Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition

This paper studies joint beamforming and power control in a coordinated multicell downlink system that serves multiple users per cell to maximize the minimum weighted signal-to-interference-plus-noise ratio. The optimal solution and distributed algorithm with geometrically fast convergence rate are derived by employing the nonlinear Perron-Frobenius theory and the multicell network duality. The iterative algorithm, though operating in a distributed manner, still requires instantaneous power update within the coordinated cluster through the backhaul. The backhaul information exchange and message passing may become prohibitive with increasing number of transmit antennas and increasing number of users. In order to derive asymptotically optimal solution, random matrix theory is leveraged to design a distributed algorithm that only requires statistical information. The advantage of our approach is that there is no instantaneous power update through backhaul. Moreover, by using nonlinear Perron-Frobenius theory and random matrix theory, an effective primal network and an effective dual network are proposed to characterize and interpret the asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on Wireless Communications are correcte

Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness

This paper addresses coordinated downlink beamforming optimization in multicell time-division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201

Cooperative Multi-Cell Networks: Impact of Limited-Capacity Backhaul and Inter-Users Links

Cooperative technology is expected to have a great impact on the performance of cellular or, more generally, infrastructure networks. Both multicell processing (cooperation among base stations) and relaying (cooperation at the user level) are currently being investigated. In this presentation, recent results regarding the performance of multicell processing and user cooperation under the assumption of limited-capacity interbase station and inter-user links, respectively, are reviewed. The survey focuses on related results derived for non-fading uplink and downlink channels of simple cellular system models. The analytical treatment, facilitated by these simple setups, enhances the insight into the limitations imposed by limited-capacity constraints on the gains achievable by cooperative techniques

Adaptive Bit Partitioning for Multicell Intercell Interference Nulling with Delayed Limited Feedback

Base station cooperation can exploit knowledge of the users' channel state information (CSI) at the transmitters to manage co-channel interference. Users have to feedback CSI of the desired and interfering channels using finite-bandwidth backhaul links. Existing codebook designs for single-cell limited feedback can be used for multicell cooperation by partitioning the available feedback resources between the multiple channels. In this paper, a new feedback-bit allocation strategy is proposed, as a function of the delays in the communication links and received signal strengths in the downlink. Channel temporal correlation is modeled as a function of delay using the Gauss-Markov model. Closed-form expressions for bit partitions are derived to allocate more bits to quantize the stronger channels with smaller delays and fewer bits to weaker channels with larger delays, assuming random vector quantization. Cellular network simulations are used to show that the proposed algorithm yields higher sum-rates than an equal-bit allocation technique.Comment: Submitted to IEEE Transactions on Signal Processing, July 201

Optimal Channel Training in Uplink Network MIMO Systems

We consider a multi-cell frequency-selective fading uplink channel (network MIMO) from K single-antenna user terminals (UTs) to B cooperative base stations (BSs) with M antennas each. The BSs, assumed to be oblivious of the applied codebooks, forward compressed versions of their observations to a central station (CS) via capacity limited backhaul links. The CS jointly decodes the messages from all UTs. Since the BSs and the CS are assumed to have no prior channel state information (CSI), the channel needs to be estimated during its coherence time. Based on a lower bound of the ergodic mutual information, we determine the optimal fraction of the coherence time used for channel training, taking different path losses between the UTs and the BSs into account. We then study how the optimal training length is impacted by the backhaul capacity. Although our analytical results are based on a large system limit, we show by simulations that they provide very accurate approximations for even small system dimensions.Comment: 15 pages, 7 figures. To appear in the IEEE Transactions on Signal Processin