Justin P. Coon

Adaptive frequency-domain equalization for single-carrier multiple-input multiple-output wireless transmissions

Channel estimation and tracking pose real problems in broadband single-carrier wireless communication systems employing multiple transmit and receive antennas. An alternative to estimating the channel is to adaptively equalize the received symbols. Several adaptive equalization solutions have been researched for systems operating in the time domain. However, these solutions tend to be computationally intensive. A low-complexity alternative is to adaptively equalize the received message in the frequency domain. In this paper, we present an adaptive frequency-domain equalization (FDE) algorithm for implementation in single-carrier (SC) multiple-input multiple-output (MIMO) systems. Furthermore, we outline a novel method of reducing the overhead required to train the proposed equalizer. Finally, we address the issues of complexity and training sequence design. Other computationally efficient adaptive FDE algorithms for use in SC systems employing single transmit and receive antennas, receive diversity, or space-time block codes (STBC) can be found in the literature. However, the algorithm detailed in this paper can be implemented in STBC systems as well as in broadband spatial multiplexing systems, making it suitable for use in high data rate MIMO applications.

Energy-Efficient Spectrum Sensing and Access for Cognitive Radio Networks

We consider a cognitive radio system with one secondary user (SU) accessing multiple channels via periodic sensing and spectrum handoff. We propose an optimal spectrum sensing and access mechanism such that the average energy cost of the SU, which includes the energy consumed by spectrum sensing, channel switching, and data transmission, is minimized, whereas multiple constraints on the reliability of sensing, the throughput, and the delay of the secondary transmission are satisfied. Optimality is achieved by jointly considering two fundamental tradeoffs involved in energy minimization, i.e., the sensing/transmission tradeoff and the wait/switch tradeoff. An efficient convex optimization procedure is developed to solve for the optimal values of the sensing slot duration and the channel switching probability. The advantages of the proposed spectrum sensing and access mechanism are shown through simulations.

Optimal training sequences for channel estimation in cyclic-prefix-based single-carrier systems with transmit diversity

We investigate a new class of training sequences that are optimal for least squares (LS) channel estimation in systems employing transmit diversity and single-carrier (SC) modulation with a cyclic prefix (CP) extension. The sequences have a constant envelope in the time domain and are orthogonal in the frequency domain. Transmission of these sequences facilitates optimal (in the LS sense) estimation of the channel impulse response at the receiver while precluding the peak-to-average power ratio problem that is inherent in other CP-based architectures such as orthogonal frequency division multiplexing.

Full connectivity: corners, edges and faces

We develop a cluster expansion for the probability of full connectivity of high density random networks in confined geometries. In contrast to percolation phenomena at lower densities, boundary effects, which have previously been largely neglected, are not only relevant but dominant. We derive general analytical formulas that show a persistence of universality in a different form to percolation theory, and provide numerical confirmation. We also demonstrate the simplicity of our approach in three simple but instructive examples and discuss the practical benefits of its application to different models.

A channel estimation algorithm for MIMO-SCFDE

This letter proposes a novel method for channel estimation in a single-carrier multiple input-multiple output (MIMO) system with frequency-domain equalization/detection. To this end, we construct novel short MIMO training sequences that have constant envelope in the time domain to preclude the peak-to-average power ratio problem encountered in many systems that utilize the frequency domain for data recovery. Simultaneously, the spectrum in the frequency domain is flat except for a grid of nulls for predefined frequency tones. Armed with these sequences, we provide an algorithm that is optimal in the least squares (LS) sense at a potentially low computational cost. Results show that the algorithm performs identically to other proposed LS techniques. Furthermore, the algorithm is extremely bandwidth efficient in that the total training overhead required to obtain full CSI is just one block.

Per-subcarrier antenna selection with power constraints in OFDM systems

In an OFDM system, antenna selection can be performed on a per-subcarrier basis, which can exploit the frequency selectivity of the channel. However, this may cause power imbalance across the antennas, which may lead to problems with the power amplifiers (PA). To avoid this problem, we devise a scheme that allocates the same number of subcarriers to all antennas, while still offering substantial diversity gains. This is achieved by using integer optimization and we show that this is solvable by linear relaxation, which results in a reduced complexity. The constrained antenna selection is analyzed and it is shown that choosing the cost function as BER offers better diversity gains than using SNR. Simulations with a nonlinear PA show that the integer optimization scheme performs better than unconstrained antenna selection and a previously published ad hoc method.

On capacity-optimal precoding for multiple antenna systems subject to EIRP restrictions

Ultra wideband transceivers promise multi-gigabit per second performance at power consumptions commensurate with portable devices. Future products are likely to adopt multiple antennas to maximize performance. Severe FCC EIRP restrictions impose an interesting system design constraint. In this paper, capacity optimal multiple antenna transmission schemes are investigated for EIRP restricted systems under the assumption that the channel is known at the transmitter. It is shown that per subcarrier antenna power allocation, which reduces to antenna selection at low SNR or when using one receive antenna, is optimal for some transmitter configurations. The improvements in capacity are quantified for representative channels.

A comparison of MIMO-OFDM and MIMO-SCFDE in WLAN environments

Recent developments in orthogonal frequency division multiplexing (OFDM) and single-carrier frequency-domain equalization (SCFDE) have sparked debate about the superiority of one method over the other. In this paper, we further this debate by comparing the theoretical performance of OFDM and SCFDE when each is implemented in one of two different multiple-input multiple-output (MIMO) architectures: spatial multiplexing and space-time block codes. This study focuses on the use of MIMO-OFDM and MIMO-SCFDE in wireless local area network (WLAN) applications. Performance is given in terms of the packet error rate (PER) and the throughput of the systems.

Difference Antenna Selection and Power Allocation for Wireless Cognitive Systems

In this paper, we propose an antenna selection method in a wireless cognitive radio (CR) system, which we term difference selection, whereby a single transmit antenna is selected at the secondary transmitter out of M possible antennas such that the weighted difference between the channel gains of the data link and the interference link is maximized. We analyze the mutual information and the outage probability of the secondary transmission in a CR system with difference antenna selection, and propose a method of optimizing these performance metrics subject to practical constraints on the peak secondary transmit power and the average interference power as seen by the primary receiver. The optimization is performed over two parameters: the peak secondary transmit power and the difference selection weight δ∈[0,1]. Furthermore, we show that the diversity gain of a CR system employing difference selection is an impulsive function of δ, in that a value of δ=1 yields the full diversity order of M and any other value of δ gives no diversity benefit. Finally, we demonstrate through extensive simulations that, in many cases of interest, difference selection using the optimal values of these two parameters is superior to the so-called ratio selection method disclosed in the literature.

Combined bulk and per-tone transmit antenna selection in OFDM systems

We present a modification of conventional transmit antenna selection schemes for OFDM systems by combining bulk and per-tone selection. We analyse the diversity and coding gains of the proposed approach with respect to symbol error and outage probability for uncoded systems, and show that these gains are identical to that which can be obtained by employing per-tone selection using all available antennas. This analysis leads to the surprising result that one can achieve optimal performance at high SNR relative to a per-tone selection system with M antennas by first selecting only two out of M antennas and performing per-tone selection on the chosen antennas.