Topological Interference Management for MIMO Interference Broadcast Channels

Multiple-input multiple-output (MIMO) wireless communication systems in combination with orthogonal frequency division multiple access and space division multiple access are flexible, spectrally efficient and of high capacity. These systems can allocate frequency, time, and space resources adaptively among mobile stations. Optimal resource allocation (RA) for maximizing the sum rate is usually too complex for practical applications, hence suboptimal algorithms are required. The performance of RA algorithms are degraded due to imperfections in channel state information at transmitter (CSIT). Thus, techniques that make the RA algorithm, robust against CSIT errors are favorable. In this paper, two novel suboptimal grouping algorithms are introduced that are shown to achieve better sum rate-complexity and fairness-complexity performances. A novel index is proposed to measure the efficiency of RA algorithms, which takes into account sum rate and complexity simultaneously. Moreover, the effect of erroneous CSIT on sum rate is investigated and it is shown that performance of the proposed RA algorithms with erroneous CSIT are superior to the existing schemes. Finally, fairness performance of the proposed RA algorithms are evaluated, which are shown to be the same if not superior to other existing algorithms.

The achievable degree of freedom (DoF) boosting has been demonstrated on a single-input single-output (SISO) X channel by using outdated and instantaneous channel state information at transmitter (CSIT) synergistically, in contrast to that of using completely outdated CSIT. However, the means by which the DoF gain can be obtained in a multiple-input multiple-output (MIMO) system remains unclear. This paper proposes an interference alignment scheme with synergistic CSIT for MIMO X channel. We show that the achievable DoF is greater than the optimal DoF obtained with outdated CSIT, and that achievable DoF equals to the optimal value with outdated CSIT and transmitter cooperation.

In a relay channel with channel states, channel state information at transmitter (CSIT) can significantly improve performance. While perfect CSIT is hard or impossible to obtain, partial CSIT obtained through finite-rate feedback from the receivers is practical. The problem is how to design such feedback jointly with the transmission strategy to optimize the performance. For discrete memoryless relay channel employing decode-and-forward, we prove the maximum achievable rate and show that the optimal feedback and transmission strategy are both memoryless and deterministic, thus, proving that an obvious intuition is indeed optimal.

The performance of adaptively grouped multilevel space---time trellis codes (AGMLSTTCs) is limited due to predefined component space---time trellis codes (STTCs) used in multilevel coding and lack of beamforming. In this paper, we present improvement in performance of AGMLSTTCs by combining beamforming and dynamic selection of component STTCs with AGMLSTTCs to design new codes henceforth referred to as weighted adaptively grouped multilevel dynamic space---time trellis codes. The channel state information at transmitter (CSI) is used to select a code set having different sets of generator sequences. The selected code set is used for generating dynamic STTCs (DSTTCs). The DSTTCs are used as component codes in multilevel coding. We use a single full-diversity DSTTC at some initial levels and multiple DSTTCs at some later levels. The single full diversity DSTTC at each initial level spans all transmit antennas and the DSTTC at each later level spans a group of transmit antennas. The CSI is further used to provide a beam forming scheme by properly weighting transmitted signals. Weights are selected that based on CSI at transmitter. The simulation results show that AGMLSTTCs combined with beamforming and DSTTCs provide significant improved error performance over grouped multilevel space---time trellis codes and AGMLSTTCs.

In this paper, the downlink rate quantization and cross-layer scheduling design are investigated in multiuser multiple-input single-output (MISO) systems with imperfect channel state information at transmitter (CSIT). We shall propose a systematic analytical design framework based on information theoretical approach. To capture the effect of the potential packet outage, we introduce the average system goodput, which measures the average b/s/Hz delivered to the mobiles successfully, as the system performance objective. Numerical results demonstrate that, by considering the statistics of CSIT errors into the design, the proposed scheduling scheme provides significant performance enhancement.

In this paper we investigate the codebook design for a time division duplex (TDD) non-antenna switching system, where the user device is equipped with two receive radio frequency (RF) chains but only one transmit RF chain. To acquire the channel state information at transmitter (CSIT), we resort to a mixture of channel sounding and limited feedback, where the former is employed to obtain the CSIT of the antenna connected to the transmit RF chain and the latter is employed for the other antenna. We propose a deep learning method to design the codebook for limited feedback. The learned codebook is distinguished from the traditional ones in two ways. First, the learned codebook is so-called personalized, which is not fixed but adapt to the partially known CSIT. Second, the codebook exhibits different beam patterns from the traditional codebook that is designed for quantization error minimization. Simulation results demonstrate that the learned codebook can achieve higher data rate with lower complexity than traditional codebook.

In many modern wireless systems that operate based on availability of channel state information at transmitter (CSIT), one of the main bottlenecks is updating CSIT of the time varying channel. In multiple-input multiple-output (MIMO) broadcast channels, CSIT is required for user scheduling and precoding. Most existing CSI feedback reduction techniques reduce the number of feedback terms by allowing only a limited number of users to feedback CSI, or allowing only partial/limited information of user channels to be send back to the transmitter. In this work, CSI is reduced over several time-slots of transmission, and not in a single snapshot at the beginning of each time-slot. A time-varying MIMO broadcast channel with limited-capacity feedback links is considered. An efficient threshold-based feedback technique that maximises net-throughput is proposed. The proposed scheme assigns different CSI feedback rates over several time-slots to different groups of users proportional to their channel strength. Analysis and simulation results for zero-forcing beamforming precoding show net-throughput superiority of the proposed scheme over the case of perfect CSI.

Adapting user selection, power allocation and rate allocation, cross layer centralized scheduling can achieve a tremendous gain due to multiuser selection diversity in wireless networks. Yet, conventional cross layer designs all require channel state information at transmitters (CSIT), which is difficult to obtain in practice. In this paper, a closed loop cross layer design with no CSIT in a block fading frequency selective environment is proposed as an extension of flat fading case in (Ka Ming Ho, 2006). The solution to optimal power, rate allocation and user selection involves stochastic programming and thus has high complexity, especially when the channel is highly frequency selective. Instead, we would like to focus on two suboptimal algorithms, namely CP-TX and OP-TX, which are based on ACK/NAK feedbacks from selected mobiles. In practical situations, OP-TX can achieve more than 67 % of the goodput of the optimal algorithm.

Obtaining accurate global channel state information (CSI) at multiple transmitter devices is critical to the performance of many coordinated transmission schemes. Practical CSI local feedback often leads to noisy and partial CSI estimates at each transmitter. With rate-limited bi-directional backhaul, transmitters have the opportunity to exchange few CSI-related bits to establish global channel state information at transmitter (CSIT). This paper investigates possible strategies toward this goal. We propose a novel decentralized algorithm that produces minimum mean square error-optimal global channel estimates at each device from combining local feedback and information exchanged through backhauls. The method adapts to arbitrary initial information topologies and feedback noise statistics and can do that with a combination of closed-form and convex approaches. Simulations for coordinated multi-point transmission systems with two or three transmitters exhibit the advantage of the proposed algorithm over conventional CSI exchange mechanisms when the coordination backhauls are limited.

In this letter, we explore interference alignment for the $K$ -user single input single output interference channel with more efficient precoder sizes and lower sensitivity to channel state information at transmitters (CSIT). The main idea in this letter is to divide transmission time into some distinct blocks and select a subset of transmitters and receivers for signaling in each block. We show that our method can achieve more sum degrees-of-freedom compared with other schemes in the limited number of channel extensions. Furthermore, our method needs no full CSIT to be applicable which can be considered as another advantage of this letter.

Multi-user multiple-input and multiple-output (MU-MIMO) wireless systems have the potential to increase system capacity significantly by separating multiple users in the space domain through appropriate signal processing. These techniques require accurate channel state information at transmitter (CSIT) for their proper operations. With inevitable channel imperfections in practice, robustness has become an important issue in the development of beamforming techniques. In this work, we propose a robust leakage-based transmit beamforming design for multi-user MIMO systems by introducing a probabilistic constraint. In a multi-user system, the main challenge for transmit beamforming is to suppress the co-channel interference (CCI) from other users. Our approach optimizes the average signal-to-interference-plus-noise ratio (SINR) performance implicitly by maximizing the average signal power subject to probabilistic leakage and noise power constraint. Moreover, both the single-stream-per-user and multiple-stream-per-user cases are considered.In the latter case, a hybrid scheme is suggested by incorporating Alamouti code into the proposed design. Simulation results show that under proper control of the probabilistic constraint, both beamformers achieve good bit-error-rate (BER) performances, reliability of SINR levels as well as robustness against channel uncertainties.

The Compute-and-Forward protocol in quasi-static channels normally employs lattice codes based on the rational integers $\mathbb{Z}$, Gaussian integers $\mathbb{Z}\left[i\right]$ or Eisenstein integers $\mathbb{Z}\left[\omega\right]$, while its extension to more general channels often assumes channel state information at transmitters (CSIT). In this paper, we propose a novel scheme for Compute-and-Forward in block-fading channels without CSIT, which is referred to as Ring Compute-and-Forward because the fading coefficients are quantized to the canonical embedding of a ring of algebraic integers. Thanks to the multiplicative closure of the algebraic lattices employed, a relay is able to decode an algebraic-integer linear combination of lattice codewords. We analyze its achievable computation rates and show it outperforms conventional Compute-and-Forward based on $\mathbb{Z}$-lattices. By investigating the effect of Diophantine approximation by algebraic conjugates, we prove that the degrees-of-freedom (DoF) of the optimized computation rate is ${n}/{L}$, where $n$ is the number of blocks and $L$ is the number of users.

Multicell cooperative processing (MCP) can overcome the detrimental effects of inter-cell interference (ICI). However, since mobile terminals (MTs) need to estimate and feed back channel state information (CSI) related to several cooperative base stations (BSs), reductions in CSI feedback overhead and delay are more demanding in MCP enabled networks. In this paper, a novel overlaid-pilot based CSI feedback scheme for multicell cooperative networks is proposed. MTs directly overlay the received downlink pilot data of all the cooperative BSs on the uplink pilot data and then broadcast it to the cooperative BSs. Each cooperative BS can recover multiple CSIs from the overlaid pilot by a presented overlaid-pilot based channel estimation algorithm. Because the overlaid pilot is fed back on the uplink pilot channel, no extra uplink channel is occupied and the downlink CSI feedback process is with zero-overhead regardless of the number of cooperative BSs. In addition, as it just needs one broadcast to feed back multiple CSIs to BSs, the feedback delay is decreased significantly. Numerical simulations show that the performance of the proposed CSI feedback scheme outperforms the traditional feedback scheme.

This paper develops an analytical framework for exploring the benefits of applying cluster-based multi-antenna non-orthogonal multiple access (NOMA) in dense wireless networks. Using the tools of stochastic geometry, a new explicit expression and a tight approximation for per-cluster average data rates are derived in terms of relevant system parameters. Moreover, simulation results are provided to validate the accuracy of our analytical results and draw some essential system design insights. Based on the tractable expressions, we further consider the analysis and optimization of area spectral efficiency (ASE). It is analytically demonstrated that: 1) In terms of the per-cluster average data rate, there exists a wide range for the power allocation coefficient within a cluster where NOMA outperforms orthogonal multiple access (OMA); 2) The per-cluster average data rate of NOMA benefits more from the additional deployment of antennas at base stations (BSs) than that of OMA when the accuracy of channel state information at transmitter (CSIT) is fixed; 3) Regarding to ASE, there exists an optimal combination of the number of clusters and power allocation coefficient to maximize ASE. The performance gain of NOMA relative to OMA becomes marginal when the requirement of user fairness is stringent; 4) As for a special case that the power allocation coefficient is fixed, the analytical results indicate that ASE scales linearly with the number of antennas when the number of clusters is set optimally.

This paper proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plus-noise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation.