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Turbo channel estimation algorithms can improve estimation accuracy in multiuser (MU) multi-input multi-output (MIMO) systems. However, the conventional least squares (LS) turbo channel estimation technique based on the maximum likelihood approach is difficult to be performed in large MU-MIMO systems since it requires a cubic complexity order $\mathcal {O}(\kappa ^3 N_T^3 N_R^3)$ for a system constant $\kappa$ and numbers of $N_T$ -transmit and $N_R$ -receive antennas. This is because the conventional technique computes inversion of a covariance matrix whose size is proportional to $\kappa N_T N_R$. We propose a new low-complexity LS estimation technique that requires a complexity order $\mathcal {O}(\kappa ^3 N_T^3)$ only by utilizing algebraic property on the covariance matrix. Simulation results shown in this letter verify that the proposed technique significantly reduces processing time without sacrificing the estimation performance. [ABSTRACT FROM AUTHOR]

In this paper, system design for a multiple-input–multiple-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at mobile stations (MSs) is considered. By exploring the structure of downlink–uplink duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of the existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then, the problem of sum power minimization is considered. Each design problem can be efficiently solved using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be further reduced. Finally, a generalization to the multihop scenario is provided to further improve power efficiency. [ABSTRACT FROM PUBLISHER]

In this paper, two protocols of device-to-device (D2D) communication are proposed to assist multi-input multi-output downlink broadcast channel in cellular networks. Devices in a D2D group are arranged to extract their own data originated from a base station (BS) in an order according to their decoding performance. At each intermediate hop, a best-decoding node is selected and responsible for the next hop transmission. In the first protocol, the selected user forwards its own data stream after decoding, then a proper interference cancellation is performed at each receiving node to eliminate this interference from the buffered reception of the broadcast channel in the first hop from the BS. A closed form representation of the outage probability of each node is derived. In the second protocol, the selected user re-constructs its own signal and cancels this signal from its received vector and forwards the resultant vector. This forwarded signal contains less interference and preserves the data information for the users who have not yet decoded their data. The receiving nodes decode their data streams by jointly considering the received vectors from all the previous hops. The comparison of the two proposed protocols in terms of computational complexity, achieved diversity order and performance are provided. We show that the proposed protocols provide promising diversity gain and avoid error propagations as compared to traditional schemes. [ABSTRACT FROM AUTHOR]

In this paper, we aim to solve the multiuser multi-input multi-output (MIMO) downlink beamforming problem where one multi-antenna base station broadcasts data to many users. Each user is assigned multiple data streams and has multiple antennas at its receiver. Efficient solutions to the joint transmit-receive beamforming and power allocation problem based on iterative methods are proposed. We adopt the group maximum signal-to-interference-plus-noise-ratio (SINR) filter bank (GSINR-FB) as our beamformer which exploits receiver diversity through cooperation between the data streams of a user. The data streams for each user are subject to an average SINR constraint, which has many important applications in wireless communication systems and serves as a good metric to measure the quality of service (QoS). The GSINR-FB also optimizes the average SINR of its output. Based on the GSINR-FB beamformer, we find an SINR balancing structure for optimal power allocation which simplifies the complicated power allocation problem to a linear one. Simulation results verify the superiority of the proposed algorithms over previous works with approximately the same complexity. [ABSTRACT FROM PUBLISHER]

Linear-assignment Gel'fand-Pinsker coding (LA-GPC) is a coding technique for channels with interference known only at the transmitter, where the known interference is treated as side-information (SI). As a special case of the LA-GPC, dirty paper coding has been shown to be able to achieve the optimal interference-free rate for SI channels with perfect channel state information at the transmitter (CSIT). In the cases where only the channel distribution information at the transmitter (CDIT) is available, LA-GPC also has good (sometimes optimal) performance in a variety of fast and slow fading SI channels. In this letter, we design filters in the nested lattice based coding to make it achieve the same rate performance as the LA-GPC in multiple-input multiple-output (MIMO) channels. Compared with the random Gaussian codebooks used in previous works, our resultant coding schemes have algebraic structures and can be implemented in practical systems. Simulations in slow-fading channels are provided, and near interference-free error performance is obtained. The proposed coding schemes can serve as the fundamental building blocks to achieve the promised rate performance of MIMO Gaussian broadcast channels with CDIT or perfect CSIT. [ABSTRACT FROM AUTHOR]