This paper analyzes the impact and benefits of infrastructure support in improving the throughput scaling in networks of $n$ randomly located wireless nodes. The infrastructure uses multi-antenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbitrary rates relative to $n$. Under the model, capacity scaling laws are analyzed for both dense and extended networks. Two BS-based routing schemes are first introduced in this study: an infrastructure-supported single-hop (ISH) routing protocol with multiple-access uplink and broadcast downlink and an infrastructure-supported multi-hop (IMH) routing protocol. Then, their achievable throughput scalings are analyzed. These schemes are compared against two conventional schemes without BSs: the multi-hop (MH) transmission and hierarchical cooperation (HC) schemes. It is shown that a linear throughput scaling is achieved in dense networks, as in the case without help of BSs. In contrast, the proposed BS-based routing schemes can, under realistic network conditions, improve the throughput scaling significantly in extended networks. The gain comes from the following advantages of these BS-based protocols. First, more nodes can transmit simultaneously in the proposed scheme than in the MH scheme if the number of BSs and the number of antennas are large enough. Second, by improving the long-distance signal-to-noise ratio (SNR), the received signal power can be larger than that of the HC, enabling a better throughput scaling under extended networks. Furthermore, by deriving the corresponding information-theoretic cut-set upper bounds, it is shown under extended networks that a combination of four schemes IMH, ISH, MH, and HC is order-optimal in all operating regimes.Comment: 26 pages, 10 figures, 1 table, Under revision for IEEE Transactions on Information Theor

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We introduce an opportunistic interference mitigation (OIM) protocol, where a user scheduling strategy is utilized in K-cell uplink networks with time-invariant channel coefficients and base stations (BSs) having M antennas. Each BS opportunistically selects a set of users who generate the minimum interference to the other BSs. Two OIM protocols are shown according to the number S of simultaneously transmitting users per cell: opportunistic interference nulling (OIN) and opportunistic interference alignment (OIA). Then, their performance is analyzed in terms of degrees-of-freedom (DoFs). As our main result, it is shown that KM DoFs are achievable under the OIN protocol with M selected users per cell, if the total number N of users in a cell scales at least as SNR (K−1)M . Similarly, it turns out that the OIA scheme with S(< M ) selected users achieves KS DoFs, if N scales faster than SNR (K−1)S . These results indicate that there exists a trade-off between the achievable DoFs and the minimum required N . By deriving the corresponding upper bound on the DoFs, it is shown that the OIN scheme is DoF-optimal. Finally, numerical evaluation, a two-step scheduling method, and the extension to multi-carrier scenarios are shown. Index TermsBase station (BS), channel state information, cellular network, degrees-of-freedom (DoFs), interference, opportunistic interference alignment (OIA), opportunistic interference mitigation (OIM), opportunistic interference nulling (OIN), uplink, user scheduling. Recently, as an alternative approach to show Shannon-theoretic limits, interference alignment (IA) was proposed by fundamentally solving the interference problem when there are two communication pairs [7]. It was shown in [8] that the IA scheme can achieve the optimal degrees-of-freedom (DoFs), which are equal to K/2, in the K-user interference channel with time-varying channel coefficients. The basic idea of the scheme is to confine all the undesired interference from other communication links into a pre-defined subspace, whose dimension approaches that of the desired signal space. Hence, it is possible for all users to achieve one half of the DoFs that we could achieve in the absence of interference. Since then, interference management schemes based on IA have been further developed and analyzed in various wireless network environments: multiple-input multiple-output (MIMO) interference network [9] [16]. These constraints need to be relaxed in order to apply IA to more practical systems. In [9], a distributed IA scheme was constructed for the MIMO interference channel with time-invariant coefficients. It requires only local CSI at each node that can be acquired from all received channel links via pilot signaling, and thus is more feasible to implement than the original one [8]. However, a great number of iterations should be performed until designed transmit/receive beamforming (BF) vectors converge prior to data transmission. Now we would like to consider practical wireless uplink networks with K-cells, each of which h...

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