A cellular multiple-input multiple-output (MIMO) downlink system is studied in which each base station (BS) transmits to some of the users, so that each user receives its intended signal from a subset of the BSs. This scenario is referred to as network MIMO with partial cooperation, since only a subset of the BSs are able to coordinate their transmission towards any user. The focus of this paper is on the optimization of linear beamforming strategies at the BSs and at the users for network MIMO with partial cooperation. Individual power constraints at the BSs are enforced, along with constraints on the number of streams per user. It is first shown that the system is equivalent to a MIMO interference channel with generalized linear constraints (MIMO-IFC-GC). The problems of maximizing the sum-rate (SR) and minimizing the weighted sum mean square error (WSMSE) of the data estimates are non-convex, and suboptimal solutions with reasonable complexity need to be devised. Based on this, suboptimal techniques that aim at maximizing the sum-rate for the MIMO-IFC-GC are reviewed from recent literature and extended to the MIMO-IFC-GC where necessary. Novel designs that aim at minimizing the WSMSE are then proposed. Extensive numerical simulations are provided to compare the performance of the considered schemes for realistic cellular systems.
We consider a coordinated multicell multipleantenna downlink transmission in a cellular network also known as network MIMO, which is expected to play a key role in future broadband cellular systems. While clustered network MIMO is a practical scheme to benefit from multicell cooperation, it still suffers from inter-cluster (uncoordinated) interference especially for the cell-edge users. In this work, a multicell network scheduling scheme is proposed to minimize the uncoordinated interference, which introduces overlapping clusters. It also offers a novel model to study network MIMO systems. The block diagonalization (BD) scheme is revised so that it can be employed as a coordinated transmission method. Further, a subgradient iterative algorithm is derived to determine the precoder matrices for BD scheme. The proposed multicell network scheduling is shown to outperform that in the conventional clustered network MIMO under the per-cell power constraints.Index Terms-Block diagonalization, interference alignment, interference channel, MIMO broadcast channel, multicell cooperation, network MIMO, network scheduling, zero-forcing beamforming.
In this paper, we multiple-input multiple-output (MIMO) systems employing transmit antenna selection and orthogonal space-time block codes (OSTBCs) are not available. We thus derive exact closed-form expressions for the BER of Gray-coded M-ary one and two-dimensional amplitude modulations when an OSTBC is employed and N transmit antennas out of total Lt antennas are selected for transmission. We also derive tight closed-form approximate BER for M-PSK constellations. Our BER expressions are valid for a frequency-flat Rayleigh fading MIMO channel and can be evaluated without numerical integration methods.
Opportunistic routing relies on the broadcast capability of wireless networks. It brings higher reliability and robustness in highly dynamic and/or severe environments such as mobile or vehicular ad-hoc networks (MANETs/VANETs). To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead and hence, their selection algorithms are critical. Common MPR selection algorithms are heuristic, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties. In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm while it does not require MPR announcement messages from the neighbors. Our evaluation results demonstrate the performance gains of our trained DeepMPR multicast forwarding policy compared to other popular techniques.
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