Full Diversity Space-Time Block Codes with Low-Complexity Partial Interference Cancellation Group Decoding

Partial interference cancellation (PIC) group decoding proposed by Guo and Xia is an attractive low-complexity alternative to the optimal processing for multiple-input multiple-output (MIMO) wireless communications. It can well deal with the tradeoff among rate, diversity and complexity of space-time block codes (STBC). In this paper, a systematic design of full-diversity STBC with low-complexity PIC group decoding is proposed. The proposed code design is featured as a group-orthogonal STBC by replacing every element of an Alamouti code matrix with an elementary matrix composed of multiple diagonal layers of coded symbols. With the PIC group decoding and a particular grouping scheme, the proposed STBC can achieve full diversity, a rate of $(2M)/(M+2)$ and a low-complexity decoding for $M$ transmit antennas. Simulation results show that the proposed codes can achieve the full diversity with PIC group decoding while requiring half decoding complexity of the existing codes.Comment: 10 pages, 3 figures

New Full-Diversity Space-Time-Frequency Block Codes with Simplified Decoders for MIMO-OFDM Systems

Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is known as a promising solution for wideband wireless communications. This is why it has been considered as a powerful candidate for IEEE 802.11n standard. Numerous space-frequency block codes (SFBCs) and space-time- frequency block codes (STFBCs) have been proposed so far for implementing MIMO-OFDM systems. In this paper, at first we propose new full-diversity STFBCs with high coding gain in time-varying channels; the construct method for this structure is using orthogonal space-time block code for any arbitrary number of transmit antenna and then we propose a decoder with linear complexity for our proposed coding scheme. Simulation results verify that the proposed STFBCs outperform other recently published STFBCs

Adaptive Communication for Wireless Massive MIMO Systems

The demand for high data rates in wireless communications is increasing rapidly. One way to provide reliable communication with increased rates is massive multiple-input multiple-output (MIMO) systems where a large number of antennas is deployed. We analyze three systems utilizing a large number of antennas to provide enhancement in the performance of wireless communications. First, we consider a general form of spatial modulation (SM) systems where the number of transmitted data streams is allowed to vary and we refer to it as generalized spatial modulation with multiplexing (GSMM). A Gaussian mixture model (GMM) is shown to accurately model the transmitted spatially modulated signal using a precoding framework. Using this transmit model, a general closed-form expression for the achievable rate when operating over Rayleigh fading channels is evaluated along with a tight upper and a lower bounds for the achievable rate. The obtained expressions are flexible enough to accommodate any form of SM by adjusting the precoding set. Followed by that, we study quantized distributed wireless relay networks where a relay consisting of many geographically dispersed nodes is facilitating communication between unconnected users. Due to bandwidth constraints, distributed relay networks perform quantization at the relay nodes, and hence they are referred to as quantized distributed relay networks. In such systems, users transmit their data simultaneously to the relay nodes through the uplink channel that quantize their observed signals independently to a few bits and broadcast these bits to the users through the downlink channel. We develop algorithms that can be employed by the users to estimate the uplink channels between all users and all relay nodes when the relay nodes are performing simple sign quantization. This setup is very useful in either extending coverage to unconnected regions or replacing the existing wireless infrastructure in case of disasters. Using the uplink channel estimates, we propose multiple decoders that can be deployed at the receiver side. We also study the performance of each of these decoders under different system assumptions. A different quantization framework is also proposed for quantized distributed relay networking where the relay nodes perform vector quantization instead of sign quantization. Applying vector quantization at the relay nodes enables us to propose an algorithm that allocates quantization resources efficiently among the relay nodes inside the relay network. We also study the beamforming design at the users’ side in this case where beamforming design is not trivial due to the quantization that occurs at the relay network. Finally, we study a different setup of distributed communication systems called cell-free massive MIMO. In cell-free massive MIMO, regular cellular communication is replaced by multiple access points (APs) that are placed randomly over the coverage area. All users in the coverage area are sharing time and frequency resources and all APs are serving all UEs while power allocation is done in a central processor that is connected to the APs through a high speed backhaul network. We study the power allocation in cell-free massive MIMO system where APs are equipped with few antennas and how the distribution of the available antennas among access points affects both the performance and the infrastructure cost

Soft-Decision-Driven Channel Estimation for Pipelined Turbo Receivers

We consider channel estimation specific to turbo equalization for multiple-input multiple-output (MIMO) wireless communication. We develop a soft-decision-driven sequential algorithm geared to the pipelined turbo equalizer architecture operating on orthogonal frequency division multiplexing (OFDM) symbols. One interesting feature of the pipelined turbo equalizer is that multiple soft-decisions become available at various processing stages. A tricky issue is that these multiple decisions from different pipeline stages have varying levels of reliability. This paper establishes an effective strategy for the channel estimator to track the target channel, while dealing with observation sets with different qualities. The resulting algorithm is basically a linear sequential estimation algorithm and, as such, is Kalman-based in nature. The main difference here, however, is that the proposed algorithm employs puncturing on observation samples to effectively deal with the inherent correlation among the multiple demapper/decoder module outputs that cannot easily be removed by the traditional innovations approach. The proposed algorithm continuously monitors the quality of the feedback decisions and incorporates it in the channel estimation process. The proposed channel estimation scheme shows clear performance advantages relative to existing channel estimation techniques.Comment: 11 pages; IEEE Transactions on Communications 201