The authors propose a generalised multi‐layer architecture for the phase shifter network (PSN) in hybrid analogue/digital beamforming (BF), which provides flexible trade‐offs between the performance and implementation complexity. By changing the layer number, the authors can adjust the complexity of the PSN and the achievable performance. A two‐stage BF algorithm is adopted, in which the analogue and digital BF matrices are designed separately. Analogue BF is adaptive to the second‐order channel statistics. A simple iterative algorithm is proposed to maximise an upper bound of the average rate of the effective channel after analogue BF. Digital BF is adaptive to the effective channel for multiuser interference cancellation. Both ideal phase shifters and discrete phase shifters with limited phase and amplitude resolutions are considered in the algorithm design. Theoretical analysis and simulation are used to demonstrate the good performance and flexibility of the proposed technique. In a 64‐antenna system, it can use 16 radio‐frequency chains to perform very closely to full digital BF with a fully‐connected PSN, or achieve 70–80% performance with a simple PSN. It is also robust against the limited phase shifter resolution. It enables us to select the proper performance and implementation complexity according to the requirement and capability of real systems.

The advantage of large antenna systems relies on the acquisition of the channel state information at the base station, which is difficult for frequency-division-multiplexing systems, as the pilot overhead increases linearly with the antenna number. In this paper, we utilize the sparse and clustering characteristics of multipath channels to solve this problem. We decompose channel estimation into three low-complexity low-overhead parts: the estimation of delay, active bins and (complex) amplitudes of clusters of propagation paths. In each part, efficient pilot transmission and channel estimation are designed to leverage the features of the parameter to be estimated. Compared with the conventional minimum-mean-square-error channel estimator, the proposed technique significantly reduces the pilot overhead. Compared with compressive sensing based techniques that exploit only the channel sparsity but not the clustering distribution of paths, it achieves better performance with a much lower complexity.

In this paper, the channel estimation related issues in FBMC systems are discussed. A single-symbol preamble structure is proposed and the corresponding channel estimation methods are provided for filter bank multicarrier (FBMC) systems. The simulation results show that compared with the existing methods, the proposed scheme achieves impressive performance gains (up to 4.0dB) with much smaller preamble resource overhead. This is obtained through effective channel filtering for the intrinsic imaginary interference and additive noise exploiting the sparse property of the channel response in delay domain. With the proposed solution, the FBMC system can even save half resources for pilots compared with OFDM systems.

This paper proposes a new full non-orthogonal integration scheme for unicast and broadcast data streams in future OFDM system, in which broadcast is deployed in single cell scenario. It is firstly analyzed why today's research results of the integration of unicast and broadcast in Single Frequency Network (SFN) manner cannot work in single cell deployment scenario. By adjusting the modulation constellation map of today's integration system, the proposed scheme, called crossed HM, works well in the discussed scenario. System level performances are investigated in this paper, which show that the proposed scheme has 40% gain against the discussed SFN integration scheme in terms of effective frequency efficiency. Performances for single cell broadcast without integration to unicast are shown for comparison.

Interference nulling (IN) is a linear precoding technique for downlink multiuser multiple-input multiple-output (MU-MIMO) systems. With perfect channel knowledge at the transmitter, IN eliminates other users' interference at each cell, thus improving the sum rate of the users in the system. To evaluate its suitability in a multicell setup, we have implemented the real-time system based on IN as well as on single-base-station closed-loop (SB-CL) precoding meeting the 3rd generation partnership project (3GPP) long term evolution (LTE)-Advanced system profile over a 20 MHz bandwidth. Our field trial in an indoor deployment of two operating base stations (BSs) and two mobile stations (MSs) shows that (1) if both MSs are located near the cell edge, IN outperforms SB-CL in terms of signal-to-interference-plus-noise-ratio (SINR) and sum throughput; (2) if two MSs are close to their respective serving BSs, IN is inferior to SB-CL. Thus, IN is more favorable for improving the transmission rate and quality of services for users near the cell-edge.

It is well known that coordinated multi-cell transmission in downlink (DL) is a promising technique to significantly increase the system throughput and cell-edge user throughput. In this paper, we propose a scalable two-stage channel feedback mechanism not only to support downlink single-cell multi-user MIMO (MU-MIMO), but also to efficiently support multi-cell coordination. This scheme yields remarkable system performance improvements with considerable uplink (UL) feedback overhead, and also has good backward compatibility for single-cell transmission scheme.

The feedback accuracy for the multiuser multiple input multiple output (MU-MUMO) system is to be enhanced in the release 10 of 3GPP LTE specification. In this paper, we propose a feedback mechanism together with associated preceding and scheduling schemes that could improve the feedback accuracy for both single user (SU) and multiuser MIMO. System level simulation results of the proposed scheme are evaluated and compared with the baseline of LTE Release 8 MU-MIMO. Results show that with a small amount of extra overhead, the proposed scheme can outperform Release 8 MU-MIMO by at least 10%.

This paper discusses compressive sensing with sparse measurement matrices. Sparse matrices have several attractive properties, like low computational complexity in both encoding and recovery, easy incremental updates to signals, and low storage requirement, etc. Typical examples of existing algorithms for sparse signal recovery with sparse measurement matrices include convex relaxation, matching pursuit, and Bayesian framework based approaches. In this paper, we propose an alternative technique to this problem. The proposed technique has a linear recovery complexity and relatively good empirical behavior. In this technique, we employ a permutation-based multi-dimensional measurement matrix, which is composed of several sub-matrices, each consisting of a block-diagonal matrix and a random permutation matrix. The measurement symbols generated from the same permutation matrix are referred to as a dimension. Such a measurement matrix brings some useful features to the measurement symbols. Fully exploiting these features enables us to design a simple and effective recovery algorithm. The proposed recovery algorithm employs an iterative process. In each iteration, the algorithm looks for measurement symbols with certain features, and uses such features to recover the source symbols related to these measurements. An iterative process is then applied, along with an interference cancellation operation, to reconstruct all source symbols gradually. The complexity of the proposed algorithm is relatively low, which grows linearly with the source signal length. Numerical results show that the proposed technique empirically offers a much lower sketch length than ℓ1-minimization-based convex relaxation and Bayesian framework based algorithms. It achieves the empirical lower bound of sketch length and linear recovery complexity at the same time.

Mutual coupling between elements in compact transmit antenna arrays is taken into account in performance prediction and system design of multiple-input multi-output (MIMO) systems. The transmitter-end coupling matrix comprised of array impedance matrix and effective load impedance matrix is derived. The impact of transmitter-end mutual coupling on spatial correlation, radiated power, and system capacity is studied. It is shown that for MIMO systems with compact transmit antenna arrays, transmitter-end mutual coupling seriously degrades system capacity. To mitigate the capacity degradation, a simple transmitter-end coupling cancelation method is proposed. It is shown through numerical experiments that the proposed method can significantly improve system capacity in the case that transmit antennas are placed closely.

This paper presents an efficient algorithm for the partial interference cancellation (PIC) group decoding. Partial interference cancellation is a linear operation that cancels interferences among symbol groups. The basic algorithm for the PIC operation requires a lot of matrix multiplications and inversions. Even though these matrix operations can be done in an efficient way by taking advantage of the Hermitian property, the computational burden is still high and may not always be acceptable. We propose a new algorithm for the PIC operation which requires much less and simpler matrix operations. The new algorithm significantly reduces the computational complexity of the PIC operation by one order of magnitude.

A multi-cell hybrid channel state information (CSI) feedback scheme was proposed, which has an ideal nested hierarchical structure to support not only the advanced multi-cell MIMO precoding, but also the traditional single-cell single-user and multi-user precoding transmissions. A quantization scheme for the multi-cell hybrid CSI feedback was provided, using codebook based vector-quantization for the per-cell CSI information part and scalar-quantization for the inter-cell CSI information part. The hierarchical structure of the multi-cell hybrid CSI feedback simplifies greatly the feedback signaling design, and implicitly supports the dynamic switch between different operation modes. Simulation results show that the proposed CSI feedback solution can enable efficient DL single-cell and multi-cell precoding transmissions.

Uplink collaborative multiple-input-multiple-output (MIMO) processing among multiple base stations (BSs), also known as uplink multi-BS MIMO, is an effective way to solve the inter-cell interference in cellular systems. However, the significantly increased overhead on the backhaul network due to inter-BS information exchange has seriously restricted its application in practical systems with limited backhaul bandwidth. In this paper, we propose a three-stage technique for inter-BS information exchange in uplink multi-BS MIMO. A sub-optimal coordinated detection algorithm based on soft combining is considered to compromise between the performance and overhead. The major improvement over the existing solutions is that the proposed technique utilizes one property of soft combining to reduce the backhaul overhead: soft information combination is only needed for a very small part of the information bits that have mismatched local decoding results in different BSs. A three-stage information exchange technique is proposed to utilize this property, which allows the BSs to first locate the bits that need soft information combination and then exchange soft information for these bits only. By doing so, the backhaul overhead can be greatly reduced without performance degradation. Both simulation results and overhead analysis are used to demonstrate the benefit of the proposed technique. Comparisons with other coordinated detection algorithms like optimal joint detection and distributed interference cancellation show that it can provide a better tradeoff between the performance and overhead.

This paper discusses compressive sensing with digital sparse signals. The motivation is that most existing sparse signal recovery algorithms, like matching pursuit, convex relaxation and Bayesian framework, do not fully exploit the digital nature of signals when dealing with digital sparse signals, which result in certain performance losses. In this paper, we solve this problem via a permutation-based multi-dimensional sensing matrix and an iterative recovery algorithm with maximum likelihood (ML) local detectors. The sensing matrix considered consists of several sub-matrices, each composed of a random permutation matrix and a block-diagonal matrix. The measurements generated from the same permutation matrix are referred to as a dimension. The block-diagonal matrices allow the use of the low-complexity ML detector in each dimension, which best utilizes the digital nature of signals. The multi-dimensional structure of the sensing matrix enables information exchange between dimensions through an iterative process to achieve a near global-optimal estimation. Numerical results are used to show the rate-distortion performance of the proposed technique. It is shown that it can achieve much better rate-distortion than the existing approaches based on convex relaxation and Bayesian framework with digital source signals.

MIMO-OFDM is widely considered as a promising framework of the future broadband mobile communication systems due to its prominent advantages such as high spectral efficiency, low-complexity equalization for multi-path fading, and sufficient exploiting of the spatial degree of freedom. However, an inherent drawback of OFDM, i.e., high sensitivity to frequency offset, limits the application scenarios of MIMO- OFDM significantly. This paper proposes an enhanced MIMO- OFDM scheme, termed double-polarized SFBC-OFDM (DP-SFBC-OFDM), which can improve the robustness to frequency offset with only very low complexity increase at the receiver. Simulation results show that even for very large frequency offset, the system only has limited performance degradation (e.g., normalized frequency offset of 0.4 only lead to performance loss of smaller than 1 dB for 64QAM for the proposed scheme while ordinary transmission scheme totally collapse in this case).

This paper proposes a hierarchical feedback technique to solve the problem of massive feedback overhead in multiuser (MU-) multiple-input-multiple-output (MIMO) transmission, which is a serious obstacle to the application of MU-MIMO in real systems, especially with a joint operation of multiuser scheduling and preceding. Motivated by the observations that the multiuser scheduling phase dominates the feedback overhead and it is less sensitive to the accuracy of channel state information (CSI) than the preceding phase, the proposed technique adopts a two-stage feedback method to reduce the feedback overhead in the scheduling phase, meanwhile guarantee good performance of the preceding phase. In the scheduling phase, long-term CSI, such as mean and covariance channel matrixes, are used to reduce the overhead. In the preceding phase, on the other hand, more accurate short-term CSI is still used, since preceding is more sensitive to the CSI accuracy and also has less feedback overhead as only users selected in the scheduling phase are required to feed back their CSI. An angular information feedback technique is introduced to provide the long-term CSI for the transmitter in the scheduling phase, along with a low-complexity scheduling technique based on angular information. Simulation results have shown that the proposed hierarchical feedback technique can significantly reduce the feedback overhead of MU-MIMO transmission, while still achieve most of the performance gain.

This paper addresses the joint design of multiuser precoding and scheduling for downlink multiuser (MU-) multiple-input-multiple-output (MIMO) systems with imperfect channel state information at the transmitter (CSIT). The major issue is to control the increased co-channel-interference (CCI) caused by CSIT error among users sharing the same time-frequency resource. We propose an advanced joint multiuser precoding and scheduling technique, which uses the statistical information of the CSIT error to predict the relationship among CCI, precoding matrixes and scheduling results, and utilizes such information to improve the precoding and scheduling algorithms for better CCI control. Simulation results have shown that considerable capacity improvement can be achieved by the proposed technique in the case of imperfect CSIT.

Imperfect channel state information (CSI) at the transmitter is one of the major challenges that the downlink multiuser multiple-input-multiple-output (MU-MIMO) technology faces in real applications. It will lead to increased co- channel interference (CCI) among users and deteriorate the capacity of MU-MIMO seriously, especially at high signal-to- noise-ratios (SNRs). In this paper, we propose an advanced MU-MIMO scheduling technique to improve the capacity of MU-MIMO in the case of imperfect CSI at the transmitter (CSIT). The rational behind the proposed scheme is the observation that the CCI level increases with the number of users simultaneously supported in MU-MIMO transmission. Thus, decreasing the number of simultaneous users can reduce CCI and sometimes even increase the sum capacity. Motivated by this observation, we design a CCI-feedback-aided scheduling technique, which adaptively adjusts the number of simultaneous users based on the CCI measurement at the user side. Simulation results have shown that considerable capacity gain can be achieved by the proposed scheduling technique at the cost of only one bit additional feedback per user and marginal complexity increase at both the transmitter and user side.

This paper presents a quasi-random approach to space–time (ST) codes. The basic principle is to transmit randomly interleaved versions of forward error correction (FEC)-coded sequences simultaneously from all antennas in a multilayer structure. This is conceptually simple, yet still very effective. It is also flexible regarding the transmission rate, antenna numbers, and channel conditions (e.g., with intersymbol interference). It provides a unified solution to various applications where the traditional ST codes may encounter difficulties. We outline turbo-type iterative joint detection and equalization algorithms with complexity (per FEC-coded bit) growing linearly with the transmit antenna number and independently of the layer number. We develop a signal-to-noise-ratio (SNR) evolution technique and a bounding technique to assess the performance of the proposed code in fixed and quasi-static fading channels, respectively. These performance assessment techniques are very simple and reasonably accurate. Using these techniques as a searching tool, efficient power allocation strategies are examined, which can greatly enhance the system performance. Simulation results show that the proposed code can achieve near-capacity performance with both low and high rates at low decoding complexity.

The vertical Bell laboratory layered space-time (V-BLAST) code transmits independent data streams from each transmit antenna, and it may perform poorly over spatially correlated channels. To overcome this drawback, we present in this paper a cross-designed V-BLAST code, each component of which is, like a linear dispersion code, a combination of several transmitted ymbols. Since the new code incorporates the advantages of V-BLAST and linear dispersion codes, it achieves robust performance to spatial correlation with low complexity, full transmission rate, and short decoding delay. Computer simulations demonstrate the efficiency of the proposed code by comparison with the V-BLAST and two typical full-diversity full-rate codes.

This paper is concerned with signal estimation over general multiple-input-multiple-output (MIMO) channels based on a jointly Gaussian (JG) approach. We show that the proposed method is equivalent to the LMMSE approach, but is conceptually more concise and computationally more efficient. A 2×2 multi-antenna system with multipath effect is considered as an example to illustrate the advantages of the JG method. Also, a simple SNR-bounding evolution tool, similar to EXIT chart technique, is developed for fast assessment of the JG method. Compared with the optimal MAP algorithm, the JG method can achieve almost the same performance with greatly reduced complexity.

This paper provides a comprehensive study of interleave-division multiple-access (IDMA) systems. The IDMA receiver principles for different modulation and channel conditions are outlined. A semi-analytical technique is developed based on the density evolution technique to estimate the bit-error-rate (BER) of the system. It provides a fast and relatively accurate method to predict the performance of the IDMA scheme. With simple convolutional/repetition codes, overall throughputs of 3 bits/chip with one receive antenna and 6 bits/chip with two receive antennas are observed for IDMA systems involving as many as about 100 users.

We study a multi-layer space-time code, based on random interleaving and proper power allocation. We show that, theoretically, the proposed code can achieve capacity for any transmission rate by superimposing many ideal low-rate codes. We also show that, with practically available codes, the proposed scheme can achieve performance close to the theoretical limit.

This work provides a comprehensive study of IDMA systems. We first outline the basic IDMA principles in single-path and multi-path environments. We then describe a simple semi-analytical technique to assess the performance of IDMA systems, based on which we develop a power allocation scheme for performance optimization. We also discuss the use of low-rate codes to further enhance the power efficiency of IDMA systems. Simulation results demonstrate the advantages of the IDMA scheme in terms of both bandwidth and power efficiencies. For example, with simple convolutional/repetition codes an overall throughput of 8 bits/chip is achieved in single antenna systems. With turbo-Hadamard codes, performance at 1.4 dB away from the theoretical limit is demonstrated in a Gaussian MAC.

This letter concerns the performance assessment of cascade trellis-block space-time codes. We focus on the case where the 2/spl times/2 orthogonal block space-time code is used as the inner code. Either bounding or hybrid bounding/simulation techniques can be used. The proposed work provides some insights into the properties of such codes.

This letter presents an improved two-state turbo single-parity-check code for applications with rates around 1/3. The new code is compared with the (15,13)/sub 8/ turbo code used in the Third Generation Partnership Project. Simulation results demonstrate that the proposed code can achieve similar performance at reduced decoding complexity.

This correspondence concerns the performance analysis of turbo-single-parity-check (SPC) codes based on the union bound of bit-error rate (BER). A treatment of the special interleavers used in turbo-SPC codes is discussed. It is shown that simple two- or four-state turbo-SPC codes with multiple component codes can perform comparably as (or even better than) the 16-state standard turbo codes. Using more complex trellis codes (with state number more than 4) appears unnecessary for such codes. Instead, performance improvement can be achieved by increasing the number of component codes, which maintains the low decoding complexity property of turbo-SPC codes.

This paper presents a family of space-time codes employing interleavers to separate signals form different antennas. Noticeable performance improvement has been observed compared with existing schemes. An interactive detection algorithm is derived. The complexity involved is very low, and increases only linearly with the number of transmit antennas.

This paper is concerned with a class of low-rate codes constructed from Hadamard code arrays. A recursive encoding principle is employed to introduce an interleaving gain. Very simple trellis codes with only two or four states are sufficient for this purpose, and the decoding cost involved in the trellis part is typically negligible. Both simulation and analytical results are provided to demonstrate the advantages of the proposed scheme. The proposed scheme is of theoretical interest as it can achieve performance of BER=10/sup -5/ at E/sub b//N/sub 0//spl ap/-1.2dB (only about 0.4 dB away from the ultimate low-rate Shannon limit) with an information block size of 65534. To the authors' knowledge, this is the best result achieved to date with respect to the ultimate Shannon limit. With regard to practical issues, the decoding complexity of the proposed code is considerably lower than that of existing low-rate turbo-type codes with comparable performance.

This paper is concerned with a family of concatenated tree (CT) codes. CT codes are special low-density parity check (LDPC) codes consisting of several trees with large spans. They can also be regarded as special turbo codes with hybrid recursive/nonrecursive parts and multiple constituent codes. CT codes are decodable by the belief-propagation algorithm. They combine many advantages of LDPC and turbo codes, such as low decoding cost, fast convergence speed, and good performance.