A robust transmission strategy for multi-cell interference networks

In this paper, we propose a robust transmission strategy for multi-cell networks equipped with multiple-antenna base stations (BSs) under universal frequency reuse and in the presence of channel estimation error. We propose a distributed optimization scheme, where each BS individually minimizes a combination of its total transmit power and its resulting overall interference inflicted on the users of the adjacent cells, subject to maintaining a desired quality of service at its local users. We transform the proposed scheme to a robust optimization problem for the worst case of errors and derive a semidefinite programming (SDP) using rank-relaxation. We prove that the derived SDP always yields exact rank-one optimal solutions. This is in contrast to the standard rank-relaxed SDP technique that requires an additionally high computational complexity to approximate the solutions with sufficient accuracies, required for an effective beamforming. A comparison of simulation results show that the proposed transmission strategy can expand the signal-to-interference-plus-noise-ratio operational range with significantly reduced power consumption levels at BSs and perform closely to its centralized counterpart

Spectrum splitting-based cognitive interference management in two-tier LTE networks

In this paper, we propose a spectrum splitting-based cognitive interference management method for LTE downlink two-tier networks (that provide closed-access mode). In the proposed method, the resource-blocks in the macrocell (in frequency and time domain) are allocated to the users with the received signal-to-interference-plus-noise-ratio greater than a threshold. The rest of resource-blocks are then allocated to the femtocells. To evaluate the effectiveness of this method, we develop a system level simulation and compare the proposed method with no interference management and also interfering resource blocking-based cognitive interference management method (IRB-CIM). It is shown that the proposed method significantly increases average throughput of femtocells' cell-edges. Furthermore, the simulation results indicate that by adjusting parameters, the proposed method results in higher average throughput for femtocells and for overall system compared to other methods. The proposed method senses control-channel of the macrocell to detect spectrum availability which is simpler and faster than IRB-CIM

Beamforming in coexisting wireless systems with uncertain channel state information

This paper considers an underlay access strategy for coexisting wireless networks where the secondary system utilizes the primary spectrum to serve its users. We focus on the practical cases where there is uncertainty in the estimation of channel state information (CSI). Here the throughput performance of each system is limited by the interference imposed by the other, resulting in conflicting objectives. We first analyze the fundamental tradeoff between the tolerance interference level at the primary system and the total achievable throughput of the secondary users. We then introduce a beamforming design problem as a multiobjective optimization to minimize the interference imposed on each of the primary users while maximizing the intended signal received at every secondary user, taking into account the CSI uncertainty. We then map the proposed optimization problem to a robust counterpart under the maximum CSI estimation error. The robust counterpart is then transformed into a standard convex semi-definite programming. Simulation results confirm the effectiveness of the proposed scheme against various levels of CSI estimation error. We further show that in the proposed approach, the trade-off in the two systems modelled by Pareto frontier can be engineered by adjusting system parameters. For instance, the simulations show that at the primary system interference thresholds of -10 dBm (-5 dBm) by increasing number of antennas from 4 to 12, the secondary system throughput is increased by 3.3 bits/s/channel-use (5.3 bits/s/channel-use

Downlink beamforming in underlay cognitive cellular networks

We propose a novel scheme for downlink beamforming design in an underlay cognitive cellular system. The beamforming design is formulated as an optimization problem with the objective of keeping the cognitive base station transmit power as well as the induced interference on the primary users, below predefined system thresholds. This is subject to providing a certain level of signal-to-interference-plus-noise ratio (SINR) to the secondary users. We then derive the corresponding semidefinite programming form for the formulated optimization problem and propose an iterative algorithm to obtain the beamforming vectors as the optimal solutions. We further analytically show the convergence of the proposed iterative algorithm. Extensive simulations verify that the proposed algorithm quickly converges to the optimal solution. We then compare the proposed scheme with a benchmarking system defined based on the previous methods proposed in the related literature. Comparisons show that the proposed algorithm outperforms the benchmarking system and induces lower interference at the primary service receivers. It is also observed that the proposed algorithm offers a higher sum rate in comparison to the benchmarking system. Simulation results further reveal that the proposed approach effectively works at a relatively high SINR level required by secondary users and strict interference threshold set by the primary system while the benchmarking system fails to do so

Provisioning statistical QoS for coordinated communications with limited feedback

The capacity performance of ICIC has been extensively studied in coordinated multi-point transmissions (CoMP). In practice however, due to limited feedback, the acquired channel direction information (CDI), which is crucial for ICIC, is often partially available. Hence one may question whether the ICIC is able to meet the Quality-of-Service (QoS) requirements. This paper considers the optimal partitioning of the feedback bits in CoMP while accounting for the inter-cell interference cancellation (ICIC). In this paper, we adopt a statistical model of QoS in CoMP by using the notion of effective capacity (EC). Utilizing EC we then formulate the system function as an optimization problem with the objective of maximizing the total EC subject to the limited feedback available to the cluster of base stations (BSs). Analytical bounds are then obtained on the EC performance which are then utilized as the base for algorithms that assign feedback bits among the user equipments (UEs) and BSs. Using simulations we then investigate the accuracy of the obtained bounds and highlight practical system designs for dealing with stringent delay requirements. Of crucial practical importance, the findings of this paper also indicates that in CoMP there is an optimal cluster size for a given feedback capacity that maximizes the corresponding EC

Coverage performance of MIMO-MRC in heterogeneous networks:a stochastic geometry perspective

We study the coverage performance of multi-antenna (MIMO) communications with maximum ratio combining (MRC) at the receiver in heterogeneous networks (HetNets). Our main interest in on multi-stream communications when BSs do not have access to channel state information. Adopting stochastic geometry we evaluate the network-wise coverage performance of MIMO-MRC assuming maximum signal- to-interference ratio (SIR) cell association rule. Coverage analysis in MIMO-MRC HetNets is challenging due to inter-stream interference and statistical dependencies among streams' SIR values in each communication link. Using the results of stochastic geometry we then investigate this problem and obtain tractable analytical approximations for the coverage performance. We then show that our results are adequately accurate and easily computable. Our analysis sheds light on the impacts of important system parameters on the coverage performance, and provides quantitative insight on the densification in conjunction with high multiplexing gains in MIMO HetNets. We further observe that increasing multiplexing gain in high- power tier can cost a huge coverage reduction unless it is practiced with densification in femto-cell tier

Coverage performance in multi-stream MIMO-ZFBF heterogeneous networks

We study the coverage performance of multiantenna (MIMO) communications in heterogenous networks (HetNets). Our main focus is on open-loop and multi-stream MIMO zero-forcing beamforming (ZFBF) at the receiver. Network coverage is evaluated adopting tools from stochastic geometry. Besides fixed-rate transmission (FRT), we also consider adaptive-rate transmission (ART) while its coverage performance, despite its high relevance, has so far been overlooked. On the other hand, while the focus of the existing literature has solely been on the evaluation of coverage probability per stream, we target coverage probability per communication link — comprising multiple streams — which is shown to be a more conclusive performance metric in multi-stream MIMO systems. This, however, renders various analytical complexities rooted in statistical dependency among streams in each link. Using a rigorous analysis, we provide closed-form bounds on the coverage performance for FRT and ART. These bounds explicitly capture impacts of various system parameters including densities of BSs, SIR thresholds, and multiplexing gains. Our analytical results are further shown to cover popular closed-loop MIMO systems, such as eigen-beamforming and space-division multiple access (SDMA). The accuracy of our analysis is confirmed by extensive simulations. The findings in this paper shed light on several important aspects of dense MIMO HetNets: (i) increasing the multiplexing gains yields lower coverage performance; (ii) densifying network by installing an excessive number of lowpower femto BSs allows the growth of the multiplexing gain of high-power, low-density macro BSs without compromising the coverage performance; and (iii) for dense HetNets, the coverage probability does not increase with the increase of deployment densities

Exploiting quantization uncertainty for enhancing capacity of limited-feedback MISO ad hoc networks

In this paper we investigate the capacity of random wireless networks in which transmitters are equipped with multiantennas. A quantized version of channel direction information (CDI) is also available, provided by the associated single antenna receivers. We adopt tools of stochastic geometry and random vector quantization to incorporate the impacts of interference and quantization errors, respectively. We first study the capacity of Aloha, and channel quality information (CQI)-based scheduling, whereby the transmissions decision in each transceiver pair depends on the strength of the CQI against a prescribed threshold. We then propose a new scheduling scheme, namely modified CQI (MCQI), by which the quantization error is effectively incorporated in the scheduling. Further we obtain the capacity of MCQI-based scheduling. Simulation results confirm our analysis and show that the proposed MCQI-based scheduling improves the capacity compared to the CQI-based scheduling and Aloha. It is also seen that the performance boost is more significant where the feedback capacity is low and the network is dense. In comparison with the case of high feedback capacity, the network capacity is not reduced by low feedback capacity in the MCQI-based scheduling. This is of practical importance since the network designer can save the feedback resources by employing MCQI-based scheduling without compromising the capacity and increasing the receivers’ complexity

A Low-Latency Interference Coordinated Routing for Wireless Multi-hop Networks

Recently, there has been an increasing interest in exploiting interference cancelation to support multiple adjacent concurrent transmissions instead of avoiding interference through scheduling. In line with these efforts, this paper propose an interference coordinated routing (ICR) scheme for wireless multi-hop networks to achieve more transmission concurrence, and thus lower the end-to-end delay. The proposed ICR scheme firstly constructs an initial path by the interference-aware routing algorithm, which captures the end-to-end latency and spatial resource cost as the routing metrics. Then, to analyze the feasibility of concurrent transmission for a given link set, we consider the interference coordination and formulate the concurrent transmission of multiple links as a linear programming (LP) problem. The solution to the LP problem indicates the power allocation. Finally, a distributed guard zone based selection (GBS) algorithm is further proposed to iteratively explore the maximum feasible link set for each time slot. The selected links are simultaneously active for packet transmission with the allocated power in the current time slot, and the remaining links will be put off to the next. Simulation results confirm that ICR reduces the end-to-end delay by 9.16% to 73.82%, and promotes better transmission concurrence compared with the existing schemes