Multi-input Multi-output Wireless Systems Research Articles

Algorithm and Architecture of Configurable Joint Detection and Decoding for MIMO Wireless Communications With Convolutional Codes

This paper presents an algorithm and a VLSI architecture of a configurable joint detection and decoding (CJDD) scheme for multi-input multioutput (MIMO) wireless communication systems with convolutional codes. A novel tree-enumeration strategy is proposed such that the MIMO detection and decoding of convolutional codes can be conducted in single stage using a tree-searching engine. Moreover, this design can be configured to support different combinations of quadrature amplitude modulation (QAM) schemes as well as encoder code rates, and thus can be more practically deployed to real-world MIMO wireless systems. A formal outline of the proposed algorithm will be given and simulation results for 16-QAM and 64-QAM with rate-1/2 and rate-1/3 codes will be presented showing that, compared with the conventional separate scheme, the CJDD algorithm can greatly improve bit error rate (BER) performance with different system settings. In addition, the VLSI architecture and implementation of the CJDD approach will be illustrated. The architectures and circuits are designed to support configurability and flexibility while maintaining high efficiency and low complexity. The postlayout experimental results for 16-QAM and 64-QAM with rate-1/2 and rate-1/3 codes show that, compared with the previous configurable design, this architecture can achieve reduced or comparable complexity with improved BER performance.

Maximising energy efficiency in antenna selection MIMO wireless systems subject to QoS constraints

Energy-efficiency (EE) in antenna selection multi-input–multi-output (MIMO) systems over Nakagami-m fading channels is investigated. An EE metric is defined as the number of successfully received bits per unit of energy consumption, which takes into account several important system parameters such as channel coding and modulation. An optimisation problem that maximises the EE metric subject to an error-performance constraint is formulated. On the basis of an analysis of this problem, the optimal value of the average energy per transmitted data symbol is obtained, such that the EE in the system is maximised. Simulation results are provided to validate the analysis.

Optimal Training Sequence for MIMO Wireless Systems in Colored Environments

In this paper, we design the training signal for a multi-input multi-output (MIMO) communication system in a colored medium. We assume that the known channel covariance matrix (CM) is a Kronecker product of a transmit channel CM and a receive channel CM. Similarly, the CM of the additive Gaussian noise is modeled by a Kronecker product of a temporal CM and a spatial CM. We maximize the differential entropy gained by receiver for a limited energy budget for training at the transmitter. Using, singular value decomposition of the involved CMs, we turn this problem into a convex optimization problem. We prove that the left and right singular vectors of the optimal training matrix are eigenvectors of the channel transmit CM and the noise temporal CM. In general case, this problem can be solved numerically using efficient methods. The impact of the optimal training is more significant in environments with larger eigenvalue spread. The expression of the optimal solution is interesting for some specific cases. For uncorrelated receive channel, the optimal training looks like water filling, i.e., more training power must be invested on the directions which have more impact. For high signal-to-noise ratios (SNRs), any orthogonal training is optimal; this means that if large amount of energy is available, it must be invested uniformly in all directions. In low SNR scenarios where low amount of energy is available for channel training, all the energy must be allocated to the best mode of channel (which has the highest ratio of the received channel variance to the received noise power).

Maximum-throughput delivery of SVC-based video over MIMO systems with time-varying channel capacity

In this paper, we present a novel scalable video transmission strategy over multi-input multi-output (MIMO) wireless systems with time-varying channel capacity. It is a great challenge to simultaneously guarantee the QoS for video delivery and maximize the system throughput over time-varying MIMO channel. We demonstrate that, by making full use of estimated channel state information (CSI) through feedback, a cascade of adaptive operations can be designed to satisfy maximum throughput for scalable video over MIMO systems. These operations include power allocation based on water-filling (WF), adaptive channel selection (ACS), and novel throughput maximizing power reallocation (PR). The proposed ACS transmission scheme enables overall increase in data throughput among enhancement layers by adaptively launching base layer bit-stream to proper sub-channel. Then, after initial power allocation with WF and proper adaptive mode selection, we obtain the surplus power across enhancement layer sub-channels which can be reallocated to some sub-channels by the proposed PR scheme. With such power reallocation, certain enhancement layers will be able to reach new level of QAM modulation through PR so as to maximize the system data throughput. We present in this paper some detailed analysis on these adaptive operations. We also present some simulation results to demonstrate that maximum throughput video transmission over MIMO wireless systems indeed can be achieved based on scalable video coding (SVC) and a sequence of appropriately designed adaptive operations.

Scalable H.264/AVC Video Transmission Over MIMO Wireless Systems With Adaptive Channel Selection Based on Partial Channel Information

In this paper, we present a novel joint application physical-layer design (JAPLD) strategy to cost-effectively transmit scalable H.264/AVC video over multi-input multi-output (MIMO) wireless systems. With this approach, the application layer cooperates with the physical layer to maximize the system performance. First, in physical layer, we propose a new layered video transmission scheme over MIMO: adaptive channel selection (ACS). ACS-MIMO is fundamentally different from parallel transmission MIMO (PT-MIMO). While each bit stream is continuously transmitted through a fixed antenna in PT-MIMO, ACS-MIMO is able to periodically switch each bit stream among multiple antennas. In application layer, Scalable Video Coding (SVC) generates layered bit streams that need prioritized delivery. Then, we obtain the ordering of each subchannel's SNR strength as partial channel information (CI) at the receiver. The partial CI is acquired via the estimated channel state information based on training sequences. The JAPLD strategy we developed in this research shall switch the bit stream automatically to match the ordering of SNR strength for the subchannels. Essentially, we will launch higher priority layer bit stream into higher SNR strength subchannel by the proposed JAPLD algorithm. In this fashion, we can implicitly achieve automatic unequal error protection (UEP) for layered SVC transmission over MIMO system without power control at the transmitter. Experimental results show that the proposed ACS-MIMO system is able to achieve UEP with the obtained partial CI and the reconstructed video peak signal-to-noise ratio demonstrate the performance improvement of the proposed system as compared with open loop PT-MIMO system.