Threshold‐based CSI feedback reduction for time‐varying multiple‐input multiple‐output broadcast channels

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degree of freedom (DoF) boosting has been demonstrated on a single-input single-output (SISO) X channel by using outdated and instantaneous channel state information at transmitter (CSIT) synergistically, in contrast to that of using completely outdated CSIT.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 We show that the achievable DoF is greater than the optimal DoF obtained with outdated CSIT, and that achievable DoF equals to the optimal value with outdated CSIT and transmitter cooperation.

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.

This paper considers the joint optimization of the transmitter beamforming filters and receiver combining filters for the multiple-input multiple-output (MIMO) broadcast channel. A low-complexity iterative algorithm to compute the filters is proposed and then a closed-form expression of the filters for the downlink with two transmit antennas is derived, avoiding the need for iterative computation in this case. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the channel structure in the closed form solution, and is independent of the number of receive antennas. Performance is evaluated by Monte Carlo simulations as a function of the amount of feedback. With two receive antennas, the proposed method performs close to the sum capacity of the MIMO broadcast channel, without the need for multiuser diversity and with only limited feedback.

We study the multiuser diversity in both multiple-input multiple output (MIMO) multiple-access channel (MAC) and MIMO broadcast channel (BC). We consider multiuser scheduling with linear receivers for uplink and linear precoders for downlink. In each time slot, a group of users are selected in either transmission or reception. Based on the performance analysis, a criterion of selecting a number of active users to optimize error performance is presented. We show that the linear processing for downlink and uplink achieves the same SNR. This makes it convenient to use a single scheduling algorithm in both downlink and uplink. Furthermore, we present an analysis of the performance of the proposed scheduling algorithm. It is shown that the analysis agrees with the simulation.

We study the Gaussian multiple-input multiple-output (MIMO) broadcast channel with common and private messages. We first obtain an outer bound for the capacity region of the two-user discrete memoryless broadcast channel with common and private messages. We next show that if jointly Gaussian random variables are sufficient to evaluate this outer bound for the Gaussian MIMO broadcast channel, the dirty-paper coding (DPC) region is the capacity region of the Gaussian MIMO broadcast channel with common and private messages. However, we can evaluate only a loosened version of this outer bound, which yields the result that extending the DPC region in the common message rate direction by a fixed amount is an outer bound for the capacity region of the Gaussian MIMO broadcast channel with common and private messages. However, this fixed amount, i.e., the gap, is not finite for all channels. We derive the necessary and sufficient conditions for this gap to be finite.

In this paper, Multi-Stream Receive-Spatial Modulation (MSR-SM) for application to the Multiple-Input Multiple-Output (MIMO) broadcast channel is introduced and studied. MSR-SM is a closed-loop transmission scheme, which applies the concept of multistream space modulation at the receiver side. An accurate mathematical framework for the evaluation of the Bit Error Rate (BER) is proposed. In addition, the diversity order and coding gain of the new architecture are derived. Note that the proposed analytical framework takes into account both the small-scale fading and the system topology, and is directly applicable to the conventional MIMO broadcast channel. Compared with the state-of-the-art MIMO transmission in the broadcast channel, it is mathematically shown that MSR-SM achieves the same diversity order and a better coding gain, in the high Signal-to-Noise Ratio (SNR) regime. Finally, the proposed mathematical framework and the new findings are validated via Monte Carlo simulation results.

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR.

In this paper, we investigate the topological interference management (TIM) for MIMO interference broadcast channels (IBC) under the fixed topology, where only topology information is needed to cancel the inter-cell interference (ICI). For the single user case, where the MIMO IBC network is reduced to the MIMO interference channel (IC) networks, we propose one novel TIM scheme by exploiting the solution to the corresponding SISO IC TIM based on topology information. And also, we present one theorem to characterize the achievable degrees of freedom (DoFs). For the multiple user case, assuming that each BS not only knows topology information but also knows the channel state information at transmitters (CSIT) within its own cell, we propose two novel TIM schemes, i.e., the virtual user compression-based scheme and the virtual base station extension-based scheme. For each scheme, we design transceivers to cancel both the ICI only based on the topology information by exploiting the solution to the corresponding SISO IC TIM and the multiple user interference (MUI) within its own cell based on the CSIT. Furthermore, we present one theorem to characterize the achievable DoF, respectively. Moreover, we prove that the DoF achieved by the virtual base station extension-based scheme is higher than or equal to that achieved by the virtual user compression-based scheme. Hence, the achievable DoF of MIMO IBC TIM is lower bounded by the optimal DoF attainable for the base station extension scheme. Furthermore, the numerical results show that our schemes can achieve significant performance gain against the traditional interference avoidance scheme.

We consider the minimization of the energy per bit (or, equivalently, the maximization of the energy efficiency) in multiple-input multiple-output (MIMO) broadcast channels with rate balancing constraints for fairness between users, and we propose a framework to find the globally optimal energy-efficient solution, which relies on dirty paper coding (DPC). The main idea is to introduce a continuous, differentiable, and concave optimal rate function, which transforms the problem into a convex-concave fractional program in one scalar variable. This not only allows an efficient solution, but also an intuitive interpretation of the method as well as the possibility to extend the method to related problems. In numerical simulations, we compare the energy efficiency with and without fairness constraints.

The capacity-achieving coding scheme for the multiple-input multiple-output (MIMO) broadcast channel is dirty-paper coding. With this type of transmission scheme the optimal number of active users that receive data and the optimal power allocation strategy are highly dependent on the structure of the channel matrix and on the total transmit power available. In the context of packet-data access with adaptive transmission where mobile users are equipped with a single receive antenna and the base station has multiple transmit antennas, we study the optimal number of active users and the optimal power allocation. In the particular case of two transmit antennas, we prove that the optimal number of active users can be a non-monotonic function of the total transmit power. Thus not only the number of users that should optimally be served simultaneously depends on the user channel vectors but also on the power available at the base station transmitter. The expected complexity of optimal scheduling algorithms is thus very high. Yet we then prove that at most as many users as the number of transmit antennas are allocated a large amount of power asymptotically in the high-power region in order to achieve the sum-capacity. Simulations confirm that constraining the number of active users to be no more than the number of transmit antennas incurs only a marginal loss in spectral efficiency. Based on these observations, we propose low-complexity scheduling algorithms with sub-optimal transmission schemes that can approach the sum-capacity of the MIMO broadcast channel by taking advantage of multiuser diversity. The suitability of known antenna selection algorithms is also demonstrated. We consider the cases of complete and partial channel knowledge at the transmitter. We provide simulation results to illustrate our conclusions.

In this paper, we present a game-theoretic approach for the purpose of deriving the problem of joint beamforming and power control in cognitive radio (CR) multiple-input multiple-output (MIMO) broadcast channels (CR MIMO-BCs), where the primary users (PUs) coexist with the secondary users (SUs) and they share the same spectrum. The cognitive base station (CBS), which is equipped with multiple antennas, is capable of transmitting data to the SU’s multiple-antenna receiver by employing the technology of beamforming. The proposed approach is an application of separable games, which are formally stated by the subgames of beamforming and power control. Furthermore, based on the model of noncooperative separate games, separable cost functions for the parameters of beamforming and power control are also proposed, showing that these cost functions are convex. Therefore, the convex theory of a noncooperative game can be employed to investigate the best response strategies as well as existence of Nash equilibrium solutions. Finally, we propose an iterative algorithm to achieve the optimal Nash equilibrium of the proposed joint beamforming subgame and power control subgame. Numerical results verify both the convergence and the tracking properties of the proposed algorithm for variant scenarios.

In this letter, the diversity order and the coding gain of Receive Space Modulation (RSM) in the Multiple-Input Multiple-Output (MIMO) broadcast channel are derived by taking into account both small and large scale fading. The considered linear precoding method is Zero Forcing (ZF). Note that the proposed framework is directly applicable to the conventional spatially multiplexed broadcast channel. Based on the derived mathematical framework, a theoretical criterion is provided, which determines the superiority between the deployment of RSM and Spatial MultipleXing (SMX) in the shadowing MIMO broadcast channel. Finally, the provided theoretical results are validated via Monte Carlo simulation results.

Recent information-theoretic results show the optimality of dirty-paper coding (DPC) in achieving the full capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC).This paper presents a DPC based code design for BCs.We consider the case in which there is an individual rate/signal-to-interference-plus-noise ratio (SINR) constraint for each user.For a fixed transmitter power, we choose the linear transmit precoding matrix such that the SINRs at users are uniformly maximized, thus ensuring the best bit-error rate performance.We start with Cover's simplest two-user Gaussian BC and present a coding scheme that operates 1.44 dB from the boundary of the capacity region at the rate of one bit per real sample (b/s) for each user.We then extend the coding strategy to a two-user MIMO Gaussian BC with two transmit antennas at the base-station and develop the first limit-approaching code design using nested turbo codes for DPC.At the rate of 1 b/s for each user, our design operates 1.48 dB from the capacity region boundary.We also consider the performance of our scheme over a slow fading BC.For two transmit antennas, simulation results indicate a performance loss of only 1.4 dB, 1.64 dB and 1.99 dB from the theoretical limit in terms of the total transmission power for the two, three and four user case, respectively.

We provide an alternative proof for the capacity region of the degraded Gaussian multiple-input multiple-output (MIMO) broadcast channel. Our proof does not use the channel enhancement technique as opposed to the original proof of Weingertan and the alternative proof of Liu et al. Our proof starts with the single-letter description of the capacity region of the degraded broadcast channel, and by using it, obtains a tight (i.e., achievable) outer bound for the capacity region of the degraded Gaussian MIMO broadcast channel, by using two main technical tools. The first one is the generalized de Bruijn identity due to Palomar which provides a connection between the differential entropy and the Fisher information matrix. The second tool we use is an inequality due to Dembo which lower bounds the differential entropy in terms of the Fisher information matrix.

This letter investigates the secure degrees of freedom (SDoF) of reconfigurable intelligent surface (RIS) aided 2-user multiple-input multiple-output (MIMO) broadcast channel with confidential messages. In this model, transmitter sends corresponding secure messages to each receiver and ensure the other receiver cannot decode them. Compared to existing studies on broadcast channel with confidential messages, our model employs an active RIS that reduces the rank of channel matrices, enlarging the nullspace for secure transmission to increase the SDoF. In particular, our model can realize secure communication even when the SDoF of original model is zero. According to the different configurations of antennas, the problem is divided into several regimes. We present outer bound and corresponding proof for each regime by utilizing theoretical methods and RIS beamforming. Then we propose universal achievable schemes for different regimes by using zero-forcing decoding. Finally, we determine the exact SDoF of RIS-assisted MIMO broadcast channel with confidential messages and its SDoF gain compared to original model.