In this paper, the sum cell rate based on altruistic and egoistic multicell distributed beamforming (MDBF) is studied with local channel state Information (CSI). To start with, we provide two sufficient conditions for implementing altruistic and egoistic strategy based on the traditional method, and give the proof of those condition. Second, a MDBF method based on the altruistic and egoistic strategy is proposed, where the altruistic strategy is implemented with the internal penalty function. Finally, simulation results demonstrate that the effectiveness of the sufficient conditions and the proposed method has the different performance and advantages.

This paper expands our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems to support multi modulus signals. The original proposal applies the channel state information (CSI) aided blind adaptive array (BAA) interference suppression after the beamspace preprocessing and the decision feedback channel estimation (DFCE). BAA is based on the constant modulus algorithm (CMA) which can fully exploit the degree of freedom (DoF) of massive antenna arrays to suppress both inter-user interference (IUI) and inter-cell interference (ICI). Its effectiveness has been verified under the extensive pilot contamination constraint. Unfortunately, CMA basically works well only for constant envelope signals such as QPSK and thus the proposed scheme should be expanded to cover QAM signals for more general use. This paper proposes to apply the multi modulus algorithm (MMA) and the minimum mean square error weight derivation based on data-aided sample matrix inversion (MMSE-SMI). It can successfully realize interference suppression even with the use of multi-level envelope signals such as 16QAM with satisfactorily outage probability performance below the fifth percentile.

This paper improves our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems by incorporating the beamspace approach. The constant modulus algorithm (CMA), a known blind adaptive array scheme, can fully exploit the degree of freedom (DoF) offered by massive antenna arrays to suppress inter-user interference (IUI) and inter-cell interference (ICI). Unfortunately, CMA wastes a lot of the benefit of DoF for null-steering even when the number of incoming signal is fewer than that of receiving antenna elements. Our new proposal introduces the beamspace method which degenerates the number of array input for CMA from element-space to beamspace. It can control DoF expended for subsequent interference suppression by CMA. Optimizing the array beamforming gain and null-steering ability, can further improve the output signal-to-interference and noise power ratio (SINR). Computer simulation confirmed that our new proposal reduced the required number of data symbols by 34.6%. In addition, the 5th percentile SINR was also improved by 14.3dB.

In this paper, the multicell distributed beamforming (MDBF) design problem of suppressing intra-cell interference (InCI) and inter-cell interference (ICI) is studied. To start with, in order to decrease the InCI and ICI caused by a user, we propose a gradient-iteration altruistic algorithm to derive the beamforming vectors. The convergence of the proposed iterative algorithm is proved. Second, a metric function is established to restrict the ICI and maximize cell rate. This function depends on only local channel state information (CSI) and does not need additional CSIs. Moreover, an MDBF algorithm with the metric function is proposed. This proposed algorithm utilizes gradient iteration to maximize the metric function to improve sum rate of the cell. Finally, simulation results demonstrate that the proposed algorithm can achieve higher cell rates while offering more advantages to suppress InCI and ICI than the traditional ones.

Multicell cooperation is a promising technique to mitigate the inter-cell interference and improve the sum rate in cellular systems. Limited feedback design is of great importance to base station cooperation as it provides the quantized channel state information (CSI) of both the desired and interfering channels to the transmitters. Most studies on multicell limited feedback deal with scenarios of a single receive antenna at the mobile user. This paper, however, applies limited feedback to cooperative multicell multiple-input multiple-output (MIMO) systems where both base stations and users are equipped with multiple antennas. An optimized feedback strategy with random vector quantization (RVQ) codebook is proposed for interference aware coordinated beamforming that approximately maximizes the lower bound of the sum rate. By minimizing the upper-bound on the mean sum-rate loss induced by the quantization errors, we present a feedback-bit allocation algorithm to divide the available feedback bits between the desired and interfering channels for arbitrary number of transmit and receive antennas under different interfering signal strengths. Simulation results demonstrate that the proposed scheme utilizes the feedback resource effectively and achieves sum-rate performance reasonably close to the full CSI case.

This letter considers the weighted sum-rate maximization (WSRMax) problem in downlink multicell multiuser orthogonal frequency-division multiplexing system. The WSRMax problem under per base station transmit power constraint is known to be NP-hard, and the optimal solution is computationally very expensive. We propose two less-complex suboptimal convex approximated solutions which are based on sequential parametric convex approximation approach. We derive provably faster convergent iterative convex approximation techniques that locally optimize the weighted sum-rate function. Both the iterative solutions are found to converge to the local optimal solution within a few iterations compared to other well-known techniques. The numerical results demonstrate the effectiveness and superiority of the proposed approaches.

This letter deals with a new cell clustering problem subject to signal-to-interference-plus-noise-ratio (SINR) constraints in uplink network MIMO systems, where multiple base stations (BSs) cooperate for joint processing as forming a cluster. We first prove that the SINRs of users in a certain cluster always increase monotonically as the cluster size increases when the receiver filter that maximizes the SINR is used. Using this result, we propose an efficient clustering algorithm to minimize the maximum number of cooperative BSs in a cluster. Simulation results show that the maximum number of cooperative BSs minimized by the proposed method is close to that minimized by the exhaustive search and the proposed scheme outperforms the conventional one in terms of the outage probability.

All points on the Pareto boundary can be obtained by solving the weighted sum rate maximization problem for some weighted coefficients. Unfortunately, the problem is non-convex and difficult to solve without performing an exhaustive search. In this paper, we propose an optimal distributed beamforming strategy for the two-user multiple-input single-output (MISO) interference channel (IC). Through minimizing the interference signal power leaked to the other receiver for fixed useful signal power received at the intended receiver, the original non-convex optimization problem can be converted into a family of convex optimization problems, each which can be solved in distributed manner with only local channel state information at each transmitter. After some conversion, we derive the closed-form solutions to all Pareto optimal points based on a game-theoretic viewpoint which indicates that linear combinations of the maximum-ratio transmit (MRT) and zero-forcing (ZF) beamforming strategies can achieve any point on the Pareto boundary of the rate region for the two-user MISO interference channel, and the only computation involved is to solve a basic quadratic equation. Finally, the result is validated via numerical simulations.

In this paper, a distributed cooperative multicell beamforming algorithm is proposed, and a detail analysis and solving method for instantaneous and statistical channel state information (CSI) are presented. Firstly, an improved distributed iterative beamforming algorithm is proposed for the multiple-input single-output interference channel (MISO IC) scenario which chooses virtual signal-to-interference-and-noise (SINR) as decision criterion to initialize and then iteratively solves the constrained optimization problem of maximizing the virtual SINR for a given level of generated interference to other users. Then, the algorithm is generalized to the multicell date sharing scenario with a heuristics power allocation scheme based on a viewpoint of the layered channel. Finally, the performance is illustrated through numerical simulations.

Coordinated multi-point processing at multiple base stations can improve coverage, system throughput, and cell-edge throughput for future cellular systems. In this paper, we study the coordinated reception of transmitted signals at multiple MIMO base stations to exploit cooperative diversity. In particular, we propose to employ cooperative multicell automatic repeat request (ARQ) protocol via backhaul links. The attractiveness of this protocol is that processing between coordinated base stations can be made completely transparent to the mobile user, and it improves the mobile user's link reliability and throughput significantly compared to noncooperative ARQ protocol. In our proposed protocol, we consider the scenario where the multicell processing involves one of the following three schemes: decode-and-forward, amplify-and-forward, and compress-and-forward schemes. We derive the average packet error rate and throughput for these cooperative multicell ARQ protocols. Numerical results show that the cooperative multicell ARQ protocols are promising in terms of average packet error rate and throughput. Furthermore, we show that the degree of improvement depends on the type of cooperative multicell ARQ protocol employed and the operating average signal-to-noise ratio of the main and backhaul links.

The biggest challenge in multi-cell MIMO multiplexing systems is how to effectively suppress the other-cell interference (OCI) since the OCI severely decrease the system performance. Cooperation among cells is one of the most promising solutions to OCI problems. However, this solution suffers greatly from delay and overhead issues, which make it impractical. A coordinated MIMO system with a simplified cooperation between the base stations is a compromise between the theory and practice. We aim to devise an effective resource allocation algorithm based on a coordinated MIMO system that largely alleviates the OCI. In this paper, we propose a joint resource allocation algorithm incorporating intra-cell beamforming multiplexing and inter-cell interference suppression, which adaptively allocates the transmitting power and schedules users while achieving close to an optimal system throughput under proportional fairness consideration. We formulate this problem as a nonlinear combinational optimization problem, which is hard to solve. Then, we decouple the variables and transform it into a problem with convex sub-problems that can be solve but still need heavy computational complexity. In order to implement the algorithm in real-time scenarios, we reduce the computational complexity by assuming an equal power allocation utility to do user scheduling before the power allocation. Extensive simulation results show that the joint resource allocation algorithm can achieve a higher throughput and better fairness than the traditional method while maintains the proportional fairness. Moreover, the low-complexity algorithm obtains a better fairness and less computational complexity with only a slight loss in throughput.

We address the problem of multiuser co-channel interference scheduling in multicell interference-limited networks. Our target is to optimize the network capacity under the SIR-balanced power control policy. Since it's difficult to optimize the original problem, we derive a new problem which maximizes the lower bound of the network capacity. Based on the analysis of this new problem, we propose an interference matched scheduling algorithm. This algorithm considers the caused co-channel interference and the channel conditions to schedule the "matched" users at the same time. We prove that this interference matched scheduling algorithm optimizes the lower bound of the network capacity for any arbitrary numbers of cells and users. Moreover, this scheduling method is low-complexity and can be implemented in a fully distributed fashion. Simulation results reveal that the performance of the proposed algorithm achieves near optimal capacity, even though it does not optimize the network capacity directly. Finally, the proposed algorithm holds a great gain over formerly proposed round robin and power matched scheduling method, especially when the scale of the network is large.

This paper proposes a MIMO-OFDM precoder that can minimize a BER upper bound of the maximum likelihood detector (MLD) under a non-cooperative downlink multicell co-channel interference (CCI) environment. Since there is no cooperation among base stations (BSs), it is assumed that information on the interference can be estimated at a mobile station (MS) and then fed back to the desired BS for the precoder. The proposed scheme controls its precoding parameters under a transmit power constraint so as to minimize the BER upper bound, which is derived from the pairwise error probability (PEP) averaged with respect to CCI plus noise. Computer simulations demonstrate that the proposed precoder can effectively improve BER performance of cell edge users and is superior in terms of BER to the eigenmode and the minimum mean squared error (MMSE) precoded transmissions which aim to maximize the channel capacity and to minimize MSE, respectively.

This paper addresses the problem of channel estimation for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM)-based wireless networks with frequency-reuse. Firstly, the optimal time-domain training sequences are derived for the multiple cells with the same frequency group and a set of suitable sequences are also presented for practical implementation. Secondly, a low complexity iterative algorithm is combined with the time domain channel estimation to suppress the co-channel interferences (CCIs). The channel estimation method is applied to synchronous and asynchronous cellular and the MSE performance of the estimator is also analyzed. Simulation results demonstrate that the presented channel estimation approach can substantially suppress CCIs and outperform the conventional LS MIMO-OFDM channel estimation over multipath fading channels in multicell environments.

The bit error rate (BER) for an uplink multicell multicarrier code-division multiple-access (MC-CDMA) system in Nakagami-m fading channels is derived and expressed in the form of a single integral. The result is obtained without the approximation for the ratio of the interference power from other-cell to the power from the user-of-interest. Numerical results demonstrate the impacts of other-cell interference and power control errors on the BER.