Interference management (IM) is essential to wireless communication networks, but interference suppression, a key component of IM, is known to degrade users' achievable spectral efficiency (SE). It is thus important to select an appropriate IM method with optimal operating parameters according to diverse network deployments, transmit power differences of various communication equipments, and dynamically changing channel conditions so as to balance the benefits brought by and the cost of IM. Interference neutralization (IN) has recently been receiving considerable attention, with which a duplicate of interference of the same strength and opposite phase w.r.t. the original interfering signal is generated to neutralize the disturbance at the intended receiver. However, to the best of our knowledge, all existing IN schemes assume that interference is completely neutralized without accounting for their power consumption. To remedy this deficiency, we propose a novel scheme, called dynamic interference neutralization (DIN). By intelligently determining the appropriate portion of interference to be neutralized, we balance the transmitter's power used for IN and the desired signal's transmission. We then present a new way to adaptively select one of DIN and other IM methods by taking into account the cost of multiple IM methods and their benefits. Our analysis has shown that DIN can include complete IN and non-interference management (non-IM) as special cases. The proposed strategy is shown via simulation to be able to make better use of the transmit power than existing IM methods, hence enhancing users' SE.

An A daptive tran S mission mechanism exploiting both interference lo C ality and the relationship between d E sired sig N al and in T erference ( ASCENT ) is proposed for uplink transmission in heterogeneous networks. The authors adopt both X channel and Z channel models according to which spatial signal processing is designed. In the X channel, the picocell base station (PBS) exploits information the macrocell base station (MBS) shares to cancel local interference, and cooperatively decodes the data carried by strong interference from a macro‐user (MU), which is then fed to the MBS. As a result, a pico‐user (PU) can transmit simultaneously with an MU on the same channel. In addition, adaptive reception is employed to achieve good tradeoff between interference suppression and the desired level of signal distortion. For the Z channel, the PU and the PBS adopt signal processing suitable for their own channel state. At a PBS, interference cancellation is adopted to eliminate disturbance from an MU via inter‐base station collaboration. ASCENT is also extended to the case of multiple picocells. The authors’ simulation results show that in X channel mode, the achievable uplink rate of an MU can be significantly enhanced. In the case of Z channel, the PU's rate is improved while guaranteeing the MU's data rate.

Both interference alignment (IA) and interference neutralization (IN) are exploited for Coordinated Multi-Point (CoMP) transmissions. With the base station's cooperation, transmit precoder and receive filter are designed jointly, and then concurrent transmissions of multiple data streams are achieved. In the design of preprocessing, IN is applied to the interferences carrying same data so as to align the interfering signals with opposite directions in a subspace. On the other hand, for interferences carrying different information, IA is employed to align them with the same direction in a subspace, thus reducing the interference signal dimension observed at the user side. Based on different precoding schemes at the transmitter's side, receivers adopt zero-forcing (ZF) so as to recover the desired data. The proposed interference alignment and neutralization based CoMP (IAN-CoMP) mechanism can achieve effective interference cancellation and suppression by exploiting limited and flexible collaboration only at the base station (BS) side. We also extend the mechanism to general cases where the antenna configurations at both transmitter and receiver side, the number of transmitters participating in CoMP and simultaneously served users are variable. In addition, the proposed scheme can also achieve a flexible tradeoff between cooperation overhead and the system's achievable degrees of freedom (DoFs). Our in-depth simulation results show that IAN-CoMP can significantly improve the spectral efficiency (SE) for cell-edge users.

Due to the hierarchical structure and heterogeneity of most deployed wireless networks, multiple data transmissions using the same communication resource, such as frequency band and time slot, are likely to occur in the same area. Hence, interference becomes critical to the decoding of interfered signals. There have already been numerous techniques dealing with interferences that can be implemented either at the transmitter, the receiver or both, to support simultaneous multi-user transmissions. This paper focuses on the reception design in multiple access channels (MACs) where multiple transmitters send data to a common receiver simultaneously. By exploiting the constructive/destructive interactions among interfering signals, we propose a receiver structure based on interference combination (ICom) incorporating zero-forcing (ZF) and successive interference cancellation (SIC). The proposed receiver structure does not require coordination at the transmitter side and can significantly reduce the number of receiving antennas at a moderate processing cost. The ICom-based reception is shown to be able to not only achieve a significant improvement of system spectral efficiency (SE) under stringent latency constraints, but also make a flexible tradeoff between the requirement of receiving antennas and signal processing complexity, thus facilitating its implementation and deployment.

A scheduling algorithm based on weighted users' correlation and fairness (WUCFS) is proposed for MU-MIMO downlink broadcast channels (BC), to address the problem that traditional fairness scheduling algorithms cannot handle, i.e., they cannot accurately compute the achievable rate for each user in their iterative user selection process, thus creating an undesirable tradeoff between user fairness and system sum-rate. The proposed WUCFS is used to activate mobile subscribers by evaluating the correlation between the candidate and the selected users as well as those who may be scheduled for data transmission. Weighted inverse-correlation maximization is then employed to determine which users to be scheduled for data transmission. By estimating potential co-channel interference (CCI) a priori, our algorithm can accurately estimate the actual transmission rate of each mobile user, and fairly activate a set of users with small mutual interference. Compared to existing schemes, the proposed algorithm is shown to improve fairness among users while ensuring high system sum-rate.