The envisioned rapid and exponential increase of wireless data traffic demand in the next years imposes rethinking current cellular networks due to the available spectrum scarcity. In this regard, 3 main drivers are considered to increase the capacity of today's most advanced (4G) and future (5G and beyond) cellular networks: use more bandwidth (more Hz) through spectral aggregation, enhance the spectral efficiency per base station (BS) (more bits/s/Hz/BS) by using multi-antenna (i.e. MIMO) systems, and increase the density of BSs (more BSs/km2) through a dense and heterogeneous deployment. We focus on the last 2 drivers. First, the use of MIMO systems allows exploiting the spatial dimension for improving the capacity of a conventional point-to-point link, increasing the number of served users, and reducing unwanted emissions (interference). Second, dense heterogeneous networks are a simple and cost-effective way to boost the area spectral efficiency by densifying the network and improving the spatial re-use of the spectrum. However, increasing the BSs density entails two main technical challenges: the interference increases because neighboring BSs/users are nearer and the amount of data traffic, as well as downlink (DL) and uplink (UL) traffic asymmetry, varies over space and time more drastically since the number of users per BS is reduced. The increase of interference makes the development of efficient interference management techniques a key enabler for MIMO dense heterogeneous networks. On the other hand, the variability of the per-BS traffic amount and the DL/UL traffic asymmetry convert flexible duplexing (i.e. flexible allocation of DL/UL resources per BS) into a necessity for an efficient resource usage. Therefore, the development of resource management schemes capable of adapting to the varying traffic load, as well as interference management, becomes crucial. Accordingly, this thesis focuses on the development of advanced interference management techniques to deal with inter-cell interference in MIMO dense networks and on the design of traffic- and interference-aware resource management schemes for flexible duplexing systems in asymmetric traffic conditions. To these goals, the wide deployment of MIMO systems is capitalized to develop advanced multi-antenna signal processing techniques when full reuse of time and frequency resources among densely deployed BSs is adopted. In the first part, different statistical characterizations of the transmitted signals are analyzed to improve the capacity of wireless interference-limited MIMO channels. Advanced signaling schemes are developed and the use of improper Gaussian signaling (IGS) is investigated, which allows exploiting the real and imaginary dimensions of MIMO channels. Majorization theory is exploited to demonstrate the strict superiority of IGS. In the second part, transmit coordination strategies are proposed to manage interference in extremely dense cellular networks. The design of BSs transmit strategies (involving design of spatial transmit/receive filters, power control, and user scheduling) is coordinated to optimize different network functions while reducing the stringent requirements needed for channel estimation in dense networks. Coordination strategies for the case in which different signaling schemes coexist in the network are also derived. Further, coordination strategies for cluster-based joint transmissions are developed, where BSs are grouped into clusters and different clusters interfere to each other. The third part focuses on the design of traffic- and interference-aware duplexing techniques to make a better use of the available resources by taking into account the asymmetric traffic conditions that arise in dense networks and managing the new kinds of interference that come up under flexible duplexing. Short-term and long-term optimizations are investigated, being therefore the interference managed instantaneously and statistically, respectively.