The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

Two decades of array signal processing research: the parametric approach

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Table Of Integrals Series And Products

A Gaussian broadcast channel (GBC) with r single-antenna receivers and t antennas at the transmitter is considered. Both transmitter and receivers have perfect knowledge of the channel. Despite its apparent simplicity, this model is, in general, a nondegraded broadcast channel (BC), for which the capacity region is not fully known. For the two-user case, we find a special case of Marton's (1979) region that achieves optimal sum-rate (throughput). In brief, the transmitter decomposes the channel into two interference channels, where interference is caused by the other user signal. Users are successively encoded, such that encoding of the second user is based on the noncausal knowledge of the interference caused by the first user. The crosstalk parameters are optimized such that the overall throughput is maximum and, surprisingly, this is shown to be optimal over all possible strategies (not only with respect to Marton's achievable region). For the case of r>2 users, we find a somewhat simpler choice of Marton's region based on ordering and successively encoding the users. For each user i in the given ordering, the interference caused by users j>i is eliminated by zero forcing at the transmitter, while interference caused by users j<i is taken into account by coding for noncausally known interference. Under certain mild conditions, this scheme is found to be throughput-wise asymptotically optimal for both high and low signal-to-noise ratio (SNR). We conclude by providing some numerical results for the ergodic throughput of the simplified zero-forcing scheme in independent Rayleigh fading.

On the achievable throughput of a multiantenna Gaussian broadcast channel

Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next-generation wireless networks As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service requirements In this paper, we present a comprehensive literature review on the research progresses in wireless networks with RF energy harvesting capability, which is referred to as RF energy harvesting networks (RF-EHNs) First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques, and existing applications Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations and review the communication protocols specially designed for RF-EHNs We also explore various key design issues in the development of RF-EHNs according to the network types, ie, single-hop networks, multiantenna networks, relay networks, and cognitive radio networks Finally, we envision some open research directions

Wireless Networks With RF Energy Harvesting: A Contemporary Survey

On the achievable throughput of a multiantenna Gaussian broadcast channels

Conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. In this paper, we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel FIR systems without requiring the knowledge of the input statistical model. A new blind identification algorithm based solely on the system outputs is proposed. Necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented.

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A least-squares approach to blind channel identification

The paper studies the problem of finding an optimal subcarrier and power allocation strategy for downlink communication to multiple users in an orthogonal-frequency-division multiplexing-based wireless system. The problem of minimizing total power consumption with constraints on bit-error rate and transmission rate for users requiring different classes of service is formulated and simple algorithms with good performance are derived. The problem of joint allocation is divided into two steps. In the first step, the number of subcarriers that each user gets is determined based on the users' average signal-to-noise ratio. The algorithm is shown to find the distribution of subcarriers that minimizes the total power required when every user experiences a flat-fading channel. In the second stage of the algorithm, it finds the best assignment of subcarriers to users. Two different approaches are presented, the rate-craving greedy algorithm and the amplitude-craving greedy algorithm. A single cell with one base station and many mobile stations is considered. Numerical results demonstrate that the proposed low complexity algorithms offer comparable performance with an existing iterative algorithm.

Computationally efficient bandwidth allocation and power control for OFDMA

While multipath induced phase rotations can be dealt with by differential encoding in orthogonal frequency division multiplexing (OFDM) communications, the loss of orthogonality due to the carrier offset must be compensated before discrete Fourier transform (DFT)-based demodulation can be performed. In this letter, we present a high-performance/low-complexity blind carrier offset estimation algorithm by exploiting intrinsic structure information of OFDM signals. The algorithm offers the accuracy of a super resolution subspace method, viz. MUSIC, without involving computationally intensive subspace decompositions.

A high-efficiency carrier estimator for OFDM communications

In orthogonal frequency-division multiplex (OFDM) communications, the loss of orthogonality due to the carrier-frequency offset must be compensated before discrete Fourier transform-based demodulation can be performed. This paper proposes a new carrier offset estimation technique for OFDM communications over a frequency-selective fading channel. We exploit the intrinsic structure information of OFDM signals to derive a carrier offset estimator that offers the accuracy of a super resolution subspace method, ESPRIT.

OFDM blind carrier offset estimation: ESPRIT

This paper presents a radio resource control (RRC) scheme for OFDMA systems where dynamic resource allocation is realized at both a radio network controller (RNC) and base stations (BSs). The scheme is semi-distributed in the sense that the RRC decision is split between RNC and BSs. RNC makes decision on which channel is used by which BS at super-frame level and BSs then make decision on which user is assigned to which channel at frame-level. Two optimization problems for RNC and BSs are formulated and computationally efficient algorithms that perform the function of interference avoidance and traffic/channel adaptation are developed. Numerical analysis is performed under several cell configurations to show tradeoffs between sector interference suppression and dynamic interference avoidance. The results indicate that with reasonable signaling overhead, the protocol and the associated algorithms yield excellent performance for both real-time and non real-time services, even under fast fading

Hui Liu

Papers

A least-squares approach to blind channel identification

Computationally efficient bandwidth allocation and power control for OFDMA

A high-efficiency carrier estimator for OFDM communications

OFDM blind carrier offset estimation: ESPRIT

Downlink Radio Resource Allocation for Multi-Cell OFDMA System