In this paper, we propose a polar coding scheme for parallel Gaussian channels. The encoder knows the sum capacity of the parallel channels (the capacity of each channel is assumed to be fractional) but does not know the capacity of any channel. By using the nesting property of polar codes, we design a coding/decoding scheme to achieve the sum capacity.

Polar codes are the first class of forward error correction (FEC) codes with a provably capacity-achieving capability. Using list successive cancellation decoding (LSCD) with a large list size, the error correction performance of polar codes exceeds other well-known FEC codes. However, the hardware complexity of LSCD rapidly increases with the list size, which incurs high usage of the resources on the field programmable gate array (FPGA) and significantly impedes the practical deployment of polar codes. To alleviate the high complexity, in this paper, two low-complexity decoding schemes and the corresponding architectures for LSCD targeting FPGA implementation are proposed. The architecture is implemented in an Altera Stratix V FPGA. Measurement results show that, even with a list size of 32, the architecture is able to decode a codeword of 4096-bit polar code within 150 μs, achieving a throughput of 27Mbps.

The performance of List Successive-Cancellation Decoding (LSCD) of Polar Codes with large list size have exceeded that of Turbo codes and Low-Density Parity-Check codes. However, large list size results in huge computation complexity and this limits the applicability of LSCD in high-throughput and power- sensitive applications. In this work, a low complexity design for LSCD with large list size based on list pruning is proposed. In particular, the property of the relative path metric (RPM) of each list candidate with respect to that of the most-likely candidate is investigated. It is found that the correct candidate has a low possibility of having a large value of RPM and based on this property, a list pruning method and the corresponding low-complexity LSCDs are proposed. From the simulation results, as compared to the conventional LSCD, the proposed LSCDs have negligible performance loss while the computation complexity is reduced by more than 80%. In addition, the proposed design is hardware-friendly and easily adaptable to the existing LSCDs hardware architectures.

A rateless coding scheme transmits incrementally more and more coded bits over an unknown channel until all the information bits are decoded reliably by the receiver. We propose a new rateless coding scheme based on polar codes, and we show that this scheme is capacity-achieving, i.e. its information rate is as good as the best code specifically designed for the unknown channel. Previous rateless coding schemes are designed for specific classes of channels such as AWGN channels, binary erasure channels, etc. but the proposed rateless coding scheme is capacity-achieving for broad classes of channels as long as they are ordered via degradation. Moreover, it inherits the conceptual and computational simplicity of polar codes.

Due to their provably capacity-achieving performance, polar codes have attracted a lot of research interest recently. For a good error-correcting performance, list successive-cancellation decoding (LSCD) with large list size is used to decode polar codes. However, as the complexity and delay of the list management operation rapidly increase with the list size, the overall latency of LSCD becomes large and limits the applicability of polar codes in high-throughput and latency-sensitive applications. Therefore, in this work, the low-latency implementation for LSCD with large list size is studied. Specifically, at the system level, a selective expansion method is proposed such that some of the reliable bits are not expanded to reduce the computation and latency. At the algorithmic level, a double thresholding scheme is proposed as a fast approximate-sorting method for the list management operation to reduce the LSCD latency for large list size. A VLSI architecture of the LSCD implementing the selective expansion and double thresholding scheme is then developed, and implemented using a UMC 90 nm CMOS technology. Experimental results show that, even for a large list size of 16, the proposed LSCD achieves a decoding throughput of 460 Mbps at a clock frequency of 658 MHz.

For polar codes with short-to-medium code length, list successive cancellation decoding is used to achieve a good error-correcting performance. However, list pruning in the current list decoding is based on the sorting strategy and its timing complexity is high. This results in a long decoding latency for large list size. In this work, aiming at a low-latency list decoding implementation, a double thresholding algorithm is proposed for a fast list pruning. As a result, with a negligible performance degradation, the list pruning delay is greatly reduced. Based on the double thresholding, a low-latency list decoding architecture is proposed and implemented using a UMC 90nm CMOS technology. Synthesis results show that, even for a large list size of 16, the proposed low-latency architecture achieves a decoding throughput of 220 Mbps at a frequency of 641 MHz.

This paper proposes a fast recursive algorithm for Group-wise Space-Time Block Code (G-STBC), which takes full advantage of the Alamouti structure in the equivalent channel matrix to reduce the computational complexity. With respect to the existing efficient algorithms for G-STBC, the proposed algorithm achieves better performance and usually requires less computational complexity.

A fast algorithm for inverse Cholesky factorization is proposed, to compute a triangular square-root of the estimation error covariance matrix for Vertical Bell Laboratories Layered Space-Time architecture (V-BLAST). It is then applied to propose an improved square-root algorithm for V-BLAST, which speedups several steps in the previous one, and can offer further computational savings in MIMO Orthogonal Frequency Division Multiplexing (OFDM) systems. Compared to the conventional inverse Cholesky factorization, the proposed one avoids the back substitution (of the Cholesky factor), and then requires only half divisions. The proposed V-BLAST algorithm is faster than the existing efficient V-BLAST algorithms. The expected speedups of the proposed square-root V-BLAST algorithm over the previous one and the fastest known recursive V-BLAST algorithm are 3.9 ~ 5.2 and 1.05 ~ 1.4, respectively.

How to achieve the closed-loop processing gain is an important issue for frequency division duplexing (FDD) based multi-input multi-output (MIMO) systems, since the channel state information (CSI) is not available by reciprocity as time division duplexing (TDD). Current technique mainly uses the codebook based precoding, where the receiver feeds back the precoding matrix index (PMI) of the codebooks to the transmitter for closed-loop processing. However, due to the suboptimality of linear processing and the quantization error of the precoding matrix / CSI, its performance gain over the open-loop processing is limited. In this paper, we propose a new precoding architecture to improve the situation. Based on lattice reduction (LR) method, we choose to feedback an integer matrix to the transmitter and perform nonlinear precoding. By utilizing the special properties of the integer matrix, the required feedback amount can be greatly reduced. Although it needs somewhat more bits than the conventional scheme to feedback the CSI, significant performance gain is achieved and full diversity can be guaranteed.

In this paper, we propose a new signal dimensioning scheme for multi-user MIMO downlink, that adjusts the signal dimensions for different users to maximize the system throughput. By adapting the signal dimensions, we can achieve better tradeoff between multiplexing and diversity, and obtain much better bit rate error performance and higher capacity.

In this paper, we propose a novel iterative interference alignment (IA) scheme for the single-input single-output (SISO) interference channel system using minimum total mean square error criterion under the individual transmitter power constraints. We show that interference alignment under such criterion could be realized through an iterative algorithm. The convergence of the proposed algorithm is discussed. Simulation results, compared with several existing IA schemes, show that the proposed scheme can effectively improve the BER performance of the SISO interference channel system while maintaining the same degree of freedom as Cadambe-Jafar scheme.

In this paper, we propose a novel interference alignment (IA) scheme via convex optimization based on minimum mean square error criterion subject to individual transmit power constraints for the MIMO interference channel system. We show that transceiver under such criterion can be realized through an efficient iterative algorithm. The convergence of the proposed algorithm is discussed. Simulation results show that the proposed scheme outperforms the existing IA schemes with fast convergence.

We propose efficient algorithms for the extended Vertical Bell Labs Layered Space-Time architecture (V-BLAST) with selective per-antenna rate control (S-PARC). Instead of computing SNIRs from nulling vectors, we compute SNIRs from diagonal entries in the estimation error covariance matrix P, which are obtained from entries in F, i.e. the triangular squareroot of P. Then the square-root V-BLAST algorithm is applied, to compute F for an antenna subset from F for another subset. Moreover, when computing F, we can reuse intermediate results to further reduce the computational complexity dramatically. Assume M transmit/receive antennas. The proposed algorithm for the S-PARC scheme minimizing total transmit power has the speedup of 0.14M + 1.82, with respect to the corresponding SPARC algorithm computing SNIRs from nulling vectors, while the proposed algorithm for the S-PARC scheme maximizing total information rate has the speedup of (M + 58)/24.

We propose efficient square-root and division free algorithms for inverse LDLTfactorization and the wide-sense Givens rotation, mainly for conventional multiply-add digital computers. The algorithms are then employed to propose a fast algorithm for vertical Bell Laboratories Layered Space-Time architecture (V-BLAST). The proposed inverse LDLTfactorization avoids the conventional back substitution of the Cholesky factor, and then has the speedups of 1.19 over the square root and division free alternative Cholesky factorization plus the back substitution. The proposed wide-sense Givens rotation requires the same computational complexity as the efficient Givens rotation. The speedups of the proposed V-BLAST algorithm over the recursive V-BLAST algorithm are 1.05~1.4. The difference between the complexity of the proposed V-BLAST algorithm and that of the square-root V-BLAST algorithm is trivial and even negligible. However, the square-root V-BLAST algorithm requires many square-root and division operations, while the proposed V-BLAST algorithm requires no square-root operations, and requires only one division for each transmit signal, which can be postponed until the signal estimation is required.

In this paper, an interference alignment system for the distributed MIMO network is investigated. We propose a new precoding scheme using interference alignment in modulation signal domain. The simulation results show that the proposed one-dimensional precoding scheme can provide significant performance improvement over the conventional schemes using interference alignment.

In this paper, we propose a new interference alignment (IA) scheme designing jointly the linear transmitter and receiver for the MIMO interference channel system, using minimum total mean square error criterion, subject to individual transmit power constraints. We show that transmitter and receiver under such criterion could be realized through a joint iterative algorithm. The convergence of the proposed algorithm is discussed. We also proposed a robust MMSE-based iterative design with imperfect channel state information (CSI). The proposed robust MMSE-based iterative interference alignment scheme is shown to be less sensitive to channel estimation errors. Simulation results show that the proposed schemes outperform the existing IA schemes with fast convergence.

This paper considers a relay-assisted bidirectional cellular network where the base station (BS) communicates with each mobile station (MS) using orthogonal frequency-division multiple-access (OFDMA) for both uplink and downlink. The goal is to improve the overall system performance by exploring the full potential of the network in various dimensions including user, subcarrier, relay, and bidirectional traffic. In this work, we first introduce a novel three-time-slot time-division duplexing (TDD) transmission protocol. This protocol unifies direct transmission, one-way relaying and network-coded two-way relaying between the BS and each MS. Using the proposed three-time-slot TDD protocol, we then propose an optimization framework for resource allocation to achieve the following gains: cooperative diversity (via relay selection), network coding gain (via bidirectional transmission mode selection), and multiuser diversity (via subcarrier assignment). We formulate the problem as a combinatorial optimization problem, which is NP-complete. To make it more tractable, we adopt a graph-based approach. We first establish the equivalence between the original problem and a maximum weighted clique problem (MWCP) in graph theory. A metaheuristic algorithm based on ant colony optimization (ACO) is then employed to find the solution in polynomial time. Simulation results demonstrate that the proposed protocol together with the ACO algorithm significantly enhances the system total throughput.

Interference alignment (IA) has evolved as a powerful technique in the information theoretic framework for achieving the optimal degrees of freedom of interference channel. In practical systems, the design of specific interference alignment schemes is subject to various criteria and constraints. In this paper, we propose novel transceiver schemes for the MIMO interference channel based on the mean square error (MSE) criterion. Our objective is to optimize the system performance under a given and feasible degree of freedom. Both the total MSE and the maximum per-user MSE are chosen to be the objective functions to minimize. We show that the joint design of transmit precoding matrices and receiving filter matrices with both objectives can be realized through efficient iterative algorithms. The convergence of the proposed algorithms is proven as well. Simulation results show that the proposed schemes outperform the existing IA schemes in terms of BER performance. Considering the imperfection of channel state information (CSI), we also extend the MSE-based transceiver schemes for the MIMO interference channel with CSI estimation error. The robustness of the proposed algorithms is confirmed by simulations.

In this paper, an interference alignment system for the MIMO network is investigated. Three different precoding schemes, which are based on zero-forcing (ZF), minimum mean square error (MMSE) and maximum SLR criteria, are proposed. The simulation results show that the proposed precoding schemes are very efficient and can provide good performance for the considered MIMO network.

A new block detection receiver is presented for noncoherent orthogonal CDMA (such as the CDMA IS-95A reverse link). This new receiver uses multiple Hadamard (or Walsh) codes for joint detection instead of using a single Walsh code as conventional receivers do. The simulation results show that the new block detection receiver is significantly better than the conventional non-block detection receivers in both additive white Gaussian noise and flat Rayleigh fading channels. A reduced-complexity algorithm for the block detection is also presented with significant complexity reduction and negligible performance degradation.