This paper proposes a linear interference alignment (IA) scheme which can be used for uplink channels in a general multicell multiuser MIMO cellular network. The proposed scheme aims to align interference caused by signals from a set of transmitters into a subspace which is established by the signals from only a subset of those transmitters, thereby effectively reducing the number of interfering transmitters. The total degrees of freedom (DoF) achievable by the proposed scheme is given in closed-form expression, and a numerical analysis shows that the proposed scheme can achieve the optimal DoF in certain scenarios and provides a higher total DoF than other related schemes in most cases.
For general multicell multiuser uplink channels, @cite_24 proposed a new scheme called unstructured IA scheme which appeared to provide a high DoF performance, but for a given uplink channel the feasibility of the scheme cannot be determined until the scheme is actually applied and checked by a numerical test. On the other hand, the IA scheme in @cite_2 aimed to align ICI into a one-dimensional subspace, and because this IA constraint was too stringent to be satisfied, only a fraction of the ICI could be aligned. This problem was then resolved in @cite_19 by consolidating ICI into a multi-dimensional subspace rather than a one-dimensional subspace. By extending the scheme in @cite_16 , which was designed only for three-cell multiuser uplink channels, the scheme in @cite_10 can be applied to a general uplink channel; however, its DoF performance decreases when the size of the uplink channel increases as the scheme can only suppress the interference caused by one interfering user regardless of the size of the uplink channel.
@cite_24 @cite_19 @cite_2 @cite_16 @cite_10
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We develop an interference alignment (IA) technique for a downlink cellular system. In the uplink, IA schemes need channel-state-information exchange across base-stations of different cells, but our downlink IA technique requires feedback only within a cell. As a result, the proposed scheme can be implemented with a few changes to an existing cellular system where the feedback mechanism (within a cell) is already being considered for supporting multi-user MIMO. Not only is our proposed scheme implementable with little effort, it can in fact provide substantial gain especially when interference from a dominant interferer is significantly stronger than the remaining interference: it is shown that in the two-isolated cell layout, our scheme provides four-fold gain in throughput performance over a standard multi-user MIMO technique. We also show through simulations that our technique provides respectable gain under a more realistic scenario: it gives approximately 28 gain for a 19 hexagonal wrap-around-cell layout. Furthermore, we show that our scheme has the potential to provide substantial gain for macro-pico cellular networks where pico-users can be significantly interfered with by the nearby macro-BS. We investigate the open problem of characterizing the multiplexing gain offered by FD in a network of M cells (compared to the gain of two available on a single link). While self-interference cancellation is fundamental in realizing full duplex (FD) capability, the more challenging problem in a network-wide deployment of FD communication is a new form of uplink-downlink interference, namely UDI, caused by transmission of uplink clients on the downlink reception of other clients operating in the same frequency band during FD. We leverage spatial interference alignment (IA) as an effective approach to address UDI and characterize the scalability of the FD's multiplexing gain (in terms of M) by providing a closed-form expression. To the best of our knowledge, this is the first characterization of FD's multiplexing gain in a multi-cell network. We also provide an IA construction that can achieve the best scaling possible. Further, we extend our results to practical settings with limited number of clients and limited information sharing between access points. In promising OFDMA systems, downlink signals originating from the same base station (BS) are orthogonal, while those from different BSs interfere with each other. As a consequence, inter-cell interference (ICI) becomes major performance degradation factor. Particularly, boundary users suffer from severe ICI in addition to the inherent near-far problem. To improve cell edge performances and support a more balanced data rate among all users, partial frequency reuse (PFR) and load-balancing schemes are investigated in this paper. We have formulated a utility maximization problem with network-wide proportional fairness (PF) as an objective in a multi-cell network with PFR. To solve this problem, we propose an offline optimal algorithm and also efficient online algorithms. Our online algorithms are based on simple inter intra-handover and cell-site selection in which a metric is changed from the signal strength to the average throughput. Through extensive simulations, we demonstrate that our online algorithms can achieve network-wide PF very closely. Compared to the conventional system with a universal frequency reuse where a user is bound to the best signal strength base station, the proposed algorithms bring two types of performance gain: interference avoidance (IA) and load balancing (LB) gain. These gains improve the system performance, especially for users at the cell boundary. The Interfering Broadcast Channel (IBC) applies to the downlink of (cellular and or heterogeneous) multi-cell networks, which are limited by multi-user (MU) interference. The interference alignment (IA) concept has shown that interference does not need to be inevitable. In particular spatial IA in the MIMO IBC allows for low latency transmission. However, IA requires perfect and typically global Channel State Information at the Transmitter(s) (CSIT), whose acquisition does not scale well with network size. Also, the design of transmitters (Txs) and receivers (Rxs) is coupled and hence needs to be centralized (cloud) or duplicated (distributed approach). CSIT, which is crucial in MU systems, is always imperfect in practice. We consider the joint optimal exploitation of mean (channel estimates) and covariance Gaussian partial CSIT. Indeed, in a Massive MIMO (MaMIMO) setting (esp. when combined with mmWave) the channel covariances may exhibit low rank and zero-forcing might be possible by just exploiting the covariance subspaces. But the question is the optimization of beamformers for the expected weighted sum rate (EWSR) at finite SNR. We propose explicit beamforming solutions and indicate that existing large system analysis can be extended to handle optimized beamformers with the more general partial CSIT considered here.