and versatility of HybRadar.

This paper presents a secure downlink communication system where a transmitter with multiple antennas sends information to multiple single antenna users under ultra reliable and low-latency communication (uRLLC) system requirement. To meet uRLLC requirement and provide secrecy against multi-antenna eavesdropper (Eve), transmitter adopts a special channel training scheme called anti-eavesdropping channel estimation (ANECE) as well as a standard transmit beamforming to send secret information in short blocklength regime. Using ANECE, two or more cooperative full-duplex radio devices obtain their receive channel state information (CSI) with respect to each other while preventing Eve from obtaining a consistent estimate of its receive CSI, which improves the secrecy of subsequent transmission of information between the devices. We derive an expression for the average secrecy throughput (AST) of ANECE assisted transmission. This easy-to-evaluate expression of approximated AST in terms of blocklength and various controllable parameters leads to useful insights to maximize AST under uRLLC requirement. Finally, numerical results are presented and discussed.

Anti-eavesdropping channel estimation (ANECE) among two or more cooperative full-duplex radio devices allows these devices to obtain consistent estimates of their receive channel state information (CSI) with respect to each other but at the same time prevents eavesdropper (Eve) from obtaining a consistent estimate of its receive CSI, which improves the secrecy of subsequent transmission of information between the devices. This paper presents an analysis of secrecy throughput of ANECE assisted transmission of information between such single-antenna devices against Eve with multiple antennas. The analysis is based on finite blocklength coding and assumes that Eve applies a standard approach for information detection. Easy-to-compute analytical expressions of secrecy throughput in terms of various controllable parameters are obtained. Numerical results are presented and discussed.

Secret key generation (SKG) is a fundamental signal processing problem for security applications including wireless network security and biometric template security. For SKG, a pair (or more) of highly correlated secret vectors (SVs) need to be respectively quantized into a pair (or more) of almost identical sequences of binary bits (i.e., keys). The literatures on SKG in the wireless community almost uniformly default on a direct quantization on the SVs. On the other hand, many works on biometric template security advocate a quantization on the output of a one-way transformation of SVs. In this paper, we present a generalized approach for SKG called continuous encryption before quantization (CEbQ). By CEbQ, a pair of SVs of limited dimension are first transformed by a continuous encryption function into a pair of sequences of quasi-continuous pseudorandom numbers (QCPRNs) of any desired length, and then these QCPRNs are quantized into keys. We show that CEbQ can yield a much lower key error rate than direct quantization subject to standardized randomness tests. Comparisons with other related methods are also provided.

This paper considers a cooperative multiple access (CMA) scheme where two full-duplex sources simultaneously send their own messages to a common destination. As part of their cooperation, each of the two sources also receives and relays part of the message from the other source. This paper develops algorithms for computing the optimal power allocation to achieve the maximal network throughput subject to residual self-interference (RSI) at the non-ideal full-duplex sources. As a generalization of a time-division duplex (TDD) scheme, we consider a three-phase CMA (3P-CMA) scheme which treats the TDD scheme as a special case. The throughput of the 3P-CMA scheme consistently exceeds that of the TDD scheme regardless of the RSI power gain. Also shown in this paper is a semi-closed form of the optimal power allocation when the three-phases are constrained to be identical, i.e., resulting in a one-phase CMA (1P-CMA) scheme. The optimal power allocation of the 1P-CMA scheme takes much less time to compute, and its network throughout is close to that of the 3P-CMA scheme when the RSI power gain is low.

This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user's choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.

This paper introduces a novel physical layer encryption method called randomized reciprocal channel modulation (RRCM) for reliable and secure transmission of information against eavesdropper (Eve) with any number of antennas and any noise level. RRCM makes it computationally complex for Eve to estimate the user's channel state information (CSI) that is used to scramble the information symbols transmitted between users. Subject to Eve's failure to overcome the physical-layer computational complexity, users can apply RRCM to achieve a virtually constant (significant) rate in bits/s/Hz of unconditional secrecy - unconditional on Eve's number of antennas, Eve's noise level and the CSI coherence time.

We present a statistical analysis of the secrecy capacity for two downlink transmission schemes: transmitantenna selection (TAS) and transmit- antenna beamforming (TAB), where the transmitter (Alice) has multiple antennas, the receiver (Bob) is a single-antenna full-duplex radio, and the eavesdroppers are randomly distributed each with a single antenna. We focus on the secrecy outage probability (SOP) or its related measure, show closed-form expressions of SOP for the two schemes, and present insights into how various system parameters such as the jamming powers from both Alice and Bob affect SOP. The SOP performance of TAB is shown to be significantly better than that of TAS although TAB requires more on channel estimation than TAS.

Through a wireless channel, two users can transmit pilot signals to each other, and then utilize the received signals to agree upon a secret key through communications in a public channel without leaking any secret about this key to anyone else. This paper addresses the optimization of the two pilots to maximize the capacity of secret key generation for a MIMO channel with any given receive and transmit correlation matrices. This study shows how the globally optimal pilots can be obtained if the transmission power is either low or high. For an arbitrary transmission power, the algorithm developed is at least locally optimal. Comparison to pilots based on minimum channel estimation errors and uniform power distribution is also presented. It is also shown that the designed pilots meet the requirement for anti- eavesdropping channel estimation.

In this paper, we study linear transceiver designs for indoor visible light communications (VLCs) with multiple light emitting diodes (LEDs). Specifically, we investigate VLCs including white emitting diodes and VLCs including red/green/blue (RGB) LEDs. The transmitter precoding and the offset are jointly designed by considering certain key practical lighting constraints, such as optical power, non-negativeness, and color illumination. Various non-convex transceiver design problems are formulated aiming to minimize total mean-square-error to improve transmission reliability. We show that for multi-input single-output white VLCs, the optimal precoding reduces to a simple LED selection strategy. For multi-input multi-output (MIMO) white VLCs, we prove that the optimization problem with multiple constraints can be equivalently simplified to a problem with single constraint, which enables us to propose efficient algorithms to search local optimal solutions. For MIMO RGB VLCs, by using certain useful transformations, we show that the precoding design is equivalent to covariance matrix design of transmit signals, which can be further transformed to a convex optimization problem. To develop an algorithm to find the optimal solution, we derive the optimal structure of the covariance matrix and show that the optimal solution can be obtained via a water-filling approach. Extensive simulation results are provided to verify the performance of the proposed designs.

Energy consumption in wireless communication systems is exponentially increasing due to growing wireless traffic. Combating adverse effects of excessive energy consumption calls for energy- aware system design, leading to a new research paradigm, green communications. To achieve energy awareness in a full-duplex (FD) bi-directional multiple antenna system, in this paper, we study a Quality-of-Service (QoS) problem, where the total transmit power is minimized subject to minimum rate constraints at each node, and propose two algorithms. We first apply a penalty-based method in order to obtain an efficient optimization strategy. Afterwards, in the second algorithm, we generalize the well-known relationship between Weighted-Sum-Rate (WSR) andWeightedMinimum-Mean- Squared-Error (WMMSE) problems, originally used to solve the sum-rate maximization problem, to tackle the sum-power minimization problem.

Due to its time-varying nature, oscillator phase noise can significantly degrade the performance of the channel estimation, carrier recovery, and data detection blocks in high-speed wireless communication systems. In this paper, we propose a new data-aided joint channel, carrier frequency offset (CFO) and phase noise estimator for orthogonal frequency division multiplexing (OFDM) relay systems. For the data transmission phase, we propose a new iterative receiver that tracks phase noise and detects the transmitted symbols. Additionally, we derive the hybrid Cramér-Rao lower bound for evaluating the performance of channel estimation and carrier recovery algorithms in OFDM relay networks. Extensive simulations demonstrate that the application of the proposed estimation and receiver blocks significantly improves the performance of OFDM relay networks in the presence of phase noise and CFO.

In this paper, we address the proportional fair (PF) issue of a K link full-duplex (FD) multiple-input multiple-output (MIMO) interference channel, where each link consists of two FD nodes exchanging information simultaneously. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from the nodes in other links due to simultaneous transmission from each link. The PF issue is important for networks with asymmetric topology and/or asymmetric traffic demands. We demonstrate that the proposed algorithm provides a good trade-off between sum achievable rate and rate distribution for asymmetric links, and moreover we show that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline half-duplex (HD) schemes.

In this paper, we propose a high-rate space-time block code (STBC) for millimeter-wave wireless communication systems that are equipped with reconfigurable multiple-input multiple-output (MIMO) antennas. We assume that each reconfigurable antenna element has the capability of forming its beam and can independently change the characteristics of its radiation pattern1. We exploit this feature of the antenna elements to construct the proposed space-time block code where each coded symbol is sent over independent beams. As a result, the received coded symbols at different receive antennas will have different power levels, a desired property that can be used in data detection. We show that the proposed code achieves a coding rate of two and requires a low-complexity maximum likelihood (ML) detector. We carry out computer simulations that demonstrate the performance of the proposed code and shows its superiority in compared to previous rate-2 STBC counterparts. We also comment on the generalization of the proposed STBC for higher MIMO dimensions.

We consider a K link multiple-input multiple-output (MIMO) interference channel, where each link consists of two full-duplex (FD) nodes exchanging information simultaneously in a bi-directional communication fashion. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from other links due to simultaneous transmission at each link. We consider the transmit and receive filter design for weighted sum-rate (WSR) maximization problem subject to sum-power constraint of the system or individual power constraints at each node of the system. Based on the relationship between WSR and weighted minimum-mean-squared-error (WMMSE) problems for FD MIMO interference channels, we propose a low complexity alternating algorithm which converges to a local WSR optimum point. Moreover, we show that the proposed algorithm is not only applicable to FD MIMO interference channels, but also applicable to FD cellular systems in which a base station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) users operating in half-duplex (HD) mode, simultaneously. It is shown in simulations that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline HD schemes.

We study two scenarios of full-duplex (FD) multiple-input-multiple-output cognitive radio networks: FD cognitive ad hoc networks and FD cognitive cellular networks. In FD cognitive ad hoc networks (also referred as interference channels), each pair of secondary users (SUs) operate in FD mode and communicate with each other within the service range of primary users (PUs). Each SU experiences not only self-interference but also interuser interference from all other SUs, and all SUs generate interference on PUs. We address two optimization problems: one is to minimize the sum of mean-squared errors (MSE) of all estimated symbols, and the other is to minimize the maximum per-SU MSE of estimated symbols, both of which are subject to power constraints at SUs and interference constraints projected to each PU. We show that these problems can be cast as a second-order cone programming, and joint design of transceiver matrices can be obtained through an iterative algorithm. Moreover, we show that the proposed algorithm is not only applicable to interference channels but also to FD cellular systems, in which a base station operating in FD mode simultaneously serves multiple uplink and downlink users, and it is shown to outperform HD scheme significantly.

In this paper, while considering the impact of the antenna correlation and the interference from neighboring users, we analyze channel estimation and training sequence design for multi-input multi-output (MIMO) two-way relay systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) phase and the broadcast (BC) phase. By considering the Kronecker-structured channel model, we derive the optimal linear minimum mean-square-error (LMMSE) channel estimators. The corresponding training designs for the MAC phase and the BC phase are then formulated and solved to improve channel estimation accuracy. For the general scenario of the training sequence design for both phases, two iterative training design algorithms are proposed that are verified to produce training sequences achieving near optimal channel estimation performance. Furthermore, for specific practical scenarios, where the covariance matrices of the channel or disturbances are of particular structures, the optimal training sequence design guidelines are obtained. The minimum required training lengths for channel estimation in both the MAC phase and the BC phase are also analyzed. Comprehensive simulations are carried out to demonstrate the effectiveness of the proposed training designs.

In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation in orthogonal frequency division multiplexing (OFDM) relaying networks. To achieve this goal, a detailed transmission framework involving both training and data symbols is first presented. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase noise parameters based on the maximum a posteriori criterion is proposed. Additionally, to evaluate the performance of the proposed channel estimation and carrier recovery algorithms, we analyze the ambiguities among the estimated parameters. Based on this analysis, a new Hybrid Cramér-Rao Lower Bound (HCRLB) is derived, which can effectively avoid such ambiguities. The simulation results show that the proposed estimation algorithm can achieve a performance close to the derived HCRLB.

In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we study the problem of channel estimation and training sequence design in multi-input multi-output (MIMO) two-way relaying (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) and the broadcast (BC) phases. By deriving the optimal linear minimum mean-square-error estimators, the corresponding training design problems for the MAC and BC phases are formulated and solved. Subsequently, algorithms and, in some special cases, closed-form solutions for obtaining the optimal training sequences for channel estimation in TWR systems are derived. Moreover, to further reduce channel estimation overhead, the minimum required length of the training sequences are determined. Simulation results verify the effectiveness of the proposed training designs in improving channel estimation performance in TWR systems.

A block-wise constellation design is presented for optical communication systems with multi-subcarrier modulation (MSM), intensity modulation (IM) and direct detection (DD). The DC-bias traditionally used only for compensating the negative peaks of the transmitter-side signals is treated as an information-carrying basis in our proposed scheme called MSM-JDCM. Designs are done for both flat-fading and frequency selective-fading scenarios, and following a principle of high dimensional sphere packing. To simplify the problem, we apply the following methods. First, we use bounds on the waveform's maximum and minimum. Second, we use the maximum and minimum constraints on a set of sufficient samples of waveforms. Third, we relax non-convex distance constraints into convex ones by iterative linearizations. With the MSM-JDCM, we minimize electrical power, optical power, and peak power with a common target bit error rate (BER). Analysis shows that the MSM-JDCM offers significant power gains over MSM-Normal and MSM-SPSS. The short-term peak to average power ratio (PAPR) and long-term PAPR constraints are combined with the MSM-JDCM to mitigate the nonlinear distortion caused by high power amplifier and laser diode, which is another novelty of our scheme. To attain lower BER, a binary switching algorithm (BSA) is applied to find the improved constellation labeling.

In this paper, we establish the signal-to-interference-noise-ratio (SINR) duality between multiple access (MAC) and broadcast (BC) multihop amplify-and-forward multiple-input-multiple-output relay systems under an imperfect channel state model, which is a generalization of several previously established MAC-BC duality results. We show that identical SINRs in the MAC and BC systems can be achieved by two approaches. The first one is to use the Hermitian transposed MAC relay amplifying matrices at the relay nodes in the BC system under the same total network transmission power constraint. The second one is to use the scaled and Hermitian transposed MAC relay amplifying matrices in the BC system under the transmission power constraint at each node of the system, where the scaling factors are obtained by swapping the power constraints of the nodes in the MAC system. Moreover, we derive the MAC-BC mean-squared error and achievable sum-rate [or mutual information] duality properties based on the SINR duality. Numerical results show the utility of the duality results established.

Wireless network coding can significantly improve the spectrum efficiency for relaying transmission when receivers can acquire accurate channel state information (CSI). In this paper, the channel estimation problem for two-way relaying channels is considered where two sources exchange information through an amplify-and-forward relay employing analog network coding protocol. By taking advantage of the apriori information of wireless channels to further improve channel estimation accuracy, the maximum a posteriori (MAP) based estimation schemes are developed to estimate the composite source-source channel coefficients and the amplitude of individual source-relay channels with apriori knowledge of channel distribution information (CDI). Variations of MAP estimation algorithms are also developed for systems under practical constraints where channel CDI needs to be estimated. In particular, scale MAP estimator as well as a long term estimation algorithm is developed to effectively control the negative impact of CDI estimation error on MAP estimation performance. The simulation results show that the MAP based estimation strategies consistently outperform maximum likelihood estimation methods in the measure of mean square error, thus establishes the advantage of presented MAP based schemes.

The demand for wireless access continues to increase rapidly in both military and civilian communities. The modern internet and modern personal-area devices have made billions of users around the world accustomed to data-hungry applications such as videos. This has an inevitable effect on the users' desire for the same through wireless media. The articles in this special issue focus on new theories and methods for advanced wireless relay technologies.

The 46 papers focusing on the theme of "Theories and Methods for Advanced Wireless Relays" have been divided into two groups to be published in two separate issues. This first issue includes 23 papers on relay performance bound, MIMO relay beamforming, relay channel estimation, two-way and shared relays, full-duplex relays, and security for relay networks. The second issue includes papers on coding for relay networks, medium access control for relays, implementation ans system performance studies.

We present a time-domain transmit beamforming (TDTB) method for self-interference cancelation (SIC) at the radio frequency (RF) frontend of the receivers on broadband full-duplex MIMO radios. It is shown that the conventional frequency-domain transmit beamforming (FDTB) method along with the orthogonal frequency division multiplexing (OFDM) framework does not generally perform SIC in the prefix region of a transmitted frame. A hardware based test of the TDTB method shows a 50 dB SIC over a bandwidth of 30 MHz.

This paper considers a system where two users exchange information via a non-regenerative half-duplex two-way MIMO relay and each of the two users and the relay is equipped with multiple antennas. We study the design of the spatial source covariance matrices (or source matrices) at the two users and the spatial transformation matrix (or relay matrix) at the relay to maximize the achievable weighted sum rate of the system. The source matrices and the relay matrix are optimized alternately until convergence. If the relay matrix is given, we show that the optimal design of the source matrices (for uniformly weighted sum rate) follows a generalized water filling (GWF) algorithm. If the source matrices are given, we show two search algorithms to optimize the relay matrix. The first algorithm is a hybrid gradient method which adaptively switches between the (steepest) gradient descent and the Newton's search. The second is an iterative weighted minimum mean square error (WMMSE) method which alternately refines the MMSE equalizers at the users and the relay matrix. We compare the convergence behaviors of the two algorithms and demonstrate their advantage over prior algorithms. We also show an optimal structure of the relay matrix, which is useful to reduce the search complexity.

This paper addresses a transmission energy problem for estimation in multihop wireless sensor networks. We consider two very different schemes of estimation: progressive estimation and consensus estimation. Progressive estimation is designed for networks about which a central planner knows a routing tree and all channel state information. Consensus estimation is commonly known for more dynamic networks. In this paper, we develop energy planning algorithms to minimize the transmission energy cost for both schemes, and compare their energy costs. This study shows that the total energy cost for consensus estimation can be much higher than that for progressive estimation, but the peak energy for the former is less than that for the latter.

This paper addresses a non-regenerative two-way MIMO relay system where two sources exchange information via a common relay. Both sources and the relay are each equipped with multiple antennas. In the first phase (time or frequency), both sources transmit their signals and the relay receives. In the second phase, the relay transmits towards both sources a transformed signal. The optimization of the source covariance matrices and the optimization of the relay transformation matrix can be performed alternately until convergence. The former can be achieved by a generalized water filling algorithm, and the latter can be achieved by two algorithms discussed in this paper. The first algorithm is a hybrid gradient optimization. The second algorithm is an iterative weighted MMSE equalization. It is shown in simulation that both algorithms converge to the same results and are much faster than a prior algorithm developed for single-antenna sources.

In this paper, a multicarrier multi-hop multiple-input multiple-output (MIMO) relay system is investigated. A linear non-regenerative strategy is applied at each relay node. We show that for Schur-concave objective functions, the optimal source precoding matrix, the optimal relay amplifying matrices and the optimal receiving matrix jointly diagonalize the multicarrier multi-hop MIMO relay channel. And for Schur-convex objectives, such joint diagonalization along with a rotation of the source precoding matrix is also shown to be optimal. Using the optimal structure of the source and relay matrices, the multi-hop relay design problem boils down to the issue of power loading among the resulting parallel multi-hop single-input single-output (SISO) relay channels. This paper provides additional details of the multicarrier version of some of our recent results.

A cooperative power scheduling algorithm developed by Wang, Krunz and Cui is extended for an ad hoc network of MIMO links. This algorithm, referred to as price-based iterative water filling (PIWF) algorithm, is a distributed algorithm by which each link computes its power scheduling through an iterative and cooperative process. The cooperation among all links is achieved by adaptive price factors applied by each link. Compared to a centralized power scheduling algorithm, the PIWF algorithm is much more efficient in computation although not as efficient in network throughput. Compared to a non-cooperative counter-part by Demirkol and Ingram where all price factors are zero, the PIWF algorithm requires additional in-network computation but is more efficient in network throughput.

In this paper, we propose a system that can detect unusual sounds and directions by observing sound environment with microphone arrays. One of the attractive features of the system is to detect the unusual information through daily environmental sound measurement. Therefore the system does not require such troublesome processes that the detected sounds must be predefined, the predefined sounds must be collected, and using the collected sounds, their features must be modeled, where the conventional systems have such troublesomeness. Moreover, unlike conventional systems using video cameras, our system is not limited by video camera angles. A simple experimental result shows the validity of the proposed system.

For a two-hop linear non-regenerative multiple-input multiple-output (MIMO) relay system where the direct link between source and destination is negligible, the optimal design of the source and relay matrices has been recently established for a broad class of objective functions. The optimal source and relay matrices jointly diagonalize the MIMO relay system into a set of parallel scalar channels. In this paper, we show that this diagonalization is also optimal for a multihop MIMO relay system with any number of hops, which is a further generalization of several previously established results. Specifically, for Schur-concave objective functions, the optimal source precoding matrix, the optimal relay amplifying matrices and the optimal receiving matrix jointly diagonalize the multihop MIMO relay channel. And for Schur-convex objectives, such joint diagonalization along with a rotation of the source precoding matrix is also shown to be optimal. We also analyze the system performance when each node has the same transmission power budget and the same asymptotically large number of antennas. The asymptotic analysis shows a good agreement with numerical results under a finite number of antennas.

Wireless mesh networks are rapidly deployable for many applications. The throughput of such a network depends on the schemes used for medium access control. In this paper, we present some of our latest results on the (intra-network) throughput of large-scale networks. In particular, we show a comparison of the throughput between the opportunistic synchronous array method and the slotted ALOHA under different traffic loading, which reveals that the former yields a higher throughput than the latter except for the region of very low traffic (less than 10% load probability). We further show that a longer distance transmission yields lower throughput than the shortest distance transmission except for the region of extremely low traffic (less than 1% load probability).

In this correspondence, we show that the uniform throughput capacity of an ultra-wideband (UWB) power-constrained ad hoc network is given by , where is the per-node power constraint, is the fading exponent of radio signal and is the number of nodes randomly distributed inside a disk of unit area. This is a stronger result than the upper bound and the lower bound previously shown by Negi and Rajeswaran. Our proof is simple given the prior work by Gupta and Kumar.

We present an optimal power scheduling scheme to maximize the throughput of a set of distributed multiple-input multiple-output (MIMO) wireless links. This scheme exploits both spatial and temporal freedoms of the source covariance matrices of all MIMO links. In particular, the source covariance matrix of each MIMO link is allowed to vary within a block of time (and/or frequency) slots. This scheme, also referred to as space-time power scheduling, optimizes an integration of link scheduling and power control for MIMO links. The computational problem involved in this scheme is non-convex. However, a gradient-projection algorithm developed for this scheme consistently yields a higher capacity than all other existing schemes.

The throughput of a large network of wireless routers critically depends on the design of distributed medium access control (MAC). In this paper, a distributed MAC scheme called opportunistic synchronous array method (SAM) is presented and analyzed. The opportunistic SAM combines multiuser diversity and interference suppression in a distributed fashion. Depending on the choice of two control parameters, the opportunistic SAM may reduce to three specializations: multiuser SAM, switching SAM or deterministic SAM. The network throughput of the opportunistic SAM is analyzed, and its throughput gain over its specializations is illustrated.

We examine signaling strategies for wireless MIMO networks with interference. Previous approaches have focused on maximizing either individual or total throughput, resulting in an inefficient or potentially unfair allocation of resources. We propose two methods motivated by game-theoretic results. First, we extend the non-cooperative Nash equilibrium proposed in previous literature. Second, we present a cooperative method based on the Nash bargaining solution which provides an axiomatic arbitration scheme. Simulation results show that the Nash bargaining solution provides a fair allocation of resources without significantly sacrificing total throughput.

A space-time optimal power schedule for multiple distributed MIMO links without the knowledge of channel state information at transmitting nodes is proposed. This new approach exploits both the spatial and temporal freedoms of distributed MIMO links. A readily computable expression for the ergodic sum capacity of the MIMO links is derived. Based on this expression, a projected gradient algorithm is developed to optimize the power allocation. For a symmetric set of MIMO links, it is observed that the space-time optimal power schedule reduces to a uniform isotropic power schedule when nominal interference is low, or to an orthogonal isotropic power schedule when nominal interference is high. Furthermore, the transition region between the latter two schedules is seen to be very small in terms of nominal interference-to-noise ratio.

This paper proposes a distributed MAC scheme for a large network of wireless routers with multiple antennas and characterizes its throughput gain over its single antenna counterpart in large network communications.

A multihop progressive decentralized estimation scheme is presented for 1-D, 2-D, or 3-D sensor networks where routing trees are available. The bit allocation for each sensor is optimized in a multihop setting. This new scheme is shown to be much more efficient in energy consumption than the nonprogressive decentralized estimation scheme available in the literature. The transmission energy model is based on spectrum-limited applications.

For four (or more) transmitters, a new design of differential space-time block code allowing symbol-wise decoding is presented in this letter. The new design not only has the minimum (symbol-wise) decoding complexity as that by Yuen, Guan and Tjhung (YGT) but also yields a lower error rate. While the YGT code uses a specially designed symbol constellation, the new code uses a conventional QAM with a rotation. At a high data rate such as 3 bps/Hz, the new design with symbol-wise decoding complexity can even yield a lower error rate than the code by Zhu and Jafarkhani that has the pair-wise decoding complexity.

Given a multiple-antenna source and a multiple-antenna destination, a multiple-antenna relay between the source and the destination is desirable under useful circumstances. A non-regenerative multiple-antenna relay, also called non-regenerative MIMO (multi-input multi-output) relay, is designed to optimize the capacity between the source and the destination. Without a direct link between the source and the destination, the optimal canonical coordinates of the relay matrix are first established, and the optimal power allocations along these coordinates are then found. The system capacity with the optimal relay matrix is shown to be significantly higher than those with heuristic relay matrices. When a direct link is present, upper and lower bounds of the optimal system capacity are discussed.

For two-transmitter systems, there are (full-rate) orthogonal space-time block codes (O-STBC). For four-transmitter systems, there are (full-rate) quasi-orthogonal space-time block codes (QO-STBC). The orthogonality of such codes makes a decoder attractively simple with only little compromise of optimality of coding performance. A complete family of O-STBC is well understood. But for QO-STBC, only ad hoc examples have been reported in the literature. In this paper, we provide a systematic construction of a complete family of 4 /spl times/ 4 QO-STBC. We show that there are only three independent QO-STBC and all other QO-STBC can be constructed by trivial variations of any three independent codes. Indeed, all 4 /spl times/ 4 QO-STBC in the literature can be constructed in such a way. Furthermore, we show a connection between three independent QO-STBC and the Hurwitz-Radon families of matrices. A complete set of the HR families of size four is also discovered.

Cooperative relays have recently been proposed and studied for mobile ad hoc networks. It has been shown that under perfect symbol synchronization, parallel relays with space-time modulation can yield more than 10 dB power savings over conventional serial relays in a highly mobile environment. We analyze the effect of synchronization errors on the performance of parallel relays, and also study several effective methods to reduce the negative impact of synch errors. Our study shows that when the synch errors are much smaller than a symbol interval, the performance of parallel relays deteriorates gracefully. We have also found that well designed space-time coding techniques, such as TR-STC and ST-OFDM, can be highly effective in combating large synch errors with only marginal reduction of data rate.

In mobile ad hoc networks, relaying is one of the most fundamental functions. In this paper, we analyze the stability of several relaying strategies. By using the notion of node lifetime instead of the commonly used link lifetime, our analysis offers a complementary and unique finding not available in the literature previously. Our results show that a route of parallel relays have a significantly longer lifetime than a route of serial relays although the latter of the two has previously predominated the networking research.

For ad hoc mobile networks, parallel wireless relays with space-time modulation have recently been discovered, and their potential to increase network capacity has been found to be significant. In this paper, we provide an analytical study of the diversity factor achievable by two relays with orthogonal space-time modulation. Unlike two transmitters or two receivers in the conventional MIMO setting for which the averaged bit error rate (BER) is proportional to 1/SNR/sup 2/, the averaged BER of two relays is shown to be proportional to ln(SNR)/SNR/sup 2/. This insight explains a difference between the diversity of multiple relays and the diversity of multiple receivers/transmitters. A switching scheme applied to two relays is also analyzed.

We first revisit the problem of optimal low-rank matrix approximation, from which a bi-iterative least square (Bi-LS) method is formulated. We then show that the Bi-LS method is a natural platform for developing subspace tracking algorithms. Comparing to the well known bi-iterative singular value decomposition (Bi-SVD) method, we demonstrate that the Bi-LS method leads to much simpler (and yet equally accurate) linear complexity algorithms for subspace tracking. This gain of simplicity is a surprising result while, as we show, the reason behind it is also surprisingly simple.

We present a new method for identification of FIR MIMO channels driven by unknown, uncorrelated and colored sources. This method, belonging to the BID (blind identification by decorrelation) family, makes use of the mutual uncorrelation of the unknown sources by first decorrelating the observed signals into two uncorrelated groups. The two decorrelators are then used to estimate the channel matrix (i.e., MIMO channel transfer function matrix) up to a constant matrix. This constant matrix is finally determined using a BID method for instantaneous MIMO channels. This new method, named BID-G, is shown to be much more robust than the subspace method that requires the channel matrix to be irreducible and column-reduced.

We present a two-step maximum likelihood (TSML) algorithm for blind identification of single-input-multiple-output (SIMO) channels modeled as an autoregressive (AR) system. The AR-TSML algorithm provides a new and useful alternative to a previously developed TSML algorithm for a moving-average (MA) system. The AR-TSML algorithm is shown to be more robust than the MA-TSML algorithm if the channel impulse responses have long tails.

We present a two-step maximum likelihood (TSML) algorithm for blind identification of single-input-multiple-output (SIMO) channels modeled as autoregressive (AR) system. The AR-TSML algorithm provides a new and useful alternative to a previously developed TSML algorithm for moving-average (MA) system. The AR-TSML algorithm is shown to be more robust than the MA-TSML algorithm if the channel impulse responses have long tails.

We consider the problem of separating colorred signals mixed by unknown convolutive channels. We introduce a variation of a previous approach of separating signals via decorrelation. This variation minimizes the mutual correlation among the output signals of a decorrelation matrix subject to a diagonal constraint. The diagonal constraint ensures a better quality of separation. The diagonal constraint is shown to be essential when the number of original signals is less than the number of distorted signals

A modification of Moulines' blind second order statistical channel subspace approach (see Moulines, E. et al., IEEE Trans. on Sig. Proc., vol.43, no.2, p.516-25, 1995) is proposed for the identification of single input multiple output finite impulse response channels. The modification exploits the transmitter redundancy introduced by a trailing zero filter bank precoder. The method is shown to be robust to common zeros and channel order over-estimation errors.

This paper presents an algorithm of blind identification and equalization of finite-impulse-response and multiple-input and multiple-output (FIR MIMO) channels driven by colored signals. This algorithm is an improved realization of a concept referred to as blind identification via decorrelating subchannels (BIDS). This BIDS algorithm first constructs a set of decorrelators which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators, and finally recovers the input signals using the estimated channel matrix. This BIDS algorithm in general assumes that the channel matrix is irreducible and the input signals are mutually uncorrelated and of sufficiently diverse power spectra. However, for channel matrix identification, this BIDS algorithm only requires the channel matrix to be nonsingular (ie, full rank almost everywhere as opposed to everywhere) and column-wise coprime. Such a channel matrix may have zeros and be of non-minimum phase.

If two wide-sense stationary time series are correlated then one can be used to predict the other. The reduced rank Wiener filter is the rank-constrained linear operator which maps the current value of one time series to an estimate of the current value of the other time series in an optimal way. A closed-form solution exists for the reduced rank Wiener filter. This paper studies the problem of determining the reduced rank FIR filter which optimally predicts one time series given the other. This optimal FIR filter is called the convolutive reduced rank Wiener filter, and it is proved that determining it is equivalent to solving a weighted low rank approximation problem. In certain cases a closed-form solution exists, and in general, the iterative optimisation algorithm derived here can be used to converge to a locally optimal convolutive reduced rank Wiener filter.

This paper presents a delayed instantaneous demixer (DID) for speech signal separation from real recordings. Based on the fact that the original signals are colored and mutually uncorrelated, a simple algorithm is derived to estimate the parameters of the demixer. This algorithm consists of two parts: a grid searching method to estimate time delays and an alternating projection method to estimate gain coefficients. Experimental result demonstrates the performance of the model and the algorithm.

This paper presents a generalized sufficient and necessary identifiability condition for second order statistics (SOS) based blind source separation (BSS). It is then shown that, even when this condition is not satisfied the sources are still partially identifiable (in blocks). Furthermore, although the identifiability condition depends on the autocorrelation coefficients of the unknown source signals, it can be tested directly from the observations. This issue is of prime importance to decide whether the sources have been well separated or else if further treatments are needed. Some simulation examples are given to assess our theoretical results.

This paper presents a new approach for blind identification of multiple colored stationary/nonstationary signals distorted by unknown FIR channels. The key idea of this approach is to use a bank of decorrelators to transform a multiple-input and multiple output system (driven by the desired signals) into a bank of single-input and single-output systems. This approach is referred to as BID, i.e., blind identification via decorrelation. The BID approach can uniquely (up to a permutation and scaling) identify the signals if (a) the signals are mutually uncorrelated and of distinct power spectra, and (b) each column of the system function is a coprime polynomial vector. No other method known to date can uniquely identify the signals under the above condition. The BID approach achieves the optimal identifiability potential predicted by Hua and Tugnait.

It is known that precoding a signal prior to its transmission through an unknown finite impulse response channel facilitates the equalisation of the channel. Moreover, since linear precoders spread the spectrum, the equalisation process mitigates the effects of channel spectral nulls caused by frequency selective fading. This paper argues that certain affine precoders are more efficient than linear precoders. Here, the efficiency of a precoder is a measure of its ability to enable the receiver to recover the source signal relative to the amount of redundancy introduced by the precoder. An efficient affine precoder is heuristically derived and simulations used to demonstrate that it is superior to both the traditional training sequence method and the filter bank precoding scheme. This precoding and equalisation scheme naturally extends to time varying channels.

In this letter, we propose an orthogonalized version of the Oja algorithm (OOja) that can be used for the estimation of minor and principal subspaces of a vector sequence. The new algorithm offers, as compared to Oja, such advantages as orthogonality of the weight matrix, which is ensured at each iteration, numerical stability, and a quite similar computational complexity.

Subspace decomposition has proven to be an important tool in adaptive signal processing. A number of algorithms have been proposed for tracking the dominant subspace. Among the most robust and most efficient methods is the projection approximation and subspace tracking (PAST) method. This paper elaborates on an orthonormal version of the PAST algorithm for fast estimation and tracking of the principal subspace or/and principal components of a vector sequence. The orthonormal PAST (OPAST) algorithm guarantees the orthonormality of the weight matrix at each iteration. Moreover, it has a linear complexity like the PAST algorithm and a global convergence property like the natural power (NP) method.

We show that a finite impulse response and multi-input-multi-output (FIR-MIMO) system with colored input is blindly identifiable up to a permutation and scaling using the second order statistics (SOS) of the system's output if (a) the system function is irreducible, and (b) the input signals are uncorrelated from each other and have distinct power spectra. Condition (a) is weaker than several conditions reported previously. It suggests a further potential of developing more robust blind algorithms.

We propose blind multiuser detection schemes with antenna arrays, which is based on signal subspace estimation. They are a multichannel extension of the decorrelating and minimum mean-square-error detectors, and therefore they share their immunity to near-far effects. The blind scheme may be seen as an extension of the results in of Wang and Poor (see IEEE Trans. Inform. Theory, vol.44, p.677-90, 1998). However, it is seen that compared with the latter results when spatial diversity is considered, the proposed spatio-temporal detectors offer, with little attendant increase in computational complexity, a better performance. A blind adaptive implementation based on a new orthogonal PAST (projection approximation subspace tracking) algorithm, which is shown to be efficient for subspace tracking, is proposed. Also, we develop a blind estimation of the spatial signature based on the orthogonality between noise and signal subspaces. It is seen that the blind adaptive multiuser detection and blind spatio-temporal signature estimation can he integrated jointly.

Separation of sources that are mixed by an unknown (hence, "blind") mixing matrix is an important task for a wide range of applications. This paper presents an adaptive blind source separation method using second order statistics (SOS) and natural gradient. The SOS of observed data is shown to be sufficient for separating mutually uncorrelated sources provided that the temporal coherences of all sources are linearly independent of each other. By applying the natural gradient, new adaptive algorithms are derived that have a number of attractive properties such as invariance of asymptotical performance (with respect to the mixing matrix) and guaranteed local stability. Simulations suggest that the new algorithms are highly efficient and outperform some of the best existing ones.

A technique for vector quantization which minimizes a maximal L/sub /spl infin// distortion is introduced in this paper. A centroid rule is obtained and a codebook design method is developed. The problem of minimizing the number of occurrences of quantization errors larger than a preselected threshold is further investigated. Compared to the methods that minimize an averaged L/sub /spl infin// distortion recently proposed by Mathews et al., the new approaches that minimize the maximal L/sub /spl infin// distortion achieve more desirable results.

The reduced-rank Wiener filter (RRWF) is a generic tool for data compression and filtering. This letter presents an iterative quadratic minimum distance (IQMD) algorithm for computing the RRWF. Although it is iterative in nature, the IQMD algorithm is shown to be globally and exponentially convergent under some weak conditions. While the conventional algorithms for computing the RRWF require an order of n/sup 3/ flops, the IQMD algorithm requires only an order of n/sup 2/ flops at each iteration where n is the dimension of data. The number of iterations required in practice is often small due to the exponential convergence rate of the IQMD.

This letter studies the problem of identifying a single-input single-output channel by linearly precoding the input. Although it is known that filterbanks induce cyclostationarity if the input is stationary, this work demonstrates that filterbanks induce a special form of spectral redundancy even if the input is deterministic. This frequency domain interpretation of filterbanks nicely characterizes all linear precoders. It not only helps to identify the strengths and aid in the design of linear precoders, it leads to frequency domain subspace methods for the identification of AR, MA and ARMA channels.

A new framework of vector quantization that minimizes maximal L/sub /spl infin// distortion is introduced. Based on the concept of minimax L/sub /spl infin// distortion, a new centroid rule and a new codebook design method are developed. The problem of minimizing the number of occurrences of quantization errors larger than a preselected threshold is also investigated. Compared to the methods that minimize an averaged L/sub /spl infin// distortion recently proposed by Mathews et al. (1997), the new methods with minimax L/sub /spl infin// distortion achieve more desirable results.

We address the problem of joint Schur decomposition (JSD) of several matrices. This problem is of great importance for many signal processing applications such as sonar, biomedicine, and mobile communications. We first present a least-squares (LS) approach for computing the JSD. The LS approach is shown to coincide with that proposed intuitively by Haardt et al. (1996), thus establishing the optimality of their criterion in the least-squares sense. Following the LS criterion, we then propose new Jacobi-like algorithms that extend and improve the existing JSD algorithms. An application of the new JSD algorithm to multidimensional harmonic retrieval is also presented.

It is a standard result that a finite impulse response channel of length L can be uniquely identified by feeding in a known (and persistently exciting) sequence of 2L-1 consecutive data points. Equivalently, given only 2L-2 consecutive data points, the channel can be uniquely identified up to a multiplicative constant. This paper significantly extends the identifiability criterion to the case when the known inputs are non-consecutively located. It is argued that by introducing 2L-1 non-consecutively spaced zeros into the input stream, for almost all input sequences, the channel can be uniquely identified up to a multiplicative constant. Furthermore, the result can be extended to the case when the known inputs are non-zero, in which case the channel can almost always be identified uniquely. To arrive at these results, general properties of systems of polynomial equations are derived. These properties do not seem to have appeared in the literature before.

We introduce a novel information criterion (NIC) for searching for the optimum weights of a two-layer linear neural network (NN). The NIC exhibits a single global maximum attained if and only if the weights span the (desired) principal subspace of a covariance matrix. The other stationary points of the NIC are (unstable) saddle points. We develop an adaptive algorithm based on the NIC for estimating and tracking the principal subspace of a vector sequence. The NIC algorithm provides a fast on-line learning of the optimum weights for the two-layer linear NN. The NIC algorithm has several key advantages such as faster convergence which is illustrated through analysis and simulation.

We present a novel generic tool for data compression and filtering: the generalized Karhunen-Loeve (GKL) transform. The GKL transform minimizes a distance between any given reference and a transformation of some given data where the transform has a predetermined maximum possible rank. The GKL transform is also a generalization of the relative Karhunen-Loeve (RKL) transform by Yamashita and Ogawa (see IEEE Trans. Signal Processing, vol.44, p.661-72, Mar. 1996) where the latter assumes that the given data consist of the given reference (signal) and an independent noise. This letter provides a very simple and yet complete description of the GKL transform and shows useful engineering insights into the GKL transform.

Oja's principal subspace algorithm is a well-known and powerful technique for learning and tracking principal information in time series. A thorough investigation of the convergence property of Oja's algorithm is undertaken in this paper. The asymptotic convergence rates of the algorithm is discovered. The dependence of the algorithm on its initial weight matrix and the singularity of the data covariance matrix is comprehensively addressed.

Blind system identification (BSI) is a fundamental signal processing technology aimed at retrieving a system's unknown information from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation, and blind image restoration. This paper reviews a number of recently developed concepts and techniques for BSI, which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input.

We present a new subspace-based method for blind identification of multichannel finite impulse response (FIR) systems. Instead of assuming spatially white additive noise as commonly used, we consider the case where the noise spatial covariance matrix is unknown. We show how a standard subspace method can be simply modified so that the channel estimate does not depend on the zero-lag correlation coefficient of the observation vector and, thus, is independent of the spatial covariance matrix of the additive noise.

We study the estimation variance performance of the matrix pair (MP) method for estimating the impulse responses of multiple FIR channels driven by an unknown input sequence. A first-order perturbation analysis of the large-data-size performance of the MP method is presented and an explicit expression for the estimation variance is derived. Both the theoretical and simulation results are used to investigate the statistical performance of the MP method and a number of new insights are revealed.

In this paper, we address the problem of the blind identification of linear-quadratic instantaneous mixture of statistically independent random variables. This problem consists in the identification of an unknown linear-quadratic transmission channel excited by temporally correlated and mutually independent source signals, using only statistical information on the observations received by an array of sensors. Herein we propose a new technique of blind identification of this non-linear mixture based on joint diagonalization of a set of data correlation matrices. Several numerical simulations are presented to demonstrate the effectiveness of the method in the case of a quadratic phase-coupling mixture.

We propose a new hypothesis testing method for detection of frequencies from a single measurement of data. The testing is performed on the least square error function. The global minimum value of the least square error function is shown to exhibit /spl chi//sup 2/ distribution which leads to an efficient threshold setup for the proposed method. The new method is a combined detection-estimation technique and provides improved performance over several existing techniques.

We study the performance of the subspace (SS), cross-relation (CR) and two-step maximum likelihood (TSML) methods for estimating the impulse responses of multiple FIR channels driven by an unknown input sequence. Assuming a large data size or/and a high SNR, the estimation variances of the SS and CR methods are derived. The performances of the three methods are studied and compared against the Cramer-Rao bound (CRB). It is shown that the TSML method significantly outperforms the SS and CR methods and attains the CRB over a wide range of SNR. It is also shown that the SS method is more robust to ill channel conditions than the CR method, while the two methods show nearly identical performances for well-conditioned channels.

This paper presents a study of blind system identification using measurements of multiple sensors. The mathematical model we consider here consists of multiple FIR channels driven by an unknown common source. We first show an orthogonal complement system matrix which is very useful in understanding the multi-channel system and developing efficient identification techniques. We then show a fast maximum likelihood method, its relation to a cross-relation method, and a new method utilizing a minimum noise subspace. We also report a study of strict identifiability of the multi-channel system and its relation to existing concepts.

This paper presents an asymptotical analysis of the MUSIC and Pencil-MUSIC methods proposed by Hua (1993) for estimating 2D angles and polarizations using crossed dipoles. The explicit first order perturbation expressions for the variances of the MUSIC and Pencil-MUSIC estimates are derived. These expressions are used to analyze and compare performances of the MUSIC and Pencil-MUSIC methods. A comparison of the angle-only Pencil-MUSIC method (Cheng and Hua, 1993) to the Pencil-MUSIC method is also presented. Simulation results agree well with theoretical results.>

A variant of the subspace decomposition matrix pencil (SDMP) method for estimating the angles of arrival of multiple transient signals is presented. The transient signals we consider here are modeled as the sum of complex exponentials. The SDMP-variant uses the same approach of the SDMP method to find the temporal poles. Then the SDMP-variant carries out a 1-D search for finding the spatial parameters (d.o.r's). The advantages of the SDMP-variant are (a) that there is no pairing routine required to match the estimated d.o.r's to the signal poles, (b) the noise sensitivity performance is better, and (c) the computation is more efficient. A sensitivity analysis is used to determine the performance, which is supported by simulation.>

A method for estimating 2D frequencies, called matrix enhancement and matrix pencil (MEMP), is presented. The MEMP method first constructs an enhanced matrix from the data samples, and then uses the matrix pencil approach to extract the 2D sinusoids from the principal eigenvectors of the enhanced matrix. The method yields the estimates of the 2D frequencies efficiently without solving the roots of a 2D polynomial or searching in a 2D space. Simulation results are provided to show that the accuracy of the MEMP method can be very close to the Cramer-Rao lower bound.>

A technique for estimating the spatial and temporal parameters of multiple transient signals impinging on a uniform linear array is proposed. Each transient signal is assumed to be the sum of complex exponentials arriving at the array from a different direction. The technique is based on the subspace decomposition and matrix pencil (SDMP) approach which can be implemented by solving for eigenvectors and eigenvalues. Simulations show that the subspace-decomposition and matrix-pencil method can achieve the Cramer-Rao bound.>

A novel method called matrix pencil method for estimating poles or frequencies from exponentially damped or undamped sinusoidal sequences is presented as an alternative to either the ESPRIT or pencil-of-functions method. A singular generalized eigenvalue problem in this method is solved in several different ways. First-order perturbation analysis reveals many fundamental perturbation properties of the new method. It is found consistently from both theoretical and simulation results that it performs better than the high-performance polynomial method of R. Kumaresan and D.W. Tufts (1982).>

The three methods (Prony's, Pisarenko's and Pencil-of-Function Methods) used for wave direction estimation based on one snapshot outputs of uniform linear array are studied for high SNR case.